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    1. Reviewer #1 (Public Review):

      In this work, Holz and colleagues develop a computational stochastic model of lamellipodial growth and turnover. The aim of this work is to compare the filament organization and rate of incorporation/detachment of actin subunits with experimental data published in the literature. This model includes many reactions: actin polymerization, depolymerization, filament branching by the Arp2/3 complex, capping, uncapping, severing, oligomer diffusion, annealing, and debranching.

      One of the difficulties of such model is to constrain as many parameters as possible. Thus, the first part of this study works on the dimensionality of the model, and determines that correct filament orientation pattern relative to the membrane requires a quasi-2D model, where new filaments are limited to branching within 10{degree sign} of the lamellipodial plane, a rather reasonable assumption for such flat structures.

      The second part of this work treats network rearrangement and dynamics during treadmilling. Most of the parameters are set to estimated values or values published in the literature. Floating parameters are severing rates (random or biased toward barbed ends) and maximum fragment size in order to test the importance of fragmentation and reannealing in the reorganization of these actin networks. The authors demonstrate that frequent severing and annealing are necessary conditions to model correctly the dynamics of actin subunits along the lamellipodium, the presence of non-negligible amount of uncapped barbed ends along the lamellipodium, and the structural remodeling of actin networks.

      The last part of the manuscript reports new speckle microscopy experiments performed at faster 0.1s time intervals. These experiments confirm that a surprisingly high fraction of actin speckles are disassembled shortly after actin filament assembly, which is supported by the model.

      One the one hand, I am impressed by these simulations, which I find very informative and provide comprehensive understanding of the reactions at play. On the other hand, multi-parameter simulations raise necessarily questions about the choices of hypotheses and parameter values (and the sensitivity of this model to fluctuations of these parameters). I appreciate parameter scans which offer a visible way to follow the behavior of the system.

    2. Reviewer #2 (Public Review):

      This is an excellent modeling study addressing the unresolved important question about lamellipodial actin network: how does the network remain wide enough, maintains angular order, and actually increases the filament length behind the leading edge?

      The modeling approach is straightforward: use Monte Carlo simulations to grow actin networks in 2d and 3d by a combination of stochastic branching, capping and elongation. Such models were used before many times, but the key here is to add fragmentation and annealing of oligomers (short filaments). The authors show that this addition is the key to explain many observations and measurements, including speckle dynamics, long filaments behind the leading edge, etc. Zcomparison with the structure of the lamellipodia from 2 different cell types allows to test a couple of different parameter sets.

      The paper is well written, contains very thorough and fair literature review, accurate, well documented. The result is novel and significant.

      I don't have any critical comments.

    1. Reviewer #1 (Public Review):

      Golgi secretion has been shown previously to be involved in cell migration, but the notion has been disputed. In this study, Vaidziulyte et al define a role of directed secretion in persistent cell migration, defined as directionality sustained beyond 20 minutes. They show that the direction of migration tends to align with the nucleus Golgi axis. This correlation is due to the Golgi reorienting towards the direction of movement of the cell. They show that cells with persistent motion display sustained polarized trafficking towards protrusion. They use ontogenetically controlled Cdc42 to show that Cdc42 rich protrusions are able to induce the reorientation of the Golgi. Finally, they propose a minimal model where coupling protrusive activity and polarised trafficking is able to recapitulate persistent migration.

      The manuscript thesis is well supported by quality imaging, quantitative analysis, use of optogenetics and modelling. However, while the analysis of the processes is of high quality, the novelty of the findings is not highlighted and not always very apparent, as many of the ideas have been discussed before in the literature. In addition, the authors did not image, study or discuss much the microtubule network, whose re-organisation is an obvious link between protrusions and Golgi re-orientation. Finally, it is not clear how the physical model yields testable predictions for future experimentations.

    2. Reviewer #2 (Public Review):

      In this paper, the authors are primarily concerned with the bidirectional link between directed secretion from the Golgi (the Nucleus-Golgi polarity axis), and events on the cell membrane associated with protrusive activity. They label the Golgi complex and track migrating cells, showing that the Nucleus-Golgi axis aligns to the direction of motion.

      Interestingly, when cells are first confined to a circular adhesion patch, and then allowed to escape, the direction of escape correlates with the NG polarity axis. The authors treat cells with microtubule-disrupting nocodazol (NZ), finding decreased migratory persistence. Using maps of morphodynamic and of Rab6-labeled Golgi secretion (trafficking maps), they find that protrusion precedes trafficking. They optogentically stimulate protrusion by activating Cdc42, showing downstream reorientation of the nuclear-golgi axis that is faster in circular confined cells that in free-moving cells.

      Finally, the authors describe a minimal model to fit their data to two parameters that govern the feedback between the axis of polarity and the protrusive activity.

      The strengths of the paper are the experimental data and the interesting tracking of cells with and without confinement, with and without MT disruption, and with ontogenetic stimuli.

      In general, it is well known that cell polarity and persistent migration are complex phenomena with multiple layers of regulation and feedback. The activities of GTPases, the feedback from F-actin, crosstalk and delivery of GEFs/GAPs along microtubules, effects of PI3K, PTEN, of lipids, and multiple interacting factors together determine the persistence and responsiveness of cells to stimuli. It is also clear that once a polarity axis of some kind is established (whether due to cytoskeleton assembly, cell shape, or organelle placement) it too, participates in the overall orchestration of cell migration and feedback to polarity persistence. Here the authors have attempted to isolate the Nucleus-Golgi axis as an important factor, while not entirely evaluating its importance relative to other factors. For example, could the NZ treated cells simply have distinct GEF/GAP activities? Is the lack of persistence in such cells explainable only by trafficking defect?

      The authors point to the fact that cell polarity and cell migration have been modelled by many others previously. This is indeed true, aa this is a field with much literature. The authors briefly mention some reviews. Many previous papers have attempted to pose hypotheses for what initiates or what maintains (or changes) polarity, and many have explored specific hypotheses for molecular interactions. In contrast, the model here is very minimal, which can be an advantage (only 2 parameters needed to fit the data). At the same time this minimality also means that there is no clear mechanistic hypothesis to test, other than the relatively well known fact that protrusion and polarity feed back on one another.

      The model essentially depicts cell edge activity by "transfer function" responses to stimuli and axis rotation by a linear combination of forces (basal and protrusive). Nowhere in the model is the secretory property of the Golgi, or indeed any specific property of the NG axis used. In short, the "axis" could just as easily relate to any other structural cell property that responds to force. This is a drawback. It would be interesting to determine what the two feedback parameters represent specifically in terms of molecular effects associated with the Golgi-nuclear axis that is unique to that axis, for example. This could possibly be achieved by starting with a more detailed model (V1) and showing that it reduces to the minimal model here, while connecting some specific molecular details to the forces or the effect of the NG axis on the cell edge activity.

      Finally, the authors have made a specific choice of representing stimuli by transfer functions, which is fine. However, it would be worth pointing out here, that this is merely one way of representing the spread of GTPase activity on the cell membrane, and that it fits well within the class of models utilizing reaction-diffusion equations to describe GTPase activity in cells. (This link would help to put the model into the context of the broader literature on the subject.)

    3. Reviewer #3 (Public Review):

      The manuscript of Vaidziulyte et al. investigates the dynamic and causal relationship between peripheral cell protrusions and the sequential events leading to the establishment of cell polarity to sustain persistence migration. Moreover, this causal analysis led to the development of a minimal physical models highlighting the importance and properties of feed-backs between protrusions and the force that control nucleus-Golgi axis on the induction of migration persistency.

      The originality of this manuscript is to develop a real causal study between cell protrusion, secretion and nucleus-Golgi orientation during long-term and persistance migration. The data are obtained and supported through state-of the-art approaches to follow and quantify migration over-time, very elegant correlation analysis, specific and dynamic control of a key regulator of cell polarity establishment, CDC42. Finally, the multiple and punctilious quantification appeared essential to sustain the development of a minimal physical model that present high interest, based on its ability to mimic clear features of observed migration behaviors with limited numbers of parameters. This manuscript is supported by many past references that appeared revisited through the help of this elegant quantitative approach. Clearly, activable adhesion on cell constrained on micro- pattern demonstrated the relationship between cell protrusions and nucleus-Golgi alignement with direction of migration. As suggested by previous studies, low concentration nocodazole treatment showed that MT dynamics was essential to connect cell protrusions and reorientation of nucleus-Golgi during persistency induction. Indeed, MT dynamics is essential to sustain secretion mechanisms that were observed with secretion of Collagen X through the RUSH system or following Rab6 vesicles outside the Golgi apparatus.

      The ability of CDC42 optogenetics activations to induce nucleus-Golgi reorientation in free cells or confined cells on micro pattern clearly confirmed the importance of this small GTPase in polarity establishment.

      Finally, the manuscript integrates all these parameters to develop a minimal physical model between CDC42-cell-protrusion-Nucleus/Golgi reorientation and cell persistency.

    1. Reviewer #1 (Public Review): 

      This study reports the novel and interesting finding that AKAP220 knockout leads to a dramatic increase in primary cilia in renal collecting ducts. AKAP220 is known to sequester PKA, GSK3, the Rho GTPase effector IQGAP-1 and PP1. Previous work from this group demonstrated that AKAP220-/- mice exhibit reduced accumulation of apical actin in the kidney attributable to less GTP-loading of RhoA. Relatedly, AKAP220-/- mice display mild defects in aquaporin 2 trafficking. In this work, Golpalan et al examine the effects of AKAP220 mutation on cilia. They demonstrate increased numbers of primary cilia decorating AKAP220-/- collecting ducts. This phenotype is striking as little is known about negative regulators of cilium biogenesis. 

      The authors also provide evidence that interaction of AKAP220 with protein phosphatase 1 (PP1) is critical for its function. Through PP1, AKAP220 may regulate HDAC6, which may in turn inhibit tubulin acetylation, which may in turn control cilia stability. Aberrant cilia function is implicated in autosomal dominant polycystic kidney disease. The authors also speculate that AKAP220 and tubulin acetylation may have clinical relevance for autosomal dominant polycystic disease. However, it remains unclear how increased cilia biogenesis may affect cell or tissue physiology. This work is of interest to cell biologists seeking to understand the biogenesis of the primary cilium, and to others interested in ciliopathies (i.e., disorders of the primary cilium).

    2. Reviewer #2 (Public Review): 

      The authors show that AKAP220 knockout in kidney collecting ducts leads to a pronounced increase in primary cilia. They go on to demonstrate that this effect holds true in multiple different preparations, before clearly demonstrating that the PP1 anchoring site is critical for the normal role of AKAP220 is limiting primary cilia formation. 

      Although the key overall finding is well supported, I did not find the specific mechanism concerning a AKAP220-PP1-HDAC6 signaling complex/axis csufficiently onvincing. The authors propose that AKAP220 interacts with HDAC6 via PP1, and that within the complex HDAC6 is stabilised through phosphorylation. The knock on effect is efficient deacetylation. Although this complicated mechanism is consistent with the data, three supporting observations towards this specific mechanism come with caveats: (i) in figure 2C, they show an increase in acetyl tubulin by immunoblotting, but the densitometry seems to be the ratio of acetyl tubulin to GAPDH - would it not be more appropriate to reference to total tubulin? (ii) In Fig. 2O, they propose an interaction between AKAP220-HDAC6 supported by proximity ligation assay data. However, no technical information is provided for the technique, and the control of imaging with HDAC6 + AKAP220deltaPP is not included. No other data (such as co-immunoprecipitations) is provided in support of protein complex formation. (iii) In Figure 3W&X (which is referred back to in the introduction), they propose that because tubacin does not increase the % ciliated cells in an AKAP220deltaPP1 knock-in background, this means that the AKAP220-PP1-HDAC6 axis is key. But there is potentially a ceiling effect at play in this experiment in this experiment since ~ 70 % of cells are ciliated in the AKAP220deltaPP1 knock-in background before the inhibitor is added. The mechanism is plausible but should not be considered concrete in the same way as the central observation that AKAP220 knockout leads to a large increase in cilia. 

      The study switches tack to focus on F-actin regulation by the AKAP220 complex, and then reveals the potential utility of tubacin to treat renal cystogenesis. Despite reservations about the exact mechanism by which AKAP220 knockout or AKAP220deltaPP1 knock-in drives increase primary cilia formation, the primary finding is interesting and well supported, and should spur on follow-up work to understand the role of this interesting signalling complex in more detail since ciliopathies are an important class of disease.

    3. Reviewer #3 (Public Review): 

      The authors had previously generated a mouse line with inactivation of AKAP220, which encodes an A-kinase anchoring protein, and observed defects in their collecting ducts (CD) leading to defects in trafficking of aquaporin 2. While further characterizing the samples, they observed that CD epithelia had increased numbers and length of their primary cilia compared to CD cells of control mice. While some AKAP proteins have been localized to the primary cilium, AKAP220 was not one of them so the authors pursued a systematic series of experiments to determine how AKAP220 has these effects. Using a combination of CRISPR-manipulated renal epithelial cell lines (IMCD cells), drugs/compounds, 3D and organ-on-a chip cell culture systems they present compelling data that show that AKAP220 anchors a complex of HDAC6 and Protein Phosphatase-1 (PP1) that controls the polymerization of actin and thereby affects cilia formation and elongation. Genetic or pharmacologic manipulations that disrupt AKAP220 or its ability to bind to PP1, inhibit HDAC6, or affect actin stability result in a similar phenotype of enhanced ciliogenesis and ciliary length. Given that polycystic kidney disease has been described as a ciliopathy, with the gene products of the two most common forms of the disease (polycystin-1 and polycystin-2) localized to the cilia, they tested whether inhibiting HDAC6 activity might affect cyst growth using a human iPSC organoid system. They found that organoids lacking polycystin-2 treated with tubacin had smaller cyst size compared to vehicle-treated mutants, leading them to propose manipulation of HDAC6 as a tentative therapeutic strategy for human autosomal dominant polycystic kidney disease and for ciliopathies characterized by defects in ciliogenesis. 

      Strengths: These findings will be of interest to the ciliary community. They have identified a new factor and its associated partners that appear to regulate ciliogenesis. The studies follow a logical progression and are generally well-done with suitable controls, rigorous quantitation, and a reasonable level of replication (all done at least three times). They have used complementary methods (ie. Genetic manipulation, pharmacologic inhibition) to support their model, sometimes in combination to show that the underlying factor targeted by either genetics or drugs work through the same mechanism. 

      Weaknesses: The major weakness of the report is in its attempt to be translational. Here, the report has a number of serious theoretical and experimental limitations. On the theoretical level, the rationale behind using an HDAC6 inhibitor is unclear given their data and their model. On the one hand, a prior study had reported that a non-specific inhibitor of HDACs slowed cyst growth in an orthologous mouse model of ADPKD. The current work could suggest that HDAC6 was the actual target in the prior work and that a specific inhibitor for HDAC6 should confer the same benefits. On the other hand, there are compelling reports that show that genetic inhibition of ciliogenesis actually attenuates cystic disease in orthologous mouse models of human ADPKD. The current paradigm is that preserved ciliary activity in the absence of Polycystin-1 or Polycystin-2 promotes cystic growth. This would suggest that any intervention that boosts ciliary function could actually worsen disease. And while the authors never directly comment on the functional properties of the "mutant" cilia that result from deletion of AKAP220 or inhibition of HDAC6, they imply that these "enhanced" cilia are functional by suggesting the use of HDAC6 inhibitors as therapy for ciliopathies that are the result of defective biogenesis. Their prior work also provides indirect support for the notion that the enhanced cilia are functional. AKAP220 knock-out mice are reported to be generally functional, apparently lacking phenotypes commonly associated with defective cilia structure or function. These contradictory observations suggest that one or more of the following conclusions: the "mutant" cilia are in fact poorly functional, the HDAC inhibitors are working through a different mechanism than that which has been proposed, or that the assay as used in this report is not a good read-out of cyst-modulating effects. The last point is particularly relevant for this report. The investigators scored effectiveness of tubacin based on the relative rate of growth of cysts treated with different concentrations of tubacin vs vehicle. In this assay, cyst growth is principally driven by rates of cellular proliferation. Tubacin is an anti-proliferative agent with some toxicity, and while it might be highly selective for HDAC6, these studies cannot distinguish between effects mediated through the AKAP22-HDAC6 pathway versus others. In sum, while tubacin or a similarly-acting drug may or may not be effective for slowing cyst growth, there are multiple reasons to think it isn't through the mechanism the authors propose.

    1. Reviewer #1 (Public Review): 

      In this paper, the authors study one of the understudied aspects of the evolutionary transition to multicellularity: the evolution of irreversible somatic differentiation of germ cells. Division of labour via functional specialisation of cells to perform different tasks is pervasive across the tree of life. Various studies assume that the differentiation of reproductive cells ("germ-role cells" in this manuscript) into a non-reproducing cell type ("soma-role cells") is irreversible. In reality, the conditions that promote the evolution of this irreversible transition are unclear. Here, the authors set out to fill in this knowledge gap. They model a population of organisms that grow from a single germ-role cell and find the optimal developmental strategy in terms of differentiation probabilities, under different scenarios. Under their model assumptions, they show that irreversible somatic differentiation can evolve when 1) cell differentiation is costly, 2) somatic cells' contribution to growth rate is large, 3) organismal body size is large. 

      Overall, I think the authors identified an interesting and neglected aspect of cellular differentiation and division of labour. I enjoyed reading the paper; I thought the writing was clear and the modelling approach was adequate to address the authors' question. 

      Some aspects that can be improved: 

      1) Throughout the manuscript, I was somewhat confused about what system the authors have in mind: a colony with division of labour or a multicellular organism? While their model can potentially capture both, their Introduction and Discussion seem to be geared towards colonies at the transition to multicellularity, whereas the Results section gives the impression that the authors have multicellular organisms in mind (e.g. very large body sizes). 

      2) From the point of view of someone who works on topics related to cancer and senescence, I think these fields are very much connected to the evolution of multicellularity. Maybe because I had multicellular organisms in mind rather than colonies with division of labour (above), I thought the manuscript missed this connection. Damage accumulation is key to Weismann and Kirkwood's theories of germ-soma divide and disposable soma, respectively, whereas dysregulated differentiation is one of the important aspects of tumour development (e.g. Aktipis et al. 2015). Making these links could also be relevant to discuss some of the model assumptions. For instance, the authors assume that fast growth comes with no cost in terms of cell damage, which may not always be the case (e.g. Ricklefs 2006) and reversibility of somatic differentiation can come at a cost of increased risk of somatic "cheaters" or cancerous cell lines. 

      3) The authors assume the differentiation strategy (D) does not change over the lifetime (which equates to ontogenesis in their model, i.e. they do not consider mature lifespan). I wonder if this is really the case, or whether organisms/cells can respond to the composition of cells they perceive. For instance, at least in some animal tissues, a small number of stem cells are kept to replenish differentiated tissue cells when needed. I understand that making D plastic can make the model really complicated, but maybe it is worth talking about what strategy would evolve if D was not stable through ontogenesis (and mature lifespan). My initial guess is that if differentiation probabilities can change through life and if one considers cellular damage accumulation, senescence and cancer (as above), the conditions that favour irreversible somatic differentiation would expand.

    2. Reviewer #2 (Public Review): 

      This works seeks to determine the conditions in which simple multicellular groups can evolve irreversibly somatic cells, that is: a replicating cell lineage that provides cooperative benefits as the group grows and cannot de-differentiate into reproductive germ cells. 

      This question is addressed with a well-constructed model that is easy to understand and provides intuitive results. Groups are composed of germ and soma cells that replicate synchronously until the group has reached a maximal size. When each type of cell divides, they may have different probabilities of producing daughter cells of each type, and the analysis determines the optimal differentiation probabilities for each type of cell depending on a variety of factors. Critically, irreversible somatic differentiation arises when the optimal probability for soma cells is to produce only soma cells. 

      The elegance of the model means that the predictions are easy to interpret. First, when there is a higher cost for soma cells to produce germ cells, then a dedicated lineage of somatic cells is more favourable. Second, when soma cells produce only soma cells and germ cells can produce both types, the proportion of soma cells in the group will increase with each division. Consequently, for irreversible somatic cells to be optimal, germ cells must produce a small number of soma cells and these few must provide large benefits. Third, larger group sizes are required for a small number of soma cells to arise and provide sufficient benefits to the group. 

      Inevitably, there is a trade-off between the benefits of a simple model and the costs of idealised assumptions. 

      Among other assumptions, the model assumes that germ cells and soma cells replicate synchronously and at the same rate, and that soma cells provide benefits throughout the growth of the group, but do not increase the fecundity of germ cells in the last generation. Consequently, it is not clear to what extent the predictions of the model apply to the notable empirical cases where these assumptions do not hold. For instance, in the often-cited Volvocine algae, soma cells do not provide any benefits until the last generation of the group life cycle. This may help to explain why many Volcocine species have a very large number of somatic cells, counter to the second prediction of the model. 

      Overall, this analysis is targeted and provides clear predictions within the bounds of its assumptions. Thus, these results provide a compelling framework or stepping-stone against which future models of germ-soma differentiation in alternate scenarios can be compared and evaluated.

    3. Reviewer #3 (Public Review): 

      This paper provides a theoretical investigation of the evolution of somatic differentiation. While many studies have considered this broad topic, far fewer have specifically modelled the evolutionary dynamics of the reversibility of somatic differentiation. Within this subset, the conditions that select for irreversible somatic differentiation have appeared conspicuously restrictive. This paper suggests that an overly simplified fitness function (mapping the soma-germline composition of an organism to its growth rate) may be partly to blame. By allowing for a more complex fitness function (that captures the effect of upper and lower bounds for the contribution of somatic cells to organism fitness) the authors are able to identify three conditions for the evolution of irreversible somatic differentiation: costly cell differentiation (particularly for the redifferentiaton of soma-cell lineages to germ line); a high/near maximal organismal growth advantage imbued by a small proportion of soma cells; a large maturity size for the organism (typically greater than 64 cells). 

      The model presented is simple and elegant, and succeeds in its aim of providing biologically feasible conditions for the evolution of irreversible somatic differentiation. Although the observation arising from the first condition (that high costs to reversible somatic differentiation promote the evolution of irreversible somatic differentiation) is perhaps unsurprising, the remaining conditions on the fitness function and the organism maturity size are interesting and initially non-obvious. Particularly tantalising is the prospect of testing these conditions, either against available empirical data, or in an experimental setting. 

      The model does however make a number of simplifying assumptions, the effects of which may limit the broad applicability of the results. 

      The first is to assume that cell division is synchronous, so that the costs of cell differentiation can be straight-forwardly averaged across the organism at each division. While the authors present a convincing biological justification for this assumption for algae such as Eudorina illinoiensis and Pleodorina californica, it is not immediately that this assumption should hold more widely. 

      The second is to assume that the development strategy (i.e. the rates of differentiation between somatic and germ-line cell types) is constant throughout the organism's growth. For instance, there may be a growth advantage in the current model (aside from the advantages with respect to reduced mutation accumulation) of producing more germ cells early in the developmental programme, before transitioning to producing more soma cells in later development. 

      Exploring such extensions to this model presents a seam of potential avenues for investigation in future theoretical studies.

    1. Reviewer #1 (Public Review):

      This is an interesting, but small study of seven ocular fluid samples examined by scRNA-seq analysis from patients with non-infectious uveitis and one sample with infectious uveitis. The authors aimed to characterize the leukocyte composition of these samples and aimed to validate their findings by multicolour flow cytometry. They further analysed the levels of cytokines by multiplex immune assay. The study confirms previous data on the dominance of lymphocytes as infiltrates in ocular fluid samples and identified the major leukocyte lineages in the samples. The major strength is the unbiased cell population identification which is the power of single cell sequencing. Despite this strength, the small samples size, variable entities studied, and substantial variability in composition between the samples - which is intrinsic to clinical samples also noted by the authors - makes the impact of the work on the field not entirely certain. Another weakness is that the 'validation' by flow cytometry work is not based on hall mark genes for the clusters identified by scRNAseq and the proportions of cell types identified by scRNAseq and flow cytometry are not comparable. The authors achieved the unbiased characterization of samples of ocular fluid, but did not achieve in linking this information with the cytometry and cytokine data. 

      It is uncertain how the cytometry and multiplex immunoassay complement the single cell work since the flow cytometry panel was based on common markers for leukocyte populations and not necessary based on key marker genes identified by scRNA-seq. For example, the marker genes shown in figure 1C are not used in flow cytometry and I believe this neutralizes the unbiased strategy by scRNAseq in the beginning of the work, which was a strong strategy. 

      In the flow cytometry proportions, the B27 samples contain almost entirely granulocytes while this leukocyte population makes up {plus minus}25% in the scRNAseq data. Also, granulocytes proportions in B27-negative sample 1 and B27-positive sample look similar in scRNA-seq, while in flow cytometry the difference is nearly 6-fold. Although this could understandably be due to inter-assay/platform variability, but this would also point towards the the uncertainty in the differences between the groups as a whole. Especially, given the large inter-sample variablity in the scRNAseq. This makes the conclusions on group differences not very robust. 

      The authors state they use the multiplex assay as a complement to transcriptomics and that this was predefined. Hovwer, based on the cell-cell interaction network it would be logical to go for cytokines such as TNFSF13B, TGFB1, CXCL9, but these are missing the the cytokine analysis. Again, the link between the proposed strategies is is a missed opportunity to really connect information.

    2. Reviewer #2 (Public Review): 

      When asked why he robbed banks, Willie Sutton is said to have replied, "Because that's where the money is." Too often researchers who study uveitis (intra-ocular inflammation) have been satisfied to look at blood or other tissue due to limited access to the eye. Kasper and colleagues have followed "Sutton's Law" to provide a comprehensive characterization of intra-ocular leukocytes in four subjects with HLA-B27-associated acute anterior uveitis, two subjects with HLA-B27 negative anterior uveitis, and one subject with bacterial endophthalmitis. Their techniques included single cell RNA Seq, multicolor fluorescence activated cell sorting, Luminex measurement of multiple cytokines in aqueous humor, measurements of cytokines in blood, software to suggest potential cell to cell interactions, and extrapolations from genome wide association studies to determine how genes identified in these studies might be influencing transcripts for cytokines within the eye. The result is an overwhelming wealth of data which is both tantalizing because of the multitude of clues to pathogenesis which have been discovered and slightly unsatisfying because of the small number of subjects involved. Perhaps the main conclusion is that dendritic cells seem especially abundant in the anterior chamber of those with HLA-B27-associated anterior uveitis. 

      In this study, the institutional review board allowed only 11 subjects due to the invasive nature of obtaining cells from the eye. Due to technical reasons, only 7 of the ocular samples could be studied by single cell RNA-Seq. As the authors recognize, multiple factors could influence the results in addition to the diagnosis. These parameters include age, sex, disease duration, local medication, systemic medication, and co-morbidities. The challenge is further complicated because HLA-B27 negative anterior uveitis is undoubtedly a collection of several diseases. It is impossible to do valid statistical comparisons on the basis of only two controls with uveitis. The validity of the comparisons weakens further because controlling for potentially important variables is also impossible. Nonetheless, this group from Muenster, Germany, has produced a pioneering study in an extremely comprehensive manner. It should serve as a roadmap for further studies to confirm or refute these preliminary findings. Ultimately the challenge will be to devise therapies based on the insights that derive from this type of big data approach.

    1. Reviewer #1 (Public Review):<br> Miyamoto and colleagues study the role of various oncogenes including MYC, HOXA9 and SOX4 in transformation of haematopoietic cells in vitro and in vivo. The authors analyze gene expression profiles and characterize leukemogenesis and cell survival resulting from manipulation of MLL-AF10 expression in myeloid leukemias. The experiments largely utilise ectopic over-expression of transgenes; hence results comparing relative "potency" of individual genes must be interpreted with caution due to supraphysiological levels of expression. 

      Specific comments: 

      1) In Figure 1A, the authors attempt to identify direct target genes of the MLL fusion protein MLL-ENL by performing ChIPseq using an anti-MLL antibody. Whether or not the signal can be attributed to MLL-ENL or wild-type MLL is unclear. Furthermore, genome-wide MLL-occupancy patterns are not shown. The work would be stronger if the authors could reconcile current data with other publicly available datasets for MLL or MLL-fusion protein occupancy in comparable contexts. 

      2) It would appear (based on capitalisation), that the authors are over-expressing human transgenes in mouse cells. This is not necessarily a concern, but should be considered when interpreting the data. Likewise, whether the primers used for qPCR are detecting expression of the transgenes, the endogenous genes or both is important (for some of the figures such as Fig. 1C there seems to be a mix e.g. Myc vs HoxA9/HOXA9). 

      3) Most of the in vivo transplantation experiments have not been performed using fluorescent reporters or congenic recipients that would enable identification of donor-derived cells. Differences between the groups could be attributed to differential engraftment, or potentially even immune rejection (assuming ectopic expression of human transgenes in an immune-competent context). Disease features in recipient mice (beyond survival) are also not shown and expression of transgenes at end-point not confirmed. 

      4) The authors propose that the data in Figure 5B confirms direct regulation of Bcl2, Sox4 and Igf1 by HOXA9. However, the regulation could also be indirect e.g. HOXA9 could regulate a transcription factor that regulates those genes, or HOXA9 depletion could induce differentiation that may result in downregulation of those genes.

    2. Reviewer #2 (Public Review): 

      The manuscript of Miyamoto et al. describes the synergistic function between HOXA9 and MYC downstream of MLL fusions in myeloid leukemogenesis. They show that MLL-AF10 expression up-regulates both HOXA9 and MYC expression. Gene expression profiles of immortalized cells (IC) indicate that distinct genetic pathways are driven by HOXA9 and MYC. Cooperativity in in vivo leukemogenesis between HOXA9 and MYC is shown. Apoptotic cell death is increased in MYC-IC and it is cancelled by overexpression of BCL2 or SOX4 that are up-regulated in HOXA9-IC but not in MYC-IC, suggesting that these genes are downstream of HOXA9 and responsible for cooperativity between MYC and HOXA9. Moreover, deletion of BCL2 or SOX4 inhibited MLL-AF10- or HOXA9/MEIS1-induced leukemogenesis. This study is well designed and experimental results are clearly presented. These results provide useful information for our understanding the mechanisms of HOX-associated leukemogenesis.

    1. Reviewer #1 (Public Review): 

      The use of DREADDs to modulate astrocyte signaling and evaluate the contribution of these glial cells to the control of the GnRH system is relevant, timely and innovative. The authors provide a combination of compelling neuroanatomical data, electrophysiological recordings and LH measures that support their key findings in males. The calcium imaging experiments are rigorously performed but the data need to be validated on a larger number of animals. The authors also explore possible sex differences in the process but several caveats need to be overcome before reaching a conclusion on this aspect. Several additional points should be addressed in order to improve the manuscript, as elaborated below. 

      1) It would be relevant to provide an estimation of the fraction of GnRH or KNDy neuron populations surrounded by infected astrocytes. This data would be interesting to discuss in relation with previous work showing that activation of only a fraction of GnRH neurons can induce LH release. 

      2) In the characterization of cell targeting, the authors should specify whether GFAP+ alpha tanycytes lining the dorsal part of the arcuate nucleus were also infected by viral constructs injected into the arcuate nucleus. 

      3) Calcium imaging analyses were performed on 1 to 2 animals per group, which is below the minimum number of animals required for statistical analyses. In all experiments, a minimum of 3 animals per group is required. 

      4) To evaluate whether PGE2 mediates the effect of astrocyte activation on GnRH neuron firing, the authors pretreated slices with a mix of EP1 and EP2 antagonists. The rationale for choosing this combination should be explained considering that EP1 was previously shown not to be involved in the stimulatory effect of PGE2 on GnRH neuronal activity (Clasadonte et al., 2011, PMID: 21896757). 

      5) It is not clear whether the characterization data shown in figure 1 are also applicable for the experiments performed on females. If it is not the case, the data obtained in female should be added. 

      6) As fairly pointed out by the authors, there are major caveats in the experiments performed in females. They indicate that recordings were not made at the same moment of the day between males and females but also that the time post-surgery significantly differed between the 2 sexes (less than 2 months in males vs 5 months in females). Therefore, any conclusion about a possible sex difference can unfortunately not be drawn from these data. These experiments need to be reproduced in a rigorously controlled manner in order to reach a definitive conclusion. 

      7) No electrophysiological recordings are shown. Representative recordings of GnRH and KNDy neuronal activity should be added to the figures.

    2. Reviewer #2 (Public Review): 

      The study by Vanacker et al builds upon previous literature demonstrating the PGE2 from astrocytes activates GnRH neurons. The authors demonstrate that chemogenetic activation of GFAP cells in the POA activates neighboring GnRH neurons via a PGE2 dependent mechanism. This may have implications on circulating LH levels as well as thermogenic and tachycardic conditions. The study is largely well done and clearly presented. There is some confusion/concerns about inclusion/exclusion of data within graphs, number of animals used for study, validation of animal models, and interpretation of physiological drivers of the activity or phenotype observed.

    1. Reviewer #1 (Public Review): 

      mTORC1 activity promotes anabolic growth and suppresses autophagy. Because mTORC1 integrates growth signals, nutrient concentrations, and other variables to coordinate metabolism with growth and division, mTORC1 dysfunction contributes to cancers, metabolic derangements, autoimmune and neurological disorders. DEPTOR is an endogenous protein inhibitor of mTORC1 that is of general interest for several reasons, including the hope that understanding how DEPTOR works will lead to new strategies for therapeutically tuning mTORC1 activity. 

      In this study, Heimhalt et al. succeeded in providing new structure/function insights into the binding and inhibitory effects of DEPTOR on mTORC1. Using in vitro kinase assays with all purified components, electron cryo-microscopy, NMR, and homology models the authors report that DEPTOR binds and partially inhibits mTORC1 via two distinct surfaces. Remarkably, DEPTOR can only inhibitor mTORC1 activity by <50%, and its inhibitory activity appears to depend at least in part on a slow, allosteric conformational change and to be limited by a negative feedback loop. Specifically, the authors build on prior work to show that DEPTOR is a phosphorylation substrate of mTORC1 and that phosphorylated DEPTOR cannot inhibit mTORC1. The authors speculate that the partial and self-limiting inhibition of mTORC1 by DEPTOR evolved so that DEPTOR can "blunt" mTORC1 activity without increasing tumorigenic PI3K signaling due to loss of mTORC1 feedback inhibition. 

      A central message of the manuscript is that, in contrast to previous cell-based studies, the authors find that DEPTOR requires both its PDZ domain and adjacent "long linker" for inhibition. The authors propose that the linker's interaction with the FRB domain of mTORC1 is crucial to the partial inhibition mechanism. As with other studies of mTORC1 complexes by cryo-EM, the maps included in this study are challenging to interpret, especially around the low-resolution periphery where DEPTOR's domains may bind. Hence, the authors used a battery of additional techniques, including HDX-MS, NMR, and homology modeling, to bolster their interpretations. However, the binding mode and role of DEPTOR's linker region remain underdetermined and are the focus of detailed recommendations to the authors. Pending the resolution of the technical questions, this study should make an impactful contribution of interest to structural biologists, kinase enzymologists, and cell biologists.

    2. Reviewer #2 (Public Review): 

      The manuscript by Heimhalt et al uses biochemical reconstitution, structural and biophysical techniques to shed light on the mTORC1 subunit, DEPTOR, and its regulatory roles toward mTORC1-dependent signaling. The authors report that DEPTOR associates with the mTOR protein via two domains, the PDZ and an unstructured linker, binding to the FAT and FRB domains, respectively. This bipartite interaction appears critical for maintaining DEPTOR bound and for partially inhibiting substrate engagement, likely via an allosteric mechanism (as opposed to direct substrate competition). Interestingly, DEPTOR-mediated inhibition is stronger on active mTORC1 than on the inactive (non-RHEB bound) complex, a claim supported by both biochemical and structural considerations. Finally, as part of a regulatory feedback, DEPTOR phosphorylation by mTORC1 in the linker region decreases DEPTOR ability to bind to and inhibit mTORC1. Overall, this is an interesting and well executed manuscript that sheds light on an important component of the mTORC1 complex. The experiments are of high quality and support the main claims.

    3. Reviewer #3 (Public Review): 

      It has been known for more than 10 years that DEPTOR is a negative regulator of mTORC1 and it has more recently come to light that this regulation may be particularly important in disease states such as multiple myeloma. In spite of the great interest in this topic, it has remained unclear exactly how DEPTOR interacts with and regulates mTORC1. It is therefore noteworthy that this study make significant progress towards defining the basis for DEPTOR-dependent inhibition of mTORC1 through a compelling combination of structural and biochemical approaches. The results define a novel bipartite binding mechanism for DEPTOR interactions with mTOR and characterize the basis for partial inhibition of mTOR by DEPTOR. Of further interest is the elucidation of a feedback loop whereby mTORC1 phosphorylates DEPTOR which suppresses the ability of DEPTOR to inhibit mTORC1. The overall quality of the data is high and the authors have offered a balanced and thoughtful description of their results and of how these findings can be integrated into existing knowledge in this field. This is a very rare example of a manuscript that where I cannot identify any major weakness.

    1. Reviewer #1 (Public Review): 

      Zappia et al investigate the function of E2F transcriptional activity in the development of Drosophila, with the aim of understanding which targets the E2F/Dp transcription factors control to facilitate development. They follow up two of their previous papers (PMID 29233476, 26823289) that showed that the critical functions of Dp for viability during development reside in the muscle and the fat body. They use Dp mutants, and tissue-targetted RNAi against Dp to deplete both activating and repressive E2F functions, focussing primarily on functions in larval muscle and fat body. They characterize changes in gene expression by proteomic profiling, bypassing the typical RNAseq experiments, and characterize Dp loss phenotypes in muscle, fat body, and the whole body. Their analysis revealed a consistent, striking effect on carbohydrate metabolism gene products. Using metabolite profiling, they found that these effects extended to carbohydrate metabolism itself. Considering that most of the literature on E2F/Dp targets is focused on the cell cycle, this paper conveys a new discovery of considerable interest. The analysis is very good, and the data provided supports the authors' conclusions quite definitively. One interesting phenotype they show is low levels of glycolytic intermediates and circulating trehalose, which is traced to loss of Dp in the fat body. Strikingly, this phenotype and the resulting lethality during the pupal stage (metamorphosis) could be rescued by increasing dietary sugar. Overall the paper is quite interesting. It's main limitation in my opinion is a lack of mechanistic insight at the gene regulation level. This is due to the authors' choice to profile protein, rather than mRNA effects, and their omission of any DNA binding (chromatin profiling) experiments that could define direct E2F1/ or E2F2/Dp targets.

    2. Reviewer #2 (Public Review): 

      The study sets out to answer what are the tissue specific mechanisms in fat and muscle regulated by the transcription factor E2F are central to organismal function. The study also tries to address which of these roles of E2F are cell intrinsic and which of these mechanisms are systemic. The authors look into the mechanisms of E2F/Dp through knockdown experiments in both the fat body* (see weakness) and muscle of drosophila. They identify that muscle E2F contributes to fat body development but fat body KD of E2F does not affect muscle function. To then dissect the cause of adult lethality in flies, the authors proteomic and metabolomic profiling of fat and muscle to gain insights. While in the muscle, the cause seems to be an as of yet undetermined systemic change , the authors do conclude that adult lethality in fat body specific Dp knockdown is the result of decrease trehalose in the hemolymph and defects in lipid production in these flies. The authors then test this model by presenting fat body specific Dp knockdown flies with high sugar diet and showing adult survival is rescued. This study concurs with and adds to the emerging idea from human studies that E2F/Dp is critical for more than just its role in the cell-cycle and functions as a metabolic regulator in a tissue-specific manner. This study will be of interest to scientists studying inter-organ communication between muscle and fat. 

      The conclusions of this paper are partially supported by data. The weaknesses can be mitigated by specific experiments and will likely bolster conclusions. 

      1) This study relies heavily on the tissue specificity of the Gal4 drivers to study fat-muscle communication by E2F. The authors have convincingly confirmed that the cg-Gal4 driver is never turned on in the muscle and vice versa for Dmef2-Gal4. However, the cg-Gal4 driver itself is capable of turning on expression in the fat body cells and is also highly expressed in hemocytes (macrophage-like cells in flies). In fact, cg-Gal4 is used in numerous studies e.g.:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4125153/ to study the hemocytes and fat in combination. Hence, it is difficult to assess what contribution hemocytes provide to the conclusions for fat-muscle communication. To mitigate this, the authors could test whether Lpp-Gal4>Dp-RNAi (Lpp-Gal4 drives expression exclusively in fat body in all stages) or use ppl-Gal4 (which is expressed in the fat, gut, and brain) but is a weaker driver than cg. It would be good if they could replicate their findings in a subset of experiments performed in Figure 1-4. 

      2) The authors perform a proteomics analysis on both fat body and muscle of control or the respective tissue specific knockdown of Dp. However, the authors denote technical limitations to procuring enough third instar larval muscle to perform proteomics and instead use thoracic muscles of the pharate pupa. While the technical limitations are understandable, this does raise a concern of comparing fat body and muscle proteomics at two distinct stages of fly development and likely contributes to differences seen in the proteomics data. This may impact the conclusions of this paper. It would be important to note this caveat of not being able to compare across these different developmental stage datasets. 

      3) The authors show that the E2F signaling in the muscle controls whether binucleate fat body nuclei appear. In other words, is the endocycling process in fat body affected if muscle E2F function is impaired. However, they conclude that imparing E2F function in fat does not affect muscle. While muscle organization seems fine, it does appear that nuclear levels of Dp are higher in muscles during fat specific knock-down of Dp (Figure 1A, column 2 row 3, for cg>Dp-RNAi). Also there is an increase in muscle area when fat body E2F function is impaired. This change is also reflected in the quantification of DLM area in Figure 1B. But the authors don't say much about elevated Dp levels in muscle or increased DLM area of Fat specific Dp KD. Would the authors not expect Dp staining in muscle to be normal and similar to mCherry-RNAi control in Cg>dpRNAi? The authors could consider discussing and contextualizing this as opposed to making a broad statement regarding muscle function all being normal. Perhaps muscle function may be different, perhaps better when E2F function in fat is impaired. 

      4) In lines 376-380, the authors make the argument that muscle-specific knockdown can impair the ability of the fat body to regulate storage, but evidence for this is not robust. While the authors refer to a decrease in lipid droplet size in figure S4E this is not a statistically significant decrease. In order to make this case, the authors would want to consider performing a triglyceride (TAG) assay, which is routinely performed in flies.

    1. Reviewer #1 (Public Review):

      Straub et al., present the first structures of membrane proteins from the XKR family of lipid scramblases. While structures of lipid scramblases from the TMEM16 family have been solved previously, it is the XKR family of proteins that have been identified as the scramblases involved in the dissipation of phosphatidyl-serine asymmetry in the plasma membrane to signal apoptosis. As such, the molecular details of these proteins has been highly sought after. Through the development of a synthetic nanobody that binds to XKR9 from Rattus norvegicus, the authors solved the full-length structure of this small 43 kDa protein by cryo-EM, with a resolution of 3.66 Å. This structure reveals a novel topology, adding to the growing repertoire of membrane protein folds. In addition, they were able to determine the structure of the caspase-3 treated protein at a resolution of 4.3 Å, which cleaves a C-terminal peptide that has been proposed to be involved with scramblase activation. In addition, both structures possess densities that are suggestive of lipids, with densities embedded within the protein core, thus mapping out a putative lipid site or pathway. There has been very little structural information about XKR proteins so far, thus, this work is impactful to the field and pushes forward our ability to investigate a new class of lipid scramblases.

      A limitation of this study is that the structures do not clearly inform on the mechanism quite yet. Unfortunately, transport function was not observable in the reconstituted liposomes, and so the connection between structure and function are limited. Certainly, it can be challenging to reconstitute function from purified proteins, but given that the previous studies of this protein are based on cellular activity, such as the rescue of PS scrambling of XKR8 knockouts by XKR9 and mutant constructs (Suzuki et al., JBC 2014), it is still not clear whether this protein provides the basic unit for transport or whether other components are required. With this, it is unclear whether the caspase cleaved protein informs on a mechanistically active structure. Thus, the paper needs to be clarified by focusing on the novel structure, potential lipid pathways and the difference in the caspase treated vs. full-length structures without speculating on the molecular mechanism.

    2. Reviewer #2 (Public Review):

      In this report by Straub and colleagues, they describe cryo-EM structures of a rat ortholog of XKR9 in full-length and caspase-9 activated states. The structure is a technical achievement due to the small size of XKR9 and provides a first view into this family of proteins, of which three members, XKR4, XKR8, and XKR9, participate in lipid scrambling. The structures are determined in complex with a synthetic monobody, resulting in an interpretable density map. To begin to understand the role of caspase cleavage in activation, a structure is determined following caspase activation. Notably, no changes could be detected in the cleaved form and it thus remains unclear how caspase activates XKR9 or how activated XKR9 mediates lipid scrambling. Overall, these results will be of broad interest and will likely serve as a foundation for future studies into this interesting family of proteins.

    3. Reviewer #3 (Public Review):

      This is a characteristically high quality report from the Dutzler lab of the atomic structure (via cryo EM, using synthetic single chain antibody for size enhancement) of a class of membrane proteins that was identified as being important for the exposure of the signaling lipid phosphatidylserine at the surface of apoptotic cells. These Xk-related proteins were proposed as caspase-activated lipid scramblases. The Dutzler paper reveals the structure of the Xkr9 homolog but the data do not allow conclusions about scramblase activity. No activity was detected and the protein in its two very similar conformations before and after caspase treatment offers no obvious clue as to its function. Nevertheless this is an important first step in this nascent field.

    1. Reviewer #1 (Public Review):

      In previous work, the authors identified a subpopulation of neocortical layer (L) 5 pyramidal excitatory neurons that were NAV1.1-positive (Ogiwara et al., 2013). However, the sub-identity of those neurons was unclear. Using a novel Scn1a-GFP BAC transgenic mouse, in this manuscript, they characterize these cells at postnatal day(P)15 and P28, to reveal an inverted expression pattern of two genes previously implicated in the determination of a corticospinal (CS) versus corticothalamic (CT) neuronal fate in L5 and L6 i.e. FEZ family zinc finger protein 2 transcriptional factor (Fezf2) and its CS fate repressor, transcription T-box brain 1 transcription factor (Tbr1) (Han et al., 2011). They found that at P15, 54 % of GFP positive neurons in L5 were FEZF2-positive(+), while minimal FEZF2 expression was observed in L2/3 and L6 i.e. 16% and 12% respectively. In contrast, TBR1+GFP+ neurons were minimal (10%) in L5 and enriched (45% and 41%) respectively in L2/3 and L6.

      Largely based on the previously reported frequency distributions for populations of CT, cortico-cortical (CC), and cortico-striatal (iCS) neurons across the cortical layers, and the aforementioned regulatory relationship between Fezf2 and Tbr1, the authors conjecture a mutually exclusive expression of Scn1a and Scn2a amongst these neuronal cell types. The premise of a mutually exclusive sub-population of Scn1a and Scn2a pyramidal neurons is indeed intriguing as it may help substantiate a circuit-based explanation for common Dravet Syndrome phenotypes. However, the manuscript is largely descriptive and can benefit by including quantitative measures such as cell counts to support text conclusions. Evidence for a mutually-exclusive expression of Scn1a and Scn2a amongst populations of CC, CT and iCS neurons can be bolstered by the use of viral-tracing strategies in combination with co-labeling counts of the relevant marker (processed by an insitu and/or protein expression assay). Additionally, subjective terminology such as "dominant", "dense", "less intense", "major" and minor, are prevalent throughout the manuscript and should be clarified by defining these terms based on objective, qualitative measures such as transcript abundance or fluorescence intensity. A substantial portion of (FEZF2+ and TBR1+) excitatory neurons in L5 express Scn1a- driven GFP. These numbers may conflict with a previous report of endogenous Scn1a antibody- expression of minimal (~5%) co-expression among excitatory pyramidal neurons in the cortex (Dutton et al., 2013). Thus, it is unclear to what extent the BAC-GFP mouse featured in this manuscript recapitulates the endogenous expression of Scn1a. Here, the inclusion of correlation plots for in situ hybridization measures for both the endogenous Scn1a and the transgene GFP, along with western blot quantifications of SCN1A and GFP expression between wild type and BAC-transgenic animals may be helpful. Finally, as the manuscript contains too many long sentences, the intent of the authors is often unclear, and the reader's comprehension of the text may be limited.

    2. Reviewer #2 (Public Review):

      The reasons for focusing on Nav1.1 and Nav1.2 by the authors is clear, the genes which code for these channels have been implicated in a multiple neurological disorders. Identifying distribution patterns and shedding light on how these channels contribute pathological neural circuits is a strong step in the right direct. It has previously been shown that Nav1.1 is mostly localized to inhibitory neurons while Nav1.2 is associated with excitatory neurons. The data shown by the authors suggest that some Nav1.1 expression can be found in excitatory neurons. The implication is interesting as it suggests that the lack of Nav1.1 activity could be beneficial in ameliorating seizure symptoms, especially if one could restrict this decreased expression to excitatory neurons alone. The hypothetical neural circuit in Figure 7 is great as it gives a conclusive theory that the authors and other interested researchers can test and work with. Underpinning some conclusions on the intensity of GFP expression makes one wonder if using another transgenic line would have led to similar conclusions (e.g. In all neocortical layers of Scn1a-GFP mice, cells with dense GFP signals are generally inhibitory interneurons (Supplemental Figure S1), and FEZF2 or TBR1 signals were found in cells with less intense GFP signals (see below) indicating that these are excitatory projection<br> neurons).<br> However, the weakness of this paper is that a few too many conclusions were drawn based on assumptions.

    3. Reviewer #3 (Public Review):

      Yamagata and collaborators describe the generation of an Scn1a-GFP transgenic mouse. Given that mutations in this gene are a frequent cause of epilepsy, including some of the most severe forms, and that the mechanisms how they result in the dysfunction of cerebral cortical networks are still under investigation; this new resource is very welcome and carries the potential to enable novel experimental approaches to the problem. The authors use a combination of protein (Western blot and immunohistochemistry -IHC-) and mRNA expression assays (in situ hybridization) to confirm the successful expression of GFP only in cells that express Nav1.1, the protein product of Scn1a.

      Nav1.1 is one of the pore-forming alpha subunits of the voltage-gated sodium channel (VGSC). It has been previously shown that it is present predominantly in inhibitory interneurons of the cerebral cortex and hippocampus, and a small subpopulation of cortical excitatory neurons. Nav1.2, the product of the gene Scn2a, is the other VGSC alpha subunit abundantly expressed in the brain; and it exhibits a mutually exclusive expression pattern with Nav1.1. The authors confirmed in the Scn1a-GFP mice the presence of Nav1.1 in interneurons and how it is only present in neurons that are negative for Nav1.2 expression; further supporting the accuracy of this new mouse model.

      The authors then focus on better characterizing the expression profile of Nav1.1 in cortical excitatory neurons, using a combination of the cortical layer location of the neurons and their expression of the transcription factors TBR1 or FEZF2. The former transcription factor identified neurons that projected within the cerebral hemispheres to the thalamus, striatum, and other cortical areas; while the latter identifies neurons that project to brainstem or spinal cord. Nav1.1 was found to be expressed predominantly in FEZF2 (+) layer V cortical neurons TBR1 (+) layer II/III neurons (which have been shown to be mostly cortico-cortical in previous reports). On the other hand, layer VI TBR1 (+) were for the most part GFP (-) and expressed instead Nav1.2. However, these associations are not absolute, as the authors found in each group cells that are GFP (+) and GFP (-), the latter of which are assumed to be Nav1.2 (+) although this was not specifically documented in IHC experiments with TBR1 and FEZF2 labelling. Future studies trying to extend this characterization will greatly benefit of the availability of this transgenic mouse line.<br> The paper concludes with a proposed mechanism through which Scn1a haploinsufficiency in mice can result in epilepsy and sudden death (an animal model of the very severe human epilepsy Dravet Syndrome, which is often secondary to SCN1A loss-of-function mutations and is associated with an increased risk of sudden death). The authors theorize that Scn1a haploinsufficiency might result in the net loss of inhibition of cortical neurons projecting to the parasympathetic brainstem centers, which can then trigger a fatal bradycardia after prolonged seizures. This is an intriguing proposal, and given how little we understand about the contributions of the different cortical and hypothalamic inputs to the autonomic cardiac centers in the brainstem (nucleus ambiguous, RVLM and less prominently dorsal nucleus of vagus) no doubt this new mouse model will prove useful in studies testing this hypothesis.

    1. Reviewer #1 (Public Review):

      The authors have studied mutations in the K13 gene that is linked to Artemisinin resistance in a range of African parasites. They show that these mutations can confer resistance in a in vitro survival assay but that they are often linked to reduced fitness. The authors also show that different parasites have less of an impact on fitness when the K13 mutations are introduced in line with the suggestion that the overall genetic background is critical for transmission of K13 mutations. The paper also shows evidence that genes potentially contributing to the genetic background are not involved.

      The overall work involves a significant amount of work that to generate a wide range of different parasite lines that allow a detailed assessment of how different mutations interact with the genetic background of the parasite. This provides a significant amount of new insights. A key conclusion the authors draw from this work relates to the relationship between fitness and resistance and by inference on why artemisinin resistance has occurred in SE Asia. While this indeed would be a striking conclusion I think the data at this stage is not strong enough to make this claim. The claim is mainly based on Figure 3 E and F as well as 5 C and D. While indeed, initially it looks like RSA has much less of a survival impact in Dd2 there is some concern that the data is generated using different baselines (isogenic WT parasite in Figure 3 and Dd2eGFP in Figure 5 D). This is noteworthy as in Figure 5C the Dd2wt parasite is used and the fitness cost appears to be different.

      A striking finding is that the UG659C560Y line appears to have a relatively small fitness cost - especially if looked at for the whole 40 generations rather than the somewhat arbitrarily picked 38 days. This data could suggest that there are parasites in Africa that have the capacity to acquire resistance with minimal cost to fitness.

      The selective sweep to C560Y in SE Asia is something that has been known for a while. It is striking that it has been selected as based on the data presented here P563L has a similar fitness and RSA profile. The authors could explore this further.<br> Overall, the main conclusion that there are K13 mutations that can confirm resistance to Art in the context of African parasites is clearly presented and convincing and this highlights the risk that exists for public health officials in African nations. What would be interesting from a readers perspective is how likely it is that this loss of fitness hurdle is going to be overcome in Africa and whether the risk of resistance development will increase as transmission rates drop.

    2. Reviewer #2 (Public Review):

      In this paper, the investigators performed two large-scale surveys of the propeller domain mutations in the K13 gene, a marker of artemisinin (ART) resistance, in African (3299 samples) and Cambodian (3327 samples) Plasmodium falciparum populations. In the African parasite population, they identified the K13 R561H variant in Rwanda, while parasites from other areas had the wild-type K13. In Cambodia, however, they documented a hard genetic sweep of C580Y mutation that occurred rapidly. They generated the C580Y and M579I mutations in four different parasite strains with different genetic backgrounds and found that these mutations conferred varying degrees of in vitro ART resistance. They further edited the SE Asian parasite strains Dd2 and Cam3.II with 7 K13 mutations and found that all the propeller domain mutations conferred ART resistance in the Dd2 parasite, whereas three of the mutations did so in the Cam3.II background. The R561H and C580Y mutations were also evaluated in several parasites collected from Thailand. In vitro growth competition analysis showed that K13 mutations caused substantial fitness costs in the African parasite background, but much less fitness costs in the SE Asian parasites. This study demonstrated the potential emergence of ART resistance in African parasite populations and offered insights into the importance of the parasite's genetic background in the emergence of ART resistance.

    3. Reviewer #3 (Public Review):

      Stokes et al address the question: Why have mutations in the K13 gene spread rapidly across South East Asia and led to widespread treatment failure with artemisinin-based antimalarials? In contrast, why do K13 mutations remain quite rare in Africa, and artemisinin-based antimalarials remain effective?

      The work combines a number of different studies on different parasites of different origins. Gene editing has been used to assess the effects of K13 mutations in different parasite backgrounds, leading to a very complex view of the competing factors of level of resistance conferred and fitness cost.

      The authors put forward the hypothesis that fitness costs associated with K13 mutations select against their dissemination in the high malaria transmission settings in Africa. However, the complexity of the genetic backgrounds of the parasites makes it difficult to tease out the contributing factors.

    1. Reviewer #1 (Public Review):

      In this study, the authors sought to uncover mechanism regulating NF90-induced antiviral pathways. They initially discovered that the checkpoint protein Tim3 was both induced upon viral infection and that loss of Tim3 is protective against the infection. Mechanistically, the authors used biochemical and cell-based approaches to discover that Tim3 binds NF90 and promotes its degradation via ubiquitination mediated by the E3 ligase TRIM47. The authors further assess the functional consequence of Tim3-mediated inhibition of NF90. As NF90 has known roles in stress granule formation, the authors demonstrated that loss of Tim3 expression is associated with increased NF90, which correlated with increased phosphorylation of PKR and eIF2a and induction of stress granule markers. To demonstrate biological relevance, the authors reveal that mice lacking Tim3 are more resistant to VSV infection.

      Strengths include diverse experimental approaches, including in vivo work, to identify and characterize the TIM3-TRIM47-NF90 axis. Weaknesses include lack of a few key experiments that will help support what is already a strong study. The impact of the work is fundamental new insight into the pathways regulating an important mechanism of viral control.

    2. Reviewer #2 (Public Review):

      In the present manuscript, the authors provide data on a cross-talk between the immune checkpoint molecule Tim-3 in macrophages and the viral sensor NF90 in the context of VSV infection. In particular, they demonstrate (by using cell lines and primary cells) that VSV infection leads to increased expression and activation of Tim-3, Tim-3 interacts with NF90 via its cytoplasmic tail and enhances NF90 ubiquitination by recruitment of TRIM47. Subsequently, NF90 is degraded and VSV replication is increased. Thus, Tim-3 can mediate viral escape in VSV infection. With this, the authors provide a novel viral escape mechanism involving the previously described immune checkpoint molecule Tim-3 and NF90 in macrophages. Although the authors thoroughly identified key players within this viral escape pathway future studies are required to comprehensively resolve subsequent steps within this pathway. The manuscript is well written and structured and the data appear reliable.

    1. Reviewer #1 (Public Review): 

      From a technical point of view this is clearly a very well-done study that does a good job of getting the details right and thoroughly describing the technical issues and the methods. The quantification of presynaptic calcium levels is very convincing. The study is enhanced by the ability to measure both presynaptic capacitance changes as well as postsynaptic AMPA currents. The results are interesting and important.

    2. Reviewer #2 (Public Review): 

      In this paper by Eshra et al., the authors have examined the Ca-dependence of exocytosis at cerebellar mossy fiber boutons. Electrophysiology, Ca imaging, Ca uncaging and capacitance measurements were used. The study reveals the presence of a high affinity Ca sensor for exocytosis, a shallow, seemingly non-saturating relationship between Ca and release or Ca and synaptic delay, a high-affinity sensory for priming of vesicles with very low (near basal) Ca levels, a late rate of release that is independent of Ca concentration (presumably due to sensor saturation), and extremely fast peak kinetics of release. In a way, this work contributes to a comparative view of synapses: these general approaches have been used at other synapses over many years by Neher and others, and they show intriguing differences among different types of synapse that are likely functionally significant. A strength of the work however is in the masterful implementation and explanation of the techniques. The recordings at physiological temperatures, pushing-the-envelope for speed of capacitance measurements, the very careful measurement of KDs of indicators, and the unbiased testing of diverse modern-day kinetic models for release, all combine to lend the paper reliability and give it lasting value.

    3. Reviewer #3 (Public Review): 

      By combining patch clamp recordings at the cerebellar mossy fiber bouton/granule cell synapse with calcium uncaging and two photon Ca2+ imaging Eshra et al, directly correlate presynaptic Ca2+ levels to neurotransmitter release rates. Subsequently, they use these quantitative measurements to test three different previously published models of neurotransmitter release to demonstrate that neurotransmitter release at the cerebellar mossy fiber bouton is best described by models with 5-site Ca2+ binding steps with either parallel or sequential pools. Furthermore, they demonstrate like many other presynaptic terminals, at the cerebellar mossy fiber bouton that there are synaptic vesicles with different intrinsic Ca2+ sensitivity. The experiments are highly rigorous, and the data quality is excellent. Furthermore, the experiments are a technical tour de force as direct presynaptic recordings are technically challenging and not trivial to perform. These results are important and novel, as they add to our understanding on the presynaptic mechanisms that are used at synapses to enable high fidelity information encoding. However, this reviewer has some criticisms for the authors to consider to strengthen their story. Overall, the study is very solid with all the appropriate experimental approaches and is extremely rigorous. Previously, it has been described by Lou et al Nature 2005 that an allosteric model, not a 5-site model best describes Ca2+ sensitivity of neurotransmitter release. However, there is no data on the the Ca2+ dose response curve at Ca2+ concentrations lower than 1 µM. This is critical to know if an allosteric model can describe release at the cerebellar mossy fiber bouton.

    1. Reviewer #1 (Public Review): 

      In this paper, the authors use an innovative staining method to visualize melanin distribution for in vivo imaging of zebrafish using micro-CT. This is an extension of prior 3D imaging work using this animal model. They show this tool is able to increase resolution of key phenotypes in mutant models that impact these pigment-producing cells.

    2. Reviewer #2 (Public Review): 

      In this paper, the authors extend their previous work using microCT in zebrafish to a new biological problem, which is how to detect and quantify melanin. This is an important question since melanin is involved in skin pigmentation, and affects things like mating and behavior. Moreover, melanin is also found in other structures such as the brain and kidney, although this is not examined. It would be useful to examine their already existing datasets for evidence of melanin at these more internal organs, since the role of melanin there is less well understood but micro CT could become a useful way of assessing this.

    1. Reviewer #1 (Public Review):

      The authors have determined the atomic resolution cryo-EM structures of M. tuberculosis cytochrome bcc at 2.7 Å resolution and in complex with anti-tuberculous drugs Q203 at 2.7 Å and TB47 at 2.9 Å resolution. The Q203 compound is a drug candidate otherwise known as Telacebec with promising results in phase 2 clinical trials. The complex structure could pave the way for rational-based drug design.


      The study builds upon the previously determined supercomplex cryo EM structures of cytochrome bcc complex from a related bacterial host Mycobacterium smegmatis. The cryo EM structures are of excellent quality and it is possible to map the detailed interactions of the drug compounds with the bcc complex. The Q203 and TB47 drugs bind to the Qp site, which is responsible for menaquinol oxidation. Some of the residues around the Q203 sites are different to other determined structures and match known acquired mutations shown to show resistance to these compounds.


      While the structure of the Q203 is of high-resolution the authors have not robustly demonstrated what determines specificity. Structures of other cytochrome bcc complexes show similar residues to M. tuberculosis. The main differences between the M. tuberculosis bcc complex and previously determined structures is that the pocket in M. tuberculosis is more open, However, in the apo structure of M. tuberculosis the pocket is also more open. Basically, since M. tuberculosis bcc shows accommodation of the drugs, it is unclear if specificity is achieved by specific interactions and/or a preferred shape to the binding pocket.

    2. Reviewer #2 (Public Review):

      Zhou et al. have resolved cryoEM structures (at 2.7-2.9 Å resolution) of cytochrome bcc from mycobacteria with two potential drug molecules (Q203 and TB47) used in the treatment of tuberculosis. This was achieved by expressing a hybrid supercomplex with the bcc part from M. tuberculosis and the cytochrome oxidase part from M. smegmatis. The structures show how the inhibitors bind and block the Qo site, with important implications for developing new drug molecules against, e.g., tuberculosis. The work is interesting from a structural biology and bioenergetic perspective, and it opens up new possibilities to understand inhibition mechanisms of respiratory enzymes.

    3. Reviewer #3 (Public Review):

      The authors modified a previously reported hybrid cytochrome bcc-aa3 supercomplex, consisting of bcc from M. tuberculosis and aa3 from M. smegmatis, (Kim et al 2015) by appending an affinity tag facilitating purification. The cryo-EM experiments are based on the authors' earlier work (Gong et al. 2018) on the structure of the bcc-aa3 supercomplex from M. smegmatis. The authors then determine the structure of the bcc part alone and in complex with Q203 and TB47.

      The manuscript is well written and the obtained results are presented in a concise, clear-cut manner. In general, the data support the conclusions drawn.

      To this reviewer, the following points are unclear:

      1. The purified enzyme elutes from the gel filtration column as one peak, but there seems to be no information given on the subunit composition and the enzymatic activity of the purified hybrid cytochrome bcc-aa3 supercomplex.

      2. It is unclear what is the conclusion of the structure comparison (Fig 6) is regarding the affinity of Q203 for M. smegmatis.

    1. Reviewer #1 (Public Review): 

      This study investigated the stimulus-specific plasticity in human visual gamma-band activity using MEG. The study found that stimulus repetition modulated gamma band activity. Gamma-band responses decreased across~10 repetitions and then increased across further repetitions. These effects were strongest in early visual cortex and increased interareal feedforward influences. The study was nicely performed and grounded well with the previous literature. Albeit the analysis were performed were state-of-the art, there were quite many unclear issues in the data-analysis, some of which are critical for the interpretation of the results.

    2. Reviewer #2 (Public Review): 

      Stauch et al linked stimulus-repetition induced changes in behavior, MEG Gamma-band responses, and pupil size. This work is conducted thoroughly, and exhibits a high degree of technical proficiency. The results speak to repetition suppression, sensory adaptation, and the flexibility of neural coding in general. The promise of the present project lies in the combination of measurement modalities in one project, which they do using a regression model approach. The patterns in the data confirm a host of different extant studies' findings, and provide a self-consistent vista on the phenomenon of interest.

    1. Reviewer #1 (Public Review): 

      This manuscript applies extensive simulations with Markov state modelling to describe the activation of a pentameric ligand-gated ion channel (pLGIC). The authors have generated libraries of microsecond trajectories to sample the interconversion of channel functional states. They have described different Markov states of the pH-gated GLIC channel, including conformations that resemble open and closed functional forms, as well as possible intermediates and a "pre-desensitised" state. They have illustrated channel modulation by capturing shifts in the free energies of gating with pH, and a shift in the distribution of states due to a mutation that affects a hydrophobic gate within the narrow transmembrane pore. The authors suggest a role for asymmetry in GLIC gating that may explain experimentally observed structural diversity of the closed state and suggests entropically driven channel closure. Overall, the sampling of channel dynamics is significant and the description of state interconversions sheds some light on pLGIC mechanisms. 

      The manuscript could include better descriptions of the simulation methods, accessible to both experts and nonexperts, avoiding jargon and better spelling out the motivations for choices made. Clearer relation to past simulation studies is needed to avoid any misapprehensions. The manuscript should include analysis to show that the MSM approach has converged and has yielded sampling independent of the starting elastic network/Brownian dynamics model. It is important that proof of equilibrium sampling is obtained in the subsequent free MD library: that it is not sampling just within the vicinity of the initial gating model path. How far afield from the initial ENM/BD path and how converged is the MSM solution? 

      The early results (around figure 2) could include better visualisation and description of the coordinates used for Markov state modelling. tICA1 is presumed to represent the slowest transition, and it appears to capture channel closure. But many readers may wonder what the tICA1/2 vectors represents physically. Perhaps some vector mapping onto the structure can illustrate protein movements for each vector, with relevant discussion. Moreover, the likely pathway through the Markov states between closed and open states could be better discussed. 

      The claims have been justified, but the importance of the findings could be better relayed. This includes newly identified states, where the roles of the intermediately closed forms could be better explained, and the role of any locally-closed form in the gating transition could be described. Note that in Fig2 both closed and LC are projected onto the state 1 cluster with narrow pore and wide ECD. Why was LC not one with compact ECD (by definition), or is this because ECD spreading vanished from the gating mechanism within this MSM? Moreover, I do not see dots for LC near the state I-II border, as the text suggests on page 8.

      The outcome of a predominantly closed channel irrespective of pH could be better related to experiments, including electrophysiology and recent cryo-EM in Ref.33. In the discussion section the authors write that the minority of channels being open is consistent with electrophysiology, apparently in contrast to what is written in the beginning of the results section. The authors previously wrote that Po is not established by electrophysiology but that cryoEM (Ref.33) may suggest it is more closed than open, regardless of pH. How do the solved "open" states compare to the proposed closed low pH state reported in that preprint (ref.33) and how do the propensities (if any) relate? 

      Finally, the relationship of ECD asymmetry to published crystal structures, and the importance of this asymmetry to the functions of pLGICs could be better explained.

    2. Reviewer #2 (Public Review): 

      The authors are trying to explain fundamental and functional aspects of ligand-gated ion channels using extensive molecular dynamics simulations. In particular they examine the effect of pH on GLIC, a pH-gated ion channel, and also the effect of (one) mutation. They successfully account for energy barriers levels as well as free energy levels in GLIC wild-type open and closed states as well as in one gain-of-function mutant, mutated in the one of the pore-lining residues. They also uncover a protonation-dependent symmetrisation in the subunits, which had seen by crystallography but not clearly demonstrated by other techniques before. The approach, based on clustering and Markov-state-models allows to find the transition rates between the different substates and could be used for other ion channels as well. 

      The study is overall well conducted and convincing. However, it suffers from the very limited scope of the mutations examined. Indeed, only one mutant is analysed, whereas dozens of mutants of GLIC have been characterised both functionally and structurally, especially some that fall in the so-called "locally-closed" (LC) state. One thus wonders how the existence of mutants that are known to adopt an intermediate conformation (LC state) fits into the scheme of this study. 

      The impact of this study would be undoubtedly strengthened if at least one more mutant was examined in details, namely one that is blocked in the LC state. Also, it is not entirely clear how much the results are sensitive (or not) to the protonation protocol.

    3. Reviewer #3 (Public Review): 

      The gating mechanism of ligand-gated ion channels offers a challenge to both the experimentalists and the modellers; existing experimental methods lack the ability to access detailed information about conformational changes during the transient events that correspond to the opening of the channel that lets ion flows, while simulations are able to access these levels of details but do not give access to the relevant timescales of the process. At a fundamental level, this makes cross-validating the two approaches a difficult task. 

      In this work, the authors tackle the second challenge by sampling the gating transition over a cumulated simulation time that exceeds 100 microseconds - thus generating very large datasets. While the analysis of these large datasets used to require a significant amount of supervised clustering (e.g. involving manual feature definitions), the authors have decided to apply the protocol of Markov State Model (MSM) construction which has matured into a semi-unsupervised approach. Indeed, it was shown that these kinetic models could be variationally optimized. 

      Major strengths:

      The authors have shown a great technical expertise in showing that such simulations could be generated and analyzed, yielding results that are overall consistent with a lot of previous results, both experimental and computational. An interesting and original observation regarding the role of pH on compaction rather than gating directly is mentioned. 

      Major weaknesses: 

      While the intention of constructing a Markov State Model is very interesting, it does not seem to have been fully executed, by lack of convergence despite a rather large computational effort. The ability to produce an (variationally) optimized kinetic model would have been a much stronger result. 

      More precisely, the authors built an MSM and optimized it using the VAMP method, but were not satisfied with the result because the kinetic model obtained emphasized "exploratory behavior" rather than "convergence of a few [slowest] interesting processes". The most likely reason for this, as pointed out by the authors, is lack of convergence: their simulations might have started to explore processes that are even slower than the ones they are interested in (desensitization? artifact? something else?) but not to convergence. To test this, maybe they should try the deflation method proposed by Husic & Noe (https://doi.org/10.1063/1.5099194) and use it to show that they did sample well the processes that they intended to sample well (gating, not desensitization)? 

      A demonstration of convergence (or lack thereof) and sampling would help clarify how the VAMP approach did not work, beyond the blanket statement that optimizing MSMs are "a feasible approach for peptide- sized systems, [but the authors] find it practically unfeasible for large-scale motions in ion channels ". 

      Also, since they were not satisfied with the variationally optimized MSM, the authors decided to work on an un-optimized one and cluster it to extract states and transitions, in a way that appears to be more supervised than unsupervised. Here too, additional details on the methods and the motivation behind the choices made for clustering would help. Since insights are drawn from these analysis, it would seem important to give a sense of how robust the conclusions would be to slightly different choices in the clustering decisions, for example. 

      Overall, the authors have shown a method that has potential in achieving their aims, and that will yield better results as more computational effort will become possible - which realistically is a lot to ask for. Given the resources available, the results obtained support the conclusions drawn. 

      Unfortunately, limitations in this respect also limits the impact on our understanding of how these molecules work. Yet, the data generated, if made available, could potentially be used beyond the aims of this paper and be made useful for drug discovery, drug design, etc.

    1. Reviewer #1 (Public Review):

      The experimental data and modeling are highly robust. The conclusions of the paper are clearly supported by the results. The sensitivity analysis is particularly impressive and suggests a system that is highly conserved across a wide parameter space. Model validation with CD8+ depletion is a nice addition that leads to interesting and surprising conclusions.The figures are highly instructive and easy to read.

      An area where the paper could be improved is conveying the actual scientific conclusions more clearly and precisely with more focused review of existing literature. The relevance of the paper's conclusions for human influenza could be discussed with more careful language.

      First, the mechanistic conclusions of the work could be emphasized along with the methodology of the work. At present, these are completely lacking from the abstract which somewhat blandly just says that the paper describes a model which fits to data. From my perspective, currently underemphasized and novel / interesting conclusions are that:

      1) CD8+ mediated killing becomes much more rapid on a per capita basis (40000 fold increase) when infected cells dip below several hundred cells approximately 7 days post infection.

      2) There is a negative correlation between infected cell clearance by innate versus CD8+ mediated mechanisms, implying that poorer initial clearance of virus may result in more effective later killing by acquired immune mechanisms.

      3) Even ~80% reduction in maximal CD8E+ levels could prolong infection by 10 days though delay in attaining these threshold CD8E+ levels due to experimental or in silico CD8+ depletion only delays viral elimination by a day.

      4) Most interesting and counterintuitively, CD8+ depletion allows for considerable reductions in the size of lung lesions as well as inflammation scores and degree of weight loss during primary influenza infection. This result suggests that CD8+ T cells have the potential to create significant bystander damage in the lung.

      Second, the introduction and discussion continue to not differentiate whether past experimental results are from humans or mice. It is somewhat misleading to cite mouse studies without acknowledging that these are from a model that in no way captures the totality of human infection conditions. For all animal models of human infection, the strengths of the model (ability to control experimental inputs and obtain frequent measurements) are counter-balanced by lack of realism. Humans have a complex background of immunity based on past vaccination and infection, different modes of exposure and other innumerable differences. In most human infections, the degree of lung involvement is minimal. Please stipulate in the review of existing literature which papers were done in mice versus humans. Please also frame conclusions of this paper in the discussion in terms of how it may or may not be relevant to human infection.

      Third, this is a primary infection model, and this point also should be emphasized. The greatest relevance of the mouse model in the paper may be for pediatric infection in humans, rather than adults who have had multiple prior influenza exposures and possibly vaccinations. Presumably CD8+ responses can be expected to be more rapid with availability of a pre-existing population of tissue resident CD8+ T cells as would occur with re-infection. The results of CD8+ depletion prior to re-infection would potentially be very different (likely harmful) in a re-infection model and this should be discussed. This is mentioned in Line 467 but is given short attention elsewhere.

      Line 60: stating that other studies have had limited success is rather insulting. Please rephrase and be more specific about why this study breaks new ground.

      Line 81:: "viral loads in the upper respiratory tract do not reflect the lower respiratory tract environment. " Please include a citation, remove or clarify that this is a possible confounding variable in the analysis.

      Line 91: define lung histomorphometry. This is a fairly novel approach for most readers.

      Line 101: This is a strong statement about viral load. Unless formal correlate studies have been done in humans (which they have not), I would day "may not be correlated" or remove altogether.

      Line 201: involved with what? I am not sure what this sentence means.

      Line 209: I would suggest denoting a separate section to the sensitivity analysis versus the parameter fitting as the fitted correlation between delta and delta_e appears separate mechanistically from the relationship between delta and viral clearance / total # of CD8E

      Line 251: Please cite the clinical correlate oof this in the discussion. Immuncompromised humans often shed influenza (and SARS CoV-2) for months. See work from Jesse Bloom's group published in Elife on this subject.

      Line 321 should this read "clear infected cells from the lung?" I am confused about what this sentence means.

      Fig 5D: why are the dots yellow? Is the magenta line CD8 depleted?

      Line 386: Has antiviral therapy been linked with extent of radiologic lung lesions in clinical trials. This would be a very atypical clinical trial endpoint so please be more precise with language. It is possible as previously mentioned in the paper that viral load may not predict lesion size or disease severity in humans.

      Line 477: add degree of immunity from prior infections as a critical variable

    2. Reviewer #2 (Public Review):

      The authors have undertaken an elaborated and extensive analysis on the question how viral and inflammation dynamics correlate with disease pathology during influenza virus infection in mice. Combining a rich set of data, comprising the time course of viral load and CD8+ T cell dynamics for mice across two weeks of influenza infection and detailed histomorphometry on lung tissue sections, with mathematical models on their interaction, they provide a mechanistic relationship between inflammation dynamics and tissue pathology. Based on their analysis, they predict that infected cells are cleared by CD8+ T cells in a density-dependent manner.

      The study is well written and thoroughly presented although some aspects on the analysis would benefit from more detailed explanations. It represents an innovative approach combining an extensive set of data with mathematical modeling. The mathematical model shows a remarkable ability in following even abrupt changes in the dynamics of the different components, leading to some questions towards the interpretability of the obtained parameterizations. Especially the fact that the CD8+ T cell mediated clearance rate delta_E seems to reach the boundary of the imposed prior range might need some additional investigation and discussion. Alternative approaches reducing model complexity could be potentially considered. In general, the authors performed a thorough analysis to address these issues, and also added additional data to address previous concerns.

      In summary, I consider this an interesting study that could provide additional insights into the relationship between viral/inflammation dynamics and induced pathology during influenza virus infection.

    1. Reviewer #2 (Public Review):

      The authors perform a series of elegant experiments to explore the role of cholecystokinin (CCK) neurons in trace fear conditioning in mice. They show that mice lacking CCK exhibit deficits in trace fear conditioning with both short and long CSs/ISIs--they previously showed these animals also have deficits in delay fear conditioning. Subsequent experiments revealed that CCK-deficient mice showed deficits in LTP-induced potentiation of auditory-evoked potentials in the lateral amygdala (LA), and that systemic activation of CCKBR receptors with CCK4 increases activity CCK in the LA and rescues the deficit in trace fear conditioning. They next used combinatorial tracing methods to reveal a CCK projection from the entorhinal cortex (EC) to the LA in CCK-Cre mice. Chemogenetically silencing LA-projecting CCK neurons in EC impaired trace fear conditioning. Lastly, optogenetic stimulation of CCK-EC axons in LA induced potentiation of auditory-evoked potentials in LA, and this was prevented by RNAi-mediated knockdown of CCK in EC neurons. Optogenetic inhibition of EC->LA CCK neurons also inhibited trace fear conditioning. This is an impressive and thorough set of experiments that reveals a role for CCK-containing EC neurons that project to the LA in trace fear conditioning. However, a shortcoming of the work is that it is not clear whether this projection is involved specifically in trace fear conditioning, or has a more general role in either delay or contextual fear conditioning.

    1. Reviewer #1 (Public Review):

      The study by Diebold et al. describes a fast and scalable method that allows to link bacterial plasmids to the organisms that harbor them. The authors then go on to apply this technique to track horizontal gene transfer in an complex bacterial population originating from clinical samples. There is no doubt that the development of such methodologies for better tracking plasmidic resistance genes and following horizontal gene transfer events is very important. The authors do a good job in optimizing their method to be a one step process that has high sensitivity and relatively low error, while it can also be scaled, automated and used with multiplex primers. Subsequently, they apply this method to two clinical patient samples for which metagenomic data is available. In this case, they correctly identify expected relationships between beta-lactamase genes and specific bacterial taxa (and in particular K. pneumoniae), but also find that the same beta-lactamase genes are associated with organisms of the microbiome. With the exception of providing evidence that the association of particular genes with multiple organisms is not due to physical association of the bacteria in question, this is an interesting study putting forward a much needed technique for the study of antibiotic resistance but also other relationships in complex bacterial mixtures.

    2. Reviewer #2 (Public Review):

      Diebold et al. developed a simplified and improved version of the epicPCR method applied to environmental samples. The results section describes well how they perform their development and support the easy to use application. They clearly demonstrate that their methods could be used to screen association of specific genes to taxonomic markers in environmental microbial populations. They then apply their methods on human gut samples ranging from hospitalized patients and demonstrate demonstrate the utility of their methods to characterize the hosts of different targeted genes (notably AMR and plasmid related genes). However, most of their results are based on previous studies on the same sample. Therefore, it appears difficult to know how their method can be used on new samples. Do they need to redo a classical metagenomic analysis in order to obtain data on new samples ? What kind of metagenomic analysis is mandatory before performing their methods ? What is the depth of the metagenomic analysis ? Those are important questions as it will be clearly more expensive to perform the whole metagenomic analysis.

      The conclusion of the paper is well supported by data but the overall approach on new sample is never discussed. Moreover, the title appear somehow misleading as their methods do not allow to clearly identify plasmids but rather to link some targeted genes to taxonomic markers.

      Here are some important remarks:

      1) The supplementary Figure 6 is missing.

      2) What is the assembly used to perform their analysis (size, N50, raw reads ?)

      3) How the authors know already the structure of the Klebsiella plasmid?

      4) the authors compare the results of their sequencing with a custom database of expected sequences but what are the results if they compare it to the NCBI database ?

      5) a comparison of their results with the HiC linkage obtained in the paper by Kent et al, could clearly strengthen their claims and their results.

    3. Reviewer #3 (Public Review):

      This manuscript is composed of two parts. The first part describes development of an emulsion-based PCR fusion method, called OIL-PCR, for matching two specific gene sequences from the same cell. In this report these are beta-lactamase genes from the V4 section of rRNA, allowing the matching of this horizontally transferred gene with its donor sequence. The second part is a demonstration project that features the use of OIL-PCR to monitor horizontal transfer of beta-lactam genes between gut bacteria from the metagenomes of two neutropenic patients. OIL-PCR was set to multiplexed class A beta-lactam genes. This is a descriptive study that largely recapitulates a previously published work on these samples showing that the relatively unstudied Romboutsia commensal genus is a carrier of these plasmid-borne genes in patient metagenomes.

      Overall, this is a well-written manuscript. Data were comprehensively analyzed with appropriate controls. The figures are excellent.

      OIL-PCR is a derived of other fusion PCR methods, especially epicPCR. There are some nice technical improvements described here, e.g efficient lysis within emulsion droplets using Ready-Lyse lysozyme. This is an incremental technical advance for a fairly niche application (where you have known target genes and are concerned about potential culture-bias) but it may be useful in particular for understanding HGT in microbiomes. There are some problems with the method that are brought to the foreground by the authors rather than quietly dropped, which is commendable. One problem appears to be that the necessary dilution for single-cell PCR reduces the taxonomic diversity of the metagenome. The only way around this to perform efficient sampling appears to be to perform multiple independent sequencing experiments and pool the results. Another feature of the system is that the accuracy falls slightly as the proportion of the target sequence in the community increases for reasons that are not discussed. However, this effect is not great (97% accuracy at 10% proportion) and most applications, the target cells will be a much lower proportion of the community.

      The results of the demonstration study on metagenomes from neutropenic patients are clearly described and provide a nicely worked example of combining this directed method with metagenome sequencing. The significance is limited but gives some descriptive hits about the mechanism of HGT between Romboutsia and Klebsiella.

      Other points:

      Unfortunately, there was no comparative test where the same samples were run against "competing" technologies (e.g sequencing of cultured beta-lactam resistant strains, epicPCR, Hi-C or single-cell) to directly compare strengths (and weaknesses) of OIL-PCR.

      As protocol development is central to this manuscript paper, and one of the main advantages claimed for OIL-PCR is ease of use, the supplement should contain a detailed protocol for control sample with a list of equipment and reagents needed and what results should be obtained. This could easily be adapted from the methods section, which is highly detailed. What is the estimated cost-per sample of this procedure and how does it compare roughly with other methods, - EPIC-PCR and culture-based?

      Line 197-198 reference needed to the Kent et al study here? What is the reason that the Hi-C results from this manuscript are not compared to the results of the OIL-PCR experiments?

    1. Reviewer #1 (Public Review):

      The manuscript by Chakraborty focuses on methods to direct dsDNA to specific cell types within an intact multicellular organism, with the ultimate goal of targeting DNA-based nanodevices, often as biosensors within endosomes and lysosomes. Taking advantage of the endogenous SID-2 dsRNA receptor expressed in C. elegans intestinal cells, the authors show that dsDNA conjugated to dsRNA can be taken into the intestinal endosomal system via feeding and apical endocytosis, while dsDNA alone is not an efficient endocytic cargo from the gut lumen. Since most cells do not express a dsRNA receptor, the authors sought to develop a more generalizable approach. Via phage display screening they identified a novel camelid antibody 9E that recognizes a short specific DNA sequence that can be included at the 3' end of synthesized dsDNAs. The authors then showed that this antibody can direct binding, and in some cases endocytosis, of such DNAs when 9E was expressed as a fusion with transmembrane protein SNB-1. This approach was successful in targeting microinjected dsDNA pan-neuronally when expressed via the snb-1 promoter, and to specific neuronal subsets when expressed via other promoters. Endocytosed dsDNA appeared in puncta moving in neuronal processes, suggesting entry into endosomes. Plasma membrane targeting appeared feasible using 9E fusion to ODR-2.

      The major strength of the paper is in the identification and testing of the 9E camelid antibody as part of a generalizable dsDNA targeting system. This aspect of the paper will likely be of wide interest and potentially high impact, since it could be applied in any intact animal system subject to transgene expression. A weakness of the paper is the choice of "nanodevice". It was not clear what utility was present in the DNAs used, such as D38, that made them "devices", aside from their fluorescent tag that allowed tracking their localization. Another potential weakness is that the delivered DNA is limited to the cell surface or the lumen of endomembrane compartments without access to the cytoplasm or nucleus. In general the data appeared to be of high quality and was well controlled, supporting the authors conclusions.

    2. Reviewer #2 (Public Review):

      The authors demonstrate the tissue-specific and cell-specific targeting of double-stranded DNA (dsDNA) using C. elegans as a model host animal. The authors focused on two distinct tissues and delivery routes: feeding dsDNA to target a class of organelles within intestinal cells, and injecting dsDNA to target presynaptic endocytic structures in neurons. To achieve efficient intestinal targeting, the authors leveraged dsRNA uptake via endogenous intestinal SID-2 receptors by fusing dsRNA to a fluorophore-labeled dsDNA probe. In contrast, neuronal endosome/synaptic vesicle (SV) targeting was achieved by designing a nanobody that specifically binds a short dsDNA motif fused to the fluorophore-labeled dsDNA probe. Combining dsDNA probe injection with nanobody neuronal expression (fused to a neuronal vSNARE to achieve synaptic targeting), the authors demonstrated that the injected dsDNA could be taken up by a variety of distinct neuronal subtypes.


      While nanodevices built on dsDNA platforms have been shown to be taken up by scavenger receptors in C. elegans (including previous work from several of these authors), this strategy will not work in many tissue types lacking these receptors. The authors successfully circumvented this limitation using distinct strategies for two cell types in the worm, thereby providing a more general approach for future efforts. The approaches are creative, and the nanobody development in particular allows for endocytic delivery in any cell type. The authors exploited quantitative imaging approaches to examine the subcellular targeting of dsDNA probes in living animals and manipulated endogenous receptors to demonstrate the mechanism of dsRNA-based dsDNA uptake in intestinal cells.


      To validate successful delivery of a functional nanodevice, one would ideally demonstrate the function of a particular nanodevice in at least one of the examples provided in this work. The authors have successfully used a variety of custom-designed dsDNA probes in living worms in numerous past studies, so this would not be a technical hurdle. In the current study, the reader has no means of assessing whether the dsDNA is intact and functional within its intracellular compartment. Another minor weakness is the lack of a quantitative assessment of colocalization in intestinal cells or neurons in an otherwise nicely quantitative study. Since characterization of the targeting described here is an essential part of evaluating the method, a stronger demonstration of colocalization would significantly buttress the authors' claims.

      While somewhat incomplete, this study represents a step forward in the development of a general targeting approach amenable to nanodevice delivery in animal models.

  2. Jun 2021
    1. Reviewer #1 (Public Review):

      In this paper by Feng et al. the authors examine the role of cholecystokinin (CCK) cells in the entorhinal cortex (EC) in fear conditioning. They find that CCK knockout mice are deficient in short and long trace fear conditioning and that this deficit could be rescued by administration of systemic CCKB receptor agonist administration. Using an in-vivo synaptic plasticity assay, they present suggestive evidence that LTP is disrupted in CCK-/- animals. To determine the source of CCK to the LA, they use anatomical tracing techniques to show that the EC contains CCK+ cells which project to the lateral amygdala (LA) and use a DREADD approach to reveal that EC-CCK+ cells are necessary for trace fear conditioning. They then take advantage of a variety of plasticity, shRNA and optogenetic approaches to show that EC-CCK+ cells contribute to plasticity in LA and are necessary for fear conditioning. These results are potentially important as they reveal a role for EC projections to the LA in fear learning and connect this to a specific population of CCK expressing cells. While the findings are compelling, there are issues with the analyses and experimental design (in some cases), validation of the shRNA knockdown technique and some of their interpretations which need to be addressed.

    2. Reviewer #3 (Public Review):

      In the present manuscript, Feng and colleagues used sophisticated techniques to elucidate the role of the neuropeptide cholecystokinin (CCK), and the neurons which produce this peptide, in a model of trace fear memory. First, using global genetic knockout mice, in vivo electrophysiology, and exogenous administration of a CCK receptor agonist, the authors showed that CCK is vital for trace fear memory and associated synaptic plasticity (long-term potentiation; LTP) assessed by changes in auditory evoked potentials (AEPs) in the lateral amygdala (LA). Anatomical tracing revealed diverse inputs to the LA, including those from the entorhinal cortex (EC). Using chemogenetics, the authors showed that the activity of EC neurons, specifically those expressing CCK, is essential to the formation of trace fear memory during conditioning. Further anatomical tracing demonstrated an abundance of CCK-expressing neurons that project from the EC to the LA, and optogenetic excitation of these cells recapitulated AEP-LTP in the LA associated with trace fear conditioning. Next, the authors used viral-genetic techniques to block production of CCK by EC neurons and found that CCK originating in EC neurons is necessary for LTP of the AEP within the LA. Finally, Feng et al. employed optogenetic inhibition of CCK-expressing neurons that project from the EC to the LA during conditioning to demonstrate that these cells are necessary for the formation of trace fear memory. Taken together, this elaborate set of experiments establish an important role of a peptide-signaling circuit in a model of fear memory.

      The results described herein will be useful to behavioral neuroscientists seeking to understand how the brain processes fear.

      This manuscript is well written and the scientific question holds translational relevance, particularly in being able to inform clinical scientists attempting to develop therapeutics targeting peptide signaling to improve symptoms of anxiety disorders. While this study has scientific and practical value, some issues of methodology, interpretation of results, and presentation of data should be addressed by the authors prior to publication.

      1) Statistical and Methodological Concerns:

      1.1) In determining the effects of experimental manipulations on freezing scores, the primary behavioral readout in this study, the authors make inappropriate use of statistical tests. While the authors' comparisons of group averages for freezing are reasonable, the use of t-tests to compare the effects of manipulations across time during trials is inappropriate and would be better suited for repeated measures ANOVAs. This issue can be easily addressed by reanalysis of this set of data.

      1.2) A couple of issues related to the use of viral techniques should be addressed, as well. In using optogenetics to induce LTP, the authors use a particular viral serotype (AAV9) that may lead to anterograde expression of their light-sensitive channel (ChETA) in neurons downstream of their target region. This concern can easily be addressed by additional histology and disclosure of this methodological caveat in the text.

      1.3) The second issue of viral-genetic techniques to be addressed is in the authors' use of shRNA to knockdown CCK expression by EC neurons. The authors failed to show validation of this technique by quantifying the expression of CCK after viral manipulation. This concern can also be easily addressed with additional histology.

      2) Concerns of Interpretation of Results: While the authors elegantly demonstrate a role of CCK-expressing projection neurons originating in the EC and terminating in the LA in their behavioral model, there exists some overextension of interpretation of these results by the authors in the present manuscript. In particular, the authors infer that synaptic release of CCK, per se, by EC neurons in the LA is responsible for the effects observed. However, the authors do not demonstrate that CCK is being released in the LA by neurons originating in the EC. The authors should limit overinterpretation of their results and discuss alternative explanations, such as the possibility of local release of CCK by CCK+ neurons in EC which could be further triggering the release of CCK from local CCK+ neurons in the amygdala.

    1. Reviewer #1 (Public Review): 

      This meta analysis addresses a double-edged sword in evolutionary biology. Group living may be beneficial for many reasons, but has costs in terms of increased rates of parasitism. Furthermore, if groups are highly related, parasites that are genetically able to infect on member of the group may be able to infect all of them, putting the entire group at risk. In the her presented meta analysis, many original studies working on questions related to parasitism, relatedness and group living are brought together in one unifying framework. The authors find that indeed, group living can facilitate the spread of infectious diseases. However, they also find that the negative effects of disease can be overcompensated by the benefits of being social. The authors stress that experimental studies are necessary to disentangle these effects. The study is of high standard and well-conducted. The take home message is clear and of general interest. 

      The study highlights that experimental work is important to understand the relationship between parasitism, relatedness and living in groups. However, I missed an important aspect here. Experiments tend to stretch factors (sometimes to extremes), which may go square to the biology of the species. In some cases, this results in non-social organisms to be pressed in a group-environment. For example, the monoculture effect as we know it in agriculture is highly artificial. Clonal lines of crop are planted in high density, promising high yield, if pathogens stay out. These plants do not have a history of evolving mechanisms to deal with the effect of high relatedness. In contrast animals living in social groups, may never experience setting with non-relatives. Social insects evolved to deal with parasites by expressing specific adaptations, such a grooming, hygiene and social structure in the colony. Many social insects may never experience conditions of low relatedness. Thus, I expect it makes a difference if you experimentally force a non-social organism to be social, or a social organism to be asocial. I would be happy if this factor could be included in the reasoning, and maybe even analyzed quantitatively. For example, I would expect that non-social species made artificially to grow in groups of relatives, suffer much more from parasites than typical social animals with the same degree of relatedness. 

      The term (and concept) "monoculture" is typically used to describe clonal populations, predominantly in agricultural settings. I understand that the authors like to expand this term (as have others done before) to include social animals. However, for most people this would be a change in terminology and may cause misunderstandings. I would prefer if you could stick with the mainstream terminology and avoid pressing this concept into a new costume.

    2. Reviewer #2 (Public Review): 

      This study uses an unusually broad comparative data set to disentangle the positive (relatedness) and negative (pathogen pressure) effects of living in groups. The authors largely succeed in this task even though the data do not allow answers to all outstanding issues. Not unexpectedly, experimental manipulation studies appear to be most informative. The results are broadly consistent with expectations based on kin-selection theory and clarify the effects of a number of important covariables. The study is thoroughly executed and innovative in its approach. I expect this study to be interesting for a broad readership and this method of searching literature data to have considerable impact. Some suggestions strengthening this paper are below: 

      - I think it would be helpful for readers to have the Discussion start with a few lines on what your study achieved in language that is complementary to the abstract, perhaps followed by a brief explanation of which angles/ambiguities/challenges you will be taking up in the paragraphs to follow. 

      - The rationale of this study is (often implicitly) that tendencies to live with relatives or not is a continuous variable. This surprised me because the senior author has written influential papers showing that family groups are different from non-family groups. In some contexts of this study it seems crucial to make that distinction. For example, a number of data points come from studies of social insects (bumblebees, honeybees, ants). Here, living with non-relatives is not an option but a given. It is well documented that these caste-differentiated colonies originated from ancestors that had exclusively full-sib colonies, so maximal relatedness was ancestral and became only diluted secondarily in some lineages. Would it be possible to check statistically whether the social insect data points always showed the same pattern as the other data points? That would test whether it matters that low relatedness is either derived or ancestral (as I think we implicitly assume to be the case in all other organisms). 

      - I wondered whether you could (interpretationally, i.e. in the discussion) do more with comparative data on pathogen pressure in the wild. The 1987 Hamilton chapter that you cite has lots of interesting natural history observations, which are now often supported by better data. I think he speculates about how altruistic soldiers evolved in aphids and thrips and connects their sociality with living in their own food (galls), which should mean low parasite pressure. The same is true for the lower termites. Would your results allow you to conjecture that all independent lineages that evolved differentiated castes (only possible in families with full siblings; or clones as in aphids) likely had to do that in disease free habitats? 

      - I think some effort should be made to make Figures 2,3 and 4 easier to interpret. The ultra-brief acronyms along the y-axis take a while to digest and to realize the nestedness of the analyses. Could you give one piece of information on the left axis (spelled out like 'experimental data' and 'observational data' and the other piece on the right axis (spelled out as 'pathogens absent' and pathogens present'? It would also be helpful if the reader could fully understand the figures without first having to go through the entire method section, so I recommend you extend the legend to explain: 1. What Zr stands for. 2. What the directionality is (so the cryptic line just below Zr can become a proper sentence in the legend), and 3. The rationale of the multifactorial analyses with four or eight combinations (as you describe in the methods; I believe Figure 4 is an example of eight, but this remains rather hazy).

    1. Reviewer #1 (Public Review): 

      The authors have shown a unique set of recordings, wherein they have collected intracranial data from parietal cortex and hippocampus, as well as scalp EEG in a number of subjects. With this unique advantage, they have examined directionality of connectivity between various regions during a working memory task. Given the growing evidence for the role of hippocampus in working memory, understanding its connectivity to the rest of the brain provides a crucial insight into the network involved in such a fundamental process. Whilst the existing content is generally of a high standard, and the analyses seem sound, there are some areas of considerable brevity that would benefit from expansion. Below are my comments on the manuscript. 

      Discussion: This is surprisingly short discussion section. I feel this should be expanded considerably, such as including some of the information that I have discussed below regarding potential considerations of the task (e.g bimodal nature), discussion of the PLV results in the context of previous non-directional findings, the differences observed between correct and incorrect trials, considering in more detail the other behavioral consequences of these results. These suggestions are not necessarily exhaustive but are all points I believe should be included. 

      Comments on results section in general: 

      ~All the results in this section seem to refer to a single electrode for each subject. It would be beneficial to know whether these electrodes were representative of activity from surrounding electrodes or not. That is, how generalizable are the PSD results shown here. 

      ~Also, many of these results are very descriptive. Whilst in some specific scenarios this is unavoidable, for the purposes of reporting results from PSD (for example) it is definitely possible to report details such as the degree of power increase. At present, this reads more like a discussion section that an informative results section. 

      ~It would be helpful to see an overlay of the parietal electrode with a topographic map of the scalp EEG recording, to truly appreciate the spatial overlap between the electrode and the generator. 

      Figures in general: many of the figures appear to refer only to single subjects. It would be useful to have more detailed summary information across subjects to understand how reliable/variable these effects are. 

      Data availability section: The bit on previously published datasets confused me a little. Is this published dataset included as part of this article? It isn't so clear in the manuscript whether these are previously published data. If they are, this should be made more apparent. 

      Line 29: Phrasing - I would add the words "rather than sequentially" here to help readers with interpretation of why this separates out encoding from maintenance 

      Line 64: This can actually extend as low as delta band (see Leszczynski et al., 2015, Cell Reports; Kumar et al., 2021, Neuropsychologia). 

      Line 88: Do the authors have behavioral data or prior knowledge of how long it takes (on average) to encode 4, 6 or 8 letters? That is, how much of the 'encoding' period is truly encoding, rather than an initial encoding followed by maintenance. Or in a similar manner, how much of maintenance is still residual encoding. 

      Line 90: Was there a particular reason as to why the encoding phase was bimodal? Do the authors think this may have influenced their results? 

      Line 94-95: Was this an instruction to the participants? If so, I would put this more explicitly, i.e. "participants were instructed to rehearse...". Of course, one cannot know for certain whether individual subjects employed this strategy. 

      Figure 2f: Where is this change in Granger relative to? A particular baseline window? 

      Line 293: Were any electrodes here included in a seizure foci? Was anything done to ensure that seizure activity did not affect recordings (e.g. not recording within xxx hours of a seizure)? 

      Line 301: Was anything done to deal with artefacts on the ECoG/sEEG electrodes? I.e. were trials with unusually large amplitudes, potentially indicative of muscle artefact (a known contaminant) removed? 

      Line 325-326: I am confused by this. You say that the individual frequencies may differ between participants - do you mean in terms of the peak frequency, or were different bands used for each subject? If different bands, why? 

      All power spectral density plots: I assume these are relative to baseline. Are they statistically-thresholded in any way?

    2. Reviewer #2 (Public Review): 

      Dimakopoulos et al. use intracranial data in humans to ask whether information flow is primarily cortical to hippocampal or the reverse during the encoding and retrieval stages of a working memory task. They find a highly reliable pattern where information in the alpha/beta range flows from auditory cortex to hippocampus during encoding and in the reverse direction during maintenance of items in WM. The authors show this pattern in a sub-selection of ECoG recordings and go on to show it is present in virtually all subjects at the EEG to intracranial hippocampus level. In addition, this directional pattern breaks down during incorrect trials. However, the current analysis suffers from possible contamination by volume conduction. 

      The study is unique in its data set and provides a valuable look into hippocampal cortical interactions during WM. However, there are multiple technical questions remaining. One of the limitations is that the study investigated primarily interactions in the alpha/beta range when looking at interactions. In contrast, their power spectral results show increases in gamma during encoding, and other studies have emphasized a role for gamma in feedforward routing. Did the authors perform a granger causality analysis in gamma?

    3. Reviewer #3 (Public Review): 

      Dimakopoulos and colleagues investigate connectivity and flow of information during encoding and maintenance of Working Memory. They use unique data, which combine human intracranial recordings from depth electrodes with ECOG and EEG. This data, combined with Granger causality analysis (GC), provides interesting results that signal from cortex (mostly from EEG electrodes located over temporal cortex) is flowing to hippocampus during encoding and this flow is reversed during maintenance. Authors interpret this as a sign of bottom-up and top-down processing. I believe that chosen methods for signal analysis are appropriate. 

      However, paper contains several statements that are unsupported by statistics and there is no clear information about why some decisions in the analysis process were made. This could give an impression that the analysis is built from arbitrarily chosen single case examples. I believe that because of below listed flaws results of the analysis do not support conclusions. 

      1) Authors do not use correction for multiple comparisons - this cast doubts on the strength of obtained results. 

      2) There is no criterion given for ECOG electrodes selected to the analysis. <br> For instance, authors state that for participant 1 for C2 electrode, increased gamma power during encoding proves that this electrode was over auditory cortex but there is no systematic analysis of gamma power. From the results we can observe that this electrode has the strongest GC with hippocampus what suggests that it was used because of this characteristic what looks like double dipping. 

      3) Why frequencies observed in PLV and GC are so different? For instance, in supplementary Figure 1 PLV shows significant differences in 18-30 Hz but GC is calculated for 8-18 Hz. Such large differences in frequencies suggest some inconsistencies in the analysis. 

      4) For analyses depicted in Fig 4 and 5 it is unknown how the highest GC is defined (is it a mean from all frequencies?) Furthermore, there is no systematic measurement or criterion that would support that indeed chosen electrodes have the highest GC. 

      5) All analysis conducted in the time domain (time to frequency and GC) does not contain any statistics supporting validity of the proposed conclusions. 

      6) There is no data that supports statement that patients used verbalization. Although material is verbal authors cannot rule out that subject uses different modalities to support information maintenance.

    1. Reviewer #1 (Public Review): 

      Dendritic cells are pivotal in the regulation of the balance between tolerance and inflammation. The transcriptional mechanisms that regulate this balance remain poorly understood. Raghav et al investigated the role of the transcriptional co-repressors SMRT and NCoR1 in the activation of conventional type I dendritic cells (cDC1) by Toll-like receptor agonists using in vitro cell model, flow cytometry and genomic analysis. The authors found that SMRT limits the activation of DC1 and the expression of inflammatory cytokines driving CD8 and CD4 T helper cells responses while supporting the expression of the anti-inflammatory IL-10. Thorough and well-presented genomic analyses reveal interesting similarities and differences in the control of genes expression by two paralogs NCOR1 and SMRT during immune activation in DC1. The paper presents a new regulatory circuit supporting the transcriptional regulation of IL-10 after TLR stimulation in cDC1.

    2. Reviewer #2 (Public Review): 

      NCOR/SMRT corepressor complexes control inflammation and their dysregulation has been reported to be of clinical importance. In this manuscript, the authors studied SMRT in dentritic cells. The authors propose that SMRT KD enhances DC inflammatory activation while reduces IL10 expression, which shows similarity with macrophages, a relatively close innate immune cell type. The authors propose the underlying mechanisms related with Nur-77, mTOR and STAT3, that SMRT KD downregulates Nur-77 and thereby inhibits the mTOR/STAT3 axis, leading to decreased IL10. The authors used multi-OMICs approaches along with several functional experiments and clinical evidence to support the relevance of SMRT regulated dentritic cells in inflammatory diseases and cancer. The findings reveal the so far unclear functions and mechanisms of corepressor-based dentritic cell (and T cells) regulation, and is important and interesting.

    1. Reviewer #1 (Public Review): 

      In this paper the authors associate genetic variation in regulatory sequences of the gene cortex with the presence/absence of a yellow band of color in the wings of two species of Heliconius butterflies. They show that cortex is spatially regulated in larval wings, but the expression of this gene does not correlate with the presence or absence of the yellow band. Then they show that the gene is expressed in the nuclei of all cells of the pupal wing. By disrupting cortex they show that black cells (Type II) become white or yellow (Type I), and red scales (Type III) become paler across the whole wing. 

      By examining open regions of chromatin around cortex, they discover that at least in one of the species, the insertion of two transposable elements in an open region of chromatin associates with the presence of the yellow band. They show that disrupting this regulatory region in a race of butterflies that does not contain the yellow band, nor the TE insertions, leads to the loss of the black color in a band-like shape, and the appearance of yellow scales in that region of the wing. They identify a different region of open-chromatin in the other Heliconius species that when disrupted also leads to the transformation of black scales into yellow scales in a band-like pattern. 

      The authors achieved their aims and the results support their conclusions. 

      The strength of this manuscript lies in the use of multiple approaches to identify the likely causal genetic variation in the cortex locus that is responsible for the presence/absence of the yellow band. The only weakness (if I can call it that) is that it is still not clear how cortex, which is also expressed in the nuclei of the yellow scales in races that supposedly have the TE insertion and closed chromatin in that enhancer region, fail to develop black scales in that region of the wing. 

      This is one of the first few papers that examines the function of specific open regions of chromatin in the DNA of butterfly species using CRISPR-Cas9. The main novelty of this paper is in identifying how a gene with a homogeneous expression pattern across the wing (during the pupal stage) can still have "hidden" modular regulatory regions that drive unique functions (albeit not expression) is specific regions of the wing. 

      This work reminds me of the regulation of the vestigial gene in the wings of Drosophila. vestigial also has homogeneous expression across the wing pouch but it achieves this homogeneous expression via two separate enhancers that have complementary expression patterns.

    2. Reviewer #2 (Public Review): 

      The gene cortex was reported to control mimicry and crypsis in butterflies (Nadeau et al. 2016). This study finds cortex function to be essential for Heliconius wing scale type determination at the transition from scale type I to type II / III. This is shown by genetic loss of function assays and characterization of scale structure by scanning electron microscopy. In particular, the authors show that cortex function is essential for scale type determination throughout the wings that mainly contain type II/III scales in Heliconius butterflies. This is revealed by a series of CRISPR/Cas9 derived somatic mosaic mutants in diverse genetic backgrounds and species. Expression of a specific yellow (type I scale) hind-wing stripe in some Heliconius melpomene and H. erato morphs was found to depend on molecular tinkering and malfunction of a discrete cortex cis-regulatory element (CRE). The authors identify distinct CRE's in both species by ATAC-seq open chromating mapping and narrow down candidate regions by genetic association to the yellow stripe. Hi-C assays were used to verify that the elements indeed interact with the cortex promoter. However, a possible regulation of other genes cannot be excluded. Tinkering of these elements appears to be a natural mechanism in wing colour pattern evolution, since a yellow stripe morph is associated with an insertion of a transposable element in the corresponding region in the morph H. melpomene timareta. Expression of cortex was investigated at different developmental stages by in-situ hybridization and immuno-staining techniques. Cortex transcripts reveal complex expression pattern that do not seem to be associated with the yellow hindwing stripe in corresponding morphs. Cortex protein is localized in the cell nucleus throughout wing cells and future studies must resolve how cortex regulatory elements determine such specific stripe-pattern. This article contrasts the widespread expression of cortex with a complex transcriptional regulation of this gene and scale type transition in discrete wing domains. The authors argue that cortex is a prime target for wing pattern evolution, acting as "input-output" module, whereby complex spatio-temporal information is translated to determine scale type and colour.

    1. Joint Public Review:

      Nature has evolved remarkably different enzymes for the essential processing of 5´ ends of pre-tRNAs. The ribonucleoprotein RNase P uses its RNA component for pre-tRNA recognition and catalysis, the protein-only RNase P (PRORP) contains a pentatricopeptide repeat domain for pre-tRNA recognition and a nuclease domain for catalysis, and more recently a new family, Homolog of Aquifex RNase P (HARP), was identified. The HARPs seem mysterious as they are quite small (~23 kDa) and form oligomers. Although they appeared to possess a catalytic domain, it was unclear how they would recognize and process pre-tRNAs. Here the authors have addressed these questions by determining a cryo-EM structure of a dodecameric HARP, Hhal2243, and using the structural information to strikingly demonstrate the essential nature of the oligomerization for enzymatic activity of the Aquifex HARP, Aq880. Enzymatic activity assays with mutant enzymes identify basic residues for pre-tRNA substrate recognition, and a preliminary HARP/tRNA model suggests a possible mode for pre-tRNA recognition and catalysis by the dodecamer.

      The cryo-EM model illustrates the overall formation of a dodecamer structure with six dimers rotated about a central screw axis. The structure of Hhal2243 was used to design C-terminal deletion mutants of Aq880 that would disrupt inter-dimer interactions. The authors used mass photometry to identify the distribution of oligomer sizes for wild-type and C-terminally truncated Aq880 and measured the enzymatic activities of the wild-type and truncated Aq880. These combined data convincingly demonstrated that enzymatic activity is lost when truncation eliminates the dodecamer form. A compelling strength of the manuscript is the correspondence of the enzymatic activity and the rich information on oligomerization from the mass photometry.

      The authors aligned their cryo-EM model with the crystal structure of the Arabidopsis PRORP1 to show that the arrangement of catalytic acidic residues is conserved in these two families, although we believe the overall structure of each protein family is distinct. To clarify this point, please explain the structural alignment more (page 9, lines 178-179). Are the folds distinct, yet the catalytic residues align? The authors should consider moving Fig. S5 to the main figures. The conservation of the arrangement of catalytic residues will likely be of interest to enzymologists fascinated by the consistent geometric arrangement produced by distinct structural scaffolds.

      With the identification of the processing active site and R125 and R129 as a substrate recognition site, the authors attempted to model pre-tRNA engagement by the dodecamer. The model is rather speculative at this stage, but the authors placed this analysis in the Discussion, which seems appropriate, and it develops a testable model for future work. We did, however, find it somewhat confusing to understand which monomers were engaging pre-tRNA, and we recommend improving the presentation of the model and how it was generated.

    1. Reviewer #1 (Public Review):

      The authors of this manuscript were trying to determine whether the stiffness of cells can influence the stiffness of the substrate the cells were migrating on and how this overall affect how cells migrate. Using the Drosophila border cell migration system the author found that by eliminating fascin expression in border cells, non-muscle myosin II activity increased, suggesting an antagonistic relationship between this actin bundler and non-muscle myosin II contractility. Further, increasing non-muscle myosin II activity in border cells increased non-muscle myosin II contractility in nurse cells (the substrate through which they migrated, suggesting a feedback mechanism between cells and their substrate.

      Some of the strengths of this manuscript capitalizing on Drosophila genetics of border cell migration allowing them to manipulate fascin expressing in different tissues (germline vs. somatic) and then determining how this affect border cell migration. Using atomic force microscopy the authors where also able to measure the biophysical properties of the egg chambers and thus were able to correlate these changes to the genetic/pharmacological manipulations. Increased non-muscle myosin II activity as assessed by increases in phosphomyosin staining inhibited border cell migration. Fascin-null border cells increase non-muscle myosin two activity in both border cells and the nurse cells through which they migrate. This could not be rescued by expression of phosphomimetic version of fascin that has been shown to inhibit fascin's bundling activity. This further suggests that it is the changes to actin architecture that is leading to changes in cellular contractility. The author's also demonstrate that this increased cellular contractility feedback loop is more generalizable as expression of constitutively active Rok (Rho kinase) also led to similar phenotype. A weakness of this manuscript is that the authors did not address any changes to the actin architecture, (actin-based structures) that likely correlated with the loss of fascin, nor did they explore exactly how increased contractility in border cells is communicated to the nurse cells. Overall, the data presented here support the authors claims.

      Increased fascin expression has been correlated with increased metastases as has increased cellular contractility, thus the results presented here begin to piece together this relationship. Furthermore, the feedback between cells and the role they play on augmented their substrates stiffness is also critical to number of migratory processes including metastasis.

    2. Reviewer #2 (Public Review):

      In this manuscript, Lamb et al. investigated the role of Fascin in regulating myosin activity and cell stiffness during Drosophila border cell collective migration. Loss of fascin results in higher levels of activated myosin (p-MRLC), and altered myosin dynamics, in border cells and in the nurse cells, the cellular substrate upon which the border cells migrate. Further, loss of fascin increases the stiffness of the nurse cells as measured by atomic force microscopy (AFM). Reducing myosin activity, either pharmacologically or by RNAi of myosin, suppresses the delayed migration found in fascin mutants. Phosphorylation of Fascin is important for regulation of myosin, as a phosphorylation-mutant that disrupts actin-bundling activity (Fascin-S52E) fails to suppress the increased levels of activated myosin found in fascin mutants. The authors then perform cell type-specific RNAi knockdown of fascin. Knockdown of fascin in nurse cells results in elevated p-MRLC in nurse cells, though not in border cells. As expected, nurse cell fascin knockdown increased the stiffness of nurse cells. In contrast, knockdown of fascin in border cells elevated p-MRLC in both border cells and in nurse cells, and non-autonomously increased the stiffness of the nurse cells. Restoring Fascin in border cells (somatic cells) of fascin mutants reduced the stiffness of nurse cells to normal levels. The authors conclude that Fascin in border cells regulates the stiffness of their nurse cell migratory substrate by limiting the levels of activated myosin. This in turn promotes normal in vivo cell migration.

      Overall, this manuscript presents novel findings with broad interest to the fields of collective cell migration and actomyosin regulation. Many of the results are well-controlled and support the conclusions. The finding that Fascin limits the levels and dynamics of myosin in a migrating collective in vivo is generally convincing. Moreover, control of substrate stiffness by migratory cells has not been well explored.<br> However, there are several key experiments that can be clarified with additional data or analyses to support the conclusions.

      First, because border cells are surrounded by nurse cells, the authors would need to more explicitly indicate how they measured p-MRLC levels in border cells versus nurse cells. How p-MRLC "puncta" are measured, and in particular what the authors mean by "length" of puncta, would need to be clarified. More notably, the p-MRLC staining looks quite different from the MRLC-GFP images shown. MRLC-GFP at the membrane should represent the phosphorylated and active pool of myosin, but somehow looks more disperse both in control and fascin mutants compared to p-MRLC staining.

      Second, the authors would need to clarify how many stage 9 follicles (egg chambers) they measured in each AFM experiment and for each genotype. In the materials and methods, it says that 2-3 follicles were measured for each experiment. This seems like a low number, although it is a technically challenging method. A recent study from the Bilder lab (Chen et al., Nature Communications 2019) appeared to measure at least 8 follicles per genotype. This is particularly important, since the data points for the stiffness measurements are generally quite broad and overlap between controls and mutants, e.g., with ~5-15 kPa in control nurse cells and ~15-45 kPa in fascin null nurse cells (e.g., Figure 2D; but also Figure 5G). There may be technical reasons why this number of follicles was measured, but it would be helpful to describe the reasoning in more detail.

      Third, the non-autonomous control of nurse cell substrate stiffness, and levels of activated myosin in nurse cells, by loss of fascin in border cells (and by overexpression of activated Rho-kinase in border cells) is interesting and novel. The authors propose that the border cells regulate the stiffness of nurse cells to facilitate border cell migration. Further clarification of this phenotype would strengthen the manuscript. Specifically, it is unclear whether the authors find elevated p-MRLC in nurse cells that are in front of the border cells, or a more general elevation of p-MRLC levels (and presumably nurse cell stiffness).

      Finally, the authors use pharmacological inhibition of myosin and/or activation of myosin to rescue border cell migration (Figure 3 and Figure 3, figure supplement 1). The Y-27632 drug and MRLC-RNAi should be fine. However, Drosophila myosin has been reported to be insensitive to blebbistatin (Straight et al., Science 2003; Heissler et al., FASEB J. 2015). Therefore, caution should be taken in assessing the results with blebbistatin in Drosophila.

    3. Reviewer #3 (Public Review):

      The authors had previously demonstrated that fascin was critical for border cell migration in Drosophila oogenesis, but were not able to fully identify the definitive molecular underpinnings.

      Here, the authors use genetic tools enabled by Drosophila system to selectively remove fascin from specific cell types, and then measure myosin 2 RLC phosphorylation, as a readout for contractility, in both the border and nurse cells. This primary method is complemented with migration analysis, rescue experiments, and what appears to be a very challenging AFM experiment to measure cell stiffness in the follicle! By doing this, they are able to modify fascin in the border cells and determine the impact on their substrate (the nurse cells).

      While taking advantage of the wonderful toolset enabled by Drosophila, this manuscript, in its current form, would benefit from a better explanation of which cells are being manipulated in each experiment. Non-Drosophila biologists might struggle with some of the terminology and could use a more "guided tour" of the work. In addition, it would be very interesting to know more about where actin is in the different cell types upon manipulation of fascin.

      Despite these limitations, the authors are able to demonstrate that fascin is somehow regulating myosin activation in multiple cell types. This is almost certainly happening in many other cells. A future challenge lies in understanding how direct this link is. It is feasible that altering the bundling of actin could be altering many myosin-modulating proteins.

      While previous works have demonstrated that migrating cells can alter the stiffness of ECM at their anterior (van Helvert and Friedl, 2016; Doyle et al. 2021), this work demonstrates this concept in cells migrating on other cells, requiring an added level of complexity, and demonstrates it in a living organism. While many studies have looked at cell migration in 2D and 3D ECM environments, semi-recapitulating physiological settings, fewer studies have carefully investigated cells migrating on other cells, as must happen with high frequency throughout multicellular life. Collectively then, this is an exciting addition to our understanding of cell migration.

    1. Reviewer #1 (Public Review):

      Kubiniok et al. study the contribution of tissue type and HLA classical class I gene allotype on the immunopeptidome. This is an understudied and critically important question for understanding CD8+T cell tolerance and immunosurveillance of cancer and other diseased cells and autoimmunity. The study is based on published data sets obtained from different samples which compromises the analysis to some extent. Ultimately, in future studies, it will be important to determine the translatome for each tissue, as a significant fraction of peptides derive from non-annotated gene products and will be missed without this data to establish the potential immunopeptidome.

    2. Reviewer #2 (Public Review):

      The study by Kubiniok et al. "Global analysis of the mammalian MHC class I immunopeptidome at the organism-wide scale" utilises data generated from human and mouse immunopeptidomic studies conducted by Marcu et al. (2021) and Schuster et al. (2018), respectively. These initial studies implemented immunopeptidomic profiling of an array of different organs in each species. For human HLA-I profiling, 51 different HLA-I alleles were present within the 21 subjects for which immunopeptidomic data were available, importantly covering many of the most frequently expressed HLA-I alleles globally. Using these previously generated data by the two aforementioned laboratories, Kubiniok et al. predicted restriction of peptides sequenced from each tissue type to respective HLA-I alleles from each sample using NetMHCpan4.0 - observing tissue-dependent variation in the proportion of peptides restricted by each HLA-I allele, and further stating that the affinities and abundance of both shared and tissue-specific peptides demonstrate unique properties. Finally, the authors correlate immunopeptidome findings from analysis of the 2 studies, Marcu et al. (2021) and Schuster et al. (2018), to a separate set of transcriptomic and proteomic tissue-based atlases from Geiger et al. 2013, Sollner et al. 2017, and Wang et al. 2019. They then sought to define correlations between abundance and expression of tissue-specific peptides presented on HLA-I to the tissue-specific atlases containing RNA and proteome expression data. Through their analyses, they also found that alternative components (enzymes) present in the antigen processing and presentation pathway may drive high levels of tissue-specific heterogeneity in the HLA-I-restricted immunopeptidome, thus informing targeted future experiments for investigating antigen processing.

      Overall, this is a study that draws attention to some of the properties of the antigen processing and presentation pathway that had not been investigated before, namely the known differential gene expression profiles between tissues resulting in the presentation of tissue-specific antigens on HLA-I molecules, and additionally provides avenues for investigation of the involvement of new enzymatic pathways involved in the generation of HLA-I restricted peptides that are presented to CD8+ T cells for immunosurveillance (e.g. the role of the four carboxypeptidases (CPE, CNDP1, CNDP2 and CPVL).

      The main points of criticism are that the tissue data has not been normalised, meaning that less material and MHC expression levels in different tissues will guide the overall sequencing depth, and therefore define the overlap of presented peptide sequences between the tissues. The bias of LC-MS acquisition towards the most abundant peptide species may further define the relationship with RNA transcript abundance. Finally, LC-MS database interpretation could lead to a bias of identifying peptides from non-variable regions if spectral interpretation did not include accurately matched personalised databases, and the conclusion that 'hyperconserved' regions are preferentially presented need very careful further validation.

    1. Reviewer #1 (Public Review):<br> Gene drive is a process by which variant genes spread through a population at a higher rate than is typical for normal inheritance patterns. There is considerable current interest in applying CRISPR technology to achieve gene drive in sexually-reproducing organisms. In this paper, Walter et al. report their studies of gene drive applied to the very different setting of infections by human cytomegalovirus (HCMV), a medically important cause of disease and death in congenitally infected newborns and in patients with weakened immune systems. The long-term goal of this work is to develop gene drive technology for use in treating viral infections. 

      This research builds on a recent publication in which these authors demonstrated that a gene drive cassette inserted into HCMV can promote specific recombination from this "gene drive virus" into another, recipient normal virus that is present and replicating in the same co-infected cell. The components of the gene drive cassette result in cutting of the normal virus genome at a specific site which is then repaired either by transfer of the gene drive cassette into that site, creating a new, "recombinant gene drive virus" or by mutations of the site that creates a "drive-resistant virus." A common obstacle in this kind of method is that it tends to select gene drive-resistant mutants over time. In this case, the data show nicely that the drive-resistant mutants have mutations at the expected site and thus are immune to the further recombination events and have the potential to replicate and cause disease. The authors test whether designing the guide RNA to target a presumably critical site within a viral gene that is needed for efficient viral replication will lessen the chances that a drive-resistant mutant virus will be able to replicate efficiently. 

      In the first part of the paper, the authors confirm and extend their prior work. Their mathematical modeling predicts that viruses resistant to gene drive will be selected and come to predominate over time. They test this idea using a gene drive system targeting the same non-essential virus gene, UL23 as they used previously. The results (Fig. 2) nicely confirm their prediction and show that even starting with only a small fraction of the gene drive virus results in spread of the gene drive cassette through the population. A striking demonstration of how robust the gene drive method is shown(in Supplementary Fig. 2. Introduction of the gene drive cassette into fibroblasts by transfection of a plasmid, a notoriously inefficient process, is nonetheless sufficient to generate enough recombinant gene drive virus to enable gene drive through the population over 50-70 days. 

      One observation in the data seems puzzling and requires clarification. Since the wild type virus, Towne-GFP, replicates 15-times more efficiently than the gene drive virus (Fig. 2B) and the experiment in Figs 2C-D was performed by starting at a low moi (0.1), most cells would be infected with only one virus. The cells infected with Towne-GFP would be expected to produce abundant virus, at least for most of the first week and so there would an increase relative and actual Towne-eGFP until there is enough virus so that a substantial population of cells are infected with both viruses. 

      The author then tried to modify gene drive viruses to target genes that are needed for efficient replication. The idea here is that the original and recombinant gene drive viruses would replicate poorly because of insertional mutation of the important viral gene. Importantly, any drive-resistant viruses that emerged would likely also replicate poorly due to mutations at the cut site, which they designed to be at specific positions within the genes that seemed likely to be very sensitive to mutation. Among the 8 other genes they targeted with this approach, only two worked. In 4 of the other cases, they unable to make the recombinant viruses, which is not really surprising since these genes are known to be essential for the virus to replicate at all. In two other cases, they observed only very limited drive, for unknown, though potentially interesting reasons. In the two cases that did work to promote gene drive, they were able to isolate drive-resistant mutants at a late time point. Their analyses nicely confirmed the presence of mutations at the expected sites and, importantly, that mutations caused these viruses to replicate somewhat less well than the wild type virus. Thus, the author showed that their strategy of targeting important loci can work, but much more needs to do to (i) understand the design rules (i.e. why did 6 or the 8 versions not work) and (ii) to understand how robust the system really is. In one of the two genes that seemed to work (UL35), the gene drive-resistant mutants did not replicate significantly less than wild type virus. In the other case (UL26), the drive-resistant viruses were isolated at a time when the titers of this population were still increasing (Fig 4D) so it is not clear if virus that replicates as well as wild type virus would emerge later. 

      Overall, these studies are well done and interesting The ultimate goal of applying gene drive methods to treat viral infection has many obstacles to overcome; these data are a small step forward.

    2. Reviewer #2 (Public Review): 

      This study describes an interesting attempt to engineer suppression gene drives for human cytomegalovirus (hCMV, herpesvirus 5). hCMV is a nuclear replicating dsDNA virus and is implicated in multiple human diseases. M. Walter and E. Verdin previously described a CRISPR/Cas9 Gene Drive (GD) targeted at hCMV's UL23 (aka. GD-UL23) that spread via recombination. Although the function of the UL23 tegument gene is dispensable for virus propagation in cell culture, it is required for evasion of immune response in vivo. Notably, loss-of-function (LOF) UL23 allele and GD-UL23 still incurred fitness cost, and the brief spread of GD-UL23 correlated with the total virus load reduction. However, viruses harboring UL23 alleles resistant to GD-UL23 rapidly evolved and blocked the further spread of GD-UL23. The goal of a current work was to engineer a next generation suppression GD in hCMV, which would be immune to resistant alleles and therefore, would spread better and cause a long-term reduction of viral levels. M. Walter et al. targeted GD into genes essential for hCMV propagation to ensure that their LOF resistant alleles could not propagate. Two GDs targeting UL26 and UL35 genes were developed and analyzed in the study. Both GDs (GD-UL26 and GD-UL35) induced a transient reduction in the total viral titer (up to 50% and 80%, respectively) before induced resistant viruses spread at the expense of each GD and brought the viral levels up. In conclusion, this study failed to develop a suppression GD that could overcome the evolution and spread of induced resistance. Nevertheless, naturally occurring or induced resistant alleles are Achilles' heel of any gene drive especially in rapidly evolving and recombinogenic viruses. Therefore, I think claims that are not supported by the data should be tempered. 


      The manuscript describes a promising approach for developing a suppression gene drive in a virus. hCMV is a good model for this type of research, since it is a nuclear replicating dsDNA virus. 

      The scale of work presented in the Table 1 is impressive. Eight essential genes were targeted by GD plasmids. It would be interesting to know some details of this massive work. Why recombinant viruses were not generated for three genes? Did each gRNA direct its target cleavage efficiently? Could Towne-GFP virus rescue these recombinant viruses? 

      I admire the choice of gRNA for GD-UL26. It looks truly conserved at both DNA and amino acid levels; and yet UL26 resistant alleles were induced that were only marginally unfit in comparison to GD-UL26. 


      I think that the presentation of findings can be improved. 

      Claims are overstated throughout the manuscript, including its title. 

      Notably, GD-UL36 works better than GD-UL26 however the GD-UL36 gRNA target contains multiple SNPs at the PAM region making it less stable in a long run for any real world application. 

      Points of potential interest: 

      Both GD-UL26 and GD-UL35 may not spread catalytically via recombination with Towne-GFP. Instead they can spread at the expense of Towne-GFP viruses (i.e. by destroying them), similarly to toxin / antidote drives in insects. 

      It would be useful for non-specialists to describe how fibroblast cell numbers were controlled during long cell culture experiments. I can image that selection between fibroblast cells can happen during 70 days, e.g. virus load may affect the probability of cells death or the speed of cell proliferation. In turn, this cell selection affects quantification of viral load and composition.

    1. Reviewer #1 (Public Review): 

      This is a most complete and very impressive study, where the authors sequentially address the role of Mvp1 in the recycling pathway off the endosome. The authors take advantage of the recently published structure of Mvp1, map the PI3P binding site and dimer interface and show that both are required for Mvp1 function. The then successfully map the consensus sequence in Vps55 and identify mutants that are defective in recycling, but now reside on the surface of the vacuole. The authors then generate a functional Vps1 allele, demonstrate its colocalization with Mvp1 and defective Vps55 recycling. They also show that Vps1 is present on endosomal tubules, and demonstrate that selective cargoes known for the retromer pathway are not affected by Mvp1. In support of this, immuno purification of retromer and Mvp1 show that both reside in distinct complexes, and have distinct cargoes. However, all three pathways seem to function together to control integrity of the plasma membrane. Even though it is just the beginning, the SNX8 analysis in Figure 7 nicely completes the study.

    2. Reviewer #2 (Public Review):

      The SNX-BAR family of sorting nexin proteins is involved in the formation of tubular carriers at endosomes. The best characterized yeast sorting nexins form part of the retromer complex, which binds sorting signals on cargo proteins to direct their recycling. There is some debate as to the role of sorting nexins in mediating cargo recognition vs tubule formation, and it is unclear which (if any) other members of the sorting nexin family bind directly to cargo. 

      In this manuscript, the authors investigate the function of the yeast sorting nexin Mvp1. This protein was previously proposed to cooperate with retromer in the formation of recycling tubules, and to recruit the dynamin-like protein Vps1 to promote their scission (Chi et al, JCB 2014). Here, Suzuki et al find that Mvp1 has a cargo-sorting role that is distinct from that of other sorting nexins. They show that Mvp1 (but not retromer) is required for the correct localization of the membrane protein Vps55, and identify a cytosolically-exposed sequence in Vps55 required for its sorting. Using structurally-guided mutagenesis, they find that dimerization and membrane binding is important for Mvp1 function. They use live cell imaging to show that Vps55 is largely sorted into different tubules compared to the retromer cargo protein Vps10, and use fractionation of vesicle fusion-deficient cells to show these cargo are present in different vesicle populations, suggesting that Mvp1 and retromer form different classes of retrograde carriers. By surveying the trafficking of other membrane proteins, they show that in some cases Mvp1 acts redundantly with two other sorting nexin complexes (Snx4 and/or retromer) to recycle cargo at endosomes. Moreover, they find that loss of all three sorting nexin complexes perturbs endosome function, lipid asymmetry, and the endosomal recruitment of the scission factor Vps1. Although Mvp1 was previously implicated in Vps1 recruitment (Chi et al, 2014), Suzuki et al use a GTPase-defective form of Vps1 to provide the first evidence that Mvp1 physically interacts with Vps1 in vivo and in vitro. Taken together, these data suggest that Mvp1, retromer and Snx4 recognize distinct sets of cargo proteins and mediate independent recycling pathways at endosomes, and imply that each sorting nexin recruits Vps1 to complete tubule scission. 

      Overall, this manuscript presents a large number of experiments that are technically well executed and makes several novel observations. It should be noted that many experiments largely repeat previous work: this was not always clearly indicated in the manuscript. For the most novel observations, some weaknesses were noted. A key novel finding was that Mvp1 binds to and sorts the cargo protein Vps55 via recognition of a cytosolic motif. The supporting data do not provide the typical burden of proof for such experiments, because: (1) the identified sequence was shown to be necessary but not sufficient, thus the mutation could indirectly affect binding at another site, and (2) Mvp1 failed to coIP with the Vps55 mutant from cell lysates, but this could be an indirect effect of Vps55 missorting to the vacuole while Mvp1 remains at the endosome, and does not prove that Mvp1 binds directly to Vps55 via this motif. 

      A second key finding is that Mvp1 and retromer form distinct classes of tubular carriers at endosomes. While the manuscript does provide data to support this conclusion, I was disappointed that there was no discussion of the work of Chi et al, who showed through careful quantitative analysis that Mvp1 and retromer frequently label the same population of tubules. Moreover, the authors claim that mvp1 mutants secrete little CPY, yet the literature indicates these mutants secrete ~65% of newly synthesized CPY (Ekena and Stevens, MCB 1995), suggesting a functional link between Mvp1 and Vps10 recycling. In fact, vps55 mutants themselves have a significant CPY missorting defect (~50% secreted) suggesting that some mvp1 phenotypes could be a secondary consequence of Vps55 mislocalization. It was not mentioned that Vps55 interacts with the transmembrane protein Vps68: these proteins are interdependent for their stability and loss of Vps68 slows traffic out of the endosome (Schluter et al MBOC 2008). This provides a simple explanation for the observed ubiquitination and degradation of overexpressed Vps55, which presumably saturates available Vps68. 

      Other experiments in this manuscript were not completely novel, including: the demonstration that Mvp1 tubules bud from endosomes and that Mvp1 is important for Vps1 recruitment to endosomes (Chi et al, JCB 2014); that Vps1 GTPase mutants accumulate Mvp1 at endosomes (Ekena and Stevens, MCB 1995); that Mvp1 plays a role in Vps55 localization (Bean et al, Traffic 2017); and that GFP-SNX8 is present on endosomal tubules when expressed in mammalian cells (van Weering et al, Traffic 2012). While in most cases the experiments presented in this manuscript build on and extend previous work, I would like to see the earlier work fully acknowledged, and any discrepancies appropriately discussed. The fact that many of the experiments presented in this manuscript are not entirely novel detracts from the overall impact of the work. Despite this, key original findings presented in this paper - including the discovery that Mvp1 is required for sorting specific cargo and binds directly to the dynamin-like protein Vps1 - will be of broad interest to the trafficking field.

    3. Reviewer #3 (Public Review): 

      This manuscript describes a very thorough characterization of Mvp1/Snx8 function in recycling proteins from the endocytic pathway to the Golgi complex. This particular sorting nexin may play a protective role against Alzheimer's disease in humans and whether or not it functions along with retromer in cargo recycling has been unclear. A major limiting component for studying Mvp1 in yeast was that no one had identified a cargo protein that specifically relied on Mvp1 for recycling. The authors identified such a cargo (Vps55) and went on to make the following impactful discoveries: 1) Mvp1 acts in a recycling pathway that functions in parallel and independently of retromer and other sorting nexins to recycle the membrane protein Vps55. 2) Mvp1 functions as a homodimer and recognizes a unique sorting signal within Vps55. 3) Mvp1 recruits the dynamin-related Vps1 to endosome-derived tubules to mediate their scission. This latter observation is particularly impressive as Vps1 studies in yeast have been plagued by nonfunctional GFP chimeras and pleiotropic phenotypes of mutants. However, the investigators have done a very nice job of developing tools to probe the specific role of Vps1 in this Mvp1-Vps55 pathway. In fact, these studies were extended to argue for a general role for sorting nexins (Snx4 and retromer complexes) in recruiting Vps1 onto endosomal membranes. 

      The major strengths of this manuscript are the high quality data supporting the conclusions, the comprehensive nature of the study, the identification of a new endosomal recycling pathway that appears to function independently of previously described routes, and clear demonstration for linkage of Mvp1 to the dynamin-related Vps1 in order to drive tubule scission. One could argue that these individual observations are unsurprising because paradigms exist in the literature for how sorting nexins function in protein trafficking and potentially recruit dynamin for membrane scission. However, seeing the full picture develop in this manuscript for Mvp1 in a genetic system that allows for multiple, well-controlled experimental approaches make this a very impactful study.

    1. Reviewer #1 (Public Review): 

      In the manuscript "Niche partitioning facilitates coexistence of closely related gut bacteria" by Brochet et. al., the authors work on the identification of the mechanisms that enable co-existence and persistence of multiple bacterial species in the gut. 

      The authors rely on a gnoto-biotic approach with Bees colonized with a defined bacterial community composed of 4 species. They studied the effect of diet, the host, and microbial interactions in enabling co-existence of these 4 species. 

      They followed gut colonization of these different species in mono-colonized animals and in co-culture under two different diets (simple sugar, or pollen). They observed that pollen could sustain persistence of these species, unlike the simpler diet where the community was dominated by a single species. 

      To disentangle between the role of the host and microbe-microbe interactions in this process they performed similar experiments in laboratory cultures. In laboratory in vitro cultures they also observed co-existence and persistence in the pollen diet, but one-species domination was observed when glucose was the main carbon source. Therefore, they concluded that a complex diet (and not the host) was key for enabling persistence, as the results were similar in the laboratory cultures.

      Their studies were complemented by transcriptomics and metabolomics and these results support the general conclusions that pollen contains diverse carbon sources which could be used in complementary ways by the different species, which have diverse metabolic capabilities encoded in their genomes. 

      One of the points that was not completely explored in the paper is what happens in the simplified diet both in vitro and in the Bee gut. They propose in the discussion that in the presence of few and simple carbon sources (sugars) there is competition for nutrients and competitive exclusion is driving loss of some species. But this is not fully addressed in the paper. 

      The system they use (with 4 closely related bacterial species) is a simplified system. Therefore, it is not clear if the same general findings will hold in more complex systems. But the results supporting that nutrient complexity (in diet) and metabolic diversity (from the microbial side) are key factors to enable co-existence and persistence of complex microbiota communities are strong and likely generalizable. Although, it is possible that with other communities and other hosts other factors will also come into play. Nonetheless, the current study is important because it sets a good example for how these questions can be addressed to study more complex systems. 

      Overall, the study described here is complete, and rigorous, except for a few points that still need to be addressed and clarified. Namely, it would be interesting to understand what drives exclusion of some members of the community in the simplified diet.

      Importantly, the current study opens the door for new studies (including in vitro studies) on the identification of network interactions that are important for Microbe-Microbe interactions that enable co-existence in other systems. Additionally, this study also highlights the importance of identifying the relevant nutritional (and metabolic) conditions for addressing those questions given the importance of the metabolic context in shaping microbe-microbe interactions.

    2. Reviewer #2 (Public Review): 

      This paper investigates the mechanisms that allow closely related species to coexist in a gut community. A simple expectation is that more closely related species will overlap more in their ecological niche, and thus tend to compete. However, factors that add complexity and heterogeneity, such as diet, immune response, gut morphology and bacteriophage may cause the realized niche of species to overlap less in the gut environment. The honeybee gut is a beautiful model and is also a good choice to test the competition-relatedness hypothesis, because the core microbiota of bees is made up of distinct phylotypes each containing closely related species. The authors select a single phylotype and compare the community assembly in a gnotobiotic colonization model and in defined culture conditions based on the bee diet. I believe that all of my concerns are easily addressable, and I think that this manuscript will be a very nice contribution to this active area of research. 

      Strengths: The use of community profiling, transcriptomics, and metabolomics adds depth, as does the comparison of defined culture conditions to the host environment. The main conclusions drawn by the authors is that the presence of pollen is necessary for gut species to coexist, and that the different species, although closely related, respond in distinct ways to nutrients in pollen and consume different profiles of nutrients from pollen. 

      Weaknesses: The main weakness I see with this work is the choice of in vitro comparison conditions. The strains are cultured either on pollen or sugar water, whereas in vivo bees are fed a diet of pollen and sugar water, or only sugar water. A direct comparison is possible between the strains grown on sugar water in vitro or in vivo, but I think that in several places, the authors may have to reconsider or modify their interpretations comparing in vitro culture on pollen/pollen extract with the in vivo growth of the community on pollen and sugar water. Because there is sugar in the bee diet, differences in assembly dynamics, transcription, or metabolite consumption between pollen-containing culture conditions and the bee gut might stem from the dietary intake of sugar, or from an aspect of the host environment.

    3. Reviewer #3 (Public Review): 

      Brochet et al. find that four species of the Lactobacillus Firm-5 lineage, one of the core bacterial lineages of the honey bee microbiome, are able to coexist because they utilize different pollen-derived flavonoids and sugars. They demonstrated this both in vivo, in gnotobiotic bees, and in vitro with laboratory co-cultures. Simple yet robust experiments involving diet or growth media with just simple sugars resulted in loss of diversity, whereas diets and media supplemented with pollen allowed the persistence of all four Firm-5 species over multiple serial passages. The authors then proceeded to examine the genes that were differentially expressed in response to different nutrient growth conditions, as well as the presence of metabolites to infer utilization of pollen-derived nutrients. The results paint a convincing picture of niche partitioning via differentiation in both encoded metabolic capabilities and in the differential expression of commonly encoded genes among co-resident bacterial species. 

      Overall, the paper is strong and the arguments and conclusions put forth are well supported by the data. I only have a few suggestions: 

      1) The study focuses on one strain each of the 4 Firm-5 species; however, there is diversity within each species. This is only briefly mentioned in the paper at the very end, and I think the authors should address this a bit more directly. In particular, they have previously generated a large amount of genomic data from some of these other strains, so it is likely possible to infer or speculate, based on this data, whether they expect different strains within each species to utilize similar nutrients. Also, I'm wondering if the authors can comment on how their findings could extend to the related bumble bee gut microbiome. Such a discussion would help enhance the applicability and importance of this study. 

      2) It is interesting that different species ended up dominating in the in vivo vs. in vitro simple sugar-based communities. What do the authors think may be behind this difference? 

      3) Since the observed coexistence of these gut microbes is largely due to nutritional niche partitioning, it would be helpful if the authors can comment on the natural variation of key pollen derived metabolites, and if/how we could expect ecological variation in the bee microbiome due to plant pollen availability based on biogeography and seasonality. 

      4) The supplementary information is nicely documented and accessible, but I think it would be even more useful if genome-wide data for the RNA-seq results, not just for select genes, are made available. Furthermore, I suggest including descriptive titles and labels within the supplementary Excel files, as there are many separate sheets and it is not always clear what each one shows.

    1. Reviewer #1 (Public Review): 

      The main question being tackled in this paper is, how do you include the unknown genes from metagenomes in analytical workflows? 

      To that end, the authors quantify the unknown fraction of genes in both genomes and metagenomes, and compare and contrast them across human-associated and marine environments. 

      The framing of the problem in the introduction, the discussion of the results, and the thinking about next steps, are particularly well done! 

      The methodology employed to generate the results, and the specific results, are high quality; and the implications for the field of both the workflows and the resulting database are immense (and clearly well understood by the authors). This is likely to spur many in-depth explorations that make use of the hypotheses that can now casually be generated. 

      Where I think the paper needs the most work is in connecting the results to the discussion. I believe all the pieces are there, but it is hard to sort through the (many) fascinating observations made by the authors and connect them clearly to the discussion.

    2. Reviewer #2 (Public Review):<br> Vanni and colleagues set out to catalog the sequence diversity and distribution of proteins identified in metagenomic data where standard methods are unable to assign functional annotations. The authors perform homology based clustering on a large collection of putative protein coding genes from metagenomic assemblies, with a focus on the HMP and TARA Oceans Survey datasets. By taking a very high-sensitivity, multi-method approach to annotating gene clusters, only clusters without detectable homology are annotated as "unknown". Their pipeline, which is built using Snakemake, involves domain annotation with Pfam/HMMER3, clustering of sequences with MMseqs, remote homology detection using HHBlits, and further grouping sequence clusters into super-clusters using MCL. The authors find that, in metagenomic assemblies the contribution of the unknown fraction to the pool of all genes is smaller than one might have been expected, and is dependent on the source of the environmental sample. Nonetheless, the ad hoc clustering of sequences into (operational) protein families shows that the unknown fraction has a very large number of potential functions, and that still more will be discovered with additional samples. Based on an analysis of taxonomic distribution, they find that the unknown fraction is largely composed of gene families that are clade specific, especially at the level of species. 

      By de novo clustering putative coding sequences, with a particular eye to identifying truly unknown protein families, the authors demonstrate the value of recently developed, scalable computational methods paired with the explosion of metagenomic data towards increasing the pace of microbial functional gene discovery. 


      - The authors take a systematic and reproducible approach to integrating data from a large corpus of metagenomic libraries and reference databases. By de novo clustering the authors are able to improve the sensitivity of their homology detection, while providing an extendable database of sequence diversity. 

      - This manuscript explores some interesting ideas about how we might structure a database of both near and remote sequence homology, specifically the use of super-clusters ("communities of gene clusters"). 

      - Uniquely, analysis parameters were chosen conservatively to minimize false negatives in homology detection. As a result, their unknown fraction is a convincing representation of the huge diversity of protein families for which functions have confidently *not* been characterized. 


      - The priority given to metagenomic protein sequences over reference genome sequences in the clustering pipeline is not sufficiently justified. Indeed, the metagenomic coding sequences are notably more likely to be fragmented due to challenges in assembly. A combined clustering of both would present a conceptually simpler and potentially less biased workflow. Likewise, the conceptual division between genomic and environmental genes belies their mutual importance in discovering unknown functions. 

      - The authors do not compare their methods to other possible ways to identify the unknown fraction. It is therefore unclear how much better than a naive approach it might be. Likewise, it is worthwhile to question the sensitive of their results to analysis parameters. As a suggestive example, in the one case where they did compare possible parameter values-the systematic selection of the inflation parameter for MCL clustering of gene clusters into super-clusters (Supplemental Figure 7-1)-the selected values resulted in distinctly different super-cluster properties compared to all other assessed parameter values. The manuscript would be strengthened by highlighting how the chosen parameters maximize sensitivity to remote homology. 

      - It is not clear why super-clusters ("cluster communities") are identified within each of the cluster classifications (Known / Genomic Unknown / etc.) instead of across all four groups. Intuitively, this would present the opportunity to detect distant homology between clusters with known and unknown function. 

      - It is not clear why small clusters and those with many fragmented members are removed entirely from downstream analyses, given that the inclusion of additional sequences in later steps would presumably improve the quality of these clusters by adding new representatives. 

      - While maximizing sensitivity to remote homology is appropriate for the overarching goal of characterizing entirely unknown protein clusters, the likely decrease in specificity means that the accuracy of functional annotations and the shared function of all sequences in a cluster are suspect (as the authors are aware). It would have been interesting and valuable to extend the hierarchical clustering framework, already partially developed here, to enable both sensitive and specific annotations.

    1. Reviewer #1 (Public Review): 

      Skrapits et al., report on a population of GnRH neurons in the putamen that dwarfs the commonly studied hypothalamic population that regulates fertility. This laboratory performs very careful immunohistochemical studies and has included a number of controls to support this claim. These primarily include comparison of an overlapping staining pattern with multiple polyclonal antibodies, in situ hybridization and measurements of GnRH decapeptide with LC-MS/MS. While these are supportive, the question of the degradation product GnRH1-5, which has been brought up as a potential caveat in prior studies of extrahypothalamic populations as pointed out by the authors, does remain. This cleavage product was detected in their samples from the forebrain, albeit at lower levels. Even the identification of a large population of cells producing the cleavage product would be of interest, but knowledge of the GnRH-related peptides in these cells is needed to point future studies in a fruitful direction. 

      These immuno studies present a more complete and state-of-the art characterization of populations that have been hinted at in past work not only in primates, which is cited, but also in rodents (Skynner et al., J Neurosci 19:5955-5966), citation of which was overlooked. The authors should also comment on the extended exposure to primary antibodies in these studies, which has been reported to increase the number of GnRH neurons visualized during development in rodents (Wu et al., J Neurobiol 1997 Dec;33(7):983-98.) Also relevant to this point the statement on lines 379-380 is incorrect; the fluorescence of eGFP in these regions in the GnRH-GFP mice used has indeed been reported (Endocrinology, September 2008, 149(9):4596-4604) as has GnRH-GFP signal in another line of mice (Prog Neurobiol 63: 673- 686), and cells were also identified using GnRH promoter to drive beta galactosidease (J Neurosci 19:5955-5966). 

      The authors also support their claims with RNAseq data. Performing these studies in human tissues is difficult because of the difficulty in controlling conditions and the data largely support their claims but some of the admitted quality limitations may warrant being more circumspect in their conclusions. 

      To extend their findings beyond enhanced anatomical characterization, the authors perform electrophysiologic studies of both putamen GnRH neurons and other putamen neurons identified in young mice. These data are not currently presented in a manner that allows a reader to determine if their conclusions from these studies are justified. Past work on GnRH action on hypothalamic GnRH neurons has indicated a dose dependence (Endocrinology 145(2):728-735), thus the current work should also examine dose effects before a putative direction of action for GnRH can be posited. Discussion of the central localization of GnRH receptors from other studies relative to their findings should also be discussed (Endocrinology 152: 1515-1526). 

      In the discussion, possible therapeutic actions of GnRH analogues are suggested. While exciting, this is not new and prior work examining patients on analogue therapy (for example Almeida et al., Psychoneuroendocrinology.2004;29(8):1071-1081 and Gandy et al JAMA.2001;285:2195-2196) should be cited.

    2. Reviewer #2 (Public Review):<br> The study beautifully illustrates the detection of a rather large population of GnRH neurons in the basal ganglia, by a convincing combination of neuroanatomical techniques in human brain specimens; techniques which are mastered by the authors and are well suited in terms of characterization of the GnRH neuronal system. The more conventional neuroanatomical techniques are further backed-up by modern molecular (RNA-seq) and biochemical (HPLC-MS) approaches. In addition, incorporation of a mouse model expressing GFP under the GnRH promoter adds some mechanistic dimension to the descriptive contents of the paper, which is a potential advantage, albeit it is not always clear that mouse and human data are fully convergent. 

      Despite the strengths of the paper, this referee has identified several limitations, which need further elaboration, in order to avoid over-interpretation of the current dataset. Among these weaknesses, the authors should better clarify the number of individuals used for each analysis, and how representative the current findings are for both sexes and range of ages (and even pathological conditions) in humans. In addition, further discussion about the potential origin and relation (similarities and dissimilarities) with the hypophysiotropic population of GnRH neurons is deserved. Further, combination of human and mouse data is difficult at some places, since the mouse model do not express GFP in adulthood, and even no confirmation is provided that striatum neurons expressing GFP are actually producing GnRH at the neonatal period in the mouse. Finally, although the implications of current findings are potentially large, the extended discussion of the present dataset in the context of neurological disease makes the paper over-speculative.

    3. Reviewer #3 (Public Review): 

      The impetus for the study was the relatively recent demonstration by Casoni et al that, in man, a large number of GnRH neurons (approx. 8000) migrating from the olfactory placode during embryonic development follow a dorsal migratory route that takes them towards pallial and or subpallial structures, rather than along the more established ventral pathway that leads them to the hypothalamus where they subserve reproduction. The primary purpose of the experiments described were to determine the fate of the embryonic GnRH neurons that follow this ventral pathway and to begin to examine the biology of this interesting group of cells. 

      By and large, the varied array of contemporary imaging and molecular methods used are well described and the results are robust. Indeed, the application of such an armamentarium of approaches to study GnRH neurons in the human brain is a major strength of the paper. 

      Quantification of extrahypothalamic GnRH neuron number was performed using IHC with a guinea pig antibody, #1018. However, it appears that the standard procedure to establish specificity of an antibody, namely pre-absorption with authentic GnRH in the case of #1018, was not performed here nor presented in the original paper cited as describing this antibody (Hrabovszky et al 2011). 

      The significance of the electrophysiological data derived from brain slices containing caudate-putamen (CPU) of a transgenic mouse (GnRH-GFP), in which GFP expressing cells were observed transiently in the CPU around postnatal day 4-7, is unclear. Regardless of what the outcome of the mouse experiments might have been, it seems highly unlikely that the discussion and implications of the data obtained from extrahypothalamic GnRH neurons in the human brain would have changed. Also the authors themselves "recognize that the neonatal mouse model has severe limitations." 

      The aims of the authors have been more than realized: they have 1) provided novel and convincing characterization of extra-hypothalamic GnRH neurons in the human brain, 2) discovered that this population of neurons (>100,000) is far larger than previously considered, and 3) tentatively suggest that the additional extrahypothalamic GnRH neurons they have discovered may not originate from the olfactory placode, 

      The authors findings will almost certainly lead to further examination of the function of extrahypothalamic GnRH in normal brain function and neurodegenerative disorders associated with aging, which in turn may lead to new therapeutic applications of GnRH1 receptor ligands. 

      Returning to the authors suggestion that the additional extrahypothalamic GnRH neurons they have discovered may not originate from the olfactory placode, the Paragraph discussing this issue (beginning Line 319) confused me. Here, the authors state that it is unlikely that the large number of extrahypothalamic GnRH neurons in the putamen and related areas are identical to the 8000 observed by Casoni et al (2016) along the dorsal migratory route (the authors original aim was to follow the fate of these cells). Instead they suggest that they are homologus to the GnRH cells that, in the monkey leave, the olfactory placode before E30 (termed "early" GnRH neurons). If "early" GnRH neurons originate from the olfactory placode then why are the large numbers of GnRH neurons observed in the human Pu, and argued unlikely to be of placode origin, considered to be homologus to "early" GnRH neurons. In this regard, the relationship between the ChAT negative GnRH neurons in the nasal region of the GW11 human fetus and the "early" and "late" GnRH cells in the monkey fetus should be provided. In clarifying the above issue, the fact that Terasawa's studies utilized fetal rhesus monkeys should be explicitly stated in the Introduction and reinforced when they are discussed with the author's results. As written, the reader does not discover the developmental origin of Terasawa's monkeys until the Discussion. 

      In the Discussion the authors refer to GnRH deficient patients (Chan 2011). Homozygous mutations of GnRH1 are very rare and therefore it's perhaps not surprising that patients with such mutation have shed little light on function of extrahypothalamic GnRH. However, GnRHR1 loss of function mutations are much more common and have been known for nearly 25 years. Surely, a review of this literature would be worthwhile to see if any insight into dysfunction unrelated to reproduction emerges.

    1. Reviewer #1 (Public Review): 

      In this manuscript, Burt and colleagues use a well-motivated neural mass modelling approach to better understand the neurobiological basis of a recently-demonstrated link between 5HT2A agonists and whole-brain signatures of increased integration - so-called, Global Brain Connectivity (GBC). The authors derive a simple model of both excitatory and inhibitory neurons in the cerebral cortex, whose activity is dependent on the weighted connections between regions, which in turn are modulated by a neural gain parameter. Crucially, this gain parameter is linked to the action of neuromodulatory neurotransmitters (and exogenous ligands), and has a heterogeneous effect that depends on the expression of a range of different classes of g-protein-coupled receptors. The authors include an estimate of this heterogeneity (mRNA maps from the Allen Human Brain Atlas) in their model by having the expression of the 5HT2A receptor mRNA separately effect the gain of both Excitatory and Inhibitory populations. Fitting their model to previously published empirical data, the authors find that the data are best explained by a relative increase in excitatory > inhibitory neural activity, which is consistent with the known mechanism of action of 5HT2A ligands. After providing a number of useful statistical checks, the authors then use a dimensionality reduction approach to relate different aspects of the functional neural signatures to unique aspects of the phenomenology of the psychedelic experience associated with 5HT2A.

    2. Reviewer #2 (Public Review): 


      The submission by Burt et al. is an interesting and timely contribution to the computational psychiatry and pharmacological fMRI literature. It advances an account of the synaptic mechanism through which LSD influences global brain activity and functional connectivity (FC), through differential modulation of synaptic gain in excitatory and inhibitory neural populations. This is done by extending a previously-introduced computational model of whole-brain neural dynamics to include regional variation in cellular and neurochemical properties (specifically, the spatial profile of serotonin receptors). This regional variation is based on maps from the Allen Human Brain Atlas, which give transcriptomic expression profiles for the HTR2A gene (encoding the 5-HT2A receptor), amongst others. The model's predictions are compared quantitatively to a human subject fMRI dataset with an LSD intervention, and provide a parsimonious and convincing account of observed LSD-induced changes in global brain functional connectivity. The general approach of augmenting a connectome-based network model of whole-brain neural activity with maps of spatially varying neurotransmitter, gene expression, cytoarchitectural, etc. features from various complementary data sources has been pioneered by this group over the past five years. 


      The study represents an extension of an established neuroinformatics-informed modelling methodology to a novel imaging dataset (LSD intervention), and appropriate use of the recently developed (and increasingly widely used) Allen Human Brain Atlas gene expression maps, with clear neurobiological rationale wrt the fMRI dataset. 

      The statistical methodologies for comparing spatial maps (spatial autocorrelation-controlled null models) are rigorous and sophisticated. 

      The central result, that HTR2A maps improve model performance better than other receptor maps (e.g. dopamine), is an important demonstration of the utility of this approach (although with some caveats; see below). 

      The paper demonstrates comprehensive understanding and utilization of a mathematical model for neural population activity in the service of the research question, including well-chosen modifications to represent neuromodulatory influences, a sensible calibration procedure, and mathematical manipulation to test novel hypotheses. 

      ...In particular: the authors have developed a modest piece of new mathematical theory that allows them to analytically incorporate global signal regression into the linear(ized) algebraic model for neural activity covariance and functional connectivity. Because the main dependent variable throughout the study is scalar maps across the cortex of global brain functional connectivity (FC), and changes thereof (ΔGBC), global signal regression (GSR) - a standard but not uncontroversial fMRI denoising technique - naturally is of major importance (because the primary effect of GSR is to remove artifactual global correlation patterns). This approach may in the future also be used to study a wide variety of other fMRI data features, for which it is important to know the potential contribution of GSR. 


      The principal result isn't hugely surprising: inclusion of the HTR2A map in the model produces ΔGBC changes with a similar spatial topography to that map in the model. The empirical ΔGBC maps are also similar to the HTR2A maps, and so the simulated ΔGBC give a good fit to the empirical ΔGBC data. Yes, the authors demonstrate convincingly that this simulated-empirical ΔGBC fit is stronger than the similarity to the HTR2A map itself, and also to that of various alternative receptor maps and surrogate null models. But the central result does have an element of 'getting out what you put in'. 

      The ΔGBC metric is a bit weak as a stand-alone outcome variable. The usual quantity used in this type of model is the goodness-of-fit of simulated to empirical FC. Indeed, the authors have used this calculation in the initial calibration step for their model, where they identified the global coupling strength parameter that yielded the best fit of empirical to simulated FC in the placebo condition, achieving reasonably good fit (Spearman rank correlation r=0.45). However the authors don't report how this FC fit changes with the inclusion of the HTR2A map modulations. It is an open question whether a model with HTR2A-modulations added that improved ΔGBC but not FC fit should be regarded as a better model than one without. 

      The authors do not make clear why it is necessary, and/or why it makes sense to perform GSR on the mathematical model FC anyway. The artifactual contributions to FC that make this necessary for empirical data are by construction not present in modelled data, after all. 

      The model description is very comprehensive but it omits the actual equations used, which are (I believe) the algebraic neural activity covariance equations at ~Eq. 21 in Deco et al. 2014. After 10 equations leading up to this, the methods section simply says "Simulated BOLD covariance matrices were derived by linearizing these equations and then algebraically transforming the linearized synaptic covariance matrix, using a procedure which we previously reported in Demirtaş et al. (2019)." The final algebraic equations should be added, and also emphasize that they are the ones used. Readers less familiar with these models could otherwise be forgiven for thinking that the neural and haemodynamic differential equations listed in Eqs 1-10 were the ones used, which is not the case.

    3. Reviewer #3 (Public Review): 

      I would like to thank the editor for the opportunity to review this work, however considering that I do not have direct experience in the biophysical modelling of dynamic systems, I will only comment on the general aspects of the manuscript. In the article entitled "Transcriptomics-informed large-scale cortical model captures topography of pharmacological neuroimaging effects of LSD" the authors integrate brain-wide transcriptomic data into the large-scale circuit modeling in order to simulate neuromodulatory effects of LSD on large-scale spatiotemporal dynamics of cortical BOLD functional connectivity. This analysis builds on their previously published experimental work which identified that LSD impacts global brain connectivity (GBC) [by elevating GBC in sensory cortex and reduced GBC in association cortex] and that these effects are attributable to the agonism of the serotonin-2A receptor (5-HT2A). Using large-scale circuit modeling in combination with high-resolution spatially-defined transcriptomic data the authors now investigate the underlying mechanisms of these LSD-induced changes showing that the model can capture the spatial topography of these changes and demonstrating that the spatial distribution of 5-HT2A [and not other receptors that have an agonistic relationship to LDS] is critical for generating the cortical topography of LSD-induced functional disruptions. 

      From the methodological point of view, this study provides incremental extensions to previously published work, however, this can be viewed both as a potential weakness and a considerable strength. In my opinion, the integration of previous findings and a hypothesis-driven approach is a significant advantage. The adaptation of well-known models for large-scale neural dynamics to investigate pharmacologically-induced changes in brain activity extends the modeling approach and provides novel and insightful contributions towards understanding the biophysical mechanisms of LSD-induced changes in functional connectivity by addressing the mechanistic gap which is frequently lacking in imaging transcriptomic studies. Moreover, the model is also capable of capturing the patterns of functional variation across individuals that are linked to their perception of these pharmacologically-induced changes in experience, going beyond group-average estimates that are commonly used in neuroimaging studies. I also appreciate the investigation of the effects of global signal regression which is still widely debated in the neuroimaging community. Overall, the manuscript is methodologically sound, very well-written, and easy to follow, the key claims presented in the article are supported by the data.

    1. Reviewer #1 (Public Review): 

      The limitations of the approach could be included in the last paragraph of the introduction. It would similarly be useful in the discussion to not only compare photopic stimulation with other approaches, but to an ideal approach. 

      Is it possible to modulate the hair bundle position continuously - e.g. sinusoidally? If not, this would be useful to state as a limitation. 

      First paragraph of results. Could you elaborate a little here (a few additional sentences is probably enough)? The methods describes nicely why reflection alone is not sufficient, and some of the argument given there would demystify this paragraph.

    2. Reviewer #2 (Public Review): 

      The manuscript by Kozlov et al., entitled Rapid mechanical stimulation of inner-ear hair cells by photonic pressure, is another in the long series of elegant publications from the Hudspeth lab. The manuscript addresses the long-standing problem of engineering a stimulation method for individual sensory hair cells in vitro that adequately provides a uniform and rapid stimulus characteristic of the native stimulus in the inner ear. The authors address this unmet need with development and characterization of a light-based stimulus to generate rapid photonic force capable of deflecting a range of hair bundle geometries, including amphibian and mammalian vestibular and auditory hair bundles. The writing is straightforward and easy to follow and figures are beautifully illustrated and informative. There are several shortcomings, attention to which, could further improve the manuscript and utility of the photonic stimulation method. 


      1) While the manuscript provides a significant technical advance, the end result does not necessarily inspire confidence that it can be widely implemented. For example, to be useful, the stimulator would need to provide a range of stimulus amplitudes to a single hair bundle. Likewise, a range of stimulus waveforms, steps, sinewaves of various frequencies, etc, would enhance the broad utility of the approach. Since the introduction section highlights the short comings of current hair bundle stimulation methods, it would also be of value for the results/discussion section address whether the current photonic stimulation method has overcome those shortcomings or whether further technical development will be needed. 

      2) In general, the results section is loosely quantified. For example, Figure 2A demonstrates significant cell-to-cell variability in the amplitude of the motion. What is the source of that variability? Biological variability in hair bundle stiffness, or variability in stimulus, probe position, light intensity, etc. Furthermore, what is the trial-to-trial variability for a single hair bundle? Fig. 2 legend states each trace in panel 2A is an average of 25 responses, thus some representation of trial-to-trial variability could be quantified and presented. This would add value and provide the reader with a better sense of stimulus reproducibility. 

      3) A technical concern needs to be addressed to reassure readers that the photodiode signal is an accurate representation of hair bundle position. This has been well established in prior publications, but needs to be revisited here, either with additional experimentation or a sufficiently persuasive explanation. The concern is, since the stimulus is light itself and the response (bundle position) depends on a measurement of light signal, the stimulus could contaminate measurement of the response. This issue needs to be addressed in the results section. If its buried in the methods section, I missed it, so please clarify. 

      4) The section entitled "Survival of mechanotransduction after laser irradiation" is important but somewhat unfulfilling. Measurement of spontaneous bundle motion is just one measure of intact mechanotransduction. It would be reassuring to know that other measures are also intact following hair bundle irradiation. Recordings of hair cell transduction current or receptor potentials, uptake of FM1-43, etc. could provide more direct evidence.

    3. Reviewer #3 (Public Review): 

      There are only small modifications to be made to the manuscript in order to better characterize the variability of the responses induced in the hair bundle, a discussion on how the method could be used and validated in mammalian hair cells and a request to provide additional paths to check the viability of the cells and the robustness of the mechanosensory response after multiple optical stimulations have been performed. 

      Major comments:

      1) The variability of the displacement to the 25 stimulations at 30mW @561nm in Figure 2A should be added as standard deviation (as a shade of light color) on top of the average depicted here. The variability in displacement for the rising as well as for the relaxation in B should also be depicted across stimulations for one cell and across cells. 

      Same indication of variability across trials and cells should apply for other figures where the average of 25 stimulations is depicted. 

      2) The authors make a point that mechanical stimulations are too slow to match the optimal frequency of activation of mammalian hair cells. However, if there is such variability in amplitude & kinetics of the displacement induced by the photonic force through the optic fiber, how can this technique be calibrated in small mammalian hair bundles? 

      3) The authors should check the viability of the cells and the robustness of the mechanosensory response after multiple optical stimulations have been performed. Currently they compare the spontaneous oscillations before and after a stimulation to illustrate that the method is not disrupting the function of the hair cell. However spontaneous oscillations are not visible on all cells. Are there other means (calcium imaging? electrophysiology?) by which the author could illustrate that the technique is not damaging the cell and altering the mechanosensory response in the hair bundle?

    1. Reviewer #1 (Public Review): 

      When an outcome is sometimes misclassified, it can blur an association between the treatment and the outcome and reduce the power of a study of the effect of the treatment on an outcome. This is a problem in studies of the effect of genotypes on severe malaria when the standard clinical definition of severe malaria is used because the standard clinical definition of severe malaria prioritizes sensitivity over specificity (because the loss from failing to treat a child for severe malaria is much greater than the loss from treating a child who doesn't have severe malaria). In this study, the authors use standardly available clinical data -- platelet count and white blood cell count -- to increase the specificity of the definition of severe malaria in studies of the effect of genotypes on severe malaria. The authors then use a data tilting approach to put more weight on clinically defined severe malaria cases that meet this more specific case definition of severe malaria. The authors show that their approach reduces false discovery rates in an empirical study. The authors also report the interesting finding that approximately one third of clinically defined severe malaria cases in a study of Kenyan children did not have severe malaria. 

      This paper presents a novel and valuable method for improving power for severe malaria genetic association studies that would also be useful for studies of other disease where there is a clinical definition that lacks high specificity.

    2. Reviewer #2 (Public Review): 

      The fundamental premise of genome wide association studies for severe malaria is to take a population with confirmed severe malaria and compare with a control group who do not have severe malaria. The author's hypothesis is that in areas with high levels of malaria transmission the severe malaria group gets diluted by patients who have been mis-classified with severe malaria (but are ill with something else). This dilution of the severe malaria group then dilutes the effect size for differences between the control group. 

      The authors propose a statistical method for correcting for the diluted severe malaria group via an approach of data tilting. The consequences of this adjustment are then followed through to a logical and sensible conclusion, namely that correcting for this dilution can lead to more hits in GWAS studies and greater effect sizes. I'm not an expert in genetic association studies, but to my untrained eye, this portion of the analysis checks out (roughly speaking Figures 4 - 6). Instead I will focus my attention on the probabilistic diagnostic model (roughly speaking Figures 1 - 3). 

      Something I struggled with was keeping track of the different datasets. To this extent, a table summarizing the cohorts with summary statistics such as geographic location, age, symptom severity, and other relevant epidemiological information would be very useful. 

      My primary concern is on the comparability of the training data (Asian adults, Asian children, African children with high PfHRP2) and testing data (Kenyan). It's crucial that the model trained on the Asian adult data (highly specific) is valid for application on African children. What I would like to see is a more explicit demonstration that what we observe about severe malaria in Asian adults applies to Asian children, applies to African children. There is evidence for this in Figure 1B and Figure S2, but there are so many different data sets, that my tired mind found it difficult to follow. 

      Figure 1B. For the grey line fitted to the FEAST data, does this also include the PfHRP2 = 1 data. As this was non-detectable, is this a valid thing to do? 

      Figure 3. Can you check the panel labels? What's the horizontal dashed line? 

      Were they significant associations between parasite density and the probability of severe malaria.

    1. Reviewer #1 (Public Review): 

      In this manuscript, Tando et al. investigated the effects of maternal exposure to Di (2-ethylhexyl) phthalate (DEHP) on DNA methylation levels in fetal male germ cells and spermatogenic cells in adult offspring. DNA methylation analysis showed DNA hypermethylation in promoter regions of spermatogenesis-related genes and RNA-sequencing analysis confirmed that expression of the corresponding genes is down-regulated in DEHP-exposed subjects compared to control. Findings from the study suggests DNA hypermethylation and the consequential down-regulation of spermatogenesis-related genes as a molecular mechanism underlying previously reported effects of maternal DEHP exposure on spermatogenesis defects. 


      Authors used the FPKM framework to estimate gene expression levels from the RNA-seq data (line 448). Use of FPKM for differential gene expression analysis has been shown to be problematic due to its limitations in terms of inter-sample variability [PMID: 22988256, PMID: 32284352]. As the normalization is sample-specific for the FPKM framework, FPKMs are not suitable for across sample comparisons. Currently, TMM or VST normalized counts are widely accepted more suitable approaches for DEG analysis [PMID: 22988256, PMID: 32284352]. It would be very important that the authors re-analyze the RNA-seq data using TMM or VST normalized counts.

    2. Reviewer #2 (Public Review): 

      The conclusion that prenatal exposure to DEHP induced epi-mutations in the germ cells is highly relevant and sustained by the work. The limitation is that the genome may have change and this is not controlled. Indeed, if epi-mutations appear in the germ cells subsequent to DEHP exposure, they may allow transposition mechanisms, notably during reprogramming of the germ cells. Such scenario may directly change the germ cells genomes. 

      The major strengths of the work are 1) to have extracted the male germ cells of the fetus directly after exposure, which is challenging, and at adulthood, providing information on how changes may persist across development, 2) the functional validation with the CpG-free plasmid. 

      The weakness is 1) the smallest possible replicates number (n=2 only) for the majors part of the experiments that were conducted, a strong limitation to mention. 2) The experimental design regarding a breeding scheme is not understandable enough, this make the work difficult to follow.

    3. Reviewer #3 (Public Review): 

      Tando et al started their study by controlling that the progeny of female mice exposed to DEHP present spermatogenesis defects as previously shown in the literature. They then collected fetal germ cells and adult germ cells at different stages (i.e. spermatogonia, spermatocytes and round spermatids) and perform RRBS and RNA-seq analyses to identify differentially methylated regions and deregulated genes. 

      The manuscript is very clear and well-written, the figures nicely presented. The chosen technical approaches are appropriate but the number of replicates (2 for each type of samples) is too small. It seems that the differences between CTL and exposed groups (both for methylation and expression) are quite subtle and indeed heatmap representation does not show clustering of replicates as one could hope for: See figure 2A, in particular adult SPG, or Figure supplement 4. Besides, for one type of sample (E19.5 DEHP RNAseq analysis) only one sample could be analyzed. The statistical analyses used for RNAseq and RRBS are not detailed enough and the lists of DMRs and DEG (differentially methylated regions and deregulated genes) which were derived from these analyses therefore appear quite uncertain. 

      Following these high throughput analyses, the authors focused on 9 genes which were found hypermethylated in fetal germ cells and adult spermatogonia, and which are known to be involved in spermatogenesis. They performed targeted methylation analyses and expression analyses on F1 spermatogonia and found hypermethylation and downregulation for 3 of them: Hist1h2ba, Sycp1, and Taf7l. These data are convincing because performed on more samples (4 and 6 replicates). Luciferase assay confirmed that hypermethylation of theses gene promoters induces downgulation. 

      The authors also mentioned in their article an effect on the F2 spermatogonia. Yet no significant changes in methylation or expression were found on these samples. Importantly, the changes which were found in F1 spermatogonia were not conserved in more differentiated germ cells, in agreement with the fact these "epi mutations" are not maintained and transmitted to the next generation. 

      In conclusion, the topic and methodological approaches are very interesting and relevant but a global effect of maternal DEHP exposure on methylation correlated with gene deregulation could not be demonstrated, probably because of the reduced number of samples which were analyzed. The 3 spermatogenesis genes which were identified are nevertheless good candidates to explain the observed defects.

    1. Reviewer #1 (Public Review):

      In their paper, Spurlock and colleagues look at the role of mitochondria fusion caused by Drp1 repression in driving the stem/progenitor-like state of skin stem cells. Prior work hinted at the possibility that mitochondrial fission/fusion activity is important in supporting neoplastic transformation, but it was unclear exactly what this role was. Here, the authors use an assay for neoplastic transformation induced by carcinogen treatment to demonstrate that diminution in mitochondrial fission activity (from increased phosphorylated Drp1 pools) can prime a stem/progenitor-like state in carcinogen-treated cells, leading to accelerated neoplastic transformation. Using genetic strategies and single cell RNAseq they additionally show that only partial repression of Drp1 is necessary for establishing the stem/progenitor-like state for driving neoplastic transformation, with too much or too little Drp1 repression having no effect. The data are therefore relevant for understanding the conditions for driving neoplastic transformation. Overall the results support the conclusions drawn by the authors and the work helps to clarify the mitochondria's role in neoplastic transformation. The paper is currrently overall difficult and in places confusing to read.

    2. Reviewer #2 (Public Review):

      The authors used a carcinogen to increase proliferation of the keratinocyte cell line HaCaT and to increase the capacity to form xenograft tumors in mice. They found that the levels of certain mitochondrial fission and fusion proteins (Drp1, Mfn1 and Opa1) were increased in the derived cell lines, but Fis1 levels was decreased in the most tumorigenic derivative as was the phosphorylation of Drp1 at position 616. Through single cell expression analysis, the author show that transformed cells have retained a subpopulation of slowly dividing cells with high expression of stem cell markers and reduced levels and phosphorylation of Drp1. This state could be mimicked by reducing Drp1 expression with shRNA. Cells with moderately reduced levels of Drp1 appeared to be more susceptible to enhanced proliferation caused by treatment with a carcinogen. The authors conclude that a moderate reduction in Drp1 levels causes an increase in proliferation and tumorigenesis of keratinocytes upon treatment with a carcinogen.

      The main strength of this paper is the use of single-cell analysis to identify a subpopulation of cells with increased stem cell gene expression and reduced levels of Drp1 and of Drp1 phosphorylation.

      A causal relation between tumorigenicity and Drp1 levels was tested by reducing levels of Drp1 with shRNA, but unfortunately, the data are very limited. The key contention that partial reduction in Drp1 levels increases proliferation is only supported by a single point and it contradicts results from other labs where it was shown that Drp1 phosphorylation and fission are increased with transformation.

      It is unclear what mechanisms connect the proposed window of Drp1 activity to tumorigenesis. In previous studies the effects of different levels of fission and fusion proteins on metabolism and tumorigenesis were analyzed in detail, showing effects on metabolism that could lead to increased tumorigenesis. That is not done here and so one is left guessing as to what functions are affected by the proposed window of Drp1 expression and how that might affect tumorigenesis.

    3. Reviewer #3 (Public Review):

      Spurlock et al. investigated how differential repression of Drp1, a master regulator of mitochondrial fission, affect neoplastic transformation of keratinocytes as well as key aspects of gene regulation and mitochondrial network dynamics. They find that "weak" repression of Drp1 in keratinocytes results in a gene expression profile reminiscent of a stem/progenitor like state, which is especially primed for neoplastic transformation. On the other hand, they show that "strong" repression of Drp1 has a very different effect and results in cells with hyperfused mitochondrial networks and less propensity towards transformation. They find that "weak" repression of Drp1 leads not to hyperfused networks but rather to small networks of fused mitochondria. These results are especially surprising as according to the authors analysis, there is less than 20% difference in the level of knockdown efficiency under the "weak" vs "strong" shRNA conditions. But the key findings in the weak vs strong knockdown conditions seem to be well supported by RNASeq analysis, mitochondrial network analysis, and immunofluorescence data (although quantification of specific data would likely strengthen their arguments).

      The authors relate these findings to those where they use differing levels of TCDD (1 nM vs 10nM) to transform HaCaT cells. While it is clear from the data that TF-1 has a different effect from TF-10 on gene expression, cell proliferation, and certain measures of stem/progenitor cell characteristics, the key findings concerning Drp1 levels that would directly relate TF-1/TF-10 to Drp1-shRNA weak/strong are not as well supported. In particular, the immunoblots of pDrp1 and Drp1 levels as well as the mitochondrial network analysis do not necessarily support the hypothesis that the differing characteristics of TF-1 vs parental or TF-10 results from Drp1/mitochondrial changes and not simply due to cell cycle or other effects of TCDD levels. Nevertheless, both sets of data are interesting and compelling and present a more nuanced view of how differing levels of transformation agents or shRNA-mediate depletion can have considerably different effects even within the same cell type. These data may also help to clarify differences seen in past studies between distinct cell types when Drp1 levels are manipulated but this remains to be tested and clarified.

      The individual conclusions of this paper are generally well supported by the data, but some aspects of data analysis need to be clarified and/or quantified.

      1) To better support the main link between the two sets of data, the levels of Drp1 (protein and activity) in TF-1 vs TF-10 conditions must be clarified and quantified (immunoblot analysis and/or in the immunofluorescence). Since the overall levels of Drp1 actually increase in both TF-1 and TF-10 compared to Parental but the authors suggest that pDrp1 decreases in TF-1, this must be quantified. Furthermore, the authors note that Drp1 is phosphorylated in a cell cycle dependent manner and go on to show significant differences in cell cycle dynamics between Parental, TF-1 and TF-10, and so the difference in pDrp1 levels could simply be a result of the cell cycle differences. While this would not change the conclusions about how differing levels of TCDD impact gene expression, transformation efficiency, and stem/progenitor cell like characteristics, it would call into question how related the effects from direct repression of Drp1 levels through shRNA are to the TCDD effects seen.

      2) There does not seem to be a big difference between the mitochondrial networks of TF-1 and parental line except possibly the spread of the Fusion5 metric. Is this statistically significant? Are any of the other measures of the mitochondrial network found to be different in Drp1-kd (W) similarly changed in TF-1? This could strengthen the connection between these data.

    1. Reviewer #1 (Public Reviews):

      This paper describes a 2D approach to a problem of identifying macromolecular complexes in cryo-ET. Surprisingly, the authors argue that the approach is more sensitive than 3D approaches, and is computationally much faster. While it is not possible to prove that the method will be superior to all possible 3D approaches, the current implementation will be a useful tool for many people.

    2. Reviewer #2 (Public Review):

      Lucas, Himes et al. present multiple practical improvements to the 2D high-resolution template-matching (2DTM) routine for cryo-EM images originally described by Rickgauer et al., eLife 2017. Moreover, the authors assess the 2DTM approach for macromolecular identification in situ using the example of the M. pneumoniae ribosome and compare it to the conventional 3D low-resolution template-matching (3DTM) approach followed by subtomogram averaging in tomograms of the same areas. Implementation of GPU-acceleration and integration into cisTEM make the approach substantially faster and easier to use than the previous CPU-based Matlab implementation. The strengths and weaknesses of the 2DTM are clearly presented and the comparison with 3DTM is thorough. At present the 2DTM approach is likely only suitable for analysis of large assemblies (e.g., ribosomes, proteasomes,etc.) in situ, future improvements in microscope hardware and the 2DTM routine itself will likely allow application of this approach to smaller complexes.

      A point of concern is the degree of reference-bias in the results of the 2DTM approach. The authors acknowledge this concern and that conventional use of the FSC is not a suitable validation metric for this approach nor for determining an appropriate filtering cutoff for a resulting reconstruction. The proposed validation metric of the emergence of additional known density features in a reconstruction, which are not present in the template, resulting from 2DTM hits is sensible. However, emergence of additional unknown densities in a reconstruction resulting from cellular data will be difficult to segregate from noise, especially since filtering of the reconstruction is determined ad hoc instead of by an objective metric.

      Nevertheless, the implementation of 2DTM is a major step forward in molecular identification in a crowded cellular environment. Even for ribosomes, 3DTM coupled with subtomogram averaging can be a time-consuming process and false-positives can persist despite extensive classification. The complementary approach presented by the authors of acquiring a nominally untitled frame-series for 2DTM that is followed by a conventional tilt-series for 3DTM of the same area could be particularly well-suited to answer questions that require an accurate "molecular census" and/or attempting a hybrid subtomogram averaging approach.

    3. Reviewer #3 (Public Review):

      The authors have implemented GPU-accelerated 2D template matching for localization and identification of macromolecules in projection images and apply it to ribosomes in M.pneumoniae cells. They optimize parameters of the workflow and compare it to 3D template matching in volumetric data. The interesting outcome of the study is that the 2D approach has higher specificity than the more time-consuming 3D strategy.

      Strengths: This work provides an experimentally and computationally fast workflow to obtain structures from whole cells. Some efforts are made to assess specificity and sensitivity of the approach, which nevertheless remain somewhat qualitative in the absence of a ground truth. The resolution assessment figures suggest that reconstructions of relatively high resolutions have been obtained. The claim that detection specificity is higher in 2D than in 3D is surprising and interesting.

      Weaknesses: The work remains on an empirical level as surprising advantages of the 2D approach compared to 3D are revealed, but there is little effort to get to the basis of these observations. Moreover, details on the compared 3D approach (and its parameter optimization analogous to the 2D approach), which the rather general conclusions would require, are missing. Lastly, the 3D approach has been applied to the strongly pre-irradiated sample, which may make observations such as a lower specificity in the 3D case almost a self-fulfilling prophecy. Thus, the 2D vs 3D comparison is not convincing in the current form.

      In summary, the 2D implementation of in situ structure determination is interesting and of potential interest to a large audience. However, the comparison to the 3D equivalent appears somewhat incomplete and the rather general conclusions require further validation.

    1. Reviewer #1 (Public Review): 

      In this manuscript, Mouat et al. investigated the contribution of viral infection to the severity of arthritis in mice. Epstein-Barr virus (EBV) infection is associated with rheumatoid arthritis (RA). By assessing arthritis progression in type II collagen-induced arthritis (CIA) induced mice with or without latent 𝜸HV68 (murine gammaherpesvirus 68) infection, authors showed that latent 𝜸HV68 exacerbates progression of CIA. Additionally, profile of immune cells infiltrating the synovium was altered in 𝜸HV68-CIA subjects - these subjects presented with a Th1-skewed immune profile, which is also observed in human RA patients. Assessment of immune cells in the spleen and inguinal lymph nodes also showed that latent 𝜸HV68 infection alters T cell response towards pathogenic profile during CIA. Lastly, authors showed age-associated B cells (ABCs) are required for the effects of latent 𝜸HV68 infection on arthritis progression exacerbation. 

      Findings presented in the manuscript provides important insights and resource to clinical RA research. 

      There are some statistical analyses that need to be updated for completeness and appropriateness of use. In addition, the authors will need to highlight that all analyses were conducted in young mice, whereas RA occurs in aged individuals.

    2. Reviewer #2 (Public Review): 

      In this study, the authors investigate the long-appreciated but little understood link between chronic infection with Epstein-Barr virus and rheumatoid arthritis (RA). Using a collagen-induced (CI)-model of arthritis and a natural murine analog of EBV (gammaherpesvirus 68, HV68), the authors demonstrate that latent infection with HV68 exacerbates clinical progression of CI-arthritis and is associated with changes in the immune cell and cytokine profile in the spleens and joints of HV68 infected mice. The most compelling finding is that an infection can indeed exacerbate the progression of secondary diseases, and the requirement of age-associated B-cells (ABCs) to the severe disease progression. While this study addresses a timely and important question-how chronic infections affect subsequent or secondary disease progression-additional work as well as a clarification of the experimental design is encouraged to understand some of the key conclusions.

    3. Reviewer #3 (Public Review): 

      The authors developed an in vivo model of EBV's contribution to RA that recapitulates aspects of human disease. They examined the role of age-associated B cells and find that they are critical mediators of the viral-enhancement of arthritis. <br> The manuscript is written in a well-structured form that facilitates the reading and following the incremental experimental setups. The manuscript is appropriate for publication after revisions. 

      Some of the statistical measures did not show significant values while the author based several statements as if there is a difference (they rather used phrases as increased/fold change). Whether this is strong enough to support their statements is not clear. 

      Overall, this report provides important insights regarding the association between latency, age-associated B cells, and the enhancement of RA in a mouse model. If these insights are translatable to RA immunology in humans is to be further investigated.

    1. Reviewer #1 (Public Review):

      FoF1-ATP synthase couples proton translocation across the membrane to ATP synthesis/hydrolysis. When the proton motive force dominates, the rotor within this complex moves in one direction, promoting ATP synthesis. On the other hand, when in ATP-driven mode, the rotor moves in the opposite direction and ATP hydrolysis is used to translocate protons against their concentration gradient. The internal symmetry of the homo-oligameric rotor (8-17 units, depending on the variant) suggests cooperative mode of action. Here an engineered variant with 10 concatenated copies of the unit is used to examine cooperativity. Single and double mutants of the key glutamic acid that shuttles the protons from the rotor to the stator are used and the results support cooperativity because of the dependence of activity on the distance between the mutated amino acids. Simulations, using a recently introduced coarse grained model, support this suggestion and provide molecular interpretation of the results. A simple kinetic model that accounts for the observed cooperativity is derived.

      Cleverly integrated experimental and computational approaches support the conclusion, and the results are honestly and modestly presented, admitting to imperfections. The one thing that bothers me some is whether the relatively minor activity differences between the double mutants with close vs more remote positions are significant enough. The statistical analysis suggests that they are, but sill... I also added a few minor suggestions to improve the manuscript further.

    2. Reviewer #2 (Public Review):

      Mitome et al. investigated the possible cooperativity among the proton-carrying subunits (c-subunit) of Fo motor in ATP synthase by biochemically analyzing the ATP synthesis/ATP-driven proton-pump activities of mutant ATP synthases. The c-subunits form the rotor oligomer ring (c10 oligomer in the Fo system that they investigated) that rotates against the stator complex of Fo composed of a-subunit and b2 dimer complex, upon transmembrane proton translocation in Fo motor. It is widely thought that each of c-subunit executes proton-transfer between c-subunit an a-subunit, coupled with 36-degree rotation of c-oligomer ring. For the investigation of the possible cooperativity among c-subunits, they prepared mutants of Fo in which 10 c-subunits were genetically fused into a single polypeptide, and they introduced a mutation at the proton-carrying residue of c-subunit, cE56 to produce the mutated c-subunit (cE56D) at particular positions in the c10 repeat. The main observation is that when cE56D mutation was introduced into two c-subunit repeats that were separated to each other, the impact of the cE56D mutation on catalysis were almost additive. On the other hand, when the mutation was introduced into two neighboring c-subunit repeats, the double mutation effect was weakened, and very close to that of single mutation. These findings suggest some cooperativity exists in c10 oligomer ring. In order to investigate the molecular mechanism of the observed cooperativity, they conducted in silico simulation where Monte Carlo simulation for the proton transfer was integrated into coarse grained molecular dynamics simulation. They observed the dwelling states for the rate-determining step on neighboring two c-subunits were often overlapped to diminish the mutation effect of the second c-subunit, while the impact of the mutation was additive when mutated c-subunit were separated in c10 ring.

      This study uncovers the cooperativity among c-subunits and provides a possible molecular mechanism for that. This work gives new and important insights on molecular mechanism of Fo motor of ATP synthase. Therefore, this paper would be suitable for the publication in eLIFE when the following concerns are addressed.

      One of the main concerns is the accuracy of biochemical assays, ATP synthesis activity measurement and ATP-driven proton pumping activity measurement. To my knowledge, it is not easy to achieve highly accurate and precise biochemical assays such as error within a few % even if we use highly purified enzymes. In this paper, the authors reported very small experimental error: around 1 % or even less. However, I have not found the description on how the author did determine the experimental errors. They used not purified enzymes but inverted vesicles of E. coli expressing the mutated enzymes. One of critical parameters for the accuracy is the quantification of the enzymes in the vesicles that were estimated from the decoupled ATP hydrolysis activity measurement. The error in this quantification should be substantially smaller than 1 % to achieve such a high accuracy. In addition, the ATP synthesis activity and ATP-driven proton pumping activity were measured from the time courses of the assays that should also include some experimental errors as found in the noise and drift in the time courses of proton pumping measurement (Fig. 2c). Because the activity difference among the double mutants were subtle, the accuracy and precision of the biochemistry part are the critical points to prove the validity of their arguments. The detailed explanation son the estimation of experimental error as well as reproducibility are required.

      The second concern is the validity of the simulation. The authors conduced Monte Carlo simulation for proton transfer step between c-subunit and a-subunit. The rate constant was represented in a simple exponential factor: exp(-A(r-r0)) where 'A' represents the decay rate, 'r' is physical distance between c-subunit and a-subunit and 'r0' is the offset value that represents the sum of sidechain length of the proton transferring residues on c-subunit and a-subunit. They assumed smaller 'r0' and larger 'A' for cE59D mutant. Although the smaller 'r0' would be reasonable considering the shorter side change of aspartic acid, the reason for higher 'A' for the mutant is not clear. In addition, different values for pKa were given to the glutamic acid in the wild type c subunit (cE59) and to the aspartic acid in the mutant (cE59D), without rationalization. These parameters should be critical for the simulation results. The validity of the different 'A' and pKa in the mutant should be explained.

    3. Reviewer #3 (Public Review):

      This is an interesting manuscript describing for a first time experimentally the cooperative effects of mutations to individual key Glu residues in the c-ring of ATP synthase. The main result is that mutations in nearby c subunits are less inhibitory than those in subunits further apart in the ring. This is explained on the basis of MD/MC simulations as a shared waiting time for delayed proton uptake in case of neighboring subunits, which appears logical. Overall the manuscript is well presented, but with some caveats described below, which should be addressed. It will be of interest to the specialists in bioenergetics, and to a wider audience working in biochemistry.

      General comment: the cooperativity is shown here in case of mutants, but it is not so obvious how it relates to WT enzyme. One clue, to which authors only briefly relate, is that according to their earlier simulations in WT the preferred pathway is when 2 or 3 Glu are unprotonated at any time rather than just one Glu being protonated/unprotonated. This kind of "cooperativity" in WT enzyme and its relation to presented here data should be discussed in more detail here.

      Also, parts of text, such as the introduction, as not very clearly written and can be improved.

    1. Reviewer #1 (Public Review): 

      This manuscript does a great job of describing their phase-targeted closed-loop auditory stimulation protocols to alter slow wave oscillations in rodents and alter behavior on a motor task. They are able to stimulate an auditory stimulus within ~5 degrees of the target, both during the ascending and descending (termed up-phase and down-phase). They showed that stimulating during the up-phase increased delta and sigma while stimulating the down-phase decreased delta and sigma. They also showed that stimulating the up-phase improved performance on a motor task while stimulating the down-phase generally decreased performance. There is translational value to this approach as this has been previously used in human subjects- altering slow wave oscillation to improve memory consolidation (a hot topic in neuroscience). Applying this tool to rodent research in future studies may allow for bridging some of the putative mechanisms underlying memory consolidation (e.g., replay during NREM sleep) and behavioral changes observed with sleep (e.g., improved hippocampus-dependent memory). It's also nice to have a non-invasive way to manipulate sleep, particularly as we want to translate rodent research to clinical work.

    2. Reviewer #2 (Public Review): 

      Numerous recent studies with human subjects have suggested that periodic auditory stimuli, delivered at a particular phase with respect to NREM thalamocortical oscillations, have the capacity to promote memory consolidation during sleep. However, the underlying neurobiological mechanisms are less well understood. In order to characterize changes occurring within the thalamocortical circuitry during such closed-loop stimulation, the authors have carried out preliminary proof of principle work here in a rat model. In the model, closed-loop auditory stimulation (CLAS) is delivered across multiple days to rats, at different phases with respect to ongoing EEG rhythms. Effects of CLAS on EEG spectral power and performance on an multi-day motor learning paradigm have been assessed. The results largely replicate what has been found previously in CLAS studies with human subjects: upstate-targeted stimulation augments NREM thalamocortical oscillations. While upstate-targeted CLAS did not have any clear effect on motor learning, downstate-targeted CLAS appeared to reduce overall engagement with the motor task. While the present study does not provide additional information regarding neurobiological underpinnings of performance improvement driven by CLAS, the developed model has potential to do so in the future.

    1. Reviewer #1 (Public Review): 

      The authors use dense electrode recordings in young mice and EEG recordings in human infants to quantitatively describe the transition from immature patterns of brain activity in sleep to more mature patterns. Interestingly, they find an intervening period when overall activity declines in both species. Although primarily concerned with describing the phenomenology of this transition, this study is interesting because it enriches our relatively impoverished view of how mature activity patterns emerge during development.

    2. Reviewer #2 (Public Review): 

      The authors employ sophisticated electrophysiological techniques and analyses to investigate ontogenetic patterns of brain activity in sleep. This is a major strength of the study. 

      Although this topic has been explored many times over the last 50-60 years, the authors make some interesting observations. The first is that there is a window of time when immature cortical activity changes from immature forms to more mature forms. The 2nd major finding is a transient condition of diminished brain activity that appears between these stages. 

      Major weaknesses:

      The first finding seems incremental in nature. Especially as no mechanistic insights are provided. It is well known that the 2nd postnatal week in rodents is when many cortical and sub cortical events coincide with a change in sleep organization--including cortical manifestations. Therefore, the first finding is more detailed than earlier studies, but not especially surprising when put in proper context. 

      The 2nd finding is interesting, but its significance is unknown.The significance of this 'state' or 'condition' is a bit overstated. For example, the authors state in their discussion that this state 'enables' the emergence of mature brain organization, but they provide no evidence for this. Their study, as interesting as it is in places, is descriptive and provides no direct evidence of mechanism or function. 

      There are also methodological issues that make the interpretation of the mouse data extremely difficult. 

      Overall, the analyses are meticulous and suggest an important phase of brain organization occurs at about the 2nd postnatal week in rodents--and possibly humans. This study could be very informative, provided that additional control experiments are performed, and direct mechanistic or functional questions are addressed.

    3. Reviewer #3 (Public Review): 

      This paper is, to my knowledge, the first to suggest that there may be 'regressive' or at least non-progressive steps in the general thrust of early activity and functional development, at least before the later stages of net synaptic elimination. The authors show that in mouse somatosensory cortex that the period after spindle-burst elimination (an early activity pattern associated with sensory stimulation either self-generated or evoked) is characterized by a 2-day 'nadir' in total activity before firing rates and synchronization as well as surface EEG power and spread begin again to increase toward adult levels. This pattern was echoed in EEG recordings from human infants, which showed a similar decrease in activity around 45 weeks of gestation (on parietal electrodes). This careful analysis of activity done similarly in the two species is a real strength and overall my confidence is high that this is a real phenomenon in the regions examined. The number of animals and analysis methods are impressive and largely appropriate. Overall the data presented make a solid and important contribution to our understanding of the developmental dynamics of neural activity development. 

      To my mind, there are a couple of critical analyses that need to be included to fully support the authors' conclusions. 

      1) The mouse experiments call for some control of developmental changes in arousal state especially as regards twitching and other movement. With the current presentation, the quiescent period could as easily be a result of reduced twitching at P8 before extensive volitional (and whisking) emerges starting on P10 as it could be explained by circuit changes in the ascending pathways. Likewise, shifts in the proportion of quiet and active sleep (which are related to twitch amount) could largely account for the differences. 

      2) The location of the analyzed contacts is incompletely described and justified. In the mouse they are described as 'somatosensory cortex' but the pictures suggest that barrel cortex is the most likely location. Better descriptions of how the locations for analysis were chosen and controlled over the wide age range are necessary. Were the contacts analyzed verified as barrel cortex by whisker deflection? Is there any possibility the quiescent period is a result of shifting the location of the grid or analyzed channels. The infant data surprisingly are taken primarily from parietal electrodes, which are not the location of sensory-evoked twitches (Milh et al 2007). Why was the analysis limited to parietal? Are the results dependent on this localization? 

      3) The authors do a number of analyses of cross-frequency co-modulation and spike-frequency modulation that are limited to 'spindle frequencies'. These results are often extrapolated to make general statements about the precision of spiking or spread of activity etc but are really just smaller snapshots of the larger activity. This would be justified if there was good reason to believe that early spindle-bursts and later sleep spindles are the same network activity. However this proposition has only weak support (and is not argued for explicitly here). In essence, the authors end up analyzing three different patterns: spindle-bursts in P5-7, unknown activity in spindle band (P8-10), and sleep spindles (P11+). That these are in the same broad range of frequencies doesn't mean they are making similar measurements across ages. It would strengthen the case that P8-10 is a unique quiescent period to show differences in power spectra and spiking not limited to spindle frequencies. Some of these are presented, but difficult to extract from the spindle analyses. In addition spiking data from layers, 4-6 are used, but these layers are both very diverse in their behavior, and the least likely to be strongly correlated with spindle-bursts (maximal in layer 2-4). A more consistent and limited analysis of spiking is important to confirm the general vs specific nature of this quiescence. 

      4) How generalizable these results are, and how they comport with previous studies is unclear. The paper is written as if this quiescent state is universal, and its identification in two species in likely different regions adds to the argument that this is the case. However, it has not been observed in similarly detailed developmental studies in other rodent regions (multiple papers by the Hanganu-Opatz lab, Minlebeav et al Science 2011, Shen and Colonnese J Neuro 2016) nor in the clinical literature. Some more careful and nuanced discussion of the relationship between these findings or expansion of the regions surveyed to show they were wrong would help situate the current findings and better comport the claims and evidence.

    1. Reviewer #1 (Public Review): 

      The manuscript describes the World Mortality Dataset, which estimates excess mortality across 89 countries and territories around the globe attributable to the COVID-19 pandemic. The method is clearly described and appropriately simple without being too simple, as it incorporates both time trends and period-specific baseline effects. 

      I have few specific comments on this paper which is mainly descriptive but very valuable. 

      My main comment is on the interpretation of excess deaths. From a causal perspective, the notion of excess deaths is 

      Observed deaths in COVID period= <br> Expected deaths in COVID period (a) - <br> Deaths averted due to COVID (eg less flu due to NPIs, less traffic death, ) (b)+ <br> Deaths directly caused by COVID (ie in people who were infected) (c)+ <br> Deaths indirectly caused by COVID (starvation from lockdown, untreated cancer) (d)+ <br> Net death from confounders (other events that were particular to that time period and caused or prevented deaths -- eg wars) (e) <br> + Random variation. 

      The main thing I would like to see is more contextualization of the "undercount" to note something like this conceptual structure, explain what should make us think that the very few examples of (e) that are in the analysis really are the main ones, and perhaps some seasonal comparisons of the undercounts so that plausible hypotheses can be proposed for which factors are at play. Otherwise, a very helpful piece of work that will likely generate many others.

    2. Reviewer #2 (Public Review): 

      The authors set out to estimate excess mortality in a large set of countries globally, and this has generated a unique impression of the mortality impact of this pandemics that were in some countries missed in the official counts. In the process they have generated a central, frequently updated repository of the all-cause mortality data across countries that is a wonderful tool for all epidemiologists to follow the development in near real time. Such data have long been available in Europe (EuroMoMo) but worldwide the publication of weekly or monthly allcause mortality data have been scarce. So all in all, this work is incredibly important and rather extraordinary. A great research tool for researchers in the field. They truly fill a gap with their collection of weekly, monthly, or quarterly all-cause mortality data from 89 countries and territories, which are openly available and will be regularly-updated: the World Mortality Data. And for this reason the paper is both original and of great importance to understand the COVID-19 crisis at a global level, and should be published as soon as possible. The database is already in use by Our World in Data, the Economist and the Financial Times. 

      The strength of the paper is the demonstration of very substantial excess mortality in several world countries like Peru, Russia, Brazil, Bolivia, and Bulgaria. This was missed so far at the country level, although such reports had been seen from select cities like Manaus, Brazil. Also, it provides several interesting metrics, such as incidence of excess deaths, and elevation above a baseline of expected deaths, and finally the uncercount ratio of these estimates compared to official data. That the top countries underreport by a factor 10 to 100 is nicely documented. Finally, it is commendable that the authors in figure 4 demonstrates the time series coincidence of reported and excess deaths. 

      Also, the authors discuss the finding of undercount ratios of as low as 0,5 in some countries such as France. The interesting discussion that ensues about the meaning of excess mortality estimates when both reductions and increases may be expected due to lockdowns (fewer accidents, suicides) and due to large epidemic sizes (poor care due to overfilled hospitals), and also other effects such as heat waves and disappeared influenza epidemics. I think the authors should discuss their thinking by also looking at what IHME has put out in this regard very recently, see here: <br> IHME on Excess Mortality http://www.healthdata.org/special-analysis/estimation-excess-mortality-due-covid-19-and-scalars-reported-covid-19-deaths 

      A few critical points about the methodology for assessing and reporting excess mortality from these data. The conclusion reached in the paper is nevertheless solid: some countries like Peru, Russia and Brazil have gone through a particularly deadly experience with COVID-19 so that as many as 0,5% of their entire population have died over a couple of pandemic waves. And much of this mortality is not always reflected in the official reports: the true death toll may be 1.6x greater than the reported numbers of death. And in some countries the mortality reporting only captures about 1/10 of excess mortality. Unfortunately, many countries do not have national vital statistics data with week, month or quarterly detail, and are not represented in the mortality database. 

      Now to the criticism: 

      1) Work is not connected to the vast literature on the topic. The authors are out-of-field statisticians and seem unaware of the literature in this domain. They had generate a baseline of expected mortality based on past years time series data, as one would do when estimating excess mortality for influenza. In this way their approach is a bit similar to that used by Murray et al (Murray, Lancet 2006) to estimate the 1918 pandemic excess mortality above an annual baseline of surrounding years for a number of countries. The authors should consider at least including a reference for excess mortality estimation for each of the past influenza virus pandemics, and ponder whether it is possible to do the same that was done in these analyses to create a baseline of expected deaths that did NOT include winter-seasonal epidemic diseases like influenza (see the collected works of Olson et al, Viboud et al, Chowell et al, Olson et al, Simonsen et al, for the pandemics of 1918, 1957, 1968 and 2009). See also the latest thinking on the problem of sorting out true excess deaths from the disappeared traffic accidents, increased mental health deaths, and other complications by IHME (see link below). 

      2) No attempt to correct baselines for seasonal influenza. The authors use past years and generate a baseline that includes mid-winter seasonal influenza mortality. By doing so, the excess mortality estimates in the present manuscript represent excess above what is normal in a season. Thus, as the authors comment on, the excess mortality estimates are affected by the too high baseline which includes mortality due to influenza, RSV and other respiratory viruses that are now largely not circulating during the COVID-19 pandemic. Particularly, the "disappeared" influenza burden in 2020-2021 results in a meaningful underestimation of the true COVID-19 excess mortality. This problem of removing seasonal influenza from the baseline has actually been worked out by epidemiologists using various statistical approaches (sometimes harmonic terms, sometimes using influenza virus data from the WHO as predictors) in the field of epidemiology the literature mentioned above, but the entire literature of excess mortality estimation is missing from the reference list. One that I am very familiar with (!) is Simonsen et al, Plos Med 2014 - but there are many many more similar published papers computing excess mortality for seasonal and recent pandemic influenza out there (look for Viboud, Chowell, Goldstein, Paget, Olson.....). I suggest you simply discuss this situation, and makee reference to this - plus suggest others to work out ways to remove influenza from the baseline, for example incorporate WHOs seasonal influenza timeseries database data (FluNet.org) in the excess mortality regression models (to identify and remove excess mortality during influenza periods). 

      3) Varying COVID-19 study time for different countries. Another problem with the way they report the excess mortality is in the difference in follow-up time. Some countries have data up to March 2021, while others only until last summer. This should be dealt with in the estimates, for example by comparing countries with complete year 2000 data. It probably cannot be helped that some countries publish their data late, but the authors should highlight these issues of comparison between countries in the text. 

      4) About the finding of a 1.6x higher excess mortality than reported deaths. It seems important to say that this is a finding for countries with national vital statistics in near-real time, so things may be very different in countries where such data to not exist. 

      5) Figure 4. Can you explain the time shift between the reported and excess deaths in the United States? Must be a data issue. Also, would be better to chose line colors or width so that one can distinguish the two in black and white.

    3. Reviewer #3 (Public Review):<br> This manuscript introduces the World Mortality Dataset, and provides estimates for 'excess' mortality for 89 countries and territories across the world over the course of the COVID-19 pandemic. These data are crucial for tracking the 'true' burden of the pandemic, and is a monumental effort on the part of the authors in collating data from many different sources. This dataset fills a gap in this field by adding countries to several existing sources of mortality data such as the Human Mortality Database. 

      While the conclusions of the paper are generally supported by the data and analysis, there are a few major concerns that need to be addressed, particularly when making comparisons across countries: 

      1) The main metric used in the paper is excess mortality, which is defined as the difference in observed mortality in 2020 and the baseline expected mortality for 2020 based on historical data from 2015 - 2019. The model adequately controls for known seasonal trends in mortality as well as a longer time trend. One of the main concerns in comparing excess mortality rates across countries is that countries have substantially different population age distributions and age is strongly associated with COVID-19 and other mortality; thus, age-adjusted measures are superior measures for comparing mortality risk across countries. Comparing 'crude' excess mortality rates can be misleading. While the authors may not be able to collect this data for all countries, age-adjusted mortality rates should be estimated for at least the subset of countries for which data is available (such as the majority of European countries). The authors do address this limitation and compute the P-scores. However, showing age-adjusted rates for comparison across countries, where possible, would greatly improve the conclusions of the paper. 

      2) The second major concern related to the comparability of data across countries is that, as the authors acknowledge in Section 2.2, the data quality across countries. The consequences of varying levels of data quality, however, is not clear, particularly when making comparisons across countries. At the very least, a discussion of what undercounting of deaths in general might mean when making cross country comparisons would be helpful.

    1. Reviewer #1 (Public Review): 

      The manuscript by Jasmien Orije and colleagues has used advanced Diffusion Tensor and Fixel-Based brain imaging methods to examine brain plasticity in male and female European starlings. Songbirds provide a unique animal model to interrogate how the brain controls a complex, learned behaviour: song. The authors used DT imaging to identify known and uncover new structural changes in grey and white matter in male and female brains. The choice of the European starling as a model songbird was smart as this bird has a larger brain to facilitate anatomical localization, clear sex differences in song behavior and well-characterized photoperiod-induced changes in reproductive state. The authors are commended for using both male and female starlings. The photoperiodic treatment used was optimal to capture the key changes in physiological state. The high sampling frequency provides the capability to monitor key changes in physiology, behaviour and brain anatomy. Two exciting findings was the increased role of cerebellum and hippocampal recruitment in female birds engaged in singing behaviour. The development of non-invasive, multi-sampling brain imaging in songbirds provides a major advancement for studies that seek to understand the mechanism that control the motivation and production of singing behavior. The methods described herein set the foundation to develop targeted hypotheses to study how the vocal learning, such as language, is processed in discrete brain regions. Overall, the data presented in the study is extensive and includes a comprehensive analyses of regulated changes in brain microstructural plasticity in male and female songbirds.

    2. Reviewer #2 (Public Review): 

      Orije et al. employed diffusion weighted imaging to longitudinally monitor the plasticity of the song control system during multiple photoperiods in male and female starlings. The authors found that both sexes experience similar seasonal neuroplasticity in multisensory systems and cerebellum during the photosensitive phase. The authors' findings are convincing and rely on a set of well-designed longitudinal investigations encompassing previously validated imaging methods. The authors' identification of a putative sensitive window during which sensory and motor systems can be seasonally re-shaped in both sexes is an interesting finding that advances our understanding of the neural basis of seasonal structural neuroplasticity in songbirds. 

      Overall, this is a strong paper whose major strengths are: 

      1) The longitudinal and non-invasive measure of plasticity employed 

      2) The use of two complementary MR assays of white matter microplasticity 

      3) The careful experimental design 

      4) The sound and balanced interpretation of the imaging findings 

      I do not have any major criticism but just a few minor suggestions: 

      # Pp 6-7. While the comparative description of canonical DTI with respect to fixel-based analysis is well written and of interest to readers with formal training in MR imaging, I found this entire section (and especially the paragraphs in page 7) too technical and out of context in a manuscript that is otherwise fundamentally about neuroplasticity in song birds. The accessibility of this manuscript to non-MR experts could be improved by moving this paragraph into the methods section, or by including it as supplemental material. 

      # Similarly, many sections, especially results, are in my opinion too detailed and analytical. While the employed description has the benefit of being systematic and rigorous, the ensuing narrative tends to be very technical and not easily interpretable by non experts. I think the manuscript may be substantially shortened (by at least 20% e.g. by removing overly technical or analytical descriptions of all results and regions affected) without losing its appeal and impact, but instead gaining in strength and focus especially if the new result narrative were aimed to more directly address the interesting set of questions the authors define in the introductory sections. 

      # The possible effect of brain size has been elegantly controlled by using a medial split approach. Have the authors considered using tensor-based morphometry (i.e. using the 3D RARE scans they acquired) to account for where in the brain the small differences in brain size occur? That could be more informative and sensitive than a whole-brain volume quantification. 

      # I think Figures Fig. 3 and Fig. 4 may benefit from a ROI-based quantification of parameters of interests across groups (similar to what has been done for Fig. 7 and its related Fig. 8). This could help readers assess the biological relevance of the parameter mapped. For instance, in Fig. 3, most FA differences are taking place in low FA (i.e. gray matter dense?) regions. 

      # In Abstract: "We longitudinally monitored the song and neuroplasticity in male.." Perhaps something should be specified after the "the song"? Did the authors mean "the neuroplasticity of song system"?

    3. Reviewer #3 (Public Review): 

      In their paper, Orije et al used MRI imaging to study sexual dimorphisms in brains of European starlings during multiple photoperiods and how this seasonal neuroplasticity is dependent in brain size, song rates and hormonal levels. The authors main findings include difference in hemispheric asymmetries between the sexes, multisensory neuroplasticity in the song control system and beyond it in both sexes and some dependence of singing behavior in females with large brains. The authors use different methods to quantify the changes in the MRI data to support various possible mechanisms that could be the basis of the differences they see. They also record the birds' song rates and hormonal levels to correlate the neural findings with biological relevant variables. 

      The analysis is very impressive, taking into account the massive data set that was recorded and processed. Whole-brain data driven analysis prevented the authors from being biased to well-known sexually dimorphic brain areas. Sampling of a large number of subjects across many time points allowed for averaging in cases where individual measurements could not show statistical significance. The conclusions of the paper are mostly well supported by data (except of some confounds that the authors mention in the text). However, the extensive statistically significant results that are described in the paper, make it hard to follow at times. 

      1) In the introduction the authors mention the pre optic area as a mediator for increase singing and therefore seasonal neuroplasticity. Did the authors find any differences in that area or other well know nuclei that are involved in courtship (PAG for example)? 

      2) Following the first comment, what is the minimum volume of an area of interest that could be detected using the voxel analysis? 

      3) It would be useful to have a figure describing the song system in European starlings and how the auditory areas, the cerebellum and the hippocampus are connected to it, before describing the results. It would make it easier for the broader community to make a better sense of the results. 

      4) In the results section the authors clearly describe which brain areas are sexually dimorphic or change during the photoperiod and what is the underlying reason for the difference. However, only in the discussion section it is clearer why some of those differences are expected or surprising. It would be useful to incorporate some of those explanations in the results section other than just having a long list of brain areas and metrics. For example, I found the involvement of visual and auditory areas in the female brain in the mating season very interesting.

    1. Reviewer #1 (Public Review): 

      The paper by Sim et al describes phospho-proteomic analysis of ATR kinase-dependent pathway in mouse spermatocytes. By administrating an ATR inhibitor, AZ20, to mice and using Rad1 (a component of 911 DNA damage clamp) conditional knockout mouse (cKO), the authors isolated testis from these mice, isolated phospho-peptides and analyzed with Tandem Mass Tag (TMT). The analyses identified 37,180 phosphorylation sites and created the data base for them. Importantly, in-depth analysis of the phosphorylation sites revealed an unique consensus site of the ATR-dependent phosphorylation; S/TPXK, whose kinase has not been identified yet. In addition, the authors showed new ATR-dependent phosphorylation sites in proteins in RNA metabolisms including piRNA biogenesis for transposon silencing and showed ATR-dependent localization of two RNA processing enzymes, SETX helicase, and RANBP3 in meiosis sex chromosome inactivation (MSCI). This is an important body of work as a good resource for phosphorylation in mouse germ cells. The study had been done in a great care with proper controls. The data set in the paper is very much useful and of great interest to researchers in meiosis and DNA damage response (DDR) field.

    2. Reviewer #2 (Public Review): 

      The authors try to identify ATR-mediated phosphorylation sites in male meiosis of mice and performed phosphoproteomics using two distinct mouse models. The paper focuses on important topics in the field. Since ATR has key functions in meiosis, successful identification of ATR-mediated phosphorylation sites would have a profound impact. 

      The study has certain technical issues in experimental design and data interpretations. 

      The rationale as to why they used Rad1-cKO was not well described. According to the co-submitted manuscript, Rad1-cKO spermatocytes experience meiotic arrest, and the cellular composition is totally different between controls and Rad1-cKO testes. The "RAD1-dependent" phenotype may simply reflect the difference in cellular composition in testis. With this criterion, any phosphorylation sites present after the mid-pachytene stage in normal spermatogenesis can be categorized as "RAD1-dependent". 

      There are two different experiments for ATR inhibitor (ATRi)-treated mice (2 pairs after 2.5-3 days of treatment, and 2 pairs 4 hours after a single dose). However, these results are not distinguished in the analysis, and there is no evaluation of testicular morphology after ATRi treatment. 

      Finally, the authors showed ATR-dependent localization of SETX and RANBP3 and discussed interesting data. However, it has not been determined whether these localization changes were due to the functions of identified phosphorylation sites or some other mechanisms.

    3. Reviewer #3 (Public Review):<br> In this study, Sims et al. perform a phosphoproteomic analysis of the ATR signaling pathway in mouse testis. By studying the different phosphorylated peptides found in testis samples from ATR inhibited mice and from mutant mice for the member of the ATR-activating 9-1-1 complex, RAD1, authors defined a comprehensive map of the ATR signaling pathway in the mouse testis. In general, the methodological approach performed is appropriate to accomplish the desired goal and the results obtained are well explained and properly discussed. The conclusions raised by the authors are supported by the results obtained and the manuscript reads easily. Thus, overall the manuscript is of high quality. Furthermore, the information provided in this study is novel since to my knowledge this is the first attempt to characterize the ATR signaling pathway in the testis. In my opinion, these data will be very relevant to better understand the role of the ATR in mouse spermatogenesis, and in meiosis in particular, in the future. 

      Nonetheless, I have a few major concerns about this manuscript. Firstly, I think an important part of the description of the results is placed in a related preprint by the authors (Pereira et al. https://www.biorxiv.org/content/10.1101/2021.04.09.439198v1). In my opinion, this manuscript lacks a more detailed analysis of the ATR signaling on DNA repair and chromosome axis structure, which are fundamental to understand the meiotic prophase. Secondly, the manuscript falls short of providing novel insights about ATR roles during the meiotic prophase. As ATR function on the meiotic prophase has been extensively studied, the ATR phosphoproteome should provide either some clues about possible novel functions ATR may do during the meiotic prophase or spermatogenesis, or provide a mechanistic explanation of how ATR performs its meiotic functions (e.g., meiotic sex chromosome inactivation or meiotic recombination). The final section of the results is an attempt at doing sol, but to me, the data provided only suppose a small incremental advance in our knowledge of how ATR promotes MSCI. I would have liked the authors to expand this section to prove the utility of the data.

    1. Reviewer #1 (Public Review):

      In this paper, the authors use a multivariate genotype-phenotype method to assess the broader association of a group of related genes to set of multivariate complex phenotypes. In particular, they investigate the genetic association of genes related to a specific gene ontology (GO) term with a multivariate representation of craniofacial shape. With this type of analysis, they demonstrated that different 'processes', e.g., different GO terms, influence different aspects of craniofacial shape. Using regularized partial least squares, the authors quantitate the proportion of variation in craniofacial shape that can be attributed to genetic differences in a particular process. The association between the process and aspects of craniofacial shape are further explored by examining the changes in those same aspects of craniofacial shape in mice that have been genetic manipulated. A web app is available to use the data and methods described in this paper to identify associations between a MPG genetic axis derived for a particular process, the aspects of craniofacial shape associated with that genetic axis, and the changes in those aspects of craniofacial shape induced by the genetic manipulation of a single gene.


      The authors have an extensive data set from Diversity Outbred mice on craniofacial shape and genetic variation. With over a thousand mice, they have ample power for these types of analyses.

      Much of traditional complex trait genetic analyses are focused on breaking complex trait down into quantitative components that can be measured precisely and examine one genetic marker at a time. However, this traditional approach is counter-intuitive to what we know about complex traits. With this method, the analytical and objective decision about how to capture the genetic influences on multiple correlated and highly interdependent quantitative measures of a biological phenomenon is driven by the data rather than by the researcher. This method also allows the user to break away from the mentality of one gene to one trait and acknowledges that disruption of any number of genes can often produce a similar phenotypic outcome and the disruption of the process is more relevant to the outcome than the disruption of any single gene.


      One of the challenges with multivariate analyses of this type is how to measure success of the model. In this case, the authors compared their genotype-phenotype results to phenotype results from genetically manipulated mice. While this methods is recognized to have advantages, there are disadvantages to this approach that there not fully addressed.

      Within the manuscript, there is an emphasis on the concordant direction of association between the process MGP axis and the axis of shape variation of a relevant mutant phenotype. The reviewers had concerns about the assumptions made and the implications of those assumptions for the interpretations of the results.

      Overall, the discussion sections is overly strongly worded.

    2. Reviewer #2 (Public Review):

      Despite the strong premise, the implementation of the multivariate genotype-phenotype (MGP) approach from biological processes also presents a few shortcomings. First, as properly introduced by the authors, candidate versus genome-wide marker set investigations are two distinct approaches, each with their respective advantages and disadvantages. The proposed methodology is based on candidate selections of processes and therefore a group of genes in support of hypothesis-driven research. In contrast to hypothesis-free investigations (e.g., genome-wide association scans, GWAS) such an approach does not allow to "discover" new associations outside the known genome annotations today, and therefore help solving the mystery of the non-coding (non-gene) parts of DNA or to discover new gene-pathways and interactions. However, combining a multitude of markers across multiple genes in an unsupervised and genome-wide manner as input to a multivariate genotype to multivariate phenotype investigation remains problematic. These issues are well discussed and acknowledged by the authors.

      The deployed MGP methodology, based on partial least squares (PLS), was presented in 2016 (1), following the citation of the authors, (in fact something similar was presented before that in 2012 (2)), but an actual genome-wide use of the technique has not been witnessed yet, to the best of my knowledge. The main reason in my opinion, is that this PLS technique is indeed prone to overfitting, as stated by the authors and the work of 2012, and further that statistical testing is obtained under permutation/randomization or cross validation. These are computationally intractable at the level of millions of SNPs to investigate in GWAS today. Alternatively, is the use of canonical correlation analysis (CCA), which resonates PLS very closely with the distinction of optimizing correlation instead of covariance in search of connecting latent dimensions between two multivariate variables. I.e. both methods are very much related (3) and both are prone to overfitting. However, CCA does have to advantage to report parametric-based p-values that are computationally tractable, which has been used in a recent GWAS on multivariate facial shape (4). The main difference with the current work is that (4) and its predecessor (5) performed a more simple SNP variant by SNP variant investigation only, to avoid overfitting, while still modeling multivariate facial shape. However, the literature on gene-based and/or haplotype- based GWAS instead of SNP-by-SNP based GWAS also lists CCA among others as a common tool to use, and it is of interest to relate the work presented here methodologically to what is done in such multivariate genotype to multivariate phenotype GWAS. It is observed that multiple SNPs within a single gene or haplotype do require extensive pruning before inputted to MV association techniques. Of great distinction and worth emphasizing, is that these remain limited at the level of a single gene at the most, and that the presented work, for the first-time associates across multiple genes (of note, all genes are represented by only an average of two genetic markers within the gene, so that a single gene is certainly not oversampled in comparison to the other genes in the group).

      On the matter of overfitting, the authors deploy a regularization and restrict themselves to the first PLS component as an outcome of the association. Although necessary from an overfitting perspective, at the same time it reduces my enthusiasm in the results presented. First, any kind of regularization is typically user-defined and tuned, making it hard to judge how robust and how well the results generalize. Unfortunately, despite the interesting overlap with mutant phenotypes, the work does not present an independent replication of the associations found, and this in a separate dataset. Second, in the case of CCA, and most likely by relationship in PLS as well, it is not always the case that the first latent dimension is the meaningful one. Therefore, the question becomes, what is missed by not including additional components, or at least testing how many components seem relevant. Third, as a by-product of the regularization, alongside the focus on a single latent component, the results as presented go from a group of genes, to a focus on one or a few of the genes only. In other words, the question now is, to what extent is the group analysis more powerful than a gene-by-gene based analysis, since regularization especially forces a sparse loading on multiple input features (in this case genes).

      While the three example processes are interesting and easy to understand or follow in terms of, how this is of interest concern remains about the interpretation of the follow-up analyses.

      It is worth noting that it is generally very hard to visualize high-dimensional data and the authors did a great job, but it is somewhat disappointing starting off introducing a complex multidimensional problem followed by a potential solution in terms of methodology (PLS) and then in contradiction working with limited dimensions throughout. Towards the future, with increasing datasets and therefore reduced danger of overfitting, it will be of great interest to expand the dimensionalities explored.

      1) Mitteroecker P, Cheverud JM, Pavlicev M. Multivariate Analysis of Genotype-Phenotype Association. Genetics. 2016 Apr;202(4):1345-63.

      2) Le Floch E, Guillemot V, Frouin V, Pinel P, Lalanne C, Trinchera L, et al. Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse Partial Least Squares. NeuroImage. 2012 Oct 15;63(1):11-24.

      3) Sun L, Ji S, Yu S, Ye J. On the equivalence between canonical correlation analysis and orthonormalized partial least squares. In: Proceedings of the 21st international jont conference on Artifical intelligence. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.; 2009. p. 1230-5. (IJCAI'09). \

      4) White JD, Indencleef K, Naqvi S, Eller RJ, Hoskens H, Roosenboom J, et al. Insights into the genetic architecture of the human face. Nat Genet. 2021 Jan;53(1):45-53.

      5) Claes P, Roosenboom J, White JD, Swigut T, Sero D, Li J, et al. Genome-wide mapping of global-to-local genetic effects on human facial shape. Nat Genet. 2018 Mar;50(3):414-23.

    3. Reviewer #3 (Public Review):

      This paper aims to generate biologically and developmentally meaningful genotype-phenotype maps of craniofacial shape variation in mice. The authors acknowledge that genotype-phenotype maps are multivariate in nature (many loci have joint effects on complex phenotypes) and therefore look for associations between multiple loci and multivariate measures of craniofacial shape. And, to gain developmentally relevant information, they constrain the analysis to genetic variation that is found in known biological processes/pathways. To find genotype-phenotype associations they use regularized partial least squares that estimates the vectors of phenotypic and genetic variation (in the genes that correspond to the biological process of interest) that have maximum correlation - as a result the overall morphological effect of the pathway is identified, as well as the relative importance of each of the genes for such phenotypic variation.

      This approach sheds new light on how natural (found in outbred mice) genetic variation in well-understood biological processes affects adult craniofacial shape, and allows the comparison between phenotypic effects of different pathways. The authors also developed a web interface that will allow anyone to explore the phenotypic effects of their biological process of interest, not restricted to the ones explored in the manuscript.

      The study offers a very useful new perspective on how genetic variation translates into phenotypic variation in a multivariate context, and it should be relevant not only for shape phenotypes but for any other complex multivariate phenotype like gene expression or behavioral measurements. However, there are two points that should be taken into consideration when assessing the novelty and predictive power of the approach:

      The novelty of this method is very overstated throughout the paper. The authors state to be using a method previously published by Mitteroecker et al 2016 with the twist of restricting the analysis to known biological processes. It is not clear in the manuscript how much of their approach is actually new and how much is Mitteroecker's applied to a subset of markers.

      The approach provides the phenotypic effect of genetic variation in already known pathways but it does not result in new genotype-phenotype associations; this is acknowledged in the text. However, the manuscript suggest that the results generate testable hypothesis which this reviewer found to be over reaching based on the data present.

    1. Reviewer #2 (Public Review): 

      The authors found EXOSC1 expression is significantly correlated with C>A transversions in coding strands in KIRC, and suggested EXOSC1 induces the mutations in VHL gene which contributes to KIRC patient prognosis. Indeed, the TCGA database indicated that KIRC patients with high EXOSC1 showed a poor prognosis, thus this finding suggested that EXOSC1 may be a potential therapeutic target for KIRC patients with high EXOSC1 expression in combination with PARP inhibitor. This work is interesting and novel. It is first report on EXOSC1 has exosome independent function to induce single strand DNA cleavage. Although the mechanism links ssDNA cleavage to C>A transversion has not been addressed in the manuscript, and VHL mutations induced by EXOSC1 have not yet characterized and tested in KIRC tumorigenesis and progression. However, the association of EXOSC1 expression with the C>A transversion is clear and association with patient prognosis is convincing. More importantly, the therapeutic potential of combination with PRAP inhibitor makes this study important.

    2. Reviewer #3 (Public Review): 

      Targeting DNA repair pathways is a critical therapeutic strategy for cancers. However, the DNA repair inhibitors markedly benefit only a part of patients necessitates the development of new strategies. In this manuscript, Xiao et al. showed that exosome component 1 (EXOSC1) leads to DNA damages and sensitizes KIRC cells to parp inhibitor. Because that endogenous source of mutation (ESM) constantly assaults genomic DNA and likely sensitize cancer cells to the inhibitor, the authors first analyzed the statistical relationship between the expression of individual genes and the mutations for KIRC. Among the ESM candidates, EXOSC1 most significantly promoted DNA damages and mutations by cleaving single-stranded DNA. Their further analyses demonstrated that high EXOSC1 patients showed a poor prognosis, and EXOSC1 sensitized cancer cells to PARP inhibitor. The topic was relative interesting as the field of synthetic lethal has attracted more intense interest recently. The whole study was logically designed, and the amount of work completed by the authors is abundant to justify the conclusion.

    3. Reviewer #1 (Public Review): 

      In this research the authors performed comprehensive genomic mutation analysis based on public database, together with that on one of the most common mutated VHL gene in KIRC and screened EXOSC1 as an endogenous source of mutation. After validation of its mutagenesis role and description of the preference for cleaving C sites in single-stranded DNA, in vitro and in vivo assay were performed to elucidate EXOSC1 damaging DNA and could sensitize KIRC to DNA repair inhibitor like PARPi. This study was innovative and rigorously designed to propose a relative novel molecule accounting for DNA damage and put forward that EXOSC1 could function as a potential biomarker for selecting potential subgroups which would benefit from PARP inhibitor, though clinical data validation lacks and requires further investigation. Experimental data were sufficient to hold back the conclusion and this article will be of significance to researchers or clinicians who were specified in DNA repair mechanism and corresponding therapy.

    1. Reviewer #3 (Public Review): 

      In this study the authors investigate the interactions between the Helicase II orthologue PcrA and RNA polymerase. This follows on nicely from previous work that showed that the C-terminal domain (CTD) of PcrA/UvrD mediates a direct interaction with RNAP. So the authors flip the experiment and use hydrogen-deuterium exchange (HDX) mass spec to reveal the location of the interaction between the CTD and RNAP, or more specifically the beta subunit rpoB. Interestingly they discover that the binding motif is conserved across a number of PcrA partner proteins (and others), including UvrB. Further investigation into HDX of full-length revealed protection near the DNA/RNA exit channel. This leads the authors to investigate the role of this interaction. They find using the S9.6 anitbody, specific to RNA/DNA hybrids and helicase assays that PcrA can unwind RNA/DNA hybrids, perhaps those formed as R-loops during transcription. These data are compiled into a model that reiterates the UvrD-like backtracking of RNAP or - based on the HDX data - an alternative that has PcrA cleaning up after RNAP as it processes. 

      Overall this is a compelling study that offers good evidence to back up many of the conclusions. The complementary approaches used help to provide wider support of their hypothesis that PcrA is involved in resolving R-loops. This view of PcrA's activity has recently received support by the work from another group (doi: 10.3390/cells10040935). The methods were clear, and appropriate. Using HDX to formulate hypotheses was a strength of this work, and the identification of a Tudor-domain binding motif in PcrA interacting proteins is of significance, because additional proteins were identified from this approach. The main weakness was that no direct evidence for how R-loops are resolved by PcrA was shown. The strongest evidence came from Figure 6A, showing a strong helicase activity on DNA/RNA hybrids, comparable to DNA/DNA, and also from Figure6D/E, showing an increase the DNA/RNA hybrids formed with the inactive PcrA-E224Q. However, this last point does not entirely clarify the mechanism, instead it simply shows that inactive PcrA enables more R-loops to form, this is not supportive of any underlying mechanism. Nonetheless, it is clear that PcrA is involved, by some mechanism, in removing R-loops.

    2. Reviewer #2 (Public Review): 

      Here, the authors aim to characterize the critical regions of PcrA/RNAP interactions and determine the function of such interactions. The manuscript's structural work is refined, elegant and leaves little room for doubt concerning the importance of the CTD PcrA-RNAP molecular interactions. This work moves the field forward in a meaningful way and unravels key aspects of PcrA/UvrD function with regards to interaction and function on RNAP. 

      Though the in vitro work and the structural studies are very convincing, the biological connotations of this newly characterized interaction are a bit premature, with the proposed models relying heavily on implications derived from their structural data. 

      The authors achieve their goal in a generally successful manner regarding the interaction domains between RpoB and PcrA. However, they focus strongly on the CTD domain. The previously suggested interactions of NTD is not explored and if pursued, could significantly improve our understanding of the structure and function of PcrA/RNAP interactions in a full manner. An additional set of experiments examining the role of NTD here would expand the scope of the study significantly. 

      The suggested processing of R-Loops by PcrA through its interaction with RNAP is informative and may be very much relevant to prior findings regarding a role for PcrA in the resolution of replication-transcription conflicts.

    3. Reviewer #1 (Public Review): 

      UvrD, Rep, and PcrA are bacterial superfamily 1 (SF1) helicase that function as motor proteins during several DNA metabolic processes. Their primary roles are during DNA repair and recombination where they: 1. bind to 3' ssDNA overhangs and translocate on the DNA in a 3'-5' manner, 2. unwind dsDNA, which is coupled to their motor activity, 3. strip or remodel other proteins bound on the DNA. In this paper, the role for the PcrA helicase is proposed based on its interaction with the RNA polymerase complex. This activity occurs during transcription and thus likely serves to resolved stalled transcription events in the cell. 

      The authors build on earlier discoveries that show UvrD interacting with the RNAP and controlling its movement. Similarly, the authors have previously reported on the interactions between PcrA and RNAP. While the interaction was shown before, the mechanistic details of how the two protein interacted were not complete and the functional relevance was elusive. In this study, using elegant HDX-MS analysis, they map the binding site on RNAP to a helicase-binding motif. Perturbations in this region interfere with PcrA-RNAP binding. This, in my opinion, is a super important finding as the corollary RNAP binding region on the helicase is conserved in other PcrA interactors. The authors have identified several new PcrA binding proteins and should be an exciting line of investigations going forward. 

      The manuscript was a pleasure to read with sound experiments, perfect interpretations of the data (and no overinterpretations). The supporting data are well reasoned and this authors does not find any fault with the experiments, results, and the discussion of the work.

    1. Reviewer #3 (Public Review): 

      The research presented by Watanabe et al. "Novel neuroanatomical integration and scaling define avian brain shape evolution and development" try to present a novel overview about how the avian brain develops and evolved. In this attemp the authors explains perfectly how integration/mopdularity worked in shaping the avian brain. 

      The data and analyses performed and sound, and the conclusions are well fundamented. The discussion section is extremelly atractive. The main strenghts of this research are the analyses (various analyses) performed with sound data. All the methodology is well described and easy to follow (altough I one of those persons who is not that friendly with R) so I think it will be good to have all the scripts used published together with the manuscript). Discussion and conclusions are well justified and excellently presented. Graphics are wonderful and self-explanatory.

    2. Reviewer #2 (Public Review): 

      Watanabe et al. investigated avian brain evolution and development via morphometric analysis of endocast size, shape, and modularity. They incorporate developmental data from extant archosaurs (Gallus and Alligator) with endocasts of crown birds and non-avialan coelurosaurian dinosaurs to identify the allometric relationship and evolution of highly encephalised crown bird brains. 

      Major strengths of the methods include the sophisticated geometric morphometric approach with digital endocast data to characterise variation in both overall brain morphology, and that of its various functional subdivisions. The authors further attempt to quantify the relative contributions of the integrated vs. modular processes between major brain regions. Further parallels with mammals, another group to have achieved highly encephalised brains, could expand the appeal of this paper to a broader audience. 

      The major findings of the paper, that crown birds possess a distinct brain shape-to-size scaling relationship with a more integrated brain structure compared to non-avialan archosaurs, are supported. Aside from those working on archosaurian brain evolution, this paper provides a valuable example in advanced methodology and approach to studying neural evolution more broadly.

    3. Reviewer #1 (Public Review): 

      Watanabe and colleagues investigated how properties intrinsic to the organism (allometry, development, morphological integration) have directed the evolution of encephalized and diverse brains in avian and non-avian dinosaurs, on the basis of 3-D imaging, high-density shape data of endocasts from 37 extant and recently extinct crown bird species, 6 non-avian coelurosaurian dinosaurs, and Archaeopteryx, as well as developmental neuroanatomical data of model archosaurs (Gallus, Alligator). Using the methods for multivariate analysis and for evaluating the pattern of integration, they demonstrate that extant birds have a distinct allometric, more integrated brain structure than non-avian dinosaurs closely related to birds. This study reveals complexity in evolutionary processes (concerted and/or mosaic patterns) that shape the evolution of encephalized and divergent brains across vertebrates, birds in particular.

      The inference in the manuscript is overall clear, and the conclusions are well supported by data, which in turn will be informative for relevant or follow-up studies. Due to the paucity of the endocast data from early birds, some claims sourced from the present data could be proposed more cautiously (e.g. in the abstract), like the way on page 10.

    1. Reviewer #1 (Public Review): 

      In this study, Hale and colleagues study the extent of co-regulation of alternative splicing events by the two RNA binding proteins RBFOX1 and MBNL1. The authors use inducible expression to look at the relationship between the dose of each factor and their impact on exon inclusion. The results support a model where for a subset of splicing events, RBFOX1 can act to buffer MBNL1 dose, and in one case the authors provide more detailed mechanistic evidence that this likely occurs through a consensus RBFOX binding site (UGCAUG) that also serves as a low-affinity binding site (YGCA) for MBNL1. 

      In general, I found the current study interesting and timely, highlighting a key challenge in our ability to understand and predict alternative splicing outcomes. Specifically, as the current study demonstrates, differing concentrations of RNA binding proteins that co-regulate alternative splicing events can often lead to diverse and complex regulatory outcomes, which we still do not fully understand. 

      A strength of this study is that it highlights the importance of understanding alternative splicing regulation as a product of interactions between multiple RNA binding proteins, where distinct concentrations of these factors can lead to diverse outcomes on splicing patterns. The work demonstrates this concept through a combination of more detailed mechanistic work on a reporter gene and through the use of in vitro binding assays. It also begins to extend these ideas through transcriptome-wide analysis of splicing patterns in cells expressing differing concentrations of two RNA binding proteins. 

      I do feel that in its current form, the study would benefit from additional genome-wide analysis to further strengthen the generality of the model put forward by the authors to explain buffering of MBNL1 dosage via the co-expression of RBFOX1. 

      Overall however, this study has increased our appreciation for the importance of moving away from studying RNA binding proteins in isolation and without consideration of other cis elements and trans factors.

    2. Reviewer #3 (Public Review): 

      In this manuscript, the authors address an important problem in understanding how alternative splicing outcomes are determined. In many cases, the influence of multiple factors accounts for the relative abundance of different splice variants and these splicing factors and their target transcripts frequently change in expression over the course of a developmental program. But how are the contributions from these multiple trans-acting regulators combined to produce a binary outcome, such as inclusion or skipping of an exon? The splicing factors examined in this manuscript are the well-studied Muscleblind (MBNL) and RBFOX families, each of which has three separately encoded paralogous loci, and which change in expression during developmental processes to control splicing of large sets of exons. These factors are particularly important in the context of muscle development, and MBNL proteins are sequestered within nuclear foci driven by expanded-repeat encoded transcripts containing canonical MBNL binding sites which is a causal factor in myotonic dystrophies. Thus it is important to understand how these factors interact to control splicing in normal and disease contexts. The interactions between RBFOX and MBNL, as well as another family of splicing regulators known as CUGBP or CELF proteins in co-regulating splicing events have all been extensively documented in the literature, including in some cases examples of overlapping binding sites. 

      The novel regulatory interaction that the authors set out to characterize here began with the identification of a non-canonical, overlapping binding site for MBNL embedded within an RBFOX motif controlling the splicing of exon 11 in the insulin receptor (INSR) transcript. RBFOX showed a 'buffering' response curve on minigene splicing, where at low concentrations of MBNL protein RBFOX significantly enhanced splicing, while the effect of MBNL was dominant when it was present at increased concentrations. The effects of each protein could be dissected by mutations pinpointing each motif, and recombinant MBNL exhibited a higher binding affinity for the hybrid site compared to RBFOX RRM, consistent with the dynamics seen in the minigene splicing assay. Through use of an innovative dual-inducible transgene system to independently manipulate expression of MBNL and RBFOX1, alternative splicing was measured first by performing a multipoint dose-response curve on individual endogenously expressed genes by RT-PCR, then examining the extreme end-points of the curve in a genome wide manner using RNA-seq. 

      A strength of the paper is in the use of these innovative cell lines with independently-titratable MBNL and RBFOX protein expression to enable dose-response testing. This system is used in a two-step process in which the coincidence of altered splicing events detected in a simplified end-point RNA-seq experiment with the dose-dependent validation of endogenous splicing responses allows the inference of complex dose-response curves from a much simpler RNA-seq experiment. However, the main weakness is the generalization of the proposed mechanism, which affects the significance of the conclusion. Simply put, although the 'buffering' type interaction on which they focus could was found among a larger group of exons in the genomewide data, these accounted for only about 1/3 of the affected splicing events. More importantly, there was little evidence provided for the role of hybrid binding sites among these buffered splicing events, with only ~30% of the exons having an identifiable motif like the one found in INSR. While the INSR minigene experiments nicely define a mechanism responsible for the splicing of that exon, and the genomic data hint at the possibility of other such interactions, the conclusions of this manuscript in its current form do not represent a major, generalizable advancement in our understanding of splicing regulation.

    3. Reviewer #2 (Public Review): 

      In this work, Hale et al. performed systematic analysis how dosage dependent splicing regulation by MBNL1 is affected by different levels of RBFOX1 expression. Starting with analysis of INSR exon 11 using minigene splicing reporters, the authors observed that the magnitude of MBNL dependent splicing is large when RBFOX1 is low, and it is reduced when RBFOX expression is high, a phenomenon denoted "buffering" co-regulatory mechanism. By mutagenesis and in vitro binding assays, the authors proposed that the RBFOX1 binding motif UGCAUG can directly bind MBNL1 through the imperfect UGCA sequence, and an increasing level of MBNL1 can outcompete RBFOX1 binding. To generalize this observation, this study elegantly generated two cell lines. In each cell line, MBNL1 and RBFOX1 are controlled by inducible promoters, which can be independent controlled by DOX/ponA titration. Exons co-regulated by MBNL1 and RBFOX1 were identified by RNA-seq analysis and were binned based on how RBFOX1 level affects the magnitude of MBNL1-dependent splicing. The authors observed that exons subject to buffering co-regulation is most abundant. The competitive binding of RBFOX1 and MBNL1 through YGCAUG appears to account for a subset of these exons. 

      Overall, systematic investigations of combinational splicing regulation by multiple RBPs are lacking and this study provided a very nice experimental system with several interesting observations. I hope the comments below can be helpful to improve the manuscript: 

      1) Previous studies reported that MBNL1 and RBFOX1 frequently regulate exon inclusion or skipping in the same direction. The authors found the magnitude of MBNL1-dependent splicing changes is smaller when RBFOX1 level is high, which was denoted "buffering" co-regulation. This is certainly correct at sementic level, but this observation does not directly imply whether there is mechanistic coordination between the two RBPs or if there is, what is the nature of such coordination. 

      a) This is because the study measured the magnitude of splicing regulation by delta_PSI, which is bounded by the baseline inclusion level. For example, when the baseline exon inclusion is 0.1 and the inclusion can readily increase to 0.4 when MBNL1 is induced if the exon is strongly regulated by MBNL1. However, if RBFOX1 increases the baseline exon inclusion level to 0.7, then the maximal possible delta_PSI upon MBNL1 expression will be 0.3, even when there is no direct coordination between the two RBPs. Therefore, it is not precise to claim the regulation is non-additive based on this observation. 

      b) The authors might refer to Baeza-Centurion et al. 2019 Cell 176:549, which addressed this issue. There might be more rigorous theoretical framework if the authors want to argue whether the regulation is additive or coordinated. 

      2) Through mutagenesis analysis of the UGCAUG elements into UCGAUG (MUT1, which disrupted the RBFOX binding site) or into CGCUUG (MUT2, which disrupted the RBFOX binding sites while creating a MBNL binding site), as well as gel shift experiment, the authors proposed that RBFOX1 and MBNL1 can compete in binding to the UGCAUG element. These data are consistent with the theory, but do not exclude other possibilities. For example, the juxtaposition of RBFOX1 and MBNL1 can stabilize the binding of the two proteins to RNA. The reduction in MBNL1-dependent splicing in MUT1 can be explained by destabilization of RNA-mediated protein-protein interactions. The restoration of splicing by MUT2 could be due to the fact that the newly created CGCU increases the affinity of MBNL1 binding to clustered YGCY motif sites, so that it is less dependent on stabilization by interacting with RBFOX1. 

      3) The observation of MBNL1 binding to UGCAUG containing sequences in vitro, or the observation that it can outcompete RBFOX1 binding at high concentration is not very surprising. This result does not prove such binding/competition occurs in cells at endogenous protein level. However, it is somewhat surprising that MBNL1 can outcompete RBFOX1 RRM in binding to UGCAUG at equivalent concentration. Is it because the substrate sequence used in gel shift has three YGCY like sites (GGCU, UGCA, and UGCG)? If this is the case, how this situation can be generalized? It might be helpful to determine Kd from the gel shift and compare the values with those reported in the literature. 

      4) Comparison of RBFOX1 motifs in different sets of exons. Before the analysis presented in Figure 3 E,F, it will be helpful to examine motif enrichment in RBFOX-dependent vs. independent exons in different regions (upstream intron, exon, and downstream intron), which will provide a positive control, as the expected patterns are very well established in the literature. In their analysis, the authors should distinguish upstream/downstream intron depending on RBFOX-dependent inclusion or skipping. 

      Some of the requests above might go beyond the immediate scope of the study, but nevertheless the authors should probably at least discuss how these issues affect the interpretation of the data.

    1. Reviewer #2 (Public Review): 

      Overzealous inflammation is a significant clinical concern in COVID-19 patients. However, mechanisms underlying this hyperinflammatory response is unclear. In the current work by Khan and colleagues, the team investigates the role of the SARS-CoV-2 spike protein in driving inflammation and demonstrates the role of TLR2 in this mechanism. Here, the team identifies a range of inflammatory cytokines and chemokines generated following treatment of human and mouse cells with the S protein. Interestingly, the team did not observe any impacts on interferon signaling, suggesting a disconnect between the cytokine/chemokine response and interferon production. Likewise, this appears to be spike-protein dependent in the research team's hands. Biochemical studies suggest that the spike protein induces NF-kB signaling through a TLR2/MyD88 dependent mechanism. In general, the studies were well conducted and the data presented support the overall conclusions of the study. However, there are a few limitations to the work noted. These include questions associated with TLR2 heterodimer formation, the use of A549 cells that are refractory to SARS-CoV-2 infection as a model system, and clear data linking the mouse studies that are critical to the mechanism back to the human studies which lack specific assessments of TLR2/MyD88.

    2. Reviewer #1 (Public Review): 

      In this manuscript, Khan et al. investigated the roles of SARS-CoV-2 proteins on activation of immune cells. The authors found that the macrophage cell lines such as human THP-1 cells and mouse RAW 274.7 cells with recombinant viral proteins, and found that only spike proteins (S1 and S2) could potently activated macrophages to produce pro-inflammatory cytokines and chemokines. 


      It was Intriguing that only spike proteins (S1 and S2) could potently activate macrophages to produce pro-inflammatory cytokines and chemokines. The authors also observed that direct contact of macrophages with spike protein transfected epithelial cells, that mimic viral infection, resulted in the activation of macrophages. Detail analyses showed that spike proteins were recognized by Toll-like receptor 2 to activate NF-kB signaling. In vivo mouse experiments further supported the in vitro experiments. This study revealed a pathogenic of the SARS-CoV-2 spike proteins that is directly activating the host inflammatory responses, which therefore may have a profound impact in understanding a novel aspect of the cytokine signaling that is involved critically in the COVID-19 pathogenesis unveiled for the first time. 


      Recent report by Shirato and Kizaki (Heliyon 7(2021) e06187: 10.1016/j.heliyon.2021.e06187) showed that SARS-CoV-2 spike protein can stimulate macrophages (RAW 264.7 cells and THP-1 cells) to produce pro-inflammatory cytokines via TLR4-dependent manner. This is likely to contradict this study. The authors must thoroughly argue these controversial observations.

    3. Reviewer #3 (Public Review): 

      Khan and colleagues evaluate the ability of purified components of the SARS-CoV-2 virus to induce inflammatory responses in macrophages and epithelial cells. They observe that the spike protein drives a TLR2-dependent inflammatory response both in vitro and in vivo. There also appears to be a potential crosstalk between epithelial cells and macrophages in response to the spike protein, however the specifics of this interaction remain unresolved.

    1. Reviewer #2 (Public Review): 

      Godet et. al have attempted to identify the cellular components of what is known as an IRESome, and conclude that paraspeckles are the sites of IRES action. They found that IRES accessory proteins known as ITAFs concentrate during hypoxia at that paraspeckle sites and are important for IRES-mediated translation. The authors show that the long non-coding RNA Neat1, and in particular isoform 2 of NEAT1, is a universal essential component that can recognize almost all cellular IRESs and contributes to their translation during the stress response in angiogenesis and/or cardio-protection. In summary, the authors propose a novel and very interesting concept, but one which is still incomplete and will require additional experimentation in order to convincingly conclude that the lncRNA NEAT1 is required for IRES mediated mRNA translation activity.

    2. Reviewer #1 (Public Review): 

      The manuscript investigates a topic of general interest to translational regulation - IRES function during hypoxia. The authors propose that nuclear paraspeckles serve as areas where cellular IRESes acquire their ITAFs and that this subsequently enables them (the IRESes) to be appropriately expressed. Among the components of the paraspeckles that the authors find associated with the FGF1 IRES is the lncRNA, Neat1, and a few resident proteins. The strengths of the current study is that the presented experiments are generally well presented and described. The manuscript is well written. The experiments cover a wide breadth in the area of FGF1 IRES activity/regulation. The weaknesses lies in several instances where correlation between datasets are taken to imply direct cause-effect relationships. Some experiments take several days to set-up (eg, knock-downs) and it thus becomes difficult to establish such direct cause-effect relationships versus effects due to secondary causes.

    3. Reviewer #3 (Public Review): 

      This study searched for IRES transacting-factors (ITAFs) that control the translation of the IRES in fibroblast growth factor FGF1 mRNA during normoxia and hypoxia in mouse cardiomyocytes. Because it has been known that several ITAFs locate to nuclear paraspeckles, the authors examined roles for a long noncoding RNA, NEAT1, that is located to these speckles, in the activation of the FGF1-IRES. Using depletion studies it was shown that NEAT1 indeed modulation of FGF1 IRES activity. Using a tagged version of p54nrb, which interacts with NEAT1, several interacting proteins were discovered by mass spectrometry. SiRNA-mediated depletion of the mRNAs encoding some of these proteins (i.e. RPS2, hnRNPM, nucleolin) showed a very modest decrease in IRES activity during normoxia, but less so during hypoxia. Finally, the authors showed that effects of NEAT1 on translation were specific for IRES-containing mRNAs that function during angiogenesis and cardioprotection. While effects of NEAT1 on FGF1 translation is supported by solid data, roles for NEAT1-interacting ITAFs is less clear. However, pre-assembly of translation-competent FGF1 in nuclear paraspeckles is a novel finding that may be very relevant in cardiomyocytes.

    1. Reviewer #2 Public Review:

      In this manuscript, Ritchey and colleagues studied an intercross of two inbred mouse strains for their inflammasome response to interrogate the genetic basis for enhanced inflammasome activity. This was spurred by the observation that bone marrow-derived macrophages (BMDM) from DBA/2 mice showed an approximately 2-fold enhanced NLRP3 inflammasome response compared to BMDMs from AKR mice. To explore this phenomenon, they stimulated BMDMs from DBA/2 and AKR intercrosses (F4 generation) with NLRP3 agonists and then studied the ensuing IL-1β response. Conducting quantitative trait locus (QTL) mapping the authors then identified a region on chromosome 7 to have the highest LOD score for the phenotype studied (this region was named Irm3). The Irm3 region encompasses the 134.80-138.45 Mb interval on chromosome 7 that encodes for 66 genes. Given its established role in inflammasome signaling and also a strong cis eQTL LOD score, the authors focused on Pycard in the following. Comparing the two mouse strains, the authors noted an SNV in the 3' UTR of the Pycard gene with differing genotypes for DBA/2 and AKR mice. This SNV is located just downstream the stop codon, a region that seems to display little conservation across different mammalian species. Comparing ASC protein expression, the authors noted increased levels of ASC in BMDMs from DBA/2 mice, a finding that also translated into higher amounts of ASC speck levels following inflammasome stimulation. Subsequent experiments indicated that Pycard mRNA levels of BMDMs from DBA/2 mice displayed a longer half-life, while Pycard mRNA transcription or splicing was not affected. Modeling the 3' UTR region of interest furthermore suggested that the SNV impacts on the structure of this region. To validate the causal role of this SNV in regulating Pycard expression, the authors generated DBA/2 ES cells, in which they changed the genotype of this SNV into the corresponding AKR variant. Comparing ES-cell-derived macrophages of the parental DBA/2 genotype to the AKR-adapted Pycard genotype, the authors found that ASC expression levels were indeed decreased and that this reduced expression translated into a reduced NLRP3 inflammasome response in these cells. Altogether, these data suggest that an SNV in the 3' UTR of the murine Pycard gene impacts the stability of its mRNA, which translates into altered ASC protein levels and thereby the activity of inflammasome pathways.


      The conclusions of this paper are well supported by data and there are no major gaps or flaws in the line of reasoning. A particular stronghold is the functional validation of the here-identified SNV using a CRISPR-based point mutagenesis approach. This set of data provides a high level of confidence for the proposed model.


      While this manuscript provides an elegant QTL mapping approach to identify differential expression of Pycard as a major regulator of inflammasome activity in murine BMDMs, the outcome of this study does not provide any new biological insight into inflammasome biology. The fact that differential expression of ASC impacts on inflammasome activity is well expected based on its firmly established role in inflammasome signaling.<br> Unfortunately, the here-identified mechanism of the differential regulation of the half-life of the Pycard mRNA is not conserved in other species, which precludes any extrapolations to other organisms. Moreover, as also correctly summarized by the authors, there is currently no evidence that genetic variants leading to differential ASC expression in humans would impact on human health or disease. These shortcomings obviously limit the conceptual advance and relevance of the here-identified mechanism.

    2. Reviewer #1 Public Review:

      Genetic differences in outbred species such as humans and differences in the epigenomic structure form the basis of the large variability in the immune response. In particular, the inflammasome is highly regulated at multiple levels, including the post-transcriptional and post-translational levels. Inflammasome responses towards a myriad of triggers are associated with disease development in murine models of disease. Furthermore, clinical trials are ongoing testing the ability of inflammasome inhibitory small molecules to prevent or ameliorate inflammasome-driven pathologies in patient populations.

      This manuscript identified that a single nucleotide change in the gene encoding for the universal inflammasome adaptor protein ASC regulates mRNA stability of Pycard and thereby inflammasome function. A particular strength of this manuscript is that the authors managed to show, using genetic alterations, that the single SNP in the Pycard gene sequence (rs33183533) between AKR and DBA/2 mice is the cause of variance in inflammasome activity. Given the relevance of inflammasome for various human pathologies, this work is important for a broad readership.

    1. Reviewer #1 (Public Review):

      In the present study Giridharan et al. identify a novel role for the phosphoinositide kinase PIKfyve in endosomal recruitment of the retriever complex and recycling of integrins as well as cell migration. Inhibition of PIKfyve with a specific inhibitor reduces endosomal levels of PI3,5P2 which in turns reduces the endosomal recruitment of VPS35L and other retriever components. The sorting nexin SNX17, which preferentially binds to PI3P, is not affected by PIKfyve inhibitors but cannot recycle integrins in the absence of endosomal retriever components. Based upon various inhibitor experiments, the authors propose a model of sequential recruitment of SNX17 and the WASH complex by PI3P followed by a shift to PI3,5P2 and recruitment of retriever, thus initiating actin mediated tubule formation and endosomal exit of SNX17 bound integrins.

      The manuscript is clearly written, the data are largely of sufficient quality, and the findings are certainly of interest to the endosomal research community. I also agree with the model the authors propose. One weak point of the study is the dependence on microscopic techniques to analyze integrin surface levels and endosomal recruitment of the retriever complex. The study would have benefited from additional methods to confirm the microscopy data. It would also be good if the authors could confirm their inhibitor studies with genetic suppression/deletion of PIKfyve, ideally followed by rescues with a kinase deficient mutant.

      The authors rely on microscopy of integrin beta 1 for most of their data. However, SNX17 and the retriever complex are not required for the recycling of all beta 1 integrins (Steinberg et al., 2012). In HeLa cells, it is mainly integrin alpha 5/beta1 that is recycled by SNX17. Therefore, the other beta 1 integrins that recycle SNX17 independently tend to mask the recycling phenotypes caused by the loss of SNX17/retriever. I think that the authors could detect a much more pronounced recycling phenotype upon PIKfyve inhibition if they stained integrin alpha 5 instead of integrin beta 1. Does integrin alpha 5 "get stuck" in a LAMP1 positive compartment similar to what Steinberg et al., 2012 or McNally et al., 2017 describe in their studies? These two studies clearly show that almost all integrin alpha 5/beta 1 accumulates in a LAMP1 or LAMP2 positive compartment upon loss of retriever/SNX17 function. If the authors are correct in their assumptions, this should be happening upon loss of PIKfyve activity. One could use the Abcam antibody against integrin alpha 5 that was used in the McNally et al. study as it works very well.

    2. Reviewer #2 (Public Review):

      Giridharan and colleagues have sought to examine the role of phosphoinositide switching on the function of endosomes in the sorting and transport of integral proteins, in particular integrins. Their focus has been on the enzyme PIKfyve which catalyses the conversion of PI(3)P, an identity feature of early endosomes, to PI(3,5)P2, an identity cue for late endosomes. Through experiments that inhibit PIKfyve function, they have examined the resulting effects on the localization and function of components of the SNX17-retriever-CCC-WASH pathway in the sorting of internalized integrins and the impact on cell migration. They conclude that the activity of PIKfyve, along with its upstream kinase VPS34 (this generates PI(3)P), are coordinated to regulate integral protein sorting through this pathway. Strengths of the study include: the research topic, analysis of the functional significance of efficient and correct endosomal sorting is an expanding area of interest; its breath, from analysis of cell migration down to molecular analysis of integrin sorting; the combination of quantitative biochemical and imaging based analysis; and, the use of genetic tools and acute chemical inhibition. However, the data supporting the conclusions could be strengthened by additional controls and the integration of published data into the final model that argues for phosphoinositide switching in the ordered assembly of the SNX17-retriever-CCC-WASH pathway could be improved.

    3. Reviewer #3 (Public Review):

      This study extends what is known about the importance of phosophoinositides in endosomal protein sorting and will be of interest to groups studying endosomal protein sorting. It is perhaps to be expected that PIKfyve, the only PI3P 5-kinase in could have a role in this process as it is important for sorting/trafficking events at other points in the endocytic pathway.<br> It remains to be determined whether the SNX17-Retriever machinery is solely responsible for integrin recycling and it is noteworthy that some of the effects of the loss of PIKfyve function are somewhat marginal. Indeed, there is evidence that SNX27, along with the WASH complex is necessary for integrin recycling and cell migration. There is a possibility that PIKfyve has rather wide-ranging roles in endosomal protein sorting and therefore many cargo proteins will be affected to a greater or lesser degree if PIKfyve function is impaired. A question left open is what machinery is involved in forming the tubular carriers (or vesicles) that transport integrins to the cell surface as SNX17 lacks the membrane-bending BAR domains in many other sorting nexins that can drive membrane tubulation.