Reviewer #3 (Public Review):

In some contexts, individual neurons in the hippocampus of rodents, called time cells, can spike selectively after a specific amount of time following a triggering event. Hippocampal neurons can also encode the traversal of a specific amount of distance (for example, running on a treadmill). Some hippocampal neurons also appear to represent mixtures of these features in addition to classical representations of place selectivity. In this manuscript, Abramson et al. hypothesize that the formation of these representations might be influenced by the task which the animal is performing in the context of the recording. To test this hypothesis, they exploit data from a previous maze-running study (Kraus et al., 2013) in which rats were trained to run on a treadmill across several trials of a session at experimentally-varied velocities. (This study had originally been done to tease apart potential confounds in the questions regarding representations of time versus distance.) In the Kraus et al. study, these walks occurred in one of two contexts or "session types." In a "fixed time" condition, on the other hand, the animal ran on the treadmill for a fixed amount of time before leaving the treadmill. In a "fixed-distance" condition, the animal ran on the treadmill for a "fixed-distance" (in the sense of self-motion). Abramson et al. conjectured that hippocampal pyramidal cells would be biased to represent elapsed time (from entering the treadmill) in the fixed-time condition, whereas they would be biased to represent elapsed distance in the fixed-distance condition. This conjecture appears to be due to the fact that the reward structure of the task motivates the prediction of elapsed time in the fixed time condition, whereas it motivates the prediction of elapsed distance in the fixed distance condition.

To test this hypothesis, the authors use the velocity of the treadmill in each trial to predict the onset of a cell's spiking activity after entering the treadmill. Such predictions would have quite different forms depending on whether the cell's representation correlates with time vs. distance, for example. The authors then use a comparison of the error in each of those two predictors, parametrically formulated, to build a classifier that predicts session type from the spiking onsets of a cell across the trials in that session. The classifier is fit to the Kraus et al. data and optimized to maximize rate of classification as distance cells in the fixed-distance sessions, and minimize rate of classification as time cells in distance sessions. By this metric, they find that 69% of cells in fixed-distance sessions are classified as distance cells, and 68% of cells in the fixed-time sessions are classified as time cells. Applying these results to a parametric hypothesis test, the null hypothesis that session type is independent of cell classifications is strongly rejected. Two other classifiers, based on similar comparisons, found similar results.

The authors conjecture that these findings may be due to the fact that the structure of the task was such that anticipation of reward would depend on "distance" traversed in the fixed-distance sessions, whereas it would depend on time elapsed in the fixed-time sessions. Thus the results are aimed to provide evidence supportive of widely-discussed theories which view the selectivity observed in hippocampal firing patterns as exemplars of predictive coding.

Weaknesses:

The original study of Kraus et al. consisted of 3 rats for which all sessions, including both training and recording, were of one type. Another 3 rats had a hybrid mixture of distance and time sessions. This is mentioned very briefly in the main text. It would appear that the theory of reward might lead to different predictions that could be verified by comparing these animals session to session at a finer grain. For example, are there examples of cells switching or transforming their "predictive" representations when a large number of trials in on session type is followed by a large number of trials of the opposite type? For another example, the transition from training to recording could give similar opportunities. It seems at least possible that ignoring these issues could cause a loss of power.

Some circularities in the construction and interpretation of the time-cell and distance-cell classifiers are not clearly addressed. The classifiers currently appear to be fit to predict the type of session a cell's response patterns are observed within. But it is tautological to use the session type to define the cell type. I sense this is ultimately reasonable because of how the classifier is built, but this concern is not addressed or explained.

Less parametric statistical thinking could be more convincing. Partly this could be a matter of explaining how and why the three classifiers were constructed and their respective scientific motivations. The strong literal finding is the rejection of the hypothesis of independence between cell response properties and session type. A measure of the strength of this effect is missing.

Reviewer #3 (Public Review):

The major strength of the study was the approach of using photosensitive protein variants to replace endogenous protein with the 1-step Crispr-based gene editing, which not only allowed acute manipulation of protein function but also mimicked the endogenous targeted protein. However, the same strategy has been used by the same first author previously in dividing cells, somewhat reducing the novelty of the current study. In addition, the results obtained from the study were the same as those from previous studies using different approaches. In other words, the current study only confirmed the known findings without any novel or unexpected results. As a result, the study did not provide strong evidence regarding the advantage of the new experimental approach in our understanding of the function of EB1. Some specific comments are listed below.

1. In Figure 1, to show that the photosensitive EB1 variant did not affect stem cell properties and their neuronal differentiation, Oct4 staining and western blot of KIF2C and EB3 were not strong evidence. Some new experiments more specifically related to stem cell properties or iPSC-derived neurons are necessary. In addition, the effect of EB1 inactivation on microtubule growth was quantified in stem cells but not in differentiated neurons, which supposed to be the focus of the study. In Figure S2D, quantification is needed to show the effect of blue light-induced EB1 inactivation in growth cones.

2. In Figure 2, the effect of blue light on microtubule retraction in the control cells was examined, showing little effect. However, it is still unclear if the blue light per se would have any effect on microtubule plus end dynamics, a more sensitive behavior than that of retraction. In Figure 2C, the length of individual microtubules in different growth cones was presented, showing microtubule retraction after blue light. Quantification and statistical analysis are necessary to draw a strong conclusion.

The results showed that EB3 did not seem to contribute to stabilizing microtubules in growth cones. It was discussed that EB3 might have a different function from that of EB1 in the growth cone, although they are markedly up-regulated in neurons. In the differentiated neuronal growth cones examined in the study, does EB3 actually bind to the microtubule plus ends? In the EB3 knockout cells without the blue light, the microtubules were stable, indicating that EB3 had no microtubule stabilization function in these cells. Is such a result consistent with previous studies? If not, some explanation and discussion are needed.

3. In Figure 3, for the potential roles of EB1 on actin organization and dynamics, only the rates of retrograde flow were measured for 5 min. and no change was observed. However, based on the images presented, it seemed that there was a reduced number of actin bundles after blue light and the actin structure was somewhat disrupted. Some additional examination and measurement of actin organization are necessary to get a clear result.

4. In Figure 4, the effect of blue light and EB1 inactivation on neurite extension need to be quantified in some way, such as the neurite length changes in a fixed time period, and the % of growth cones passing the blue light barrier compared with growth cones of the control cells.

5. For the quantification of growth cone turning, a control condition is needed to show that blue light itself has no effect on turning.

Reviewer #3 (Public Review):

Noonan et al. developed a clever reporter of TGFbeta signaling using human A375 melanoma cells to identify a TGFbeta-induced enhancer and generated a zebrafish transgenic line to monitor TGFbeta activation during the development of melanoma. They found that few discrete cells in advanced melanoma express the TIE:EGFP reporter, and used single-cell sequencing to identify differences in gene expression between these TGFbeta-responsive melanoma cells and the remaining population. They found that these cells downregulate interferon signaling and upregulate a gene signature compatible with chronic TGFbeta signaling that favours metastasis and requires AP-1 binding to regulatory elements of the target genes. Then they overexpressed SATB2, a known inducer of TGFbeta activation, in whole melanoma to increase the amount of TIE:EGFP positive cells for better characterization. Among the TIE:EGFP positive cells they retrieved a population of macrophages (Marco positive in single-cell analysis) and interpreted this observation as due to the phagocytic activity of macrophages that preferentially phagocytose TIE:EGFP positive melanoma cells. Since melanoma cells expressing TGFbeta upregulate a chronic TGFbeta signature that favours metastasis, downregulate interferon signaling, and are preferentially phagocytosed by macrophages that, as a consequence, turn on M2 markers (immunosuppressive), the authors conclude that this work highlights the need for the identification of a chronic TGFbeta biomarker signature to predict patient response to TGFbeta inhibitors.

The conclusions of this paper on melanoma cells are mostly well supported by data, while the data concerning macrophages and their interpretation need strengthening with better images and additional data analysis.

Reviewer #3 (Public Review):

The manuscript by Jia, Ratzan et al. is elegant and makes an important contribution to the hair cell and PCP field. Using a subtractive approach involving deep sequencing of the mouse Emx2 mutant and control mice, they identified Stk32a as a candidate gene regulated by EMX2. Next, they made a Stk32a mouse mutant and showed that STK32a is necessary/sufficient to determine hair bundle orientation in the vestibule. Moreover, they show that STK32A governs GPR156. The images are compelling. I have no major concerns.

Reviewer #3 (Public Review):

The manuscript by Ray et al. reports a massive body of work targeting the transport cycle of a class of LeuT-fold transporters that specializes in metal transport, the Nramps. The Gaudet laboratory has published extensively on this family of proteins and here they ask the question of how Nramps can transport one of their physiological substrates Mn2+ and how that differs structurally from a toxic metal like Cd2+. The authors capitalize on previously published mutations to trap the transporter in three states with and without Mn2+. Together with ITC data and MD simulations, they put together a plausible, albeit oversold, model of transport. I am not an expert on the details of the technical elements but overall given they appear sound and the corresponding author is a noted expert in crystallography. The structures recapitulate previously seen conformational changes. Nevertheless, the mechanistic story is new and of interest.

Reviewer #3 (Public Review):

In this work, the authors explored some of the oculomotor mechanisms that humans put in place when observing other people looking somewhere. This tendency is generally known as 'gaze following' and represents a fundamental behaviour to obtain fluid social interactions with both others and the environment.

The strengths of this work can be found in the approach of the analysis, which provides a rich perspective on how human eye movements are shaped by social cues. I have appreciated the combination of more traditional analyses with more sophisticated approaches such as artificial intelligence.

At the same time, the complexity of the data analysis could lead to difficulties in understanding the whole picture emerging from here. The task itself should be described in more detail. In addition, I have also the feeling that some theoretical aspects concerning gaze following and social attention, in general, have been little discussed, leaving room for more technical and formal aspects. For instance, I am wondering if a control condition in which the gazer is looking towards a non-social item (such as an object) could be of interest and potentially important to better qualify these data within a social dimension.

Reviewer #3 (Public Review):

In this paper, Baker and colleagues present a model for the evolutionary dynamics of PRDM9 - the protein that determines where recombinations occur in many species. PRDM9 is one of the most rapidly evolving proteins and theoretical models have been developed to understand why it evolves so rapidly. The most popular of these models assumes that PRDM9 (indirectly) causes double-strand breaks where it binds DNA, and this in turn causes the erosion of its binding sites. Over time, this reduces the number of double-strand breaks, ultimately imperiling the proper segregation of chromosomes and hence causing selection for a new PRDM9 allele that can bind new sites. Unfortunately, recent experimental evidence has shown that PRDM9 merely positions where double-strand breaks occur and that the number of double-strand breaks is controlled independently of PRDM9. This new understanding of the biology of PRDM9 then casts doubt on the previous model for why PRDM9 evolves so rapidly, demanding a new explanation.

This paper takes this updated view of the biology of PRDM9 and formalizes it into a mathematical model of how evolution will act on different PRDM9 alleles and their binding sites. The model is very carefully couched in our current understanding of PRDM9 and is solidly analyzed. Altogether, this paper convincingly reconciles the rapid evolution of PRDM9 and the rapid erosion of its hotspots with the biological finding that PRDM9 itself does not drive double-strand break formation.

Reviewer #3 (Public Review):

Bavard & Palminteri extend their research program by devising a task that enables them to disassociate two types of normalisation: range normalisation (by which outcomes are normalised by the min and max of the options) and divisive normalisation (in which outcomes are normalised by the average of the options in ones context). By providing 4 different training contexts in which the range of outcomes and number of options vary, they successfully show using 'ex ante' simulations that different learning approaches during training (unbiased, divisive, range) should lead to different patterns of choice in a subsequent probe phase during which all options from the training are paired with one another generating novel choice pairings. These patterns are somewhat subtle but are elegantly unpacked. They then fit participants' training choices to different learning models and test how well these models predict probe phase choices. They find evidence - both in terms of quantitive (i.e. comparing out-of-sample log-likelihood scores) and qualitative (comparing the pattern of choices observed to the pattern that would be observed under each mode) fit - for the range model. This fit is further improved by adding a power parameter which suggests that alongside being relativised via range normalisation, outcomes were also transformed non-linearly.

I thought this approach to address their research question was really successful and the methods and results were strong, credible, and robust (owing to the number of experiments conducted, the design used and combination of approaches used). I do not think the paper has any major weaknesses. The paper is very clear and well-written which aids interpretability.

This is an important topic for understanding, predicting, and improving behaviour in a range of domains potentially. The findings will be of interest to researchers in interdisciplinary fields such as neuroeconomics and behavioural economics as well as reinforcement learning and cognitive psychology.

Reviewer #3 (Public Review):

Mtb antigens were traditionally discovered through crude direct methods such as immune-blotting of Mycobacterium tuberculosis (Mtb) culture filtrate (or whole cell lysate), or indirectly through T cell / APC stimulation experiments. The manuscript addresses the critical question of which Mycobacterium tuberculosis (Mtb) antigens are presented in peptide form on the surface of macrophages that are actually infected with Mtb. The identification of such antigens is particularly important for defining targets for TB vaccine design since CD8 T cells are an important component of the adaptive immune response to Mtb and macrophages are the most important phagocyte target of Mtb infection. The authors directly isolate MHC-I molecules from human monocyte-derived macrophages, elute MHC-I bound peptides from several HLA types, and screen for sequences found among Mtb antigens, which they find to represent only 0.1% of all peptides screened. The authors make the interesting observation that the majority of peptides identified (13 of 16) correspond to antigens secreted by the unique Type-7 Secretion System (T7SS) of Mtb. Another strength is the experiments to determine whether these T7SS substrates preferentially gain access to the cytosol for MHC-I loading via phagosome permeabilization by identifying the colocalization of Mtb with markers of phagosomal membrane damage (rather than MHC-I). The authors used quantitative mass spec to quantify and compare the expression of two peptides presented on HLA-A*02:01 and -B*57:01, demonstrating similar expression after infection with H37Rv, but that infection with an Esx1-deficient Mtb mutant did not lead to the presentation of either peptide even though one of these peptides was part of a separate Esx locus. Although only two peptides were assessed and compared using quantitative mass spec, these data imply that Esx1 was required for the presentation of the antigens from which both peptides were derived. While the exact mechanism of antigen processing for HLA-I presentation is still unclear for the EsxA and EsxJKPW peptides, the authors tested several pathways including inhibitors of proteasome activity, cysteine cathepsin activity, and lysosomal acidification. In future follow-up studies, it would also be useful to know whether the pulldown of a broader selection of HLA-I alleles would yield the same peptides/classes of peptides vs. a broader repertoire. The conclusions of this paper are well-supported by the data. This rigorous analysis of peptides presented on macrophages in the context of Mtb infection will establish a precedent for use of these techniques to discover additional antigens and will inform vaccine development efforts.

Reviewer #3 (Public Review):

T-tubules are an elaborate series of membrane invaginations that bring membrane voltage-activated Ca2+ channels in close apposition to the sarcoplasmic reticulum containing RyR, allowing for Ca2+-induced Ca2+ release. They serve as critical hubs of excitation-contraction coupling and play a central role in myopathies and inherited and acquired cardiomyopathies. Several membrane structures and proteins have been implicated in striated muscle t-tubule biogenesis, but the specific mechanisms of early t-tubule biogenesis are not defined.

Lemerle et al here investigate the biogenesis of transverse tubules in skeletal muscle. They use skeletal myoblasts from murine and human muscle as well as sophisticated high-resolution microscopy, live cell imaging, and adenoviral targeting to forward a model of BIN1 mediated caveolae ring formation which give rise to DHPR enriched t-tubules and associate with SR. While they demonstrate that BIN1 and Cav3 enriched caveolae act together to form t-tubules, the precise pathophysiological mechanisms by which this process acts in disease remain unclear.

Strengths of the study consist in the use of both murine and human skeletal muscle experiments, suggesting a conserved molecular mechanism; the innovative approach of correlative light and electron microscopy, and the use of pathological specimens. The live cell timelapse provides imaging evidence of Cav3-enriched caveolae-rings forming in centers of high BIN1 enrichment, from which t-tubules emanate. This is novel evidence in support of the biogenesis model proposed by the authors.

The pathological correlation of their model is promising but limited. Specifically, while the study of Cav3 mutant specimens is used to show the Cav3 dependence of BIN 1 action (in experiments using BIN 1 overload), the authors have not tested the sufficiency of their proposed mechanism by rescuing the pathologic state. Moreover, the conditions of development likely have an important effect on the studied mechanism - such as mechanical loading, contractile state, neurohormonal environment, and so on. Furthermore, a more complete description of the precise molecular binding sites between BIN1 and Cav3 would be important. While exon11 is required for tubulation, BIN1 not expressing exon 11 appears sufficient to assemble caveolar rings, suggesting this is mediated by other specific BIN1 regions.

Overall, the study provides new details on early t-tubule biogenesis in skeletal muscle (likely shared with other striated muscle) and lays the foundations for further definition of the precise molecular mechanisms.

Reviewer #3 (Public Review):

This study aims to define the factors that regulate the material properties of the viral inclusion bodies of influenza A virus (IAV). In a cellular model, it shows that the material properties were not affected by lowering the temperature nor by altering the concentration of the factors that drive their formation. Impressively, the study shows that IAV inclusions may be hardened by targeting vRNP interactions via the known pharmacological modulator (also an IAV antiviral), nucleozin, both in vitro and in vivo. The study employs current state-of-the-art methodology in both influenza virology and condensate biology, and the conclusions are well-supported by data and proper data analysis. This study is an important starting point for understanding how to pharmacologically modulate the material properties of IAV viral inclusion bodies.

Reviewer #3 (Public Review):

This work aims to elucidate the evolutionary origins of disulfide-rich spider toxin superfamilies and to determine the modes of natural selection and associated ecological pressures acting upon them. The authors provide a compelling line of evidence for a single evolutionary origin and differing factors (e.g., prey capture strategies and methods of anti-predator defense) that have shaped the evolution of these toxins. Additionally, the two major spider infraorders are claimed to have experienced differing selective pressures regarding these toxins.

The results presented here are novel and generally well-presented. The evidence for a single origin of DRP toxins in spiders is exciting and changes the paradigm of spider venom evolution.

The data are well analyzed, but the methods lack enough detail to reproduce the results. More information regarding the parameters passed to each software package, version numbers of all software employed, and models of molecular evolution employed in phylogenetic analyses are among the necessary missing information.

The differences in the evolutionary pressures between mygalomorphs and RTA-clade spider DRP toxins are clear, but expanding RTA results to all araneomorphs may be overreaching. Additional araneomorph sequence data is available, despite the claims within this manuscript (e.g., see Jiang et al. 2013 Toxins; He et al. 2013 PLoS ONE; and Zobel-Thropp et al. 2017 PEERJ). These papers include cDNA sequences of spider venom glands and contain representatives of inhibitory cysteine knot toxins, which are DRP toxins. These data would greatly enhance the strengths of the results presented herein.

Goitein's zettelkasten is comprised of about 20,000 3 x 5" index cards and 7,000 5 x 8" index cards.

Link to: https://hypothes.is/a/TEiQ5H1rEe2_Amfzi4XXmg

While not directly confirmed (yet), due to the seeming correspondence of the number of cards and their corpus descriptions, it's likely that the 20,000 3 x 5" cards were his notes covering individual topics while the 7,000 5 x 8" cards were his notes and descriptions of a single fragment from the Cairo Geniza.

Reviewer #3 (Public Review):

This is an important paper anchored by the observation that cultures of Neurospora undergoing amino acid starvation lose circadian rhythmicity if orthologs in the classic GCN2/CPC-3 cross-pathway control system are absent. Data convincingly show that Neurospora orthologs of Saccharomyces GCN2 and GCN4 (CPC-3 and CPC-1 respectively) are needed to promote histone acetylation at the core clock gene frequency to facilitate rhythmicity. While the binding of CPC-1 and thereby GCN-5 are plainly rhythmic, the explanation of exactly where rhythmicity enters the pathway is incomplete.

Figure 1 shows that inhibition of the HIS-3 activity affected by 3-AT, which should trigger cross-pathway control, is correlated with a graded reduction in the amplitude of the rhythm, and eventually to arrhythmicity at 3 mM 3-AT. While normalized data are shown in Figure 1B, raw data should also be provided in the Supplement as sometimes normalization hides aspects of the data. Ideally, this would be on the same scale in wt and in mutant strains.

Figure 2. The logical conclusion from Fig 1 is that circadian frq expression driven by the WCC has been impacted by amino acid starvation in the mutants. If so, either WC-1/WC-2 levels might be low, or else they might not be able to bind to DNA. When this was assessed, ChIP assays showed a loss of DNA binding. Although documented, an interesting result is that WCC protein amounts are sharply increased, especially for WC-1. The authors could comment on possible causes for this.

Line 176, "hypophosphorylation of WC-1 and WC-2 (which is normally associated with WC activation . . . )". While the authors are correct that this is often the case it is not always the case and this introduces a potentially interesting caveat. That is, the overall phosphorylation status of WCC does not always reflect its activity in driving frq transcription. This was first noticed by Zhou et al., (2018 PLOS Genetics) who reported that even though WCC is always hyperphosphorylated in ∆csp-6, the core clock maintains a normal circadian period with only minor amplitude reduction. This should be noted, cited, and discussed.

Figure 2 and Figure 2 Suppl. report different gel conditions and show that the sharply increased WC1/WC-2 levels seen in Fig 2 resulting from 3-AT treatment of the cpc pathway mutants are due to the accumulation of hypophosphorylated WC-1/2. The conclusion would be stronger if the gels in the Supplement showed the same degree of difference between wt and mutants as seen in Fig 2. In any case, these hypophosphorylated WC should be active and able to bind DNA but plainly are not based on Fig 2.

Figure 3 correlates the unexpected loss of DNA binding by hypophosphorylated WCC with reduced histone H3 acetylation at frq. The 3 mM 3-AT reported to result in arrhythmicity in cpc mutants in Figures 1 and 2 results in a small (~20%?) and not statistically significant reduction in H3 acetylation in wt, compatible with the sustained rhythms seen in wt in Figure 1, but in a substantial (~5 fold) loss of binding in the ∆cpc-1 background; so CPC-1 is needed for H3 acetylation at frq to sustain the rhythm during amino acid starvation. The simplest explanation here then is that the hypophosphorylated WCC cannot bind to DNA because the chromatin is closed due to decreased AcH3.

Figure 4. Title:" Figure 4. CPC-1 recruits GCN-5 to activate frq transcription in response to amino acid starvation"; the conditions of amino acid starvation should be mentioned here for the reader's benefit. (In the unlikely case that there was no amino acid starvation here then many things about the manuscript need to be reconsidered.)<br /> Based on the model from yeast where amino acid starvation activates GCN2 (aka CPC-3 in Neurospora) kinase which activates the transcriptional activator GCN4 (aka CPC-1) which recruits the SAGA complex containing the histone acetylase GCN5 to regulated promoters, CPC-1 was tagged and shown by ChIP to bind rhythmically at frq. Co-IP experiments establish the interaction of components of the SAGA complex in Neurospora and Neurospora GCN-5 indeed is bound to frq, likely recruited by CPC-1. This part all follows the Saccharomyces model with the interesting twist that the binding CPC-1 is weakly rhythmic and GCN-5 strongly rhythmic in a CPC-1-dependent manner. Based on the figure legend title, these cultures should always be starved for amino acids (although as noted this should be made explicit in the figure legend). In any case, given this, from where does the rhythmicity in GCN-5-binding arise? This question is developed more below.<br /> Line 224, "low in the cpc-1KO strain, suggesting that CPC-1 rhythmically recruit GCN-5".<br /> Because ChIP was done only for a half circadian cycle (DD10-22), it is hard to conclude "rhythmically". The statement should be modified.

Figure 5 shows that rhythmicity in general and of frq/FRQ specifically requires GCN-5 even under conditions of normal amino acid sufficiency, and that normal levels of H3 acetylation and its rhythm at frq require GCN-5. Not surprisingly, high H3 acetylation at frq correlated with high WC-2 DNA binding, and ADA-2 is required for SAGA functions.<br /> As earlier, raw bioluminescence data corresponding to panel B should be provided in the figure or Supplement.<br /> Also, if CPC-3 and CPC-1 regulate frq transcription through GCN-5, why is the frq level extremely low in the cpc-3KO or cpc-1KO(Fig.1D) but remains normal in gcn-5KO (Fig. 5D)?

Figure 6 documents the counter effects of TSA which inhibits histone deacetylation and shortens the period versus 3-AT which decreases (via CPC-3 to CPC-1 to GCN-5) histone acetylation and causes period lengthening or arrhythmicity. HDA-1 is necessary for histone deacetylation at frq.

Figure 7 documents extensive changes in gene expression associated with 3-AT-induced amino acid starvation and the CPC-3 to CPC-1 pathway. How do these results compare with other previously studied systems, particularly Saccharomyces, where similar experiments have been done? Are the same genes regulated to the same extent or are there some interesting differences?

Figure 8 provides a model consistent with the role of the CPC-3/GCN2 pathway in regulating genes in response to amino acid starvation. It seems this could be any gene responding to amino acid starvation.<br /> Not accounted for in the model is the data from Fig 4 which show the rhythmic binding of CPC-1 and stronger rhythmic binding of GCN-5 to frq, both under amino acid starvation. In the presence of 3-AT, amino acid starvation is constant, which should mean that CPC-3 and CPC-1 would always be "on". Why doesn't CPC-1 recruit GCN5 at the same level at all times leading to constant high H3 acetylation rather than rhythmic H3 acetylation as seen in Figure 3? Perhaps, unlike the statement in lines 345-34, it is WCC that regulates rhythmic GCN-5 binding and facilitates rhythmic histone acetylation at frq. Or perhaps the clock introduces rhythmicity upstream from GCN5. Without an answer to the question of where rhythmicity comes into the pathway, the story is only about how the CPC-3/GCN2 pathway in regulating genes in response to amino acid starvation; without explaining the rhythmicity the story seems incomplete.

Reviewer #3 (Public Review):

In this work the authors show, using different computational methods (molecular dynamics simulations, Markov state modeling, docking) that the probability of pocket opening in the isoforms of the protein myosin is an important determinant of the potency of the allosteric inhibitor blebbistatin. The data from the work supports the conclusions, and clearly shows that blebbistatin inhibits more potently myosin isoforms with a higher probability of pocket opening. The authors developed a protocol combining the probability of pocket opening from Markov state modeling with docking scores to estimate the IC50 values of blebbistatin for different myosin isoforms, achieving a good correlation between computed and experimental IC50 values (coefficient of determination of 0.82). The authors also tested their computational protocol prospectively, providing an estimate of IC50 for blebbistatin for the myosin isoform Myh7b which was in line with the experimental results. The computational protocol developed by the authors can be very useful for the community, since it can be applied to any protein containing cryptic pockets.

A major strength of the work is the prospective test of the computational protocol they developed, and the subsequent conclusion that the IC50 estimated by their method, 0.67 µM, was similar to the value obtained in the experiments, 0.36 {plus minus} 0.08 µM.

A major weakness of the work is the use of docking scores to compute the IC50 of blebbistatin for the different isoforms of myosin. Docking scores are usually empirical and previous works have shown that they are usually poorly correlated with experimental binding affinities.

Reviewer #3 (Public Review):

In this study, the authors use recently published single nucleus RNA sequencing data and a newly generated single cell RNA sequencing dataset to determine the transcriptional profiles of the different cell types in the Drosophila ovary. Their analysis of the data and experimental validation of key findings provide new insight into testis biology and create a resource for the community. The manuscript is clearly written, the data provide strong support for the conclusions, and the analysis is rigorous. Indeed, this manuscript serves as a case study demonstrating best practices in the analysis of this type of genomics data and the many types of predictions that can be made from a deep dive into the data. Researchers who are studying the testis will find many starting points for new projects suggested by this work, and the insightful comparison of methods, such as between slingshot and Monocle3 and single cell vs single nucleus sequencing will be of interest beyond the study of the Drosophila testis.

Reviewer #3 (Public Review):

This manuscript uses the NDUFS4-/- mouse, which models severe mitochondrial disease Leigh Syndrome, to examine if changes in iron homeostasis modify disease progression. They report that iron limitation delays the phenotype of "clasping", a neurologic change associated with loss of NDUFS4. The study is mostly observational and has little mechanism regarding how possible alterations in iron homeostasis contribute to disease progression. Therefore, it does not advance our understanding of how changes in iron homeostasis add to the progression of Leigh Syndrome.

Strengths:

The authors propose that iron homeostasis may be altered in the absence of NDUFS4 in mice, which is utilized as a model for the human disease Leigh Syndrome. To test this hypothesis, the authors show that limiting iron by either iron chelation or restriction in the diet delays disease progression (clasping is delayed and longer survival). They show by ICP-MS elevated iron in NDUFS4-/- mouse livers, kidney and duodenum and that "overall" tissue iron levels are elevated in the absence of NDUFS4. They show the predicted changes in iron levels in those tissues when the iron content in the diet is limited. They also show that other metals are changed in the absence of NDUFS4 and that when iron is limited in the diet there are increased levels of other metals with the most significant changes in Mn. They show a significant correlation between increased peroxidation of PUFAs in the liver of NDUFS4-/- mice and increased clasping, a neurologic measure of disease progression.

Weaknesses<br /> Unfortunately, the authors do not detect changes in iron levels in neurologic tissues (brain) in the absence of NDUFS4 nor do they show changes in iron levels in the brain upon limiting iron in the diet. In addition, the authors do not provide any imaging of the brain or brain stem to support slowed progression of lesions in this model. That a change in iron in the diet affects RBC levels simply confirms that the diet is limiting for erythropoiesis and does not provide supporting evidence that iron levels may be changing in the brain.

The authors spend a lot of experimental effort measuring metal levels in all tissues without evidence of changes in neurologic tissues and then focus on changes in metals in the liver and increased lipid peroxidation in the liver. It is unclear to this reviewer if the authors are suggesting that the iron loading in the liver is contributing to the neurologic phenotypes associated with loss of NDUFS4 or if they are suggesting that there must also be inappropriate iron loading in neurologic tissues (with no supportive data) that gives rise to disease progression. The authors did not measure if iron loading in the liver, kidney or duodenum in NDUFS4-/- mice resulted in decreased organ function thus leaving open the possibility that other organ dysfunction contributes to the observed neurologic phenotypes associated with this disease.

It is unclear why the authors did not measure lipid peroxidation in the brain tissue or other neurologic tissues, nor did they measure lactate levels in blood and CSF upon dietary iron limitation.

There is no mechanistic experimental data that inform on how iron changes accelerate the progression of disease.

Fig 4 -They measure metal levels in different tissues, however, they do not show any changes in iron levels in neurologic tissues nor do they assess iron protein levels in neurologic tissues.

Together, this study does not determine the how of increased iron in tissues of NDUFS4-/- mice, and if there are changes in mitochondrial function upon dietary iron restriction, whether the location of iron in tissues is different (e.g., it is unclear whether there is increased mitochondrial iron, often a phenotype associated with mitochondrial dysfunction).

Reviewer #3 (Public Review):

Chen et al. perform an innovative screen using retinal organoids derived from rd16 mice to identify small molecules to treat CEP290 hypomorphic mutations linked to ciliopathies such as LCA. The authors identify reserpine which promotes photoreceptor development and viability in retinal organoids derived from LCA patient iPSCs and rd16 mouse retinas. The authors finally propose a mechanistic model where reserpine restores proteostasis thereby improving ciliogenesis.

The authors present a highly effective drug screen that utilizes the benefits of retinal organoids while also accounting for the inherent variability of retinal organoids by performing a screen on 2D cultures derived from the organoids. This is an innovated approach to using retinal organoids in drug screens and is of interest to the greater community. The success of the screen is reflected in the effectiveness of reserpine in the in vivo rd16 mouse retinal model where it promotes photoreceptor survival. However there are multiple issues with the LCA patient organoid screen that must be resolved.

The patient derived iPSC lines are not controlled sufficiently enough to make conclusions stated in the manuscript. The authors rely on single iPSC clones from disease patients to perform experiments, and it is not clear whether karyotyping to validate normal chromosomal integrity was performed. In the case of the RNAseq experiment one patient clone does not show any differences calling into question the findings from the other clone. Patient derived iPSC studies would benefit from the use of multiple independently derived iPSC clones per patient, or rescuing the LCA10 mutation using CRISPR editing to validate the correlation of the mutation with the differences observed.

This study could be strengthened by parallel RNAseq studies is the rd16 mouse retina and patient iPSC retinal organoids.

Reviewer #3 (Public Review):

The authors set out to examine the roles of multiple cell death pathways during a Shigella infection. Shigella oral infection has been classically difficult to perform because wild type C57BL/6 mice naturally resist Shigella infection, likely reflecting the fact that Shigella species are human-specific pathogens. The authors recently developed an oral Shigella infection model that successfully allows mice to be infected orally with Shigella. In this model, NLRC4 inflammasome knockout mice are treated with streptomycin 1 day prior to infection. Streptomycin depletes the microbiota and opens a microbial niche for intestinal infection (this is the same method that is used for Salmonella typhimurium oral infections in C57BL/6 mice). The Nlrc4 deletion removes one innate immune barrier to infection. Now the authors examine additional deletions in other regulated cell death genes on this Nlrc4-/- background.

Their results show that three distinct pathways to cell death are important in defending against Shigella infection, but that some pathways are more protective than others. In their previous paper, the authors showed a difference in phenotype between Nlrc4-/- mice on a C57BL/6 (B6) versus a 129S1/SvImJ (129) mouse background. The authors now show that the difference in these phenotypes is primarily driven by Casp11, in which 129 mice are naturally genetically deficient. The authors show that Casp11 is capable of protecting IECs from colonization. This is conceptually at odds with the knowledge that Shigella encodes OspC3, which is a type III secretion effector that inhibits caspase-11. However, it turns out to be that both inhibition by OspC3 and defense by caspase-11 occur in parallel with partial efficiency. The attenuation of the ospC3 Shigella mutant was abolished in mice lacking Casp11.

Further, they show that counter to assumptions in the field, neither myeloid pyroptosis nor IL-1 affected Shigella pathogenesis during this oral infection model.

The authors next examine the role of TNF driven cell death through caspase-8 and RIPK3. They show that TNF does contribute to defense against Shigella infection, but that this protection is secondary to the roles of Nlrc4 and Casp11. Finally, the authors show that quadruple knockout Casp1/11/8-/-Ripk3-/- mice lacking all four of these pathways display far worse disease pathogenesis than any of the other knockout mice studied.

In summary, NLRC4 provides the strongest defense, and caspase-11 and caspase-8/RIP3 provide weaker defense. The authors show that the weakness of the caspase-11 pathway is caused by the OspC3 effector that inhibits caspase-11. We can extrapolate form this to speculate that the weakness of caspase-8 is caused by OspC1 inhibiting it, and the weakness of RIPK3 is caused by OspD3 inhibiting it. This could be proved in future work.

One formal weakness is that Figure 1 is data from just one experiment, however, the key conclusion is verified in Figure 2 by the use of targeted Casp11 knockout.

One omission from the paper is that in Figure 3 and Figure 4, WT mice were not infected with an ospC3 mutant to show the baseline attenuation. It is stated that oral infections have not been studied with this mutant.

One weakness inherent in the use of Casp8-/- mice is that they are not viable unless they carry the Ripk3-/- or equivalent mutation. Therefore, the authors can only assess the simultaneous loss of both pathways. This can be compared to a single Ripk3-/- situation, but, here caspase-8-driven apoptosis could be sufficient. Often RIPK3 serves as a backup defense when a pathogen inhibits caspase-8, thus a hypothetical Casp8-deficient Ripk3-sufficient mouse might remain resistant due to RIPK3 activation. This might be achieved in future work by using recently developed mouse lines that carry specific Casp8 point mutations that cause the loss of apoptosis while retaining mouse viability.

One limitation of the study is that littermate controls arising from heterozygous by knockout breeding were not always used. Co-housing was used for at least 3 weeks, which often, but not always, normalizes the microbiota. This noted, it should be acknowledged that littermate controls would be extremely burdensome to accomplish in the case of some strains where multiple knockouts are used.

Reviewer #3 (Public Review):

The findings by Latshaw et al. identify Amtyr1 as a major regulator of latent inhibition - a neurological mechanism whereby non-productive stimuli are down-ranked in reward:stimuli association - in honeybees. The authors utilize intracolony variation in exhibited latent inhibition in male honey bees to map Quantitative Trait Loci associated with this phenotype, then use the identified regulatory regions (associated with Amtyr1) to target Amtyr1 using several perturbation methods to demonstrate the centrality of this locus to latent inhibition, and neurophysiology methods to assess the neuronal effect. Overall their results are convincing and approaches appear rigorous.

Overall I found this paper to be relatively easy or hard to review, depending on how you rate reviewing a paper that does many of the things you would have suggested to assess the functional centrality of a target gene to an observed phenotype.

I really do not have many criticisms of the approach, findings, or rigor of major note. I would appreciate it if the authors noted (acceptable as supplementary) the other QTL loci identified (lines 123-124), as the text implies other genes may have been identified in their QTL mapping. If so, this may be of interest to the general community.

I also personally prefer exact p-values reported (e.g., line 253) instead of the "<<0.01" or (line 257) "<0.01".

Honestly, I'm a little disappointed in how little I could criticize, which is only partially related to my not being an expert in the field. The paper was clear, well written, rigorous (as far as I could tell), validates findings via multiple routes, and extends their locus-focused (lol) results into neurotransmitter differences, empirically determined.

Reviewer #3 (Public Review):

The authors analyse the role of bisphosphoglycerate mutase (BPGM), an enzyme unique to erythrocytes and placental cells. The authors assess the role of BPGM in the pathogenesis of fetal growth restriction (FGR).

Strength of the work: The authors have analysed a murine model of hypoxia (acute and chronic) as well as human placental samples.

Impact of the work on the field: FGR is linked to many short- and long-term medical complications. The authors have done important efforts to understand the role of hypoxia and the placenta in the pathogenesis of FGR. The identification of BPGM as a potential link between FGR and adverse intrauterine is relatively novel.

Reviewer #3 (Public Review):

DeCalciOn is an innovative contribution to the toolbox of real-time processing of calcium imaging data. It provides calcium traces from hippocampal CA1 neurons with a roughly two-millisecond latency and uses them to decode the position of rats running along a linear track - setting the stage for closed-loop experiments requiring fast interpretation of neural activity. The manuscript would be strengthened by a more systematic, empirical comparison to other, currently available alternative approaches. In addition, the decoding analysis does not fully account for the possibility of artifactual motion in the imaging video being informative of position.

We suggest strengthening this manuscript by addressing the following four points:

1) In the discussion of other platforms, the authors state that "Any system that lacks motion stabilization would also be vulnerable to artifactually decoding behavior from brain motion (which can be correlated with behavior) rather than neural activity." It follows that the same problem might also occur with incomplete motion correction. While the motion-corrected video shown in Supplementary Video 1 has reduced motion compared to the raw video, motion is still visible, including outside of the marked jitter. It remains possible that the linear decoders for the position in the linear track are utilizing brain motion-induced, as opposed to calcium fluorescence-induced, signal changes. A critical first step to assess this issue is to ask whether the motion in the video is related to the rat's behavior. One could test whether the 2D motion displacement traces can be used to predict rat position using linear classifiers.

2) The manuscript would benefit from repeating the experiment in a more complex environment, such as a 2D arena. This would increase the generalizability of the findings. In addition, increasing the complexity of the environment would reduce the possibility that particular types of brain motion are closely linked with positions in the environment.

3) The authors present an interesting comparison between "contour-free" and traditional contour-based source extraction. A more comprehensive discussion on the history or novelty of "contour-free" calcium imaging processing would contextualize this result.

4) In the discussion, the authors compare DeCalciOn to two previous online calcium imaging algorithms. The technical innovations of this work would be better highlighted by directly testing all three of these algorithms, ideally on similar datasets.

Reviewer #3 (Public Review):

This is an interesting study to examine how alveolar bone responds to oral infection using unbiased scRNA-seq. The manuscript is well-written and the results are convincing.

1) The authors should revise the abstract. The study did nothing with the understanding of healing. The whole conditions were performed under infection and inflammation which actually induce bone loss, but not healing.

2) Since periapical inflammation causes progressive bone loss, how MSC with increasing osteogenic potentials contributes to bone loss? The authors should discuss it.

3) Did the authors detect osteoclasts by scRNA-seq? If not, are there any precursors of osteoclasts identified in inflammatory alveolar bones? 1) I suggest that the authors provide a more detailed analysis of inflammation since this is a unique model to study oral bone inflammation.

4) It is known that macrophages can be classified into M1 and M2. Based on scRNA-seq, did the authors observe these two types?

Reviewer #3 (Public Review):

The study titled "MCT1-dependent energetic failure and neuroinflammation underlie optic nerve degeneration in Wolfram syndrome mice" has illustrated one of the possible molecular mechanism of Retino-ganglion cells (RGCs) degeneration leading to optic atrophy observed in the patients of Wolfram syndrome (WS). It is very crucial to understand the molecular details of optic atrophy and progressive vision loss in patients of WS. The main reason for the optic atrophy and loss of vision in Wolfram syndrome is the degeneration of specific cells in the retina- Retino-ganglion cells (RGCs). There have been many studies in different model systems of WS but the study addressing the loss of vision is limited. A recent study in a zebrafish model of WS shows thinning of the optic nerve layer, loss of RGCs, and loss of vision, however, the molecular mechanism for the specific degeneration of RGCs is limiting. Therefore, it is of utmost need to understand the molecular mechanism/s which could be the possible reason for the loss of RGCs in WS.

In this study, the authors have illustrated one of the possible molecular mechanisms leading to the loss of RGCs and eventually resulting in progressive loss of vision in mice models of WS. The study shows that MCT1 and Wolframin interact with each other and help in lactate transport to meet the high energy demand of RGCs. In the absence of wolfamin, MCT1 dependant energy failure leads to demyelination of optic nerve axons further leading to the degeneration of RGCs and progressive loss of vision. This study is one of its kind which investigates the molecular mechanism for the selective loss of RGCs in the wolfram syndrome. This finding will enable therapeutic screening of promising drug molecules that could rescue the RGCs degeneration.

Reviewer #3 (Public Review):

The authors explore the use of SRT as a host-directed therapy for use in combination with other first-line TB antibiotics. This manuscript is of substantial importance since TB is a major world health concern, and there is growing interest in the development of host-directed therapies to augment existing therapies for TB. Demonstrating the effectiveness of adding an FDA-approved drug to existing cocktails of anti-TB drugs has potentially exciting implications.

The manuscript is bolstered by their use of multiple in vitro and in vivo models of infection, as well as a clinically relevant strain of TB. While their findings generally support the use of SRT as an effective HDT/treatment, the mechanistic details underlying the effectiveness of SRT remain somewhat obscure, and as presented, the in vitro experiments support more limited conclusions.

Major concerns:

In vitro studies (i.e. bacterial culture) were only performed with SRT up to 6 uM while the cultured cell experiments used a range up to 20 uM. 5 uM had almost no effect on the viability/growth of Mtb in macrophages. The authors should use the same concentrations in vitro as their macrophage studies to test whether SRT directly impacts Mtb viability to be able to rule in/out that SRT does not impact Mtb viability when cultured.

The mechanism of action of SRT during TB infection and the conclusions drawn by the authors are not supported by the limited experimentation. SRT is presented as an antagonist of polyI:C-induced type I IFNs, but during TB infection, cytosolic DNA sensing via the cGAS/STING axis constitutes the major pathway through which type I IFNs are induced in macrophages.

To offer more support that SRT inhibits type I IFN, the authors should consider measuring the the actual amount of type I IFN using an IFNb ELISA. Additionally, the authors should use human/mouse primary macrophages (not just THP1 reporter cells) and measure transcript levels (at key time points post infection) and protein levels of type I IFN and other proinflammatory mediators (e.g. TNFa, IL-1, IL-6) +/- SRT to determine if SRT is specific to the type I IFN response. If this is indeed the case, other NFkB genes/cytokines should not be impacted.

Moreover, to draw the conclusion that "augmentation property of SRT is due to its ability to inhibit IFN signaling" a set of experiments using an IFN blocking antibody would enhance Figure 2, as both cGAS and STING KO macs have significant differences in basal gene expression and their ability to respond to innate immune stimuli.

Because the first half of the paper focuses on type I IFNs during macrophage infection to explain the mechanism of action for SRT, additional analysis of the mouse infections to examine levels of type I IFNs, as well as IL-1B and IFN-g (in serum/tissues?), is important for connecting the two halves of the manuscript. The in vivo data would also be strengthened by quantitative analysis of histological changes by, for example, blinded pathology scoring. This type of quantitation would also permit statistical analyses of this important pathology readout.

The authors conclude that SRT functions through an inflammasome-related function, but this conclusion requires further support of actual inflammasome activation, such as IL-1B secretion by ELISA or IL-1B processing by western blot analysis, rather than Il1b gene expression alone. Additional functional readouts of inflammasome activation like cell death assays would also strengthen this conclusion.

What strain of TB was used in these studies? The results and methods do not indicate the strain used, which is critical to know since different strains have varying pathogenesis phenotypes.

Minor concerns:

It might be worth consistently using the more common INH and RIF abbreviations to increase the clarity/readability of the MS and figures.

What is the physiological concentration of SRT when taken for depression and how does that compare to the concentrations used in vitro? Are the in vitro concentrations feasible to achieve in patients?

In Figure 3B, why is there a spike in TNF-a in the HRS treated cells only at 42h?

Was statistical analysis performed on the data in Figure 3B and D?

A description/discussion of the different mouse strains use in infection - what benefits each has as a model and why several were used - would help convey the impact of the in vivo studies.

Since antibiotics and SRT were administered ad libitum, how did the authors ensure that mice took enough of the antibiotics and especially SRT? Is it known whether these drugs affect the water taste enough to affect a mouse's willingness to drink them?

Was statistical analysis performed on time-to-death experiments?

Were CFUs measured in mice from Figure 4 to determine empirically how effective the antibiotic treatments were? And if SRT impacted their effectiveness?

The H&E images could use some additional labels to more easily discern what groups they belong to.

Reviewer #3 (Public Review):

The Rcs phosphorelay plays an important role in regulating gene expression in bacteria; most of the current knowledge about the Rcs proteins is from E. coli. Yersinia pestis, carrying mutations in two central components of the Rcs machinery, provides an interesting example of how evolution has shaped this system to fit the life cycle of this bacteria. In bacteria other than Y. pestis, most Rcs activating signals are sensed via the outer membrane lipoprotein RcsF; from there, signalling depends on inner membrane protein IgaA, a negative regulator of RcsD. Histidine kinase RcsC is the source of the phosphorylation cascade that goes from the histidine kinase domain of RcsC to the response regulator domain of RcsC, from there to the histidine phosphotransfer (Hpt) domain of RcsD, and finally to the response regulator RcsB. RcsB, alone or with other proteins, regulates transcription of many genes, both positively and negatively. These authors have previously shown that RcsA, a co-regulator that acts with RcsB at some promoters, is functional in Y. pseudotuberculosis but mutant in Y. pestis, and that this leads to increased biofilm in the flea. The authors also noted that rcsD in Y. pestis contains a frameshift after codon 642 in this 897 aa protein; in theory that should eliminate the Hpt domain from the expressed protein. However, they found evidence that the frame-shifted gene had a role in regulation. This paper investigates this in more depth, providing clear evidence for expression of the Hpt domain (without the N-terminal domain), and demonstrating a critical role for this domain in repressing biofilm formation. The Y. pseudotuberculosis RcsD does not express a detectable amount of the Hpt domain nor does it repress biofilm formation. The ability of the Hpt domain protein to keep biofilm formation low explains most of what is observed for the full-length frame-shifted protein.

1. The authors provide a substantial amount of data supporting the expression of the C-terminus of RcsD is sufficient and necessary for low biofilm levels, and that this is dependent upon the active site His in the RcsD Hpt domain (H844A) as well as other components of the basic phosphorelay (RcsC and RcsB). However, it is only possible to see this protein by Western blot in 100-fold "Enriched" lysates (Figure 2). No small protein was detected in the RcsDpstb strain, although the enriched lysate was not shown for this. Without that experiment, it is not possible to evaluate whether the small protein is also made from the rcsDpstb gene. Either answer would be interesting, and would allow other conclusions to be drawn. Is the RBS and start codon the same for the HPT region of this rcsD gene (it could be added to Supplementary Table 6). If the small protein is made, is its ability to function blocked by the excess full length protein in terms of interactions with RcsC? Or is the expression of the small protein dependent upon loss of overlapping translation from the upstream start?<br /> 2. In many phosphorelays, the protein kinase also acts as a phosphatase, and which direction P flows is critical for regulation. It is often difficult to follow what the model for this is in this paper, and that is important to understand for evaluating the results. Most of this paper uses two assays, biofilm formation and crystal violet staining (also related to biofilm formation) to assess the functioning of the Rcs phosphorelay. Based on the behavior of the rcsB mutant, it would seem that functional Yersinia pestis Rcs (RcsDpe) represses this behavior, and this correlates with RcsB phosphorylation (Fig. 4). What is the basis (line 443-44) for saying that RcsD phosphorylates RcsB while RcsDHpt dephosphorylates? Yersinia pseudotuberculosis RcsD(pstb) shows no difference with the rcsB mutant. Doesn't that suggest that RcsDpstb is no longer repressing (phosphorylating)? In the presence of the RcsDpstb as well as multicopy RcsF, an activating signal in other organisms, RcsDpstb seems able to phosphorylate. This all suggests that the full-length protein, like the Hpt domain, is capable of phosphorylating, but that it may be doing nothing in the absence of signal (or dephosphorylating). Given these results, saying that RcsDpstb is positively regulating biofilm formation (Fig.1 title, and elsewhere) is somewhat misleading. What it presumably does is prevent the Hpt domain, expressed from the chromosomal locus in Fig. 1b, from signalling to RcsB. By itself, it is not clear it is doing anything. Understanding this clearly is important for interpreting this system and the tested mutants. A clear model and how phosphate is flowing in the various situations would help a lot. Currently Supplementary Fig. 3 seems to reflect the appropriate directional arrows, but the text does not. Moving the rcsB data earlier in the paper (after Fig. 1, 2, or maybe earlier, before Fig. 3) would certainly help.<br /> 3. The authors show (in their pull-down) that there is a bit of full-length RcsD even in the frame-shifted protein. Is there any clear evidence this does anything here? Does the N-terminus (truncated after the frame-shift) have a function?<br /> 4. While the RNA seq data is useful addition here, it is difficult to interpret without a bit more data on the strain used for the RNA seq, including the biofilm phenotypes of the WT and mutant derivatives, as well as the relevant rcsD sequences, and maybe expression of a few genes or proteins (Hms or hmsT). Are these similar in the parallel strains used earlier in the paper and the one for RNA seq, in WT, rcsB- and the RcsDpstb derivative? It would appear that rcsB- and rcsDpstb have opposite effects, at least at 25{degree sign}C, while in Fig. 4, these two derivatives have similar effects on biofilm. Is this due to temperature, strains, or biofilm genes that are not shown here? It is certainly possible that the ability of the full-length RcsD changes its kinase/phosphatase balance as a function of temperature, or dependent on other differences in these Y. pestis strains.

Reviewer #3 (Public Review):

The study addresses a tough question in the study of wild bats: what and where they eat, using both acoustic bio-logging and DNA metabarcoding. As a result, it was found that greater mouse-eared bats made more frequent attack attempts against passively gleaning prey with lower predation success but higher prey profitability than aerial hawking with higher predation success. This is a precious study that reveals essential new insights into the foraging strategies of wild bats, whose foraging behavior has been challenging to measure. On the other hand, the detection of capture attempts, success or failure of predation, and whether it was by passively gleaning prey or aerial hawking were determined from the audio and triaxial accelerometer analysis, and all results of this study depend entirely on the veracity of this analysis. Also, although two different weights and a tag nearly 15% of its weight were used, it is essential for the results of this data that there be no effect on foraging behavior due to tag attachment. Since this is an excellent study design using state-of-the-art methods and very valuable results, readers should carefully consider the supplemental data as well.

Reviewer #3 (Public Review):

In this manuscript, Chu and colleagues first studied the differentiation of hypertrophic chondrocytes into osteoblasts using lineage tracing and single-cell transcriptomics on dissociated bone tissues. In analyzing these data, they identified MMP14 as upregulated in immature osteoblasts derived from hypertrophic chondrocytes. This observation prompted them to study the relationship between MMP14 and signals that regulate osteoblast differentiation such as a parathyroid hormone. Interestingly, MMP14 was found to cleave the ectodomain of the PTH receptor and blunt its signaling activity. Accordingly, MMP14 deficiency in these cells augmented PTH-induced bone anabolism.

This work builds upon multiple previous studies demonstrating that a subset of hypertrophic chondrocytes (or, at least, cells marked by collagen X Cre strategies) can become osteoblasts. The use of lineage tracing to try to divide osteoblasts into those derived from HCs or other progenitors is interesting, although technical challenges are present in data interpretation. The study then pivots dramatically into loosely-connected mechanistic studies investigating links between MMP14 (identified from their single-cell RNA-seq studies) and the PTH receptor. Gaps exist in the logic linking this work to the beginning of the paper, and major questions remain about MMP14-mediated PTH receptor cleavage. The work then returns to in vivo studies investigating the skeletal and cellular phenotype of PTH-treated mice where MMP14 is deleted using collagen X Cre.

While several interesting threads are suggested by these findings, the scope of the work is quite broad and it is difficult to appreciate the direct relationship between some of the findings that are presented in successive figures. GPCR cleavage by an MMP is exciting and interesting. However, the cleavage patterns observed in vitro do not match the PTH receptor fragments noted in vivo. Moreover, much remains to be described regarding differences in PTH efficacy in cells with and without MMP14. Of course, the possibility remains that MMP14 targets other than the PTH receptor contribute to the phenotypes that are observed in mice.

This work adds to an already-large body of evidence demonstrating that collagen X-labeled cells contribute to the osteoblast pool. The use of single-cell RNA-seq here is appealing and demonstrates the heterogeneity of collagen X-labeled cells and their descendants for the first time. The scRNAseq data will be useful for the entire bone biology community. In addition, a comparison between global and ColX-mediated MMP14 deletion is well done and of interest. Overall, my impression of the impact of the work is mixed. The most novel/exciting finding here is that MMP14 cleaves the PTH receptor and regulates its activity: the evidence supporting this new finding is incomplete, and the other data presented on hypertrophic chondrocyte differentiation may be viewed as a distraction to the central message of this manuscript.

Reviewer #3 (Public Review):

Resting stage fMRI studies have revealed functional associations between cerebral cortical networks and cerebellar regions. However, it remains unknown whether specific regions of the cerebellar cortex integrate information from functionally related areas of the cerebral cortex. Here, the authors used a task-based fMRI approach to infer the degree of convergence of cerebral cortical inputs at the level of the cerebellar cortex. Models that allow for integration of cerebral cortical inputs, rather than one-to-one relationships between cerebral cortical and cerebellar regions best explained cerebellar task-related activity. A higher degree of convergence was needed to explain activity in non-motor cerebellar regions.

Strengths:<br /> - Innovative task-based approach to assess the level of cerebral cortical inputs to the cerebellar cortex.<br /> - Used a large multi-domain battery of fMRI tasks.<br /> - Multiple models of interactions between the cerebral cortex and cerebellum were assessed.<br /> - Predictive accuracy of models was assessed across multiple parcellations of the cerebral cortex.<br /> - Connectivity models can be useful in predicting new cerebellar functional data in new participants.

Weaknesses:<br /> - One limitation of the approach that is not discussed is that the motor responses that can be performed in the scanner are inherently simple, whereas non-motor tasks can be more varied and have a higher degree of complexity. Thus, it is unclear if the types of tasks used in multi-domain batteries are sufficient to substantiate the finding that there is less functional integration in non-motor regions of the cerebellar cortex.

Likely impact and utility:<br /> - The study provides insightful evidence that regions of the cerebellar cortex may integrate inputs from different regions of the cerebral cortex. This finding is useful for theories of cerebellar function and for guiding future studies of how integration may occur at the level of the cerebellar cortex.

https://www.govinfo.gov/content/pkg/CHRG-107jhrg96166/html/CHRG-107jhrg96166.htm

Reviewer #3 (Public Review):

The authors sought to propose a mechanism by which cancer-causing mutations in the thrombopoietin receptor (TpoR) activate the receptor. To do so, they used a systematic approach of introducing non-native and naturally occurring mutations into the receptor and use a combination of in-vivo and cell-based assays and solid-state NMR spectroscopy. They propose that the proximity of the asparagine mutations to the cytosolic boundary influences the secondary structure of the receptor and suggests that this structural change induces receptor activation.

The strengths of this work are the importance of the system being studied and tackling a problem that is not yet fully resolved. The authors acquired a large and convincing set of biological data, including in vivo experiments that support the gain-of-function/activating role of the mutations studied. The solid-state NMR data are of high quality as well. In particular, the INEPT data in figure 6a display very clear differences within one region of the wild-type compared to the mutants.

One significant weakness is the validity of the conclusions given the limited atomistic measurements presented. Namely, the authors make rather specific conclusions about protein folding based on a single set of 13C alanine carbonyl chemical shifts in the wild-type and mutant TM peptides. Essentially, the authors observe chemical shift perturbations at this carbonyl carbon when mutations are introduced into a protein and use this information to make conclusions about secondary structure. I am not convinced that the authors have presented sufficient evidence to justify the conclusion that the helix unwinds and that this is responsible for the mechanism of activation. While the other cell-based experiments in mutations are interesting, deciphering such a specific folding mechanism with limited atomistic data is not justified.

Reviewer #3 (Public Review):

Soutschek and Tobler provide an intriguing re-analysis of inter-temporal choice data on amisulpride versus placebo which provides evidence for an as-yet untested hypothesis that dopamine interacts with proximity to bias choices.

The modeling methods are sound with a robust and reasonably exhaustive set of models for comparison, with good posterior predictive checks at the single subject level, and decent evidence of parameter recoverability. Importantly, they show that while there is no main effect of drug on the proportion of larger, later (LL) versus smaller, sooner (SS) choices, this obscures conflicting-directional effects on drift rate versus starting point bias which are under-the-hood, yet anticipated by the hypothesis of interest.

While I have no major concerns about methodology, I think the Authors should consider an alternative interpretation - albeit an interpretation which would actually support the hypothesis in question more directly than their current interpretation. Namely, the Authors should re-consider the possibility that amisulpride's effects are mediated primarily by acting at pre-synaptic receptors. If the D2R antagonist were to act pre-synaptically, it would drive more versus less post-synaptic dopamine signaling.

There are multiple reason for this inference. First, the Authors observe that the drug increases sensitivity to differences in the relative offer amounts (in terms of effects on the drift rate). With respect to the canonical model of dopamine signaling in the direct versus indirect pathway, greater post-synaptic signaling should amplify sensitivity to reward benefits - which is what the Authors observe.

Second, the Authors also observe an effect on the starting bias which may also be consistent with an increase in post-synaptic dopamine signaling. Note that according to the Westbrook & Frank hypothesis, a proximity bias in delay discounting should favor the SS over the LL reward, yet the Authors primarily observe a starting bias in the direction of the LL reward. This contradiction can be resolved with the ancillary assumption that, independent of any choice attribute, participants are on average predisposed to select the LL option. Indeed, the Authors observe a reliable non-zero intercept in their logistic regression model indicating that participants selected the LL more often, on average . As such, the estimated starting point may reflect a combination of a heightened predisposition to select the LL option, opposed by a proximity bias towards the sooner option. Perhaps the estimated DDM starting point is positive because the predisposition to select the LL option has a larger effect on choices than the proximity bias towards sooner rewards does in this data set. To the extent that amisulpride increases post-synaptic dopamine signaling (by antagonizing pre-synaptic D2Rs) it should amplify the proximity bias arising from the differences in delay, shifting the starting bias towards the SS option. Indeed, this is also what the Authors observe.

Note that it remains unclear why an increase in post-synaptic dopamine signaling would amplify one kind of proximity bias (towards sooner over later rewards) without amplifying the other (towards a predisposition to select the LL option). Perhaps the cognitive / psychological nature of the sooner bias is more amenable to interacting with dopamine signaling than the latter. Or maybe proximity bias effects are most sensitive to dopamine signaling when they are smaller, and the LL predisposition bias is already at ceiling in the context of this task. These assumptions would help explain why a potential increase in post-synaptic dopamine signaling both amplified the proximity effect of delay when it was smallest (when the differences in delay were smaller), and also failed to amplify the predisposition to select the LL option (which may already be maxed out). More importantly, the assumption that there are opposing proximity biases would also help explain why there is a negative effect of delay magnitude on the estimated starting point on placebo. Namely - as the delay gets larger, the psychological proximity of sooner over later rewards grows, counteracting the proximity bias arising from choice predisposition / repetition.

Regardless of the final interpretation, showing that pharmacological intervention into striatal dopamine signaling can simultaneously modify a starting point bias and drift rate (in opposite directions - thus having systematic effects on choice biases without altering the average proportion of LL choices) provides crucial first evidence for the hypothesis that dopamine and proximity interact to influence decision-making. These results thereby enrich our understanding of the neuromodulatory mechanisms influencing inter-temporal choice, and take an important step towards resolving prior contradictions in this literature. They also have implications for how striatal dopamine might impact decision-making in diverse domains of impulsivity beyond inter-temporal choice, ranging from cognitive neuroscience (e.g. in numerous cognitive control tasks) to psychiatry (treating diverse disorders of impulse control).

Reviewer #3 (Public Review):

The primary objectives of this manuscript were to characterize "the baseline phenotypic diversity in B cells and B cell receptors (BCRs) in the Kymouse" and draw comparisons to existing mouse and human datasets. Specifically, the authors place an emphasis on investigating whether the BCR repertoire has characteristics in common with repertoires from healthy humans (as expected), rather than wild-type mice (C57BL/6). In my opinion, the authors have met these basic objectives. The authors conclude that while the Kymouse repertoires have distinct heavy chain variables, diversity, and joining gene usage profiles and that their CDRH3 length is intermediate to the group averages of their control samples (C57BL/6, n=5; human, n-10), other important features, such as kappa and lambda light chain ratios, and CDRH3 structures were more "human-like". The data presented here will be useful for setting a foundation for the use of this model in future studies (as well as other similar transgenic models). Ultimately, how the Kymouse model can be best utilized for different objectives will be important to determine. As outlined by the authors in the first paragraph of the introduction, whether the repertoires and associated immune responses mounted by these animals can be "considered representative of humans" will likely need additional demonstration through investigations under various experimental conditions.

Reviewer #3 (Public Review):

This work uses an agent-based model of SARS-CoV-2 transmission (calibrated to the first wave in the Netherlands) to examine how the societal impact of interventions could have been reduced - while maintaining epidemiological impact - if they were implemented at a subnational (eg, municipality) rather than a national level. After more than two years of lockdowns and mobility restrictions, the societal cost of such measures is becoming better understood, and it is important to evaluate the effectiveness of such measures and reflect upon how they can be deployed in a minimally disruptive fashion. Mathematical and computational models are a natural choice for such investigations as they enable researchers to explore counter-factual scenarios ("what might have happened had we acted differently?")

The authors conclude that subnational interventions, triggered via prevalence in a particular municipality, could have controlled the first wave of SARS-CoV-2 in the Netherlands with minimal health cost but less societal disruption than national interventions. This claim is supported by reference to Figure 4 showing the impact on (a) hospital admissions and (b) municipalities without interventions through different phases of the outbreak. For more remote/rural municipalities, the use of interventions is delayed by ~1 week, although some (6%) of municipalities avoid interventions altogether.

Strengths:

As noted above, the general objective of this study is important and of potentially broad interest. The agent-based model is complex, but not unreasonably so, and makes good use of rich demographic, mobility, epidemiological/clinical, etc. data for calibration. The simulations conducted using the model support the specific conclusions of the manuscript.

Weaknesses:

While the motivation and approach are strong points of this work, the analysis and interpretation would benefit from further development. The robustness of model behaviour to the threshold used to trigger subnational interventions is explored; however, there are other aspects of the model that are not subjected to sensitivity analysis, including:

1. The impact of imperfect surveillance (eg, due to asymptomatic transmission, reporting delays, etc);

2. The impact of non-compliance, which could potentially differ for subnational versus national interventions;

3. The impact of pathogens/variants with transmission/severity characteristics different from the original SARS-CoV-2 strain.

In the absence of such analyses, it is difficult to generalise the findings beyond "this is how subnational interventions could have been used to control the first wave of SARS-CoV-2 in the Netherlands" to "this is how subnational interventions could be used effectively in the event of future outbreaks" (of a SARS-CoV-2 variant or other pathogen).

The discussion focuses on limitations associated with the model but does not consider other potential implications of subnational interventions. For example:

1. Subnational interventions may produce unintended consequences if populations respond by relocating from regions with interventions (high prevalence) to regions without interventions (low prevalence).

2. Subnational interventions would require extremely effective public health messaging to avoid confusing populations. Particularly in densely populated regions where municipalities may be small and tightly connected, the feasibility of communicating (and enforcing compliance with) interventions may be challenging.

3. A proposal to implement subnational interventions - following the results of this work - may raise ethical questions about cost-benefit trade-offs (eg, whether 355 additional hospital admissions is an acceptable price to pay for 36 million person-days without interventions; ie, two days per citizen, on average). The fact that such decisions would (in the even of a future outbreak) need to be made rapidly, in the face of potential uncertainty about pathogen characteristics, heightens the need for clear understanding of how situational factors may affect the likely effectiveness of interventions (at any scale).

Impact and broader utility:

As noted, the question addressed - how we can reduce the disruption caused by interventions for transmission control - is important. Thus, the work presented in this manuscript has the potential for broad utility. Currently, this is limited by the focus on specific outbreak instance.

Reviewer #3 (Public Review):

The authors first demonstrated in bone marrow-derived macrophages (BMMs) that IL-4 treatment of BMMs led to a significant reduction of BCG- and TDB-induced MINCLE expression (Fig. 1). While IL-4 treatment did not impact BCG phagocytosis by BMMs, it led to a reduced production of the cytokines G-CSF and TNF by BMMs (Fig. 2). In an elegant model using hydrodynamic injection of mini-circle DNA encoding IL-4, the authors show that IL-4 overexpression abrogated the increased MINCLE expression in monocytes upon BCG infection in vivo. Similar findings were observed in a co-infection model with the hookworm Nippostrongylus brasiliensis, where MINCLE expression on inflammatory monocytes from BCG-infected mice was reduced compared to control mice infected only with BCG (Fig. 3). The key findings of the manuscript include the two murine helminth infection models, S. mansoni as a chronic infection, and N. brasiliensis as a transient infection, in both of which the authors showed an organ-specific inhibition of the Th17 response in a vaccination setting with a MINCLE-dependent adjuvant (Fig. 4 and 5).

Data shown in the manuscript represents a major advance over previous studies because for the first time a relation between IL-4 and MINCLE expression and function is demonstrated in vivo in relevant co-infection models. All experiments have been done with care. Appropriate controls have been included and conclusions are largely supported by the data. Future studies in human patients will be needed to determine the clinical relevance of the findings observed in the murine helminth infection models.

Reviewer #3 (Public Review):

This manuscript by Jagoda et al. addresses the genetic mechanism of the haplotype at chromosome 3 where introgressed from Neanderthals shows the strong association with COVID-19 severity in Europeans. They re-evaluate the adoptively introgressed segment using Sprime and U and Q95 methods and analyze cis- and trans- eQTLs based on the whole blood dataset. All the 361 Sprime-identified introgressed variants act as eQTLs in the whole blood and alter the expression of 14 genes including seven chemokine receptor genes.

Then they tested the 613 variants using a Massively Parallel Reporter Assay (MPRA) in K562 cells and narrow downed the 20 emVars. In the end, they selected the four variants based on four criteria regarding the association of COVID-19 severity, eQTL data, chromosomal interaction, and epigenetic marks in immune cells. They highlighted variant rs35454877 (CCR5 regulation), rs71327024, rs71327057, and rs34041956 (CCR1 regulation).

Narrowing down the four critical variants from the around 800 kb introgressed region is impressive work. However, MPRA and eQTL data are not consistent, and these data don't support clinical gene expression data (increased expression of CCR1 in severe COVID-19 patients).

Reviewer #3 (Public Review):

Previous work on HO-2 null mice suggest that the increased occurrence of central and obstructive sleep apneas observed in these animals is linked to hyperactivation of carotid bodies through a CO-dependent H2S increase mechanism within the carotid bodies. Hyperoxia, genetic ablation or pharmacological bloc of CSE (a CO-dependent H2S producing enzyme) reduced the occurrence of both central and obstructive apneas.

Here, the authors propose an alternate, or complementary view to address occurrence of OSA in HO-2 null mice. In in vitro medullary slice preparations they used the same pharmacological and genetic approaches to manipulate levels of CO and H2S and observe that inhibition or elimination of HO-2 induces a transmission failure between the preBotC and hypoglossal motoneurons that is potentially linked to a post-synaptic effect on hypoglossal motoneurons, in particular at the level of apamin sensitive small conductance potassium channels.

Although drugs were bath applied rather than local applied to XII motoneurons, the authors provide evidence that HO-2 and CSE modulation affects the Input/Output relationship between the preBötC and the hypoglossal nucleus.

Given the occurrence on central apneas in these mice in vivo, the potential effects of H2S on preBotC neurons, the use of bath application in these experiments and the apparent effects on rhythmogenesis, additional assessment of preBötC function in these mice would benefit the study.

Reviewer #3 (Public Review):

In this research, the authors explore a novel mechanism of CDK4/6 inhibitor dalpiciclib in HER2+HR+ breast cancers, in which dalpiciclib could reverse the process of ER intra-nuclear transportation upon HER2 degradation. The conclusions are significant to gain insight into the biological behavior of TPBC and provided a conceptual basis for the ideal efficacy in the published clinical trial. The findings are supported by supplemented in vivo assay and transcriptomic analysis.

Reviewer #3 (Public Review):

In this manuscript, the authors found upregulation of PCA3 and downregulation of PRUNE2 in prostate cancer as compared with normal prostate in two retrospective and independent patient cohorts, supporting that PCA3 and PRUNE3 function as an oncogene and a tumor suppressor gene, respectively. The findings presented here represent additional evidence for the functional reciprocal co-regulation of PCA3 and PRUNE2 in the setting of early tumorigenesis but not in late events in human prostate cancer. But further studies of PCA3/PRUNE2 dysregulation are still needed.

Reviewer #3 (Public Review):

This study examines cellular deficits in human neural precursor cells derived from ASD individuals and unaffected controls. The ASD cases include idiopathic ASD individuals and 16p Deletion ASD individuals. These are rigorous studies employing multiple differentiations and multiple clones for all experiments. The authors report common deficits in neurite outgrowth and migration in these distinct ASD samples, and further demonstrate common deficits in downstream mTOR signaling. Outgrowth and migration deficits could be mimicked or rescued by manipulating mTOR signaling, further supporting a role for mTOR in these deficits. Differences in EF responsiveness identify ASD-subtype specific deficits, and the effects of subthreshold concentrations of EFs represent novel and interesting findings.

The results on the mTOR signaling pathway as a point of convergence in these particular ASD subtypes is interesting, but the discussion should address that this has been demonstrated for other autism syndromes, and in the present manuscript, there should be some recognition that other signaling pathways are also implicated as common factors between the ASD subtypes.

The conclusions of this paper are mostly well supported by data, but for the cell migration assay, it is not clear if the authors control for initial differences in the inner cell mass area of the neurospheres in control vs ASD samples, which would affect the measurement of migration. Also, in Fig 5 and 6, panels I and J omit the effects of drug on mtTOR phosphorylation as shown for other conditions.

Reviewer #3 (Public Review):

A problem in synthetic ecology is that one can't brute-force complex community design because combinatorics make it basically impossible to screen all possible communities from a bank of possible species. Therefore, we need a way to predict phenomena in complex communities from phenomena in simple communities. This paper aims to improve this predictive ability by comparing a few different simple models applied to a large dataset obtained with the use of the author's "kchip" microfluidics device. The main question they ask is whether the effect of two species on a focal species is predicted from the mean, the sum, or the max of the effect of each single "affecting" species on the focal species. They find that the max effect is often the best predictor, in the sense of minimizing the difference between predicted effect and measured effect. They also measure single-species trait data for their library of strains, including resource niche and antibiotic resistance, and then find that Pearson correlations between distance calculations generated from these metrics and the effect of added species are weak and unpredictive. This work is largely well-done, timely and likely to be of high interest to the field, as predicting ecosystem traits from species traits is a major research aim.

My main criticism is that the main take-home from the paper (fig 3B)-that the strongest effect is the best predictor-is oversold. While it is true that, averaged over their six focal species, the "strongest effect" was the best overall predictor, when one looks at the species-specific data (S9), we see that it is not the best predictor for 1/3 of their focal species, and this fraction grows to 1/2 if one considers a difference in nRMSE of 0.01 to be negligible.

The same criticism applies to the result from figure 2-that pairs of affecting species have more negative effects than single species. Considered across all focal species this is true (though minor in effect size, Fig 2A). But there is only a significant effect within two individual species. Again, this points to the effects being focal-species-specific, and perhaps not as generalizable as is currently being claimed.

Another thing that points to a focal-species-specific response is Fig 2D, which shows the distributions of responses of each focal species to pairs. Two of these distributions are unimodal, one appears bimodal, and three appear tri-modal. This suggests to me that the focal species respond in categorically different ways to species addition.

These differences occur even though the focal bacteria are all from the same family. This suggests to me that the generalizability may be even less when a more phylogenetically dispersed set of focal species are used.

Considering these points together, I argue that the conclusion should be shifted from "strongest effect is the best" to "in 3 of our focal species, strongest effect was the best, but this was not universal, and with only 6 focal species, we can't know if it will always be the best across a set of focal species".

My second main criticism is that it is hard to understand exactly how the trait data were used to predict effects. It seems like it was just pearson correlation coefficients between interspecies niche distances (or antibiotic distances) and the effect. I'm not very surprised these correlations were unpredictive, because the underlying measurements don't seem to be relevant to the environment tested. What if, rather than using niche data across 20 nutrients, only the growth data on glucose (the carbon source in the experiments) was used? I understand that in a field experiment, for example, one might not know what resources are available, and so measuring niche across 20 resources may be the best thing to do. Here though it seems imperative to test using the most relevant data.

Additionally and relatedly, it would be valuable to show the scatterplots leading to the conclusion that trait data were uninformative. Pearson's r only works on an assumption of linearity. But there could be strong relationships between the trait data and effect that are monotonic but not linear, or even that are non-monotonic yet still strong (e.g. U-shaped). For the first case, I recommend switching to Spearman's rho over Pearson's r, because it only assumes monotonicity, not linearity. If there are observable relationships that are not monotonic, a different test should be used.

In general, I think the analyses using the trait data were too simplistic to conclude that the trait data are not predictive.

Reviewer #3 (Public Review):

In this manuscript, Caspy et al. present a detailed structural analysis of eukaryotic photosystem II (PSII) isolated from the green alga Dunaliella salina. By combining single-particle cryo-EM with multibody refinement, the authors not only reveal a high-resolution (2.4Å) structure of the eukaryotic PSII, but also demonstrate alternate conformations and intrinsic flexibility of the overall complex. Stretched and compact conformations of the PSII dimer were readily identified within the single-particle dataset. From this structural analysis, the authors propose that excitation energy transfer properties may be modulated by changes in transfer distance between key chlorophyll molecules observed in different conformational states of the PSII dimer. Due to the high resolution of the maps obtained, the authors identify post-translational modifications and a sodium binding site based on the observed cryo-EM maps. Additionally, the authors analyze PSII complexes in stacked and unstacked configurations, and find that compact and stretched states also exist within the stacked PSII complexes. From their cryo-EM maps, the authors demonstrate that there is no direct protein-protein interaction between stacked PSII complexes, and rather propose a model wherein long-range electrostatic interactions mediated by divalent cations such as magnesium, can facilitate PSII stacking.

The conclusions and models presented in the manuscript are mostly well justified by the data. The cryo-EM maps are high quality and the models appear generally well refined. However, some aspects of data processing and analysis, as well as the resultant conclusions need to be clarified.

1. In general, it is not clear from the cryo-EM processing workflow (suppl. Fig 1) or the methods section when exactly symmetry was applied during 3D classification and refinement. In the case of C2S2 unstacked particles, when was symmetry first applied in the overall processing workflow? To identify the compact and stretched configurations of C2S2, did the 3D classification without alignment (and/or the refinement preceding this classification) have C2 symmetry applied? If so, have you considered the possibility that some particles may actually be asymmetric in some regions?

2. Following multibody refinement in Relion individual maps and half-maps for each body will be generated. There is no mention in the methods of how these individual maps for each C2S2 "monomer" were combined to produce an overall map of the dimer following multibody refinement. There are several methods currently used to combine such maps, including taking the maximum or average of the two maps or using a model-based approach in phenix. The authors should be explicit about the method they used, any potential artifacts that may develop from this map combination process, and/or the interface between masks used in multibody refinement.

3. In addition to the point raised above, following multibody refinement there will be an individual FSC curve and resolution for each body. However, in supplemental figure 2 and supplemental table 1, only a single FSC curve and resolution are reported. Are these FSC curves/resolutions only reported for the better of the two bodies? If not, how was a single resolution calculated for the overall map of combined bodies?

4. One of the major conclusions from the 3D classification and multibody refinement is that conformational changes and inherent flexibility of the PSII dimers have the potential to change distances between cofactors in the complex, ultimately leading to altered excitation energy transfer. However, it is unclear whether or not the authors believe one conformation over another may more readily support the evolution of oxygen. It would be nice if the authors could elaborate slightly upon this topic in the discussion.

5. Along the lines of point 4 above, on line 95 the authors claim that "the high specific activity of 816 umol O2/ (mg Chl * hr) suggest that" both the C2S2 compact and stretched conformation are highly active. However, it is not clear to me why this measure of specific activity would suggest that both PSII conformations should have "high" activity. Maybe a reference here would help guide readers to previous measures of specific activity?

6. It is claimed that "more than 2100 water molecules were detected in the C2S2 compressed model", and the water distribution is shown in Figure 3. Obtaining resolutions capable of visualizing waters with cryo-EM is still a significant challenge. Upon visual inspection of the map supplied, it appears that several of the waters that were built into the atomic model simply do not have supporting peaks in the coulomb potential map above the level of noise. While some of the modeled waters are certainly supported by the map, in my opinion, there are many waters that simply are not, or at best are questionable. What method or tool was originally used to build waters into the model, and how were these waters subsequently validated during structure refinement?

7. The authors claim to identify several unique map densities during model building. One of these is a sodium ion close to the OEC, which is coordinated by D1-His337, several backbone carbonyls, and a water molecule. When looking closely at the cryo-EM map supplied, it appears that the coulomb potential map is quite weak for this sodium, and is only visible at quite low contour levels. In fact, the features for the coordinating water, and chloride ions located ~7-9A away are much stronger than the sodium. Do the authors have any explanation for why the cryo-EM map is significantly weaker for the sodium compared to the coordinating water or chloride ions in the same general vicinity? Similar to what they did for the other post-translational modifications, the authors should consider showing the actual cryo-EM map for the bound sodium in supplemental Figure 10 a,b.

8. The cryo-EM maps showing CP29-Ser84 phosphorylation and CP47-Cys218 sulfinylation are quite convincing. However, it is interesting that these modifications are only observed in the compact conformation, and not in the stretched conformation. Can the authors elaborate on whether or not they believe the compact and stretched conformations could be a result of these posttranslational modifications, or vice versa?

9. Do the authors believe that PSII dimers in the solution can readily interconvert between compact and stretched conformations? Or is the relative ratio of these conformations fixed at the time of membrane solubilization with decyl-maltoside?

10. The model proposed for divalent cation-mediated stacking of PSII dimers is compelling, and seems to be in agreement with previous investigations that observed a lack of stacked dimers in cryo-EM preparations lacking calcium/magnesium. However, my understanding from reading the methods section is that the observed lack of density between the stacked PSII dimers was inferred from maps obtained after multibody refinement. Based on the way the masks to define bodies were created for multibody refinement (Fig. 4A), the region between stacked dimers would be highly prone to map artifacts following multibody refinement. Have the authors looked closely at the interfacial region between stacked dimers following conventional 3D classification/refinement to ensure that there are indeed no features observed in the interfacial region even at low contour levels?

Reviewer #3 (Public Review):

This study aims at classifying central amygdala neurons based on their expression of marker genes along with their spatial, morphological, and connectivity properties. The use of state-of-the-art experimental and analysis approaches to disentangle the functional complexity and heterogeneity of this brain region is a clear strength of the study. The detailed spatial description, including rostral-caudal dimensions and specific CeA subnuclei in all of their analyses as well as the co-expression of multiple marker genes and description of various long-range projection targets, will be valuable, potentially allowing for the targeting of anatomically distinct CeA subregions in future mechanistic studies. The major weaknesses are the exclusion of female subjects in their experiments and the incomplete acknowledgement of previous studies that have addressed transcript expression and cell-type specific function in the CeA.

Reviewer #3 (Public Review):

The nuclear transport machinery is aberrantly regulated in many cancers in a context-dependent fashion, and mounting evidence with cultured cell and animal models indicates that reducing the activity or expression of certain nuclear transport proteins can selectively kill cancer cells while sparing nontransformed cells. Here the authors further explore this concept using a zebrafish model for hepatocellular carcinoma (HCC) induced by liver-specific transgenic expression of oncogenic krasG12V. The transgene causes greatly increased liver size by day 7 in larvae, associated with a gene expression profile that resembles early-stage human HCC. This study focuses on Ahctf1, a nuclear pore complex (NPC) protein known to be essential for postmitotic NPC assembly. Using the krasG12V background, the authors analyze animals that are heterozygous for a recessive mutation in the ahctf1 gene that leads to ~50% reduction in ahctf1 mRNA (and likely the encoded protein). The authors show that the ~4-fold increase in liver volume of krasG12V animals is reduced by ~1/3 in the ahctf1 heterozygous mutants. This is associated with increased apoptosis, decreased DNA replication, up-regulation of pro-apoptotic and cdk-inhibitor genes, and down-regulation of anti-apoptotic gene. These effects found to be substantially Tp53-dependent. Consistent with previous Ahctf1 depletion studies, hepatocytes of ahctf1 heterozygotes show decreased NPC density at the nuclear surface, elevated levels of aberrant mitoses and increased DNA damage/double stranded breaks. Finally, the authors show that combining the achtf1 heterozygous mutant with a heterozygous mutation in another NPC protein- RanBP2- or treating animals with a chemical inhibitor of exportin-1 (Selinexor) can further reduce liver volume. Overall they suggest that combinatorial targeting of the nuclear transport machinery can provide a therapeutic approach for targeting HCC.

This is an interesting study that bolsters the notion that reduction in the levels of discrete nucleoporins (and/or inhibiting specific nuclear transport pathways) can result in cancer cell-selective killing. Moreover, the work extends previous studies involving cultured cell and mouse xenografts to a new cancer model (HCC) and nucleoporin (Ahctf1). Whereas the authors describe multiple aberrant cellular phenotypes associated with the dosage reduction in ahctf1, the exact causes for reduction in liver size in the krasG12V model remain unclear. Although it would be desirable to parse effects of Ahctf1 related to NPC number, aberrant mitoses, licensing of DNA replication and chromatin regulation, this is a tall order at present, given the limited understanding of Ahctf1. However, useful insight on these and related questions could be gained with further analysis of the system as outlined below.

1) In the krasG12V model, it would be helpful to distinguish the contribution of increased cell death vs decreased cell proliferation to the change in liver size seen with heterozygous ahctf1. Is this predominantly due to decreased proliferation?

2) It would be good to know whether the heterozygous ahctf1 state blunts the overall level of Ras activity in krasG12V animals.

3) Notwithstanding the analysis of Tp53 target genes presented in this study, it would be helpful to see detailed transcriptional profiling of hepatocytes in the krasG12V model with the heterozygous ahctf1 mutation, and to assess the effects of Selinexor. GSEA type analysis offers a way to start untangling the effects of these pathways. Moreover this analysis could provide insight on the relevance of this model to human HCC.

4) Functions of Achtf1 in regard to chromatin regulation could be compromised in this model. Scholz et al (Nat Gen 2019) report that Ahctf1 is involved in increasing Myc expression via gene gating mechanism. It would be good to know what the effects are in this system. Indeed, anti-cancer effects from depletion of Nup93 in a breast cancer model was reported to involve a role of Nup93 in chromatin regulation (Bersini et al, Life Sci Alliance 2020).

5) If feasible, it would be important to know if loss/reduction in Tp63, proposed to compensate with Tp53 loss, would alter the effects of Achtf1 depletion.

6) The synthetic lethality argument pressed in this manuscript seems exaggerated. Standard anti-cancer treatments typically target several cellular pathways, and nucleoporins directly affect a multiplicity of pathways besides nuclear transport.

Reviewer #3 (Public Review):

The manuscript by Hoces et al uses a small set of genetically barcoded B. theta strains to quantify population bottlenecks during colonization based on a Poisson model. They then estimate the decrease in niche size as a function of microbiota complexity. Although there was a surprising similarity between the WT and CPS mutant when colonizing separately, they showed that the competitive disadvantage during co-colonization was due to a lag before growth initiation in the gut. Overall, they make the interesting finding that capsule may be more important to deal with microbiota interactions rather than the host. In general, I find the manuscript well-constructed and interesting, using a clever method to understand an important question in microbiome biology. The titration experiments in particular led to very clean results.

Reviewer #3 (Public Review):

In this manuscript, the authors compared the accuracy of 3 machine learning (ML) algorithms for predicting incident diabetic kidney disease (DKD) by using longitudinal data from 1,365 Chinese, Malay, and Indian participants from the Singapore Epidemiology of Eye Diseases (SEED) study cohort (median follow up 6 years). They report that their ML model "Elastic Net" had the highest AUC (0.85) of the 3 ML models, compared to a logistic regression model (AUC 0.79). The LR model was based on age, sex, ethnicity, duration of diabetes, systolic blood pressure, HbA1c, and body mass index. In 3 ML models, the authors included a range of variables including > 200 blood metabolites, single nucleotide polymorphisms, and eye imaging parameters.

A major weakness of this study is the definition of incident DKD and the lack of albuminuria data - the authors define incident DKD as eGFR < 60 cc/min/1.73 m2. This may underestimate the incidence of DKD, and further may label non-DKD as DKD (e.g. in an individual who experiences acute kidney injury without full recovery). Another major weakness is the treatment of ethnicity as a biological variable - in the strongest prediction model, Chinese vs Malay vs Indian ethnicity was one of the top 15 variables in the ML model. More explanation is needed around why ethnicity was included in both the ML models and the LR model. Further, a subgroup analysis of each of these groups was not performed. Finally, the rationale for the selection of the >200 metabolites is unclear. Several of the top 15 variables in all 3 models are these metabolites. Another top-15 variable in one of the models was noted to be "anti-diabetes medications", though the authors do not separate insulin vs non-insulin medications.

Reviewer #3 (Public Review):

Caligaris and colleagues show a new mechanism by which AMPK/Snf1 inhibits TORC1 signaling during glucose starvation. They propose that under glucose starvation, Snf1 inhibits the unscheduled activation of TORC1 by phosphorylating Pib2 and Sch9, upstream and downstream effectors of TORC1. The study also provides a resource for novel substrates of Snf1 which can be useful for future studies. Specific comments are below.

1. Conceptually, the manuscript shows that Snf1 activity is important for the acute inhibition of TORC1 during glucose starvation. However, this is mainly restricted to 10 and 15 minutes of glucose starvation. After 20 minutes, TORC1 is inhibited by some unknown mechanisms independent of Snf1 (Hughes Hallet et al). This raises concern regarding the physiological relevance of Snf1-mediated TORC1 inhibition during acute glucose stress. The authors show that this regulation is important for the survival of cells under TORC1 inhibition. How do the authors envision that the acute role of Snf1 plays an important long-term physiological relevance during rapamycin treatment? Providing more support for the physiological relevance of this regulation will make this study of interest to a broad readership.

2. Another major concern of the manuscript is the inconsistencies between the various representative immunoblots and their quantifications. The effect of AMPK activity on TORC1 signaling under glucose starvation seems very subtle. A few specific concerns are mentioned below:

a) In figure 1A, the increase in TORC1 activity upon inhibition of analogue sensitive Snf1as by 2NM-PP1 is very marginal. Although quantification shows a significant increase, a representative western blot figure should be shown.

b) Does deleting Snf1 itself have any effect on TORC1 activity? Lane 4 of figure 1A shows reduced activity compared to lane 1.

c) To show the effect of Snf1 on the repression of TORC1, the time-course experiments are run on two separate gels in figure 1C. Hence, it is difficult to compare the effect of Snf1 on unscheduled reactivation of TORC1 under glucose starvation.

d) In figure 1E, the effect of Reg1 deletion on TORC1 activity seems minor as both phospho- and total levels of Sch9 are reduced.

Since further mechanistic insights are based on these initial findings of figure 1, solidifying these observations is very important.

3. In figure S1, the analogue sensitive Snf1as shows significant reduction in its activity (reduced S79 phosphorylation of ACC1-GFP). This raises the concern of whether this genetic background is an ideal system to resolve the mechanism of TORC1 suppression.

4. In figure 2, during glucose restimulation, there is increased retention of Snf1as-pThr210 in the presence of 2NM-PP1. This suggests that the upstream glucose sensing pathway as well as Snf1 might be more active than in DMSO-treated cells. This also raises concerns regarding the suitability of the genetic background for the study. Can authors comment on why this phosphorylation persists? Does the phosphoproteomic analysis give any hint for this phenotype?

5. In figure 4H, where authors claim reduced binding of Kog1 to Pib2SESE, levels of Kog1 in input are also reduced. Can authors provide further support using colocalization studies? Also, does Pib2SESE has any defect in forming Kog1 bodies?

6. In figure 5F, where the authors claim the Sch9SE mutant has lower TORC1 activity, the difference is very minor. Furthermore, corresponding lanes also show reduced levels of Snf1as expression. Hence, improved blots are required here. Also, an in vitro kinase assay with full-length Sch9 KD with and without the Ser288 mutation could solidify the observation that phosphorylation of Ser288 indeed affects TORC1-mediated phosphorylation.

7. In figure 6E, the Sch9SE mutant shows no effect in the presence of rapamycin. Thus, in vivo, phosphorylation at Ser288 may not be perturbing the phosphorylation of Sch9 by TORC1.

8. According to the author's proposed mechanism, TORC1 activity in Pib2SASA or Pib2SASA/Sch9SA backgrounds should be higher during glucose starvation compared to the control strains. However, glucose starvation shows a similar level of reduction in TORC1 activity in these backgrounds. This raises concern regarding the proposed mechanism. The authors mainly base their conclusions on Ser to Glutamate mutants. The authors should be cautious that Ser to Glutamate changes may also affect the protein structure which can confer similar phenotypes. How do the authors justify this discrepancy?

Reviewer #3 (Public Review):

The authors were trying to describe and document the grooving behaviour of nuts in two species of flying squirrels (Hylopetes Phayrei electilis and H. alboniger) as well as related such behaviour to tool use or that the squirrels are smart. To achieve these objectives, the authors conducted three field surveys. They also set out a camera later to capture animal species that interacted with these nuts. They found that these nuts with grooves are fixed between twigs and can be found in different small plant species. Both species of squirrels made grooves a nut. More shallow grooves are found in nuts that are fixed on alive than dead trees. Ellipsoid nuts have deeper grooves than oblate nuts. They concluded that these nut grooving behaviours are evolved or learned in those flying squirrel populations, and related these behaviours to tool use as well as that the squirrels are smart.

One strength of this work is that the data were collected in the field, which may provide hard evidence with video footage showing the two flying squirrel populations made grooves on nuts as well as fixing them between twigs. This evidence will induce new interests to understand the causes and consequences of such nut grooving behaviour. It may be bold to claim that such behaviour involves advance cognition or cognitive process without proper, systematic, experiments. Accordingly, whether the squirrels are 'smart' remains unclear.

The authors did well in describing and documenting the nut grooving behaviours of the two species of flying squirrels, which has achieved their first aim. However, as mentioned above, whether such behaviour is 'smart' will need more systematic investigations.

https://www.zylstra.org/blog/2022/12/22660/

Reviewer #3 (Public Review):

The paper from Liu et al. investigates the mechanism of speed control in the fruit fly where movement is generated by the coordination of contraction across several segments (peristaltic wave). They show very convincing behavioural data demonstrating that the interwave phase is regulated to control speed and that one of the Lateral transverse muscles (LT2) is constantly contracted during this period. They describe two presynaptic inhibitory neurons to LT2 motorneuron (A31c and A26f) that have patterns of activity that suggest they could be involved in the process. When the neuronal activity has manipulated the contraction of LT2, the interwave time and the speed of locomotion seems to be modified. The data regarding the pattern of activity of A31c and A26f neurons in the isolated nervous system is not completely convincing due to the clear overlap of the neuronal activity with the contraction of abdominal segments. Also, the n number of certain behavioural experiments is very low. Overall, it is a very interesting paper that describes a new mechanism of speed control, but several points need to be solved.

The main strength of the paper is that it describes a new mechanism of speed control. The behaviour data demonstrating that the time of the interwave is modified to control speed during crawling is very convincing.

Then, they analysed the contraction of LT2 muscles and found that they only contract during the interwave phase. The behavioural setup and the tool to evaluate muscle contraction (tendon driver) are different from the ones that have been used in the past, but the striking difference in the pattern of contraction should have been explored in more detail. For example, repeating the experiment with a muscle reporter, or analysing more precisely the movies from Figures 5, 6, or 7. More clarity regarding the difference between their data and the data showing how LT muscles contract during the wave (Heckscher et al 2012; Zwart et al. 2016; Zarin et al. 2019) is needed.

Liu et al. also discover two inhibitory neurons that are connected with the MN21-22 and could potentially control speed. They perform behavioural optogenetic experiments inhibiting or increasing neuronal activity and found interesting data that strongly suggest that the neurons are indeed controlling the interwave time. However, doubts are raised by the low n number of animals analysed (n often = 5 or 7). In order to compensate for a low n number the authors decided to use bootstrapping, but working with Drosophila they should have increased the number of animals analysed.<br /> These behavioural experiments are the strongest evidence the authors have of the mechanism of control of speed, they should be immutable.

Another weakness is that the phase of activity of A31c and A26f neurons overlaps with part of the peristaltic wave, not matching the suggested pattern of activity during the interwave phase. When observing waves in fictive crawling (for example the long recordings from Pulver et al. 2015), it seems that there is no interwave time and that waves happen one after the other in bouts. It is possible that the sensory feedback is essential to set the interwave time and that the slightly un-phased activity is due to this lack. The authors do not give us any explanation. Is the activity of the Bar-H1+ motor neurons inhibited when A31c activity is high? What happens when A26f neurons are active? Or is it that the role of these neurons is somewhat different from what is stated? What is the actual phase relationship between A26f and A31c? Figure 4F shows us two different segments the A31c presynaptic that has an anteriorly projecting connection in a4 and the postsynaptic in a5. We should see the pattern of expression of all the segments where A26f is expressed.

Overall, the paper is very interesting data but more rigour in the description and interpretation of the data is required. Also, a few replicates are needed to confirm what, at the moment, are very suggestive data.

Reviewer #3 (Public Review):

Normal levels of UBE3A expression in neurons are from the maternal allele whereas its paternal allele is repressed by its antisense transcript (UBE3A-ATS). In Angelman syndrome, a severe neurodevelopmental disorder, the combined lack of maternal UBE3A expression and paternal repression led to UBE3A deficiency. The oligonucleotide therapeutic approach holds a promise to treat Angelman syndrome by suppressing UBE3A-ATS and reactivating the paternal UBE3A allele. Previous studies showed the effectiveness of this method in early development. This narrow and rather restricted therapeutic window limits its potential in treatment. This research aims to test the idea that it is possible to expand the therapeutic window. They first developed a new maternal Ube3a knockout mouse model of Angelman syndrome and then use antisense oligonucleotides to repress Ube3a-ATS targeting both juvenile and adult mice. This approach increased UBE3A expression from the paternal locus and to a large degree rescued the abnormal EEG rhythm and sleep quality, two core clinical symptoms of patients. Overall, this is a well-designed and executed study. The authors did a thorough analysis of UBE3A expression levels in different brain regions under different conditions which correlated well with functional data. Further, the manuscript is also well written. This reviewer had several concerns, including Western blot data presentation, ICV injection validation, and possible improvement in cognitive functions. The reviewer believes that the authors should be able to address these issues readily.

Reviewer #3 (Public Review):

The manuscript presents a theory of generalization performance in deterministic population codes, that applies to the case of small numbers of training examples. The main technical result, as far as I understand, is that generalization performance (the expected classification or regression error) of a population code depends exclusively on the 'kernel', i.e. a measure of the pairwise similarity between population activity patterns corresponding to different inputs. The main conceptual results are that, using this theory, one can understand the inductive biases of the code just from analyzing the kernel, particularly the top eigenfunctions; and that sample-efficient learning (low generalization performance with few samples) depends on whether the task is aligned with the population's inductive bias, that is, whether the target function (i.e. the true map from inputs to outputs) is aligned with the top eigenfunctions of the kernel. For instance, in mouse V1 data, they show that the top eigenfunctions correspond to low frequency functions of visual orientation (i.e. functions that map a broad range of similar orientations to similar output value), and that consistent with the theory, the generalization performance for small sample sizes is better for tasks defined by low frequency target functions. In my opinion, perhaps the most significant finding from a neuroscience perspective, is that the conditions for good generalization at low samples are markedly different from those in the large-sample asymptotic regime studies in Stringer et al. 2018 Nature: rather than a trade-off between high-dimensionality and differentiability proposed by Stringer et al, this manuscript shows that in the low-sample regime such codes can be disadvantageous for small sample sizes, that differentiability is not required, that the top eigenvalues matter more than the tail of the spectrum, and what matters is the alignment between the task and the top eigenfunctions. The authors propose sample-efficient learning/generalization as a new principle of neural coding, replacing or complementing efficient coding.

Overall, in my opinion this is a remarkable manuscript, presenting truly innovative theory with somewhat limited but convincing application to neural data. My main concern is that this is highly technical, dense, and long; the mathematical proofs for the theory are buried in the supplement and require knowledge of disparate techniques from statistical physics. Although some of that material on the theory of generalization is covered in previous publications by the authors, it was not clear to me if that is true for all of the technical results or only some.

Fixed population code, learnable linear readout: the authors acknowledge in the very last sentences of the manuscript that this is a limitation, given that neural tuning curves (the population neural code) are adaptable. I imagine extending the theory to both learnable codes and learnable readouts is hard and I understand it's beyond the scope of this paper. But perhaps the authors could motivate and discuss this choice, not just because of its mathematical convenience but also in relation to actual neural systems: when are these assumptions expected to be a good approximation of the real system?

The analysis of V1 data, showing a bias for low-frequency functions of orientation is convincing. But it could help if the authors provided some considerations on the kind of ethological behavioral context where this is relevant, or at least the design of an experimental behavioral task to probe it. Also related, it would be useful to construct and show a counter-example, a synthetic code for which the high-frequency task is easier.<br /> Line 519, data preprocessing: related to the above, is it possible that binning together the V1 responses to gratings with different orientations (a range of 3.6 deg per bin, if I understood correctly) influences the finding of a low-frequency bias?

I found the study of invariances interesting, where the theory provides a normative prediction for the proportion of simple and complex cells. However, I would suggest the authors attempt to bring this analysis a step closer to the actual data: there are no pure simple and complex cells, usually the classification is based on responses to gratings phases (F1/F0) and real neurons take a continuum of values. Could the theory qualitatively predict that distribution?

Reviewer #3 (Public Review):

In this study, the authors set out to test several new optogenetic tools in zebrafish. They motivate the study by citing differences in ion selectivity of channelrhodopsins and the potential utility of photoactivatable anenylyl and guanylyl cyclases to control cell functions. Although the study provides some useful new information about the utility of these tools in zebrafish, the characterization is limited and there are serious caveats around interpretation of behavioral responses.

The latency of behavioral responses is often extremely long and there is a lack of control data from opsin negative animals, raising serious doubts as to whether these responses are optogenetically mediated.<br /> In other words, many of these responses may not result from optogenetic activation of V2a cells, but instead arise from indirect effects such as visual stimulation of the animal. Previous zebrafish studies have shown swimming responses in opsin-negative control animals at latencies above ~100 ms and used a 50 ms cut-off for optogenetically evoked swims. One can see evidence suggestive of this issue in the authors' data: latency data for GtCCR4 appears bimodal with a cluster of short latency swims and a second spread at latencies >2s; this could be a mix of fast optogenetic and slow artifactual responses. As the authors have already tested opsin negative control animals, they should examine the latency distribution of these responses. The long latency is even more striking in the case of BeGC1, pPAC and OaPAC where in all cases mean latency exceeds 2 seconds. No short latency responses are apparent and the delay is too long to be solely a result of second messenger action (e.g. activation of cyclic nucleotide gated ion channels). In any case, no explanation is provided.

Although this study is motivated by the need to precisely control the flux of specific ions and modulate specific second messenger pathways, there is almost no characterisation of these processes in zebrafish cells. As such, the degree to which these tools are useful to "precisely control second messengers in vivo" is unclear and the lack of mechanistic data also leaves open questions about unexpected aspects of behavioral results (e.g. the long latency of presumed cyclic-nucleotide induced behavior, above).

Finally, there is little comparison with other commonly used optogenetic actuators. CrChR2[T159C] is used as the only control but more recent tools (e.g. CoChR, Chrmine, ChroME) are not considered. Thus, beyond showing that the new tools have behavioral effects in zebrafish, the usefulness of this report for researchers wanting to compare and select between tools is limited.

Reviewer #3 (Public Review):

CRISPR-Cas immune systems protect bacterial cells from bacteriophages by acquiring DNA-based molecular memories of infection called "spacers". Spacers are transcribed into RNA guides that direct Cas nucleases to cleave matching targets, thereby providing bacterial cells with adaptive immunity. Many studies focus on the mechanisms of CRISPR-Cas immunity, but less is known about how immune diversity emerges and evolves over time in complex populations. Here, the authors develop a computational framework to model stochastic CRISPR-Cas immunization events as well as phage mutations that enable escape. The authors use a rigorous set of parameters and analytical tools to simulate the arms race between bacteria and phage over many generations, allowing them to ask fundamental questions about whether and how host and pathogen are able to coexist. By altering an extensive set of variables, including population size, mutation rates, and spacer uptake and efficacy, the authors show that complex and stochastic dynamics emerge with exciting implications for the effective length of CRISPR arrays. They further show that these dynamics are affected by spacer cross-reactivity, which is likely an important factor in natural settings where distinct phages often share large regions of homology.

A limitation of the study is that many of the conclusions are drawn from simulations in which each phage contains a single CRISPR-targetable site - or "protospacer", such that a single mutation allows escape from many or all extant spacers in the population. In reality, phages harbor hundreds of protospacers, many of which are sampled by different bacterial cells during immunization. Therefore, bacterial populations encountering a new phage will quickly establish high spacer diversity. In this case, a phage that escapes one spacer by mutating the corresponding protospacer will still be killed efficiently by most other CRISPR immune cells in the population that harbor a different spacer. Nonetheless, the authors establish a rigorous and flexible platform through which existing experimental data can be analyzed, and new hypotheses can be generated. While experiments involving large, complex, and dynamic bacterial and phage populations are challenging, they will be buoyed by recent advances in NGS sequencing depth and complex microbial model systems, as well as the theoretical framework provided by Bonsma-Fisher and Goyal.

Reviewer #3 (Public Review):

The rod shape of cardiomyocytes (CM) as well as their distinct specialized membrane microdomains are crucial for normal cardiac function while alterations of such architecture are central to the pathogenesis of a host of cardiac diseases. However, mechanisms regulating this 3 D organization of CM during cardiac development and adult heart are still poorly known. The group C Galès had already done an important contribution to this domain by describing a distinct highly organized architecture of the lateral membrane with periodic crest containing transmembrane proteins claudin-5 and ephrin B1, of, however, unknown function. Following these previous studies, the group now investigated the maturation of this CM crest domain during the post-natal period as well as the consequences of loss of this organization on heart function. This study performed by an expert team clearly provides new and original knowledge on cardiac maturation heart development and on the relation between CM ultrastructure and cardiac function. A major finding of the study is that the protein ephrin-B1 plays a key role in adult crest-crest interactions between CM that appears to be a major determinant of the normal diastolic cardiac function. Therefore, beyond providing new insights in CM maturation, this study opens perspectives for the understanding of the pathogenesis of the so-called heart failure with preserve ejection function, a rising cause of HF with a prevalence increasing with the ageing of the population and without yet specific biomarker and therapeutic target. The article, well written and clearly illustrated, uses an impressive number of approaches including cutting edge imaging techniques.

Reviewer #3 (Public Review):

Gomolka et al. are trying to establish how aquaporin-4 (AQP4) water channels, a key component of the glymphatic system, facilitate brain-wide movement of interstitial fluid (ISF) into and through the interstitial space of the brain parenchyma. Authors employ a number of advanced non-invasive techniques (diffusion-weighted MRI and high-resolution 3D non-contrast cisternography), invasive dynamic-contrast enhanced (DCE-) MRI along with ex-vivo histology to build a robust picture of the effects of the removal of AQP4 on the structure and the fluid dynamics in the mouse brain. This work is a further step for the implementation of non-invasive tools for studying the glymphatic system.

The main strengths of the manuscript are in the extensive brain-wide and regional analysis, interrogating potential changes in the structural composition, tissue architecture, and interstitial fluid dynamics due to the removal of AQP4. The authors demonstrate an increase in the interstitial fluid volume space, an increase in total brain volume, and a higher brain water content in AQP4 knockout mice. Importantly, an increase in apparent diffusion coefficient (ADC) was reported in most brain regions in the AQP4-KO animals which would suggest an increase in the movement of the fluid, which is supported by an increase in interstitial fluid space measures by real-time iontophoresis with tetramethylammonium (TMA). There is a reduction in the ventricular CSF space compartment while the perivascular space remains consistent. A reduction in gadolinium-based MRI tracer influx into many regions of the AQP4 KO mouse brain parenchyma is found, which supports conclusions of slowing down of fluid transfer while noting that the tracer dynamics in the main CSF compartments show no significant differences.

The interpretation of non-invasive measures of the interstitial fluid dynamics in relationship to regional AQP4 expression is less well supported. The regional AQP4 channel expression in WT mice positively correlates with the ADC and extravascular diffusivity (D) measures. However, their finding that regional ADC also increases when AQP4 is removed weakens the conclusion that the removal of AQP4 leads to interstitial fluid stagnation.

Reviewer #3 (Public Review):

I agree with the authors that DCIS is a very common but understudied problem and longer-term follow-ups of cohort studies and randomized trials are needed.

I think the study described in this submission is a useful description of Chinese practice and patterns of care particularly with reference to the criteria that may have been used to select patients for endocrine therapy. It may also be of value for local and regional audits of care. Unfortunately, it does not help with any understanding of outcomes for the following reasons:

• It is retrospective in nature;<br /> • The number of events is very small;<br /> • The method of collecting information on side effects is not adequately described. I assume this is from case note review and ascertainment bias will therefore represent a major problem.

It would be very misleading to assert causal relationships from this study.

Reviewer #3 (Public Review):

This is an intriguing manuscript with a rigorous experimental and computational methodology looking at the interaction of Pseudomonas aeruginosa (Pa) and Staphylococcus aureus (Sa). These two pathogens frequently co-habit infections but in standard liquid media often show a winner-take-all outcome. This study seeks to be mechanistically predictive as to the outcome of the co-culture based on the addition of specific carbon sources as filtered through the lens of metabolic efficiency or, as the authors term - absolute growth. Overall, the study is sound, but there are some specific caveats that I would like to present:

1. The study undersells the knowledge in the literature of what allows or prohibits the stability of the Pa and Sa co-cultures. While most of the correct papers are cited, the outcomes of those studies are downplayed in favor of the current predictive study. While the current study is indeed more "predictive", it strays exceedingly far from an infection-relevant media, whereas other studies show reasonable co-existence in host-relevant media.<br /> 2. The major weakness in the ability of this study to be extrapolatable to infection conditions is the basal media selected for this analysis. The authors choose TSB, which is an incredibly rich media from the start, and proceed to alter only 11% of the available carbon (per mass) with their carbon source manipulations. This suggests an underappreciation for the amino acid metabolism routes of these two pathogens that are taking advantage of the roughly 89% of carbon as amino acid content in the TSB components of tryptone and soytone (17g and 3g, respectively vs the 2.5g carbon source). There are a few major issues with this basal formulation:<br /> a. Comparison to all extant literature on Pa - The media historically used to assess Pa include (rich) LB, BHI, MH; (minimal) MOPS, M63, M9; (host-associated) ASM, SCFM, SCFM2, Serum, and DMEM. TSB is not a historically evaluated formulation for Pa (though it is often for non-mammalian pathogenic Pseudomonads and environmental species). Thus, this study is not inherently integrated into the Pa literature and presents an offshoot study for which a direct connection to extant literature is difficult. Explicitly testing these predictions in the most minimal media possible and then in a host-relevant model would be optimal.<br /> b. TSB is not remotely host-relevant. The Whiteley lab has done monumental work evaluating in vitro models that mimic human infection (scrupulously matching transcriptomes) and TSB is about as far as you can get. Thus, the ability to extrapolate from the current work to infection without testing in host-relevant media is limited.<br /> c. The experimental situation has a component that is both good and bad- O2 tension. By overlaying with mineral oil, the authors immediately bias Staph (a more versatile fermenter) to success, whereas Pa deals with most of these carbon sources better at body level or higher O2 levels. The benefit of this is that many of the infection sites in which these two species co-occur are low in O2.<br /> d. Some of the tested metabolites are osmotically active (sucrose), while others are not (acetate), confounding the interpretation of what absolute metabolism means in the context of this study since the concentrations of all tested metabolites vary from above to below physiologic-dependent on the metabolite. A much better approach would have been to vary a single metabolite or combination to alter 'absolute metabolism' and test whether the stability of the co-culture held.<br /> e. The manuscript never goes into the fact that for some of these "the carbon source" sources, they are catabolite repressed compared to the basal TSB amino acids (or not). Both organisms show exquisite catabolite repression control, yet this is not addressed at all within the text of the manuscript. Since this response in both organisms is sensitive to relative proportions of the various C-sources, failure to vary C-sources or compare utilization compared to the massive excess tryptone and soytone in the media makes the 'absolute metabolism' difficult to interpret.<br /> f. The authors left out the 'favorite' sources of Pa that are known to be relevant in vivo - the TCA intermediates: citrate, succinate, fumarate (and directly relevant to host-pathogen interactions, itaconate)<br /> 3. Statistics: Most of the experiments presented are comparisons in which there are more than two experimental groups and the t-tests employed therefore need to be corrected for multiple comparisons. The standard way to do this is to employ an ANOVA with the appropriate multiple-comparison-corrected post-test. These appear to be appropriate for Dunnett's post-testing but the comparator group is not directly defined within the figure legends. Multiple comparison testing is critical for this analysis, as the H0 is that all are the same - the more samples potentially pulled from the same distribution will result in a higher likelihood that one or more will appear as from a distinct population (i.e. H0 rejected). Multiple comparisons correct for this and are absolutely critical for the evaluation of the data presented in this manuscript.<br /> 4. The authors missed including Alves et Maddocks 2018 in relation to priority effects and other contributing factors to stable Pa/Sa co-culture.

Reviewer #3 (Public Review):

This manuscript postulates that uterine stroma cells undergo a stage of activation between the resting state and the differentiated decidual state in order to support embryo implantation. Using in vivo mouse and in vitro mouse and human stroma cells they demonstrate that during decidualization the stroma cells express the marker genes for activated stroma. They then trace an axis from the embryo-producing TNF to prostaglandin production and activin A that is required for this process. They propose data to show that activation of the stroma is altered in infertility due to fetal trisomy 16.

The strengths of this manuscript are:

1. This is a comprehensive study using both in vivo and in vitro studies and in both mouse and human stroma cells.<br /> 2. The experiments use a combination of ligands, agonists, and inhibitors to map the signaling axis regulating stroma activation.<br /> 3. The data shown support the conclusions in this manuscript.

The weaknesses of this manuscript are:

1. The conclusion that Acitvin A is the regulator of stroma activation as mentioned by this manuscript is correlative. What is needed is a knockdown of Activin A and then assess stroma activation to prove Activin A is the major regulator and not one of many TGFb family members.<br /> 2. The use of uterine epithelial cells is problematic. The in vitro co-culture approach is not a state-of-the-art co-culture. Removal of epithelial cells from the uterus results in loss of the epithelial phenotype. If the manuscript used an epithelial organoid stroma cell coculture approach it may better reflect the role of the epithelial cells in this process. Otherwise, it is not clear that the epithelial cells are actual participants in the signaling axis. The treatments could be directly on the stroma cells.<br /> 3. Ishikawa cells are endometrial cancer cells. They do not really reflect uterine epithelium and it is not clear that any epithelial cell could be substituted for these cells.<br /> 4. The activation of stroma cells in the fetal trisomy 16 experiments at the end is very superficial. Data should show that these cells decidualize with decidual markers. This appears to be an experiment to show the translational value of the signaling axis. This experiment, again, is not well developed, does not add much to the manuscript, and should be omitted.<br /> In summary, the concept of stroma cell activation as part of decidualization is nicely developed and will add to the field. Normally investigators consider decidualization a mesenchymal to epithelial transition while some consider it stromal activation. This manuscript demonstrates that stroma cell activation is a critical part of the process of decidualization.

Reviewer #3 (Public Review):

This is a comprehensive study of the effects of aging of the function of red pulp macrophages (RPM) involved in iron recycling from erythrocytes. The authors document that insoluble iron accumulates in the spleen, that RPM become functionally impaired, and that these effects can be ameliorated by an iron-restricted diet. The study is well written, carefully done, extensively documented, and its conclusions are well supported. It is a useful and important addition for at least three distinct fields: aging, iron and macrophage biology.

The authors do not explain why an iron-restricted diet has such a strong beneficial effect on RPM aging. This is not at all obvious. I assume that the number of erythrocytes that are recycled in the spleen, and are by far the largest source of splenic iron, is not changed much by iron restriction. Is the iron retention time in macrophages changed by the diet, i.e. the recycled iron is retained for a short time when diet is iron-restricted (making hepcidin low and ferroportin high), and long time when iron is sufficient (making hepcidin high and ferroportin low)? Longer iron retention could increase damage and account for the effect. Possibly, macrophages may not empty completely of iron before having to ingest another senescent erythrocyte, and so gradually accumulate iron.

Reviewer #3 (Public Review):

This work addresses a long-standing gap in the literature, showing that the medial temporal lobe (MTL) is involved in representing simple feature information during a low-load working memory (WM) delay period. Previously, this area was suggested to be relevant for episodic long-term memory, and only implicated in working memory under conditions of high memory load or conjunction features. Using well-rounded analyses of task-dependent fMRI data in connection with a straightforward behavioural experiment, this paper suggests a more general role of the medial temporal lobe in working memory delay activity. It also provides a replication of previous findings on item-specific information during working memory delay in neocortical areas.

Strengths:<br /> The study has strengths in its methods and analyses. Firstly, choosing a well-established cueing paradigm allows for straightforward comparison with past and future studies using similar paradigms. The authors themselves show this by replicating previous findings on delay-period activity in parietal, frontal, and occipito-temporal areas, strengthening their own and previous findings. Secondly, they use a template with relatively fine-grained MTL-subregions and choose the amygdala as a control area within the MTL. This increases confidence in the finding that the hippocampus in particular is involved in WM delay-period activity. Thirdly, their combined use stimulus-based representational similarity analysis as well as Inverted Encoding Modeling and the convergence on the same result is encouraging. Finally, despite focusing on the delay period in their main findings, extensive supplementary materials give insight into the time-course of processing (encoding) which will be helpful for future studies.

Weaknesses:<br /> While the evidence generally supports the conclusions, there are some weaknesses in behavioural data analysis. The authors demonstrated fine stimulus discrimination in the neural data using Inverted Encoding Modeling (IEM), however the same standard is not applied in the behavioural data analysis. In this analysis, trials below 20 degrees and trials above 20 degrees of memory error are collapsed to compare IEM decoding error between them. As a result, the "small recall error" group encompasses a total range of 40 degrees and includes neighbouring stimuli. While this is enough to demonstrate that there was information about the remembered stimulus, it does not clarify whether aLEC/CA3 activity is associated with target selection only or also with reproduction fidelity. It leaves open whether fine-grained neural information in MTL is related to memory fidelity.

Moreover, the authors could be more precise about the limitations of the study and their conclusions. In particular, the paper at times suggests that the results contribute to elucidating common roles of the MTL in long-term memory and WM, potentially implementing a process called pattern separation. However, while the paper convincingly shows MTL-involvement in WM, there is no comparison to an episodic memory condition. It therefore remains an open question whether it fulfils the same role in both scenarios. Moreover, the paradigm might not place adequate pattern separation demands on the system since information about the un-cued item may be discarded after the cue.

Reviewer #3 (Public Review):

In this study Kershberg et al use three novel in vivo biotin-identification (iBioID) approaches in mice to isolate and identify proteins of axonal dopamine release sites. By dissecting the striatum, where dopamine axons are, from the substantia nigra and VTA, where dopamine somata are, the authors selectively analyzed axonal compartments. Perturbation studies were designed by crossing the iBioID lines with null mutant mice. Combining the data from these three independent iBioID approaches and the fact that axonal compartments are separated from somata provides a precise and valuable description of the protein composition of these release sites, with many new proteins not previously associated with synaptic release sites. These data are a valuable resource for future experiments on dopamine release mechanisms in the CNS and the organization of the release sites. The BirA (BioID) tags are carefully positioned in three target proteins not to affect their localization/function. Data analysis and visualization are excellent. Combining the new iBioID approaches with existing null mutant mice produces powerful perturbation experiments that lead and strong conclusions on the central role of RIM1 as central organizers of dopamine release sites and unexpected (and unexplained) new findings on how RIM1 and synaptotagmin1 are both required for the accumulation of alpha-synuclein at dopamine release sites.

It is not entirely clear how certain decisions made by the authors on data thresholds may affect the overall picture emerging from their analyses. This is a purely hypothesis-generating study. The authors made little efforts to define expectations and compare their results to these. Consequently, there is little guidance on how to interpret the data and how decisions made by the authors affect the overall conclusions. For instance, the collection of proteins tagged by all three tagging strategies (Fig 2) is expected to contain all known components of dopamine release sites (not at all the case), and maybe also synaptic vesicles (2 TM components detected, but not the most well-known components like vSNAREs and H+/DA-transporters), and endocytic machinery (only 2 endophilin orthologs detected). Whether or not a more complete collection the components of release sites, synaptic vesicles or endocytic machinery are observed might depend on two hard thresholds applied in this study: (a) "Hits" (depicted in Fig 2) were defined as proteins enriched {greater than or equal to} 2-fold (line 178) and peptides not detected in the negative control (soluble BirA) were defined as 0.5 (line 175). How crucial are these two decisions? It would be great to know if the overall conclusions change if these decisions were made differently.<br /> Given the good separation of the axonal compartment from the somata (one of the real experimental strengths of this study), it is completely unexpected to find two histones being enriched with all three tagging strategies (Hist1h1d and 1h4a). This should be mentioned and discussed.

It would also help to compare the data more systematically to a previous study that attempted to define release sites (albeit not dopamine release sites) using a different methodology (biochemical purification): Boyken et al (only mentioned in relation to Nptn, but other proteins are observed in both studies too, e.g. Cend1).

Reviewer #3 (Public Review):

In this paper, the authors studied the influence of topological defects on extrusion events using 3D multi-phase field simulations. By varying cell-cell and cell-substrate parameters, this study helps to better understand the influence of mechanical and geometrical parameters on cell extrusion and their linkage to topological defects.

First the authors show that extrusion events and topological defects of nematic and hexatic order are typically found in their system, and then that extrusions occur, on average, at a distance of a few cell sizes from a + and - 1/2 defects. Next, the author analyse at extrusion events the temporal evolution of the local isotropic stress and the local out-of-plane shear stress, showing that near the instant of extrusion, the isotropic stresses relax and the shear stresses fluctuate around a vanishing value. Finally, the authors analyse both the distribution of isotropic stress and the average isotropic stress pattern near +1/2 defects.

Reviewer #3 (Public Review):

The manuscript reports two separate lines of evidence whereby in individuals with Malignant Hyperthermia susceptibility, the increased cytosolic calcium levels caused by leaky RYR1 mutant channels boost Calpain1 activity resulting in the activation of two different pathways, where one results into impaired glucose metabolism, while the other is expected to stimulate glucose utilization by skeletal muscles.

In the first set of data, the authors report evidence that muscles fibers of MHS patients contain increased levels of the 40kDa activated form of GSK3ß, which is generated by Calpain1-mediated cleavage of 47kDa full length GSK3ß protein. The activation of GSKß activity is associated to impaired glucose utilization by skeletal muscle and fits well with previous data on alterations of glucose storage in MHS patients reported by the same authors in a previous paper (Tamminemi et al., 2020).

In the second set of data, the authors report evidence indicating that skeletal muscles from individuals with MHS present reduced levels of JPH1 in the presence of a 44 kDa fragment of JPH1 (JPH44) that corresponds to the C-terminal region of JPH1, a cleavage again generated by the calcium-induced activation of Calpain1 proteolytic activity. They then go on to present data indicating that the JPH44 fragment, although expected to contain the transmembrane segment of JPH1, migrates to the nucleus where it activates the transcription of genes correlated with increased glucose metabolism, an activity that would oppose the effect of GSK3ß activation. These data on JPH44 show some analogy with the reported calcium-induced cleavage of JPH2 in cardiomyocytes, where a fragment of JPH2 translocate to the nucleus, where it activates a protective program to counteract cardiac stress conditions (Guo et al., 2018).

1) Figure 1 A and B show a western blot of proteins isolated from muscles of MHN and MHS individuals decorated with two different antibodies directed against JPH1. According to the manufacturer, antibody A is directed against the JPH1 protein sequence encompassing amino acids 387 to 512 while antibody B is directed against a no better specified C-terminal region of JPH1. Surprisingly, antibody B appears not to detect the full-length protein in lysates from human muscles, but recognizes only the 44 kDa fragment of JPH1. However, to the best of the reviewer's knowledge, antibody B has been reported by other laboratories to recognize the full-length JPH1 protein.<br /> Thus, is not obvious why here this antibody should recognize only the shorter fragment. In addition, in MHS individuals there is no direct correlation between reduction in the content of the full-length JPH1 protein and appearance of the 44 kDa JPH1fragment, since, as also reported by the authors, no significant difference between MHN and MHS can be observed concerning the amount of the 44 kDa JPH1.<br /> Based on the data presented, it is very difficult to accept that antibody A and B have specific selectivity for JPH1 and the 44 kDa fragment of JPH1.

2) In Figure 2B staining of a nucleus is shown only with antibody B against the 44 kDa JPH1 fragment, while no nucleus stained with antibody A is shown in Fig 2A. Images should all be at the same level of magnification and nuclear staining of nuclei with antibody A should be reported.<br /> In Figure 2Db labeling of JPH1 covers both the nucleus and the cytoplasm, does it mean that JPH1 also goes to the nucleus? One would rather think that background immunofluorescence may provide a confounding staining and authors should be more cautious in interpreting these data.<br /> Images in 2D and 2E refer to primary myotubes derived from patients. The authors show that RyR1 signals co-localizes with full-length JPH1, but not with the 44 kDa fragment, recognized by antibody B. How do the authors establish myotube differentiation?

3. Figure 3 A-C. The authors show images of a full-length JPH1 tagged with GFP at the N-terminus and FLAG at the C-terminus. In Figure 3Ad and Cd the Flag signal is all over the cytoplasm and the nuclei: since these are normal mouse cells and fibers, it is surprising that the FLAG signal is in the nuclei with an intensity of signal higher than in patient's muscle.<br /> Can the authors supply images of entire myotubes, possibly captured in different Z planes? How can they distinguish between the cleaved and uncleaved JPH1 signals, especially in mouse myofibers, where calpain is supposed not to be so active as in MHS muscle fibers?

4. If the 44 kDa JPH1 fragment contains a transmembrane domain, it is difficult to understand the dual sarcoplasmic reticulum and nuclear localization. To justify this the authors, in the Discussion session, mention a hypothetical vesicular transport of the 44 kDa JPH1 fragment by vesicles. Traffic of proteins to the nucleus usually occurs through the nuclear pores and does not require vesicles. Even if diffusion from the SR membrane to the nuclear envelope occurs, the protein should remain in the compartment of the membrane envelope. There is no established evidence to support such an unusual movement inside the cells.

5. In Figure 5, the authors show the effect of Calpain1 on the full-length and 44 kDa JPH1 fragment in muscles from MHS patients. Can the authors repeat the same analysis on recombinant JPH1 tagged with GFP and FLAG? Can the authors provide images from MHN muscle fibers stained with JPH1 and Calpain1.

6. In Figure 6, the authors show images of MHS derived myotubes transfected with FLAG Calpain1 and compare the distribution of endogenous JPH1 and RYR1 in two cells, one expressing FLAG Calpain1 (cell1) and one not expressing the recombinant protein. They state that cell1 shows a strong signal of JPH1 in the nucleus, while this is not observed in cell2. Nevertheless, it is not clear where the nucleus is located within cell2 since the distribution of JPH1 is homogeneous across the cell. Can the authors show a different cell?

7. In Figure 7, panels Bb and Db: nuclei appear to stain positive for JPH1. It is not clear why in panels Ac, Bc they show a RYR1 staining while in panels Cc and Dc they show N-myc staining. The differential localization to nuclei appears rather poor also in these panels.

8. The strong nuclear staining in Figure 8, panels C and D is very different from the staining observed in Fig. 2 and Fig. 3. Transfection should not change the ratio between nuclear and cytoplasmic distribution.

Reviewer #3 (Public Review):

In order to study memory Tfh cell subsets the authors develop an in vitro assay to generate Ovalbumin (OVA) specific Tfh1, Tfh2 and Tfh17 cells. In vitro, these subsets express the expected hallmarks of successful differentiation. These subsets are able to mostly maintain their phenotype upon adoptive transfer and reactivation (by immunisation) in vivo providing an experimental system to test their function. The transferred cells can support germinal centres and antibody production, with iTfh17 having a larger effect after a long in vivo rest period, proposed to be due to enhanced expression of CCR7.

The authors then focus on human CXCR5+CD45RA-CD4+ cells that they call circulating Tfh-like (cTfh) cells, and divide these into CCR7+PD-1- TfhCM and PD-1+CCR7low TfhEM. RNAseq shows that there are different pathways enriched in these groups, with TfhCM having superior survival and proliferation in vitro as compared to TfhEM. The authors then further subdivide TfhCM and TfhEM into Tfh1/2/17 and show that there are differences in the ratios of these subgroups, and that the TfhEM have more pronounced effector characteristics that are typically associated with Th1/2/17 cells. In an HBV vaccination cohort, antigen specific cTfh17 cells were expanded in people who produced an early antibody response to HBV, but not in those who responded later. The authors then used a publicly available dataset of scRNAseq of HA-specific CD4+ T cells to identify an enrichment of T cells Tfh17 signature prior to vaccination and with a Tfh1 signature 12 days after vaccination, the latter finding is consistent with previous reports. Finally, the authors examine long term immunity by focusing on antigen-specific cells that likely were generated during childhood vaccination. cTfh17 cells were the most abundant cTfh subset recalled. Further these appear to accumulate with increasing age, indicating that these cells are likely retained as memory. Together, this body of work makes the case that CCR6+CXCR3-CXCR5+CD45RA-CD4+ cells (cTfh17 cells) are memory cells that are recalled upon challenge.

Reviewer #3 (Public Review):

Wilkinson et al. report the biochemical and structural characterization of two bacteriophage-encoded modifiers of E. coli RecBCD, which has both helicase and nuclease activities. In addition to a function in double-stranded DNA break repair, RecBCD also degrades the genomic DNA of an invading phage and generates phage DNA fragments to be incorporated into CRISPR-based defense systems. Bacteriophages often encode inhibitors to block the RecBCD nuclease activity as the first line of defense. Furthermore, some bacteriophages also encode modifiers of RecBCD to hijack it for phage propagation. The phenomena and effects of phage-encoded Abc2 and Gam were characterized and reported in a series of papers by KC Murphy in the 1990s, of which the 1994 JBC paper is specifically cited as Reference 15.

In this paper, the authors chose to study phage T7 encoded RecBCD inhibitor gp5.9 and Salmonella phage P22 encoded RecBCD modifier Abc2. Based on prior knowledge and amino acid composition, it was proposed that gp5.9 is a DNA mimic and blocks DNA binding and hence the enzymatic activity of RecBCD. The authors verified these properties, which are similar to the phage lambda encoded RecBCD inhibitor Gam, whose structure in complex with RecBCD is known. However, gp5.9 shares no sequence similarity with Gam. The cryoEM structure of RecBCD-gp5.9 was thus determined by the authors and reveals that gp5.9 dimerizes to generate a pair of parallel negatively charged alpha helices that mimic a DNA substrate and block DNA binding by RecBCD. Meanwhile, GamS dimerizes in an orthogonal fashion, and only one GamS subunit extends an alpha helix into the DNA binding site of RecBCD. This study shows the diversity in biology and convergent evolution of bacteriophage in blocking RecBCD.

Interestingly, Abc2 cannot be purified by itself alone but is stable only in complexes with RecBCD. Because of a Proline residue (Pro68) in Abc2, which is a substrate of prolyl-isomerase (PPI), WT Abc2 is tightly associated with PPI, but the mutant Abc2P68A can be separated from PPI. Therefore, the authors have prepared both RecBCD- Abc2P68A and RecBCD- Abc2-PPI. The biochemical characterization of the effects of Abc2 on RecBCD is a repeat of KC Murphy's paper, but different from KC Murphy's in the effects of Abc2 on dsDNA-end binding (2-4 fold increase, by Murphy) and helicase activity (3-4 fold reduced, by Murphy) of RecBCD (reference 15). Here, both RecBCD- Abc2P68A and RecBCD- Abc2-PPI have comparable enzymatic activities as RecBCD alone and both can be blocked by gp5.9 as by Gam (Murphy). The cryoEM structures reveal Abc2 binds the Chi-recognition RecC subunit and potentially modifies RecBCD in response to the Chi sequence. But in the absence of DNA, the structure does not explain the in vivo function of Abc2 hijacking RecBCD, nor how Abc2 alters dsDNA binding and helicase activity of RecBCD as reported by Murphy.

The biochemical experiments are expertly carried out. The cryoEM structures are of good quality. While the RecBCD-gp5.9 structure explains the inhibiting mechanism of gp5.9, the lack of functional effects of Abc2 on RecBCD in the in vitro assays is peculiar.

Reviewer #3 (Public Review):

The lissencephaly 1 protein, LIS1, is key regulator of cytoplasmic dynein-1. Gillies et al., (2022) had previously reported a 3.1 Å structure of yeast dynein bound to Pac1, the yeast homologue of LIS1. This structure revealed the details of their interactions but mutational studies based on sequence homology indicated that it did not completely represent how Lis1 binds to human dynein. To mitigate this lack of knowledge, in this manuscript, the authors have solved the structure of Lis1 bound to human cytoplasmic dynein-1 using cryo-EM.

The authors solved structures of human dynein bound to one and two LIS1 β-propellers to 4.0 Å and 4.1 Å, respectively. These structures revealed that while the overall structure of dynein's interaction with LIS1/Pac1 is conserved from yeast to humans, there are important differences in the specifics of the dynein-LIS1/Pac1 and LIS1/Pac1-LIS1/Pac1 interactions. The authors further suggest residues/interfaces that can be targeted in the future to probe the role of LIS1 in promoting the assembly of active dynein complexes.<br /> This structure is an important piece in the puzzle of how LIS1 activates human dynein. The information on how to better disrupt the human dynein-LIS1 interface and where the human disease-causing mutations lie will be very important for future studies.

Reviewer #3 (Public Review):

This manuscript generates a valuable new genetic resource for mosquito research. The ribosomal RNA (rRNA) data generated for 33 mosquito species will ultimately enable physical subtraction of rRNA from mosquito RNA preps prior to sequencing, something that has not been possible for most mosquito species. This will dramatically improve the power of RNA sequencing in the mosquito field. Since mosquitoes harbor many RNA viruses, this is very important and removes a major roadblock to the study of mosquitoes and their viruses.

In addition, the authors seem to show that rRNA-based taxonomical identification of mosquitoes is superior to traditional COI-based taxonomy. This would be a very important finding if true, but the authors never unequivocally conclude this.

Reviewer #3 (Public Review):

In this study, the authors provide the first molecular clue to the apparent dispensability of RLC phosphorylation at S35,S36 (equivalent of RLC T18,S19 in non-muscle myosin II) for cytokinesis in Schizosaccharomyces pombe. Using point-mutant alleles, they successfully demonstrated that the S35 residue of Rlc1 is phosphorylated during cytokinesis in cells growing on glucose and that a mutant expressing the Rlc1-S35A allele is inviable on glycerol. The mutant cells exhibit slow CAR constriction and disassembly, multi-septated phenotype, and occasional cell lysis.

Rlc1 phosphorylation at S35 increases glycerol, which requires either Pak1 or Pak2. Although the localization of endogenously tagged Pak2-GFP was not detectable, the authors showed that Pak2-GFP expressed from the pak1+ promoter can localize to the division site in both glucose and glycerol conditions. Next, the authors elucidated the physiological significance of Rlc1 phosphorylation by looking at the regulation of formin For3. Previously, the authors showed that For3 is downregulated at the protein level (probably through degradation) in response to latrunculin A treatment in a Sty1-dependent manner. Similarly, the shift from glucose to glycerol caused phosphorylation of Sty1 and concomitant downregulation of For3 protein levels, which in turn caused a reduced actin cable-to-path ratio. Because expression of For3-DAD (a constitutively active allele) or a lack of For3 downregulation was sufficient to fully rescue mutants in which Rlc1-S35 phosphorylation is impaired in glycerol conditions, the authors concluded that this phosphorylation compensates for the reduced actin-cable nucleation.

Finally, the authors hypothesized that ROS production during respiratory growth is responsible for Sty1-dependent For3 downregulation, and showed that the addition of the antioxidant GSH was sufficient to rescue the reduction in For3 levels and (as expected) the inviability of mutants lacking Rlc1-S35 phosphorylation in glycerol.

This will be the first report on the cellular response in the regulation of cytokinesis to a shift from fermentative to respiratory growth. It provides a new and important context to the value of fission yeast as a model to study animal cytokinesis and the effects of oxidative stress during the process. Data are generally well presented and clear-cut, and the components of two molecular pathways involved (SAPK-For3 and PAK-Rlc1) appear to behave in manners consistent with the authors' conclusions.

Some areas of weakness are as follows:<br /> (1) Lack of use of phosphomimetic Rlc1 alleles (e.g., Sladewski et al., MBoC 2009) to strengthen the author's conclusions.<br /> (2) It is not very clear how the two pathways (SAPK-For3 and PAK-Rlc1) interact with each other. Fig. S6 suggests that the authors favor the model they are regulated independently under respiratory conditions. However, alternative models are possible and testable.<br /> (3) The authors conclude that oxidative stress causes Sty1 phosphorylation and that this phosphorylation is ultimately responsible for For3 downregulation and dependency on phosphorylation at Rlc1-S35. However, it is formally possible that all of these are independent events, which could easily be tested by using the sty1∆ mutant that the authors have used in publication.

Reviewer #3 (Public Review):

This important study uses high resolution imaging of single synaptic vesicle fusion events to look at the localization of individual vesicle vGlut-pHluuorin fusion events. Using this approach, the authors were able to determine with high resolution the location of single vesicle fusion. The authors find that a significant percentage of asynchronous events occur ectopically outside the synapse, but that most still fuse within the synapse and that the fluorescent decay rates, as a proxy for vesicle endocytosis change with localization within the synapse.

Reviewer #3 (Public Review):

Definitive endoderm is an important transient, progenitor tissue formed in the embryo that gives rise to most of the internal organ systems. Studying how definitive endoderm arises in development is important for understanding several common diseases and also for improving methods to specialise pluripotent stem cells in culture towards functional cell types with applications in regenerative medicine. The aim of the current study was to identify and characterise new genetic factors that contribute to these processes. The authors identified a previously-overlooked gene that they named LNCSOX17 and showed that this gene is needed for cells in culture to maintain their definitive endoderm identity. Similar genes have been shown previously to function by controlling other nearby genes, but the authors showed that this is not what is happening for LNCSOX17. Instead, it is likely that LNCSOX17 affects other processes in the cell, beyond the nearby gene. This research provides a nice example of how a noncoding gene that is expressed in a very restricted developmental stage can have strong effects on cell lineage control. Because there are thousands of other long, noncoding transcripts, most of which are largely uncharacterised, this study emphasises the urgent need to examine this type of transcript in further detail.

Overall, the main conclusions of the manuscript are well supported by the evidence.

A key strength of the work is that the authors use state-of-the-art genetic methods in human pluripotent stem cells to address the function and regulation of LNCSOX17 and nearby regulatory elements. It is clear that disabling LNCSOX17 does not affect SOX17, establishing that the long noncoding transcript does not function in cis.

Robust cellular assays also provide strong evidence that the LNCSOX17 transcript is required for the continued development of endoderm cells (but not for the initial specification).

Whether LNCSOX17 operates in trans is not fully established, but the authors present evidence that supports this viewpoint and they put forward a plausible model for how this might be mediated (albeit very preliminary, as they acknowledge).

Reviewer #3 (Public Review):

Meng, Shi et al determined the crystal structure of the Bre1 RBD-Rad6 complex from Kluyveromyces lactis and found that RBD forms an asymmetric dimer binding to a single Rad6 molecule. Subsequently, the author confirmed the binding mode of RBD-Rad6 complex by structure-based mutagenesis. They show that the binding of Bre1-RBD to Rad6 is important for both Rad6-mediated ubiquitin chain production and ubiquitin discharging of the E2~ubiquitin conjugate. In addition, they show that the interaction between Bre1 RBD and Rad6 is crucial for Bre1-mediated H2B mono-ubiquitination or homologous recombination repair inside the cell.

This study presents a useful finding on the mechanism of Bre1/Rad6-mediated ubiquitination and the conclusions of this paper are mostly well supported by data, but some aspects of claims need to be clarified and extended.

Reviewer #3 (Public Review):

This manuscript provides evidence of the correlation of Gdf11 expression to MeCP2 protein levels, demonstration of phenotypic improvement of mice overexpressing MeCP2 by genetic reduction of Gdf11 levels, and characterization of the phenotypic effects of loss of one copy of Gdf11 on mouse behavior and survival. Significance of the work is driven by the understanding that both gain and loss of MeCP2 function, a transcriptional regulator, causes severe neurodevelopmental disease associated with widespread transcriptional changes. Furthermore, recent work has identified people with neurodevelopmental problems associated with heterozygous mutations in Gdf11. The results are potentially impactful in that the identification of a specific gene target of MeCP2 relevant to pathophysiology and the underlying molecular abnormalities associated could provide insight into future novel therapeutic interventions, as well as the initial characterization of an animal model of a different neurodevelopmental disorder. Furthermore, the work expands the understanding of aspects of the importance of gene dosage in neurodevelopmental disorders and outlines interesting approaches to dissect the underlying genetic network interaction.

Strengths:<br /> 1. Careful bioinformatic evaluation of gene expression changes in MDS mice responsive to anti-sense oligonucleotide treatment that reduces MeCP2 RNA and protein levels to identify a set of genes whose expression was highly correlated with MeCP2 protein levels, restriction to genes of interest based on human predictive algorithms of loss-of function intolerance, followed by analysis of existing transcriptional profiles from multiple species (human, rat, mouse) to restrict focus to Gdf11<br /> 2. Combinatorial use of reporter mouse lines and modern molecular genetic techniques to establish relationship between MeCP2 protein levels and Gdf11 locus binding and regional histone epigenic modifications to support model of direct transcriptional relationship between MeCP2 protein and Gdf11 transcription.<br /> 3. Systematic phenotypic evaluation of the effect of reducing Gdf11 copy number in MDS mice to demonstrate amelioration of some phenotypes observed in MDS mice, as well as evaluation of the effect of Gdf11 copy number reduction on mouse phenotypes to demonstrate mouse phenotypic abnormalities that suggest that this mouse line can be a mouse model of the human disease caused by the heterozygous loss of function mutations in Gdf11

Weaknesses<br /> 1. There is a lack of detailed information on the exact composition of the various cohorts of animals used, the age and order of the specific behavioral assessments, and any accounting for the multiple behavioral test performed (to adjust for the multiple statistical tests).<br /> 2. A number of the behaviors that showed improvement with genetic reduction of Gdf11 in MDS mice were behaviors in which the Gdf11 heterozygous mice showed the opposite behavioral abnormality as the MDS mice. For example, total distance in the open field in MDS mice was reduced compared to WT mice, whereas in Gdf11 het mice there is an increased amount of total distance traveled. Similar opposite directions are present in a number of the key phenotypic measures (elevated plus, conditioned fear). The presence of these opposing phenotypic abnormalities between MDS and Gdf11 het mice make interpretation of a partial amelioration of MDS phenotypes by genetic reduction of Gdf11 less clear, as the final "normalization" could reflect an additive effect of opposing phenotypes resulting in a pseudonormalization resulting from aberrant changes in completely independent underlying mechanisms, rather than directly associated with correcting underlying problems directly associated with MDS. Potentially most interesting, and worth commenting upon, are those opposite behavioral abnormalities (such as rotarod) that do not show improvement in the double mutant animals.<br /> 3. The transparency and availability of the entirety of the data contributing to the manuscript (including behavioral data) could be improved by inclusion as supplemental tables or deposition into freely and readily available data repositories or websites (rather than indicating that it is available from corresponding author upon request).

Reviewer #3 (Public Review):

In recent work, Prigge and collaborators reported the essential function of the apicoplast in the synthesis of isoprenoids which serves as a precursor of several biochemical processes. The pathway involving the synthesis of IPP includes Fe-S enzymes IspH/IspG. Thus, the inactivation of the gene products promoting the assembly of Fe-S clusters for these enzymes in the apicoplast indirectly affects IPP formation and makes the function of these genes likewise essential. Recently, the authors established that the essential requirement of IspH/IscG can be bypassed if an alternate IPP pathway is provided. The mevalonate (MEV) pathway does not require the involvement of Fe-S enzymes and allows for the mevalonate-dependent organism's survival even after disruption of IspH/IspG or ferredoxin (involved in Fe-S cluster formation). The MEV bypass genetic construct provides a valuable experimental handle to expand the analysis of additional functions essential to the apicoplast. Using this genetic tool, this report provides experimental evidence demonstrating the essentiality of sufS, sufE, sufC, sufD, and sufB in IPP synthesis and supporting their previously proposed roles in Fe-S cluster biosynthesis. Although the results from these experiments were anticipated, the novel finding of this study is that phenotypes associated with sufS inactivation differ from the phenotypes associated with the inactivation of other components of the Fe-S cluster biosynthetic apparatus pointing to additional function(s) of this enzyme.

Cysteine desulfurases are enzymes involved in sulfur mobilization for the synthesis of Fe-S cluster and other sulfur-containing cofactors. Thus, the inactivation of sufS would likely lead to the depletion of additional sulfur-containing biomolecules in the apicoplast. Using the MEV bypass, the authors showed sufS inactivation led to the loss of the apicoplast genome, indicating the involvement of SufS in additional essential functions in this organelle. Based on this premise, the authors tested the hypothesis that tRNA thiolation was also an essential process in this organelle. Experimental validation supporting this hypothesis included 1) genetic evidence that the putative tRNA 2-thiouridylase MnmA is also essential and that mnmA inactivation leads to phenotypes that mirror those of sufS inactivation in the MEV bypass genetic background, 2) B. subtilis MnmA or MnmA-YrvO fusion complements the PfMnmA inactivation, and 3) B. subtilis MnmA-YrvO fusion is able to complement PfsufS inactivation in an MEV bypass. Collectively, these results support a model in which SufS is involved in two essential functions Fe-S cluster formation and tRNA thiolation. Interestingly, genetic analysis suggests that SufS but not SufE are involved in tRNA thiolation, indicating the occurrence of a direct SufS-MnmA sulfur transfer reaction, a mechanistically distinct feature from other characterized SufS-like enzymes that require a dedicated E-like sulfur transferase. Thus the absence of SufS-like sequences in the host cells combined with the essentiality of this enzyme for the parasite life cycle offers an attractive target for metabolic intervention.

Reviewer #3 (Public Review):

The authors sought to develop an efficient protocol for granulosa-like cells by identifying and testing transcription factors identified through secondary analyses of RNA-seq data. The transcription factors were exogenously expressed in human iPSCs and tested for their ability to induce expression of granulosa cell genes, produce estradiol, and form ovaroids with human primordial germ cell-like cells.

There are weaknesses in some descriptions of experiments and results. Additionally, the follicle formation in the ovaroid experiments was not adequately identified or described. Finally, additional lines of human iPSCs (biological replicates) to demonstrate granulosa cell expression after the final transcription factors were determined, would increase the robustness of the granulosa-like cell differentiation protocol.

The major strengths of this manuscript include the comparison of granulosa-like cells in vitro and in the ovaroid aggregates to previously published RNA-seq analyses of human fetal ovaries. Additionally, several human, murine, and cell line controls were used where appropriate to compare cell expression.

Overall, the authors have achieved their aims of identifying transcription factors that induce a granulosa-like phenotype in human pluripotent stem cells. The production of estradiol and the presence of DAZL4+ cells in an aggregate culture that includes human primordial germ cell-like cells confirmed the functionality of the granulosa-like cells (with the caveat that the cell origins within the ovaroid culture need to be confirmed).

There are several challenges to studying human fetal ovary development and an efficient, robust granulosa-like cell protocol for human pluripotent stem cells, as described here, will lead to major advancements in this field.

Reviewer #3 (Public Review):

The manuscript by Cover et al. follows up on their recent work examining a poorly characterized connection from nuclei in the rostral intralaminar thalamus to the dorsal striatum. Their previous work demonstrated that mice self-administer optogenetic activation of this pathway, which promotes dopamine release in the striatum (in a multi-synaptic fashion).

In terms of thalamostriatal connectivity, there has been a greater focus on the more robust striatal inputs from the center median and parafascicular thalamic nuclei. Notably, the rostral intralaminar thalamic inputs are thought to be morphologically distinct from their parafascicular counterparts in that they have stronger thalamocortical projections, their axons preferentially synapse on the spines of striatal output neurons (as opposed to the dendritic shafts of these neurons or cholinergic interneurons), and they may relay information from the cerebellum to striatum. As such, the author's functional characterization of the striatal projection from the less understood intralaminar thalamic nuclei is an important conceptual advance.

By using projection-specific calcium imaging, the authors show that these projections activate during lever pressing or the initiation of well-learned lever-pressing sequences and during the receipt of reward. Notably, the authors found no correspondence between the expected value of the lever presses, since devaluing the rewards or extinguishing their delivery altogether had no effect on the magnitude of this pathway's activation at the time of lever pressing. Devaluation also had no impact on the magnitude of activation at the time of reward delivery.

By contrast, the magnitude of activation in this pathway did inversely correlate with the animal's latency to initiate pressing and retrieving the reward. Moreover, activity in this pathway was positively correlated to spontaneous movement in an open-field arena. In conjunction with the author's earlier study, these findings suggested this pathway could be important for goal-directed action selection. In agreement with this idea, optogenetically manipulating this pathway bi-directionally modulated performance in their lever-pressing task.

The data presented overall support the claim that this pathway is important for operant conditioning. One weakness is that the optogenetic inhibition experiments produced very small effect sizes. This could be related to the technical difficulty of inhibiting enough of these sparse projections to the striatum. Another potential drawback (related to this weakness) is an over-interpretation of the importance of this projection and the underemphasis on the importance of somatically driven dopamine release, ideas that could be better addressed in the abstract and discussion.

Reviewer #3 (Public Review):

Zhang et al focus on investigating the role of pericytes in the vasculature of the inner ear. They propose that pericyte-derived VEGF is required for vessels and SGN survival. Functionally, they show that pericyte ablation leads to hearing loss.

This work is interesting to the scientific community. It describes a very specific organ vasculature and its potential crosstalk with the neuronal compartment in the peripheral nervous system.

Major strengths and weaknesses:

- The study is well explained, written, and discussed;<br /> - The design of the experiments is adequate;<br /> - The study is performed in vivo, in vitro, and with functional readouts;<br /> - Results are convincing.

The main conclusion of the study is that pericyte-derived VEGF acts on inner ear vessels and SGNs to maintain their functionality and survival. While all presented data supports this model, there could be other potential interpretations that should be tested and validated with further evidence:

- The in vitro experiments are performed with SGN explants. Using this system the authors see that pericyte-derived conditioned medium or exosomes lead to increase vessel branching and SGN neurite outgrowth. As explants contain vessels and neurons, there is the possibility that VEGF is primarily acting on endothelial cells, which then in turn signal to neurons (independent of VEGF, even when neurons express VEGFR2). This should be tested. Perhaps by targeting VEGFR2 specifically in neurons, or by culturing isolated SGN neurons and testing the effect of pericyte-derived exosomes.

- Pericyte ablation via DTA might result in the activation of the immune system, which could also influence vessels and neuronal survival. It should be checked whether there is immune activation upon pericyte ablation.

Reviewer #3 (Public Review):

The use of mutagenic drugs in combating new viral diseases is increasing, so it is imperative to understand how they might impact the evolutionary trajectory of RNA viruses and weigh their potential benefits versus their harms. The authors examined the impact on treatment outcomes and virus populations of treatment with mutagenic drugs (ribavinin and favipiravir) in a child with severe combined immunodeficiency syndrome and RSV pneumonitis. The authors report that despite a three-fold increase in viral mutation within-host evolution was still slow with only minor gain in viral fitness. The patient's clinical status was stable despite virus non-clearance by the drugs.

Despite looking at only one case, this study illustrates the potential impacts of widespread use of antiviral mutagenic drugs in the event of a viral epidemic. The authors warn in the discussion of the study that the results should be interpreted with caution if the same drugs are given to individuals who are immunocompetent, which I agree.

Reviewer #3 (Public Review):

This is interesting research that uncovers a novel inhibition mechanism for serotonin (SERT) transporters, which is akin to traditional un-competitive inhibitors in enzyme kinetics. These inhibitors are known to preferentially bind to the enzyme-substrate complex, thus stabilizing it, resulting in a decrease of the IC50 with increasing substrate concentrations. In contrast to this classic enzyme inhibition mechanism, the authors show for SERT, through detailed kinetic analysis as well as kinetic modeling, that the inhibitor, ECSI#6, binds preferentially to the inward-facing state of the transporter, which is stabilized by K+. Therefore, inhibition becomes "use-dependent", i.e. increasing substrate concentrations push the transporter to the inward-facing configuration, which then leads to the increased apparent affinity of ECSI#6 binding. Interestingly, this mechanism of action predicts that the inhibitor should be able to rescue SERT misfolding variants. The authors tested this possibility and found that surface expression and function of a misfolding mutant SERT is increased, an important experimental finding. Another strength of the manuscript is the quantitative analysis of the kinetic data, including kinetic modeling, the results of which can reconcile the experimental data very well. Overall, this is important and, in my view, novel work, which may lead to new future approaches in SERT pharmacology.

With that said, some weaknesses of the manuscript should be mentioned. 1) The authors suggest that serotonin and ECSI#6 cannot bind simultaneously to the transporter, however, no direct evidence for this conclusion is provided. 2) How does ECSI#6 access the inward-facing binding site? Does it permeate the membrane and bind from the inward-facing conformation, or is it just a very slowly transported low-affinity substrate that stabilizes the inward-facing state with much higher affinity? Including ECSI#6 in the recording electrode may provide further information on this point. Additionally, it is not clear why displacement experiments were not carried out with cocaine. Since cocaine is a competitive inhibitor but does not induce transport (i.e. doesn't induce the formation of the inward-facing conformation), it should act in a competitive mechanism with ECSI#6. 3) Why are dose-response relationships not shown for electrophysiological experiments? These would be a good double-check for the radiotracer flux data.<br /> Despite these weaknesses, I believe that this is important work, which adds to our understanding of the pharmacology of serotonin transporters, which are of critical nature due to being a target of anti-depressant drugs. The data make a case for the proposed inhibition mechanism and the interpretation of results, as well as conclusions, are generally sound.

Reviewer #3 (Public Review):

Molecular-level interpretations of SAXS data are challenging, especially for oligomeric systems of variable length with intrinsic flexibility and the possibility of multiple association interfaces. In order to make this challenge tractable, a number of assumptions are made here: 1) There is a single pathway by which individual domains associate first into homodimers and then into longer oligomers; 2) the association kinetics is isodesmic, which allows the direct calculation of oligomer distributions based on the given value of a single dissociation constant; 3) the internal dynamics within dimers is restricted essentially to relative domain-domain motions, that are sampled comprehensively via MD simulations. As a result, excellent fits to the SAXS data are obtained and the underlying conformational ensembles are highly plausible. The resulting models are useful to further understand SPOP function, especially in the context of liquid-liquid phase separation.

Reviewer #3 (Public Review):

Wernet et al. show that there are intrinsic protein oscillations at the hyphal tips of A. flagrans, a nematode trapping fungus, that become coordinated when two hyphae become close. They create a mathematical model of this synchronization phenomenon, and then go on to show that calcium is critical to the functioning of these oscillations and hyphal fusion. The concept of inter-hyphal communication through signal synchronization is fascinating, and the visual matching of the output of the model to the data is compelling. However, given that the authors already showed synchronized oscillations in the SofT protein in A. flagrans in Hammadeh et al. 2022 (Figure 4), this diminishes the novelty of the findings in this study. Additionally, as it also has been established that calcium drives other oscillatory communications, the characterization of calcium dependence is not especially novel or bringing new insights into the problem especially since it is unclear if the chelation is having effects due to loss of intracellular supplies and/or because it is the key signal in the dialogue. Right now the mathematical model feels a bit vague with discussion of hypothetical molecules, so the paper would be greatly strengthened if any key regulatory molecules that promote desychronization could be identified or there were some manipulations of the core known proteins that examined consequences of altering the oscillations. As it is, the reader is left intrigued but there are few concrete conceptual advancements.

Reviewer #3 (Public Review):

The issues raised in this review are more conceptual in nature and my suggestions are designed to sharpen the focus of the paper. The paper does a good job of explaining how prices of NIH project have changed over time but leaves the reader wanting a clearer understanding as to why this has happened. The paper raises the issue of price effects compared with compositional effects at the beginning and the very end of the paper. It would have been helpful for the paper to be more explicit about examining price changes and composition changes in the organizing structure of the paper (e.g. the solicited v. unsolicited is a compositional change and should be highlighted as such). The authors conclude that changes in NIH prices are associated with changes in the composition of NIH funding, and the evidence supports that. However, the NIH has inordinate control over prices because of the salary cap imposed in 2012. It would be helpful to see the relative weights of the various components of the BRDPI index in the paper graphed over time. I suspect the personnel salaries receive the highest weight. Figure 1B indicates BRDPI dropped by over 1.5 percentage points once the salary cap was put into place. When the NIH mechanically caps the price increases in salaries, they will hold research inflation (BRDPI) in check.

In addition, many of the notable trends in the data deserve further discussion. For example, in Figure 1A, awards are much higher than awardees, indicating that there are many PIs with multiple awards. This difference narrowed after 2013, but by 2021, there are ~5,000 multiple RPG awardees. This deserves some discussion. Furthermore, in Figures 2 through 4, the real value of NIH funding per project has fallen since the NIH doubling. This is a hugely important point and deserves more discussion. Eyeballing the real drop in value in Figures 3 and 4, it's approximately ~$50,000 (about 10%) close to the cost of one postdoc on an RPG. Clearly, by keeping the real costs of funding per project down, NIH is able to fund more projects. But what are the tradeoffs of this kind of policy? This may be beyond the scope of the paper, but it would be helpful for the authors to discuss the possibility that imposing the salary cap may have had some unintended consequences.

On Page 9 the authors state: "From 2012 through 2021 whisker ranges increased, exceeding levels for the doubling for untransformed costs, and not quite reaching doubling levels for logtransformed costs." Later the authors argue that this is the result of changes in the composition of research grants-that solicited grants are a larger share and cost more. However, it may be possible that the variance of funding costs is a by-product of the salary cap in 2012. When PIs could no longer charge full personnel costs, they may have developed different approaches to maximizing funding from NIH. This should be commented on in the paper. For example, are certain institutions (perhaps those that receive a lot of NIH funding in the first place) better at this kind of budget request than others.

While the authors attribute much of the change in the variance of costs to composition effects (solicited vs. unsolicited projects), the timing of the variance changes is interesting. It's very telling that during the doubling, the variance in grants was higher and then when NIH funding fell in real terms, the variance in funding narrowed (Figure 6). After the salary cap and the 2015 budget increases, the variance in funding increased again. This suggests that when money is tight the variation in funding narrows. I know the authors ran a regression on the time effects of actual funding costs (Figure 13) but not on the variance. Again, the time series of the variance in funding begs for further explanation.

Since much of the change in the composition of NIH grants is between solicited vs. unsolicited projects, it would be helpful to provide more information on the nature of solicited proposals and why NIH has shifted to funding more of them. For example, are these one-time solicitations? Are these U-mechanisms? Some combination of both? How would COVID-related funding appear in the NIH portfolio? A paragraph describing this change in emphasis and the types of projects being solicited would be very helpful.

In the conclusion, it would be helpful to mention the NIH salary cap during the discussion of the Baumol cost disease. While it is true that services will cost more overtime relative to goods (since robots can replace production workers in manufacturing but not postdocs in laboratories), the NIH effectively has its thumb on the price level with the salary cap. Cost disease is not going to be as problematic as long as the salary cap remains in place. However, there is growing evidence that the effective price cap that NIH has in place on NRSA stipend levels is generating shortages of postdocs (see https://www.science.org/doi/pdf/10.1126/science.add6184 and https://www.statnews.com/2022/11/10/tipping-point-is-coming-unprecedented-exodus-of-young-life-scientists-shaking-up-academia/). The authors should comment on the growing reports of labor shortages and consider how NIH may have to respond to this in the coming years.

Reviewer #3 (Public Review):

The authors perform a thorough investigation of the role of Islet2 in the specification of lumbar motor pools. They use a number of approaches, including RNA-seq, behavioral testing, and imaging to establish a role for this transcription factor (TF) in the organization and axonal and dendritic morphology primarily of the Gl motor pool. The experiments are clear, well-presented, and convincing. Concerns about this work stem from the fact that the authors use a null mouse instead of a conditional. While this is not so problematic when examining MN properties such as organization, it makes data on connectivity and behavior hard to interpret. Since the authors perform one experiment with the conditional mouse (showing Pea3 downregulation), it is a bit puzzling that they did not use these mice for the rest of the experiments.

Reviewer #3 (Public Review):

In this study, the authors studied the underlying mechanism of obesity-related inflammation in OA synovitis. They found more pronounced synovitis and enhanced macrophage infiltration accompanied by dominant M1 macrophage polarization in obese OA patients and ApoE-/- mice synovial tissues. Enhanced M1-polarized macrophages in obese synovium decreased growth arrest-specific 6 (GAS6) secretion, which resulted in impaired macrophage efferocytosis in synovial apoptotic cells. Intra-articular injection of GAS6 restored the phagocytic capacity of macrophages, reduced the accumulation of local apoptotic cells, and decreased the levels of TUNEL- and caspase-3-positive cells, preserving cartilage thickness and preventing the progression of obesity-associated OA. The main strengths of the paper are the discovery of the underlying mechanism of obesity-associated osteoarthritis. However, some claims and conclusions were not well supported by their data.

Reviewer #3 (Public Review):

In the human disease multiple sclerosis (MS) and in inflammatory demyelinating mouse models of MS, a subset of oligodendroglia express MHC genes. The role of MHC-expressing oligodendroglia in disease is unknown but thought to relate to a novel antigen-presenting function in these cells.

This study represents a fundamental advancement in approaches to detect and quantify the spatial and temporal expression of MHC I and MHC II genes in vivo through the generation of two reporter mice encoding CD74- or B2m-TdTomato fusion genes. This affords a highly quantitative method to isolate cells expressing the relevant fusion proteins and study their differential gene expression. The study advances the recent concept of oligodendroglia heterogeneity and in particular the presence of MHC expressing immune oligodendroglia.

Prior work has shown oligodendrocyte heterogeneity, induction of MHC I and/or MHC II genes in "stressed" oligodendrocytes, and immunologic OPCs in MS at the transcriptional level (Schirmer 2019, Jakel 2019, Absinta 2021). Authors of the current work have shown that OPC differentiation is impaired by effector T cells, that IFNγ induces the MHC class I in these cells and that class I expressing OPC can present antigen, in vitro, to CD8 T cells (Harrington 2020, Kirby 2019). However, a deeper understanding of 1) how common is this process under different pathologic conditions, 2) where and when does MHC I and MHC II expression in oligodendroglia occur during a multistep pathophysiologic process, and 3) what is the full transcriptional characterization of immune oligodendroglia and how do they differ from other oligodendroglia, is lacking. The work presented in this manuscript address this gap and provides a tool for investigation into these questions for the community.

The investigators created two reporter mice - a CD74-TdTomato (class II) and a B2m-TdTomato (class I) strain. Figure 1 shows the targeting strategy, genotypes, and transgene expression in CD45, CD19, and CD3 cells from blood and secondary lymphoid tissue, demonstrating anticipated expression. Fig 1F and G show expression of CD74-TdT and B2m-TdT, respectively, in transverse histologic sections through the spinal cord of EAE mice with clinical scores of 0, 1.5, and 3.0 (baseline expression in naïve mice is shown in Fig 1 supp 3 and 4). Finally, supplement 5 shows higher power images, and quantitation of TdT as a function of other immunologic markers. The data nicely shows the fidelity of expression in relevant cell types and induction in vivo in EAE. In addition, the data shows that the transgene does not obviously impact expression quantitatively (Fig 1 sup 2).

The data in Figure 2 are central to the overall concept. The authors nicely demonstrate the induction of CD74-TdT and B2m-TdT in interferon gamma-treated oligodendrocytes as well as other cell types. Oligodendrocytes identified by olig2 are present in the spinal cord of mice with EAE and their frequency increases as the EAE severity increases. A strong correlation is seen between the severity of EAE and the percent of olig2 cells expressing the class I or class II gene.

In figure 3, scRNA seq performed on cells isolated from CD74-TdT or B2m-TdT mice with EAE reveals multiple subclusters of oligodendrocytes, one of which is high in MHC l as well as in other genes involved with antigen processing. The experiments are carefully conducted and contaminating cell populations were eliminated from the analysis.

An outstanding accomplishment, providing a resource to study multiple aspects of MHC I and MHC II cell-specific expression, transcriptional profiles in relevant cell types, and temporal course of activation. Most importantly, this resource will allow for a deeper quantitative analysis of the immune oligodendroglia phenotype and explore potential function in disease models.

Reviewer #3 (Public Review):

The manuscript is very well written and the graphics are quite iconic. Moreover, the hypothesis is clear and the rationale is very convincing. Overall, the paper has the potential of being of paramount importance for the TMS-EEG community because it provides a valuable tool for a proper interpretation of several previously published TMS-EEG results.

Unfortunately, in my opinion, the dataset used to train and validate the method does not support the implication and interpretation of the results. Indeed, as clearly visible from most of the figures and mentioned by the authors of the database, the data contains residual sensory artefacts (auditory or somatosensory) that can completely bias the authors' interpretation of the re-entrant activity.

Reviewer #3 (Public Review):

This work by Ishikawa et. al is focused on testing the hypothesis first proposed by Rosenbaum that Ca2+ levels in the primary cilia act as an internal regulator of cilia length by negatively regulating intraflagellar transport (IFT) injection and/or microtubule assembly. The authors first built a mathematical model for Ca2+ based regulation of cilia length through the activity of a Ca2+ dependent kinase. They then tested this model in the growing cilia of Chlamydomonas cells expressing an axonemal localized GCaMP. Ca2+ levels were manipulated genetically with a calcium channel deficient mutant line and with the addition of EGTA. While increases in Ca2+ levels do correlate with cilia length as expected by the model they found that IFT injection was positively correlated with IFT injection and increased axonemal stability which contradicts its potential as a mechanism for the cell to internally regulate cilia length.

Overall the conclusions of the paper are supported by their data. They greatly benefit from first establishing their model in a clear form and then experimentally interrogating the model from multiple angles in order to test its viability. The importance of cilia length to our understanding of human health has only become greater in recent history and the authors are making a significant contribution to our understanding of ciliary length regulation.

Reviewer #3 (Public Review):

The mitochondrial NADH dehydrogenase complex (complex I) is of prime importance for cellular respiration. It has been biochemically and structurally characterized for several groups of organisms, including mammals, fungi, algae, seed plants and protozoa. Furthermore, different complex I conformation have been reported, which are considered to possibly represent distinct physiological states of the enzyme complex. E.g. in mammalian mitochondria, two resting states can be distinguished, designated 'ready-to-go' resting state, and 'deactive' resting state. To better understand the physiological relevance of these states, complex I is here investigated from the fruit fly Drosophila melanogaster, which represents a model for insects but beyond for metazoan in general and which can be easily genetically modified.

Complex I from Drosophila is presented at up to 3.3 Angstrom resolution. It includes 43 of the 45 complex I subunits defined for mammalian complex I. Subunit NDUFA3 has been found in Drosophila complex I for the first time. Overall, Drosophila complex I is remarkably similar in its composition and structure to the mammalian enzyme. Only minor topological differences were found in some subunits. Furthermore, three different complex I states are described, termed Dm1, Dm2 and Dm3. The three states are extensively discussed and compared to the states found in mammalian complex I. Dm1, which is the dominating class, likely represents the active resting state. In Dm2, the two complex I arms are slightly twisted with respect to Dm1. In Dm3, the membrane arm appears to be 'cracked' at the interface between ND2 and ND4. It possibly represents an artefact resulting from detergent-induced loss of stability in the distal membrane domain of the Dm2 state. Both, Dm2 and Dm3 most closely correspond to the mammalian active state. A state resembling the mammalian deactive state could not be found. This result is further supported by biochemical experiments. It is concluded that Drosophila complex I, despite its remarkable similarity to the mammalian enzyme, does not undergo the mammalian-type active/deactive transition.

In conclusion, complex I structure from Drosophila is of limited value for the better understanding of the states of mammalian complex I (which could be stated more clearly). However, insights into complex I structure and function of an insect is highly interesting. The conclusions are justified by the presented data. The manuscript is well written and the figures are thoroughly prepared. The discussion very much focusses on the interpretation of the three complex I states. The deactivate state, which is interpreted to protect mammalian mitochondria from ROS production during reverse electron transfer, might be dispensable in species characterized by a comparatively short life cycle like Drosophila, which is in the range of weeks.

Reviewer #3 (Public Review):

This paper uses single-cell transcriptome sequencing to identify and characterize some of the neuronal populations responsible for sex-specific behaviour and physiology. This question is of interest to many biologists, and the approach taken by the authors is productive and will lead to new insights into the molecular programs that underpin sexually dimorphic development in the CNS. The dataset produced by the authors is of high quality, the analyses are detailed and well described, and the authors have made substantial progress toward the identification and characterization of some of the neuron populations. At the same time, many other cell types whose existence is suggested by this dataset remain to be identified and matched to specific neuron populations or circuits. We expect the value of this dataset to increase as other groups begin to follow up on the data and analyses reported in this paper. In general, the value of this paper to the field of Drosophila neurobiology will be high even if it is published in close to its present form. On the other hand, the current manuscript does not succeed in presenting the key take-home messages to a broader audience. A modest effort in this direction, especially re-writing the Conclusions section, will greatly enhance the accessibility and broader impact of this paper.

While the biological conclusions reached by the authors are generally robust and of high interest, we believe that some conclusions are not sufficiently supported by the analyses that have been performed so far and need to be reexamined and confirmed. A major question concerns the authors' ability to distinguish a shared cell type with sex-biased gene expression from a pair of closely related, sex-limited cell types. There appear to be many cases that fall into this grey area, and the current analysis does not provide an objective criterion for distinguishing between sex-specific and sexually dimorphic clusters. Below we suggest some technical approaches that could be used to examine this issue. A second problem, which we do not believe to be fatal but that needs to be discussed, concerns potential differences in developmental timing and cell cycle phase between males and females, and how these differences might impact the inferences of sexual dimorphism in cell numbers and gene expression. Finally, we identify several areas, including the expression of transcription factors in different neuronal populations, that we believe could be described in more biologically insightful ways.

For our review, we focus on three levels of evaluation:

1). Is the dataset of high quality, useful to a large number of people, well annotated, and clearly described?

The data appear to be high quality. The authors use reasonable neuronal markers to infer that 99% of their cells are neuronal in origin, suggesting extremely low levels of contamination from non-neuronal cells. Moreover, the gene/UMIs detected per cell are high relative to what has been reported in previous Drosophila scRNA-seq neuron papers (e.g. Allen et al., 2020). The cluster annotations are incomplete - which is not surprising, given the complexity of the cell population the authors are working with. 46 of the 113 clusters in the full dataset are named based on published expression data, gene ontology enrichments of cluster marker genes, and overlap with other CNS single cell datasets. This leaves rather a lot outstanding. It is probably unrealistic to aim for a 100% complete annotation of this dataset. But if we're thinking about how this dataset might be used by other researchers, in most cases the validation that a given cluster corresponds to a real, distinct neuron subpopulation will be left to the user.

A major comment we have about the quality of the dataset relates to how doublets are identified and dealt with. The presence of doublets, an unavoidable byproduct of droplet-based scRNAseq protocols (like the 10x protocol used by the authors), could affect the clustering or at least bias the detection of marker genes. In large clusters, one might expect the influence of doublets on marker gene detection to be diluted, but in smaller clusters it could cause more significant problems. In extreme cases, a high proportion of doublets can produce artifactual clusters. The potential for problems is particularly high in cases where the authors identify cells with hybrid properties, such as clusters 86 and 92, which the authors describe as being serotonergic, glutamatergic, and peptidergic. Currently, the authors filter out cells with high UMI/gene counts, but it's unclear how many are removed based on these criteria, and cells can naturally vary in these values so it is not clear to us whether this approach will reliably remove doublets. That said, we acknowledge that by limiting their 'FindMarkers' analysis to genes detected in >25% of cells in a cluster the authors are likely excluding genes derived from doublets that contaminate clusters in low (but not high) numbers. We think it would be useful for the authors to report the number of cells that are filtered out because they met their doublet criteria and compare this value to the number of expected doublets for the number of cells they recovered (10x provides these figures). We would also recommend that the authors trial a doublet detection algorithm (e.g. DoubletFinder) on the unfiltered datasets (that is, unfiltered at the top end of the UMI/gene distribution). Does this identify the same cells as doublets as those the authors were filtering out?

2). What is the value of this study to its immediate field, Drosophila neurobiology? Are the annotation and analysis of specific cell clusters as precise and insightful as they could be? Has all the most important and novel information been extracted from this dataset?

This is the part that we are least qualified to assess, since we, unlike the authors, are not neurobiologists. We hope some of the other referees will have sufficient expertise to evaluate the paper at this level.

One thing we noticed (more on that in Part 3) is that the authors rely on JackStraw plots and clustree plots to identify the optimal combination of PCs and resolution to guide their clustering. This represents a relatively objective way of settling on clustering parameters. However, in a number of the UMAPs it looks like there are sub clusters that go undiscussed. E.g. in Fig. 2E clusters 1 and 3 are associated with smaller, distinct clusters and the same is true of clusters 2 and 6 in Fig 4b. Given that the authors are attempting to assemble a comprehensive atlas of fru+ neurons, it seems important for them to assess (at least transcriptomically) whether these are likely to represent distinct subpopulations.

3). How interesting, and how accessible is this paper to people outside of the authors' immediate field? What does it contribute to the "big picture" science?

Here, we think the authors missed an important opportunity by under-utilizing the Conclusions section. The manuscript has a combined "Results and Discussion" section, where the authors talk about their identification and analysis of specific cell clusters / cell types. Frankly, to a non-specialist this often reads like a laundry list, and the key conclusions are swamped by a flood of details. This is not to criticize that section - given the complexity and potential value of this dataset, we think it is entirely appropriate to describe all these details in the Results and Discussion. However, the Conclusions section does not, in its present form, pull it all back together. We recommend using that section to summarize the 5-8 most important high-level conclusions that the authors see emerging from their work. What are the most important take-home messages they want to convey to a developmental biologist who does not work on brains, or to a neurobiologist who does not work on Drosophila? The authors can enhance the value of this paper by making it more interesting and more accessible to a broader audience.

Reviewer #3 (Public Review):

This manuscript is well organized, and the author has generally shown good rigor in generating and presenting results. For instance, the author utilized TCRdist and structure-based metrics to remove redundancies and cluster complex structures. Additionally, the consideration of only recent structures (Fig. 2B) and structures that do not overlap with the finetuning dataset (Fig. 2D) is highly warranted.

In some cases, it seems possible that there may be train/test overlap, including the binding specificity prediction section and results, where native complexes being studied in that section may be closely related to or matching with structures that were previously used by the author to fine-tune the AlphaFold model. This could possibly bias the structure prediction accuracy and should be addressed by the author.

Other areas of the results and methods require some clarification, including the generation and composition of the hybrid templates, and the benchmark sets shown in some panels of Figure 2. Overall this is a very good manuscript with interesting results, and the author is encouraged to address the specific comments below related to the above concerns.

1. In the Results section, the statement "visual inspection revealed that many of the predicted models had displaced peptides and/or TCR:pMHC docking modes that were outside the range observed in native proteins" only references Fig. S1. However, with the UMAP representation in that figure, it is difficult for readers to readily see the displaced peptides noted by the author; only two example models are shown in that figure, and neither seems to have displaced peptides. The author should provide more details to support this statement, specifically structures of example models/complexes where the peptide was displaced, and/or summary statistics noting (out of the 130 tested) how many exhibited displaced peptides and aberrant TCR binding modes.

2. The template selection protocol described in Figure 1 and in the Results and Methods should be clarified further. It seems that the use of 12 docking geometries in addition to four individual templates for each TCR alpha, TCR beta, and peptide-MHC would lead to a large combinatorial amount of hybrid templates, yet only 12 hybrid templates are described in the text and depicted in Figure 1. It's not clear whether the individual chain templates are randomly assigned within the 12 docking geometries, as an exhaustive combination of individual chains and docking geometries does not seem possible within the 12 hybrid models.

3. Neither the docking RMSD nor the CDR RMSD metrics used in Figure 2 will show whether the peptide is modeled in the MHC groove and in the correct register. This would be an important element to gauge whether the TCR-pMHC interface is correctly modeled, particularly in light of the author's note regarding peptide displacement out of the groove with AlphaFold-Multimer. The author should provide an assessment of the models for peptide RMSD (after MHC superposition), possibly as a scatterplot along with docking RMSD or CDR RMSD to view both the TCR and peptide modeling fidelity of individual models. Otherwise, or in addition, another metric of interface quality that would account for the peptide, such as interface RMSD or CAPRI docking accuracy, could be included.

4. It is not clear what benchmark set is being considered in Fig. 2E and 2F; that should be noted in the figure legend and the Results text. If needed, the author should discuss possible overlap in training and test sets for those results, particularly if the analysis in Fig. 2E and 2F includes the fine-tuned model noted in Fig. 2D and the test set in Fig. 2E and 2F is not the set of murine TCR-pMHC complexes shown in Fig. 2D. Likewise, the set being considered in Fig. 2C (which may possibly be the same set as Fig. 2E and 2F) is not clear based on the figure legend and text.

5. The docking accuracy results reported in Fig. 2 do not seem to have a comparison with an existing TCR-pMHC modeling method, even though several of them are currently available. At least for the set of new cases shown in Fig. 2B, it would be helpful for readers to see RMSD results with an existing template-based method as a baseline, for instance, either ImmuneScape (https://sysimm.org/immune-scape/) or TCRpMHCmodels (https://services.healthtech.dtu.dk/service.php?TCRpMHCmodels-1.0; this only appears to model Class I complexes, so Class I-only cases could be considered here).

6. As noted in the text, the epitopes noted in Table 1 for the specificity prediction are present in existing structures, and most of those are human epitopes that may have been represented in the AF_TCR finetuning dataset. Were there any controls put in place to prevent the finetuning set from including complexes that are redundant with the TCRs and epitopes being used in the docking-based and specificity predictions if the AF_TCR finetuned model was used in those predictions? For instance, the GILGFVFTL epitope has many known TCR-pMHC structures and the TCRs and TCR-pMHC interfaces are known to have common structural and sequence motifs in those structures. Is it possible that the finetuning dataset included such a complex in its training, which could have influenced the success in Figure 3? The docking RMSD accuracy results in Fig. 5A, where certain epitopes seem to have very accuracy docking RMSDs and may have representative complex structures in the AF_TCR finetuning set, may be impacted by this train/test overlap. If so, the author should consider using an altered finetuned model with no train/test overlap for the binding specificity prediction section and results, or else remove the epitopes and TCRs that would be redundant with the complex structures present in the finetuning set.

7. The alanine scanning results (Figure 6) do not seem to be validated against any experimental data, so it's not possible to gauge their accuracy. For peptide-MHC targets where there is a clear signal of disruption, it seems to correspond to prominently exposed side chains on the peptide which could likely be detected by a more simplistic structural analysis of the peptide-MHC itself. Thus the utility of the described approach in real-world scenarios (e.g. to detect viral escape mutants) is not clear. It would be helpful if the author can show results for a viral epitope variant (e.g. from one of the influenza epitopes, or the HCV epitope, in Table 1) that is known to disrupt binding for single or multiple TCRs, if such an example is available from the literature.

Reviewer #3 (Public Review):

The goal of this study was to probe the transition from the IF to OF conformations inside the cells of a multidrug ABC transporter, ABCG2. In order to do so the authors used an antibody that specifically recognized the IF state (the epitope is 'disorganized' in the OF conformation) and this tool was particularly useful to address the conformational changes of ABCG2 that take place inside the permeabilized cells, depleted or not in ATP, and complemented with different combinations of nucleotides, drugs, and inhibitors. This technique was also used to show that the drugs increase the transition from the IF to the OF state.

By using confocal microscopy, the authors showed that ATP depletion led to a majority of ABCG2 that reside in a mitoxantrone-bound IF conformation.

The fluorescence correlation spectroscopy was another powerful approach used by the authors to convincingly demonstrate that the mitoxantrone drug could bind to ABCG2 in the IF conformation only.

Overall, the experiments are sound and the main conclusions drawn by the authors are very well supported by their data. This study unravels the first steps of the catalytic cycle of ABCG2 inside the cells, from drug-binding to a high-affinity site in the IF conformation to drug release from a low-affinity site in the OF conformation. It helps us to better understand how this transporter works in an environment that is physiologically relevant.

Here’s a lovely blog by Jason Wei that pulls together 137 examples of ’emergent abilities of large language models’. Emergence is a phenomenon seen in contemporary AI research, where a model will be really bad at a task at smaller scales, then go through some discontinuous change which leads to significantly improved performance.

Reviewer #3 (Public Review):

This manuscript presents an analysis of the cellular integration properties of a specific mushroom body output neuron, MBON-α3, using a combination of patch clamp recordings and data from electron microscopy. The study demonstrates that the neuron is electrotonically compact permitting linear integration of synaptic input from Kenyon cells that represent odor identity.

Strengths of the manuscript:

1) The study integrates morphological data about MBON-α3 along with parameters derived from electrophysiological measurements to build a detailed model.<br /> 2) The modeling provides support for existing models of how olfactory memory is related to integration at the MBON.

Weaknesses of the manuscript:

1) The study does not provide experimental validation of the results of the computational model.<br /> 2) The conclusion of the modeling analysis is that the neuron integrates synaptic inputs almost completely linearly. All the subsequent analyses are straightforward consequences of this result.<br /> 3) The manuscript does not provide much explanation or intuition as to why this linear conclusion holds.

In general, there is a clear takeaway here, which is that the dendritic tree of MBON-α3 in the lobes is highly electrotonically compact. The authors did not provide much explanation as to why this is, and the paper would benefit from a clearer conclusion. Furthermore, I found the results of Figures 4 and 5 rather straightforward given this previous observation. I am sceptical about whether the tiny variations in, e.g. Figs. 3I and 5F-H, are meaningful biologically.

Reviewer #3 (Public Review):

In this retrospective study, the authors intend to demonstrate the utility of serum procalcitonin in reducing the use of antibacteral agents in cancer patients with COVID-19, by identifying the subset of their highly immunocompromised population where early discontinuation of antibacterial therapy would not be harmful.

This study has a large population size > 500 patients over the span of 16 months. The groups with low procalcitonin and high procalcitonin have similar baseline characteristics, which makes the subsequent comparisons valid and relevant. The authors have considered all the relevant variables that could affect the outcomes being studied, and used sound statistical methods.

This study has some limitations. It is retrospective by nature, with possibility for confounders. In addition to the limitations mentioned by the authors, the study spans the period from March 2020 to June 2021 through which our knowledge of COVID has evolved, multiple variants have emerged, immunization has become available in the later part of the study period, more therapies (antivirals, monoclonal antibodies) became available, all of which have definitely affected COVID-related mortality, and could be an important confounder here. While the authors report the level of severity of the infection, using proxies such as supplemental oxygen and ICU admission, the use of COVID-directed therapies, including immunosuppressants such as steroids and tocilizumab (which in turn can increase the risk of bacterial infections and decrease the risk of mortality) is not reported. It also seems that the management of antibacterial therapy was left at the discretion of the treating physician, which can lead to a wide variety of practices, the nature of antibacterials administered is not reported here.

The results presented here support the conclusions made by the authors, and one has to appreciate the difficulty of antimicrobial stewardship efforts in an immunocompromised population such as the one being studied here. Many of these patients have been immunosuppressed for prolonged periods of time, could have profound defects in their immune systems, and could have had multiple previous infections, sometimes with atypical presentations. These patients are typically excluded from most large clinical trials, thus retrospective studies such as this one are usually the most informative pieces of literature available to support evidence-based medicine in this special patient population. I think this study should encourage clinicians to consider the use of serum procalcitonin as one additional clue to support their pursuit of antibacterial de-escalation or discontinuation in cancer patients with COVID-19.

Reviewer #3 (Public Review):

This is an interesting study in which the authors record simultaneously from neurons along the medial bank of the rodent PFC as rats perform the restaurant row task, an economic decision-making task in which subjects are offered different reward types with a specified delay, and they need to decide whether to accept or reject the offer. The authors find functional correlates of anatomical subdivisions of the mPFC; interestingly, they find that PL perhaps should be subdivided into dorsal and ventral subregions, a finding that is consistent with some known anatomical features. They characterize the task-related responses of neurons in these different subdivisions and find that in general, the dorsal regions (ACC, dPL) encode decision-related variables, whereas the ventral regions (vPL, IL) encode more motivational variables, such as the trial number in the session and the amount of lingering time.

Strengths:<br /> - The observed dichotomy between decisional and motivational factors mapping onto dorsal and ventral aspects of mPFC is interesting and, as far as I am aware, novel.<br /> - There are a number of rich, interesting observations, such as a lack of encoding of the reward delay in the offer zone, but then encoding of that variable in the wait zone (in all areas except ACC). This is intriguing given that their previous work has suggested that the decisions made in the offer and wait zones are in some ways dissociable, implying that they might rely on distinct neural circuits.<br /> - Overall, the data and analyses are of high quality, and the results are interesting.<br /> - The finding that PL should be subdivided into two distinct subregions will be of broad interest to researchers studying the mPFC. The approach and finding will also be of interest to the growing number of groups using linear silicon (including Neuropixels) probes to record from multiple brain areas simultaneously.

Weaknesses:<br /> - The authors find that dorsal regions of mPFC, particularly ACC, encode the upcoming decision of the animal. However, the upcoming choice will be correlated with animal movements (as is often the case). Given that ACC is adjacent to the motor cortex, and more posterior parts of the cingulate have been documented to reflect particular types of movements, it would be helpful to know if these signals would be observed for movements outside of the task, or if they really reflect the upcoming decision in this behavioral context.<br /> - I think some of the statistical analyses can be strengthened. For instance, the authors correlate neural activity against a large number of behavioral variables, some of which are correlated with each other. I would encourage a regression-based approach, which takes into account the correlations between variables for error bars/significance tests for each regressor.

In general, I think the authors' claims about their data are justified.

Reviewer #3 (Public Review):

KV7 channels play an important role in setting the resting membrane potential of neurons. As such, modulation by reactive oxygen species is an important and physiologically relevant form of channel regulation. Here, the authors propose a mechanism for this modulation in which ROS disrupts the interaction between the S2S3 loop of the channel and CaM, resulting in an overall enhancement of channel activity. The authors propose that this S2S3/CaM interaction is selectively mediated through CaM EF3, and is dependent on Ca2+. The results are supported by patch-clamp data, as well as NMR measurements and a FRET-based binding assay. The paper contains a considerable amount of data that point towards the conclusion.

The authors conclude that the EF3 of CaM is 'by itself sufficient and necessary for the oxidative response of KV7 channel complex and for gating the calcium responsive domain of KV7 channels." This is a very strong conclusion, and while much of the data points towards an important role for EF3, it is difficult to conclude that it is sufficient and necessary. The sparse description of the experiments makes the interpretation of the results a bit challenging. Based on the description provided, some of the results appear contradictory, limiting the conclusions drawn by the authors.

Reviewer #3 (Public Review):

This study by Kato et. al used a combination of computational modeling, in vitro experimentation, and confirmatory in vivo mouse work to define what influences collective cell invasion in squamous cell carcinoma (SCC). Looking at a multitude of parameters, the authors found that cancer cell-cancer cell contacts and matrix degradation work cooperatively in SCC invasion.

The authors provide a rigorous and systematic approach to querying the importance of the parameters tested; first setting their hypothesis computationally followed by in vitro experimentation in two different cancer cell culturing methods (organotypic and spheroid). Importantly, the experimental data convincingly confirmed the computational predictions, lending credence to their methodology. This is a major strength of the manuscript and will be beneficial to the field with regard to investigating invasion in other cancer types.

Additionally, the varied parameters tested (cancer cell-cell adhesion, cancer cell-matrix adhesion, cancer cell-fibroblast adhesion, fibroblast-matrix adhesion, cell-intrinsic motility, matrix displacement, and matrix proteolysis) were thoughtful and rooted in the literature. However, though considerate of the role the extracellular matrix (ECM) may play (via interrogating cancer cell-matrix adhesions as parameters), the characteristics of the matrix itself (e.g. stiffness, alignment) were not investigated. These attributes have been previously shown to affect collective cell invasion. Indeed, while investigating the contributions of matrix proteolysis on invasion, the authors found a parabolic relationship where both too much and too little matrix negatively impacted the ability of SCC cells to invade. Moreover, it is unclear what the role of fibroblast-matrix adhesions was to this system, though it was originally stated as a tested parameter.

Reviewer #3 (Public Review):

This manuscript provides a helpful and transparent guide on the application of granger-causality (GC) to calcium datasets. This is a useful entry point toward understanding the suitability and limitations of GC to neural data. However, it is not entirely convincing that the variations of GC analysis provided in this manuscript can be effectively applied to large-scale calcium datasets without prior knowledge of the underlying circuit, especially when such networks are likely to contain redundancy and recurrent links.

I would like to acknowledge that, at the outset, I held an unfavorable prior belief toward GC, for reasons that are well addressed in this manuscript, including the dangers of applying spectral GC to nonlinear networks, as well as a variety of pathologies that can undermine naive GC.

The manuscript has been helpful, both for its effective presentation of both bivariate GC and its multivariate extension, as well as the practical considerations that are essential to applying it to real-life data. It was particularly helpful to see a treatment of the challenges and their possible resolutions. I commend the authors for their transparency - they should certainly be rewarded rather than punished for their transparency.

Major<br /> 1. Redundant signals: throughout the brain, it's expected that a population of neurons can encode the same information. It's unclear how GC (both the original and the modified versions) can handle this redundancy. Given how pervasive redundant signals are in the brain, this should be addressed in both simulation and experimental data. For example, in one of the manuscript's simulated networks, replace one neuron with 10 copies of it, each with identical inputs and outputs but with the weights scaled by 1/10. Such a network is functionally equivalent to the original but may pose some challenges for the various versions of GC. I believe this issue also accounts for the MVGC results in the hindbrain dataset. It might be more appropriate to apply GC to groups of neurons (as indeed the authors cited), instead of applying it at the single-cell level with redundant signals.<br /> 2. Similarly, there is recurrent connectivity throughout the brain. The current manuscript appears to assume feedforward networks. Is the idea that GC cannot be applied to recurrent networks? If so, this needs to be clearly stated. If the authors believe that GC can recover casual links even in the presence of recurrent connectivity, this needs to be demonstrated.<br /> 3. Both BVGC and MVGC appear to be extremely sensitive to any outlier signals. The most worrying aspect is that the authors developed their corrections and pipelines with the benefit of knowing the structure of the underlying system, whereas in the case where GC would be most useful, the user would be unable to rely on prior knowledge of the underlying structure. For instance, the motion artifact in Fig 3a-c was a helpful example of a vulnerability of naive GC, but one could easily imagine scenarios involving an unmeasured disturbance (e.g. the table is bumped) causing a similar artifact, but if the experimenter is unaware of such unmeasured disturbances then they will not be included in Z, and hence can result in the detection of widespread spurious links.<br /> There is a circularity here that's concerning. It seems that one already needs to have the answer (e.g. circuit connectivity) in order to clean up the data sufficiently for BVGC or MVGC to work effectively. Perhaps the authors would be interested in incorporating ideas from the systems identification literature, which can include the estimation of unmeasured disturbances, perhaps in conjunction with L1 regularization on the GC links. This is certainly out of scope for the present work, but it would be worth acknowledging the difficulties of unmeasured disturbances and deferring a general solution to future work. Similar considerations apply to a common unmeasured neuronal input (e.g. from a brain region not included in the field of view of the imaging).<br /> 4. Interpretation - would it be correct to state that BVGC identifies plausible causal links, while MVGC identifies a plausible system-level model? I think these interpretations, carefully stated, might provide a helpful way of thinking about the two GC approaches. Taking the results of the paper together, neither BVGC nor MVGC is definitive - BVGC may overestimate the true number of causal links but MVGC is prone to a winner-take-all phenomenon that may represent just one of many plausible system-level models that can account for the observed data. This should be more clearly stated in the manuscript.<br /> 5. "correlation completely misses the structure" - links are signed, so they should be shown with "bwr" colormap, with zero mapped to white (i.e. v_min is blue, 0 is white, v_max is red, |v_min| = |v_max|, this is natively supported in PyPlot and can be trivially implemented or downloaded in MATLAB). It is misleading that correlation appears to miss certain links marked in black, until one realizes that these links are inhibitory. It would substantially aid clarity and consistency if all panels followed this signed "bwr" convention. I think the emphasis for the GC panels is on whether links are detected, rather than the weight of the link, so I would suggest indicating detected inhibitory links as -1 (blue) and detected excitatory links as +1 (red), and link not detected as 0 (white).

Reviewer #3 (Public Review):

In this work, Zhou et al. employed the polarization microscope (PM) method to track the orientations of helix 6a in the bacterial amino-acid transporter AdiC. It is very impressive that the authors were able to optimize the technique to achieve an overall resolution of 5{degree sign} for detecting changes in the inclination and rotation angles (𝜃 and 𝜓). However, I am deeply concerned about how the authors linked PM-detected conformational states to the structural states obtained using crystallography. Overall, I think it was an overstatement that the work resolved the equilibrium conditions for the major states in AdiC's transport cycle, and I urge the others to be more transparent with the readers about the limitations of their technique and be more thorough in considering alternative interpretations.

Reviewer #3 (Public Review):

This paper combines experimental structures with careful molecular dynamics to address a crucially important topic in cellular biology - how are mechanosensitive ion channels gated by the membrane? There are many flavors of mechanosensitive proteins, and here the authors study MscS from e. coli and the eukaryotic homolog MSL1 from Arabidopsis. The key finding is that the closed states of both channels induce high curvature in the inner leaflet due to the membrane protruding into the cytoplasm to lipidate exposed hydrophobic patches on the protein. The open state structures exhibit far less membrane deformation. Moreover, comparing the open and closed state structures reveals that the membrane-protein surface area is not significantly different in the two states - hence all of the mathematical models to date (and many experimental models too) that posit that tension-induced gating is driven by expansion of the in-plane area of the protein must be revised. Instead, the authors convincingly argue that the role of tension is to increase the energy of the protein-membrane system in the closed state (with its large membrane deformations) compared to the flat-membrane open state. Forgive me for not going on more about the structures that have been solved here, and how they are likely more representative of the native open state than previously solved structures - I agree with the authors' assertions, and they represent a major step forward in elucidating the full gating transition in both bacterial and eukaryotic systems. This is an important discovery, and it would have been impossible without the structure and simulation coming together. Future work attempting to quantify the energy of the membrane deformations, protein free energy difference between the channels in open and closed states, and the role of tension will be essential but outside the scope of what the authors were trying to do here.

Reviewer #3 (Public Review):

In this manuscript, the authors leverage new single-cell sequencing data to unravel cell type diversity in the head of Loligo vulgaris hatchlings. This analysis recovers 33 clusters and the authors describe the cell type populations with HCR in situ hybridization. This work provides an important next step in describing neural and sensory cells in an understudied class of invertebrates that goes beyond traditional morphological characterization.

Reviewer #3 (Public Review):

In this manuscript the authors ask whether finger movements in non-human primates can be predicted from neural activity recorded from the primary motor cortex. This question is driven by an ultimate goal of using neural decoding to create brain-computer interfaces that can restore upper limb function using prosthetics or functional electrical stimulation systems. More specifically, since functional use of the hand (real or prosthetic) will ultimately require generating very different grasp forces for different objects, these experiments use a constant set of finger kinematics, but introduce different force requirements for the finger muscles using several different techniques. Under these different conditions (contexts), the study examines how population neural activity changed and uses decoder analyses to look at how these different contexts affect offline predictions of muscle forces and finger kinematics, as well as the animals' ability to use different decoders to control 1 or 2-DOF online. In general, the study found that when linear models were trained on one context from offline data, they did not generalize well to the other context. However, when performance was tested online (monkeys controlling a virtual hand in real time using neural activity related to movement of their own hands) with a ReFIT Kalman filter, the animals were able to complete the task effectively, even with a decoder trained without the springs or wrist perturbation. The authors show data to support the idea that neural activity was constrained to the same manifold in the different contexts, which enabled the animals to rapidly change their behavior to achieve the task goals, compared to the more complex requirement of having to learn entirely new patterns of neural activity. This work takes studies that have been conducted for upper-limb movements and extends them to include hand grasp, which is important for creating decoders for brain-computer interfaces. Finally, the authors show using dPCA can extract features during changes in context that may be related to the activity of specific muscles that would allow for improved decoders.

Strengths:

The issue of hand control, and how it compares to arm control, is an important question to tackle in sensorimotor control and in the development of brain-computer interfaces. Interestingly, the experiments use two very different ways of changing the muscle force requirements for achieving the same finger movements; springs attached to a manipulandum and changes in wrist posture. Using both paradigms the decoder analysis clearly shows that linear models trained without any manipulation do not predict muscle forces or finger kinematics well, clearly illustrating the limitations of common linear decoders to generalize to scenarios that might encompass real grasping activities that require forceful interactions. Using a well-described real-time decoder (ReFIT Kalman Filter), the authors show that this performance decrease observed offline is easily overcome in online testing. The metrics used to make these claims are well-described, and the likely explanations for these findings are described well. A particular strength of this manuscript is that, at least for these relatively simple movements and contexts, a component of neural activity (identified using dPCA) is identified that is significantly modulated by the task context in a way that sensibly represents the changes in muscle activity that would be required to complete the task in the new contexts.

Weaknesses:

The differences between exemplar data sets and comprehensively tested contexts was difficult to follow. There are many references to how many datasets or trials were used for a particular experiment, but overall, this is fragmented across the manuscript. As a result, it is difficult to assess how generalizable the results of the manuscript were across time or animal, or whether day-to-day variations, or the different data collection schedules had an effect.

The introduction allocates a lot of space to discussing the concepts of generating (computing) movements as opposed to representing movements and relates this to ideas of neural dynamics. The distinction between these as described in the introduction is not very clear, nor is it clear what specific hypothesis this leads to for these experiments. Further, this line of thinking is not returned to in the discussion, so the contribution of these experiments to ideas raised in the introduction are unclear.

The complexity of the control that was possible in this task (1 or 2 DOF finger flexion/extension) was low. Further, the manipulations that were used to control context were simple and static. Both these factors likely contribute to the finding that there was little change in the principal angles of the high-variance principal components. While this is not a criticism of the specific results presented here, the simplicity of the task and contexts, contrasted with the complexity of hand control more generally, especially for even moderately dexterous movements, makes it unclear how well the finding of stable manifolds will scale. On a related point, it is unclear whether the feature, identified using dPCA, that could account for changes in muscle activity, could be robustly captured in more realistic behaviors. It is stated that future work is needed, but at this point, the value of identifying this feature is highly speculative.

The maintained control in online BMI trials could also be explained by another factor, which I don't think was explicitly described by either of the two suggestions. Prism goggle experiments introduce a visual shift can be learned quickly, and some BCI experiments have introduced simple rotations in the decoder output (e.g. Chase et. al. 2012, J Neurophys). This latter case is likely similar in concept to in-manifold perturbations. Regardless, the performance can be rapidly rescued by simply re-aiming, which is a simple behavioral adaptation. In a 1DOF or 2DOF control case like used in these experiments, with constant visual feedback on performance, the change in context could likely be rapidly learned by the animals, maybe even within a single trial. In other words, the high performance in the online case may be a consequence of the relatively simple task demands, and the simple biomechanical solution to this problem (push harder). What is the expectation that the results seen in these experiments would be relevant to more realistic situations that require grasp and interaction?

Some of the figures were difficult to read and the captions contained some minor incorrect information. The primary purpose of some of the figures was not immediately clear from the caption. For example, the bar plots in Figures 5 and 6 were very small and difficult to read. This also made distinguishing the data from the two different animals challenging.

There is no specific quantification of the data in Figures 4D and 5D. In Figure 4D it seems apparent that the vast majority of the points are below the unity line. But, it remains unclear, particularly in Figure 5D whether the correlations between the two contexts truly are different or not in a way that would allow conclusive statements.

Reviewer #3 (Public Review):

Precise methods were developed to validate the expression of channelrhodopsin in inner hair cells of the Organ of Corti, to quantify the relationship between blue light irradiance and auditory nerve fiber depolarization, to control light stimulation within the chamber of a high-pressure freezing device, and to measure with good precision the delay between stimulation and freezing of the specimen. These methods represent a clear advance over previous experimental designs used to study this synaptic system and are an initial application of rapid high-pressure freezing with freeze substitution, followed by high-resolution electron tomography (ET), to sensory cells that operate via graded potentials.

Short-duration stimuli were used to assess the redistribution of vesicles among pools at hair cell ribbon synapses. The number of vesicles linked to the synaptic ribbon did not change, but vesicles redistributed within the membrane-proximal pool to docked locations. No evidence was found for vesicle-to-vesicle fusion prior to vesicle fusion to the membrane, which is an important, ongoing question for this synapse type. The data for quantifying numbers of vesicles in membrane-tethered, non-tethered, and docked vesicle pools are compelling and important. These quantifications would benefit from additional presentation of raw images so that the reader can better assess their generality and variability across synaptic sites.

The images shown for each of the two control and two experimental (stimulated) preparation classes should be more representative. Variation in synaptic cleft dimensions and numbers of ribbon-associated and membrane-proximal vesicles do not track the averaged data. Since the preparation has novel stimulus features, additional images (as the authors employed in previous publications) exhibiting tethered vesicles, non-tethered vesicles, docked vesicles, several sections through individual ribbons, and the segmentation of these structures, will provide greater confidence that the data reflect the images.

The introduction raises questions about the length of membrane tethers in relation to vesicle movement toward the active zone, but this topic was not addressed in the manuscript. Seemingly quantification of this metric, and the number of tethers especially for vesicles near the membrane, is straightforward. The topic of EPSC amplitude as representing unitary events due to variation in vesicle volume, size of the fusion pore, or vesicle-vesicle fusion was partially addressed. Membrane fusion events were not evident in the few images shown, but these presumably occurred and could be quantified. Likewise, sites of membrane retrieval could also be marked. These analyses will broaden the scope of the presentation, but also contribute to a more complete story.

Overall, the methodology forms the basis for future studies by this group and others to investigate rapid changes in synaptic vesicle distribution at this synapse.