Reviewer #3 (Public Review):

This study aims to determine whether the chromosome defects induced by a bacterial endosymbiont in insects in developing embryos are a direct result of paternal chromosome defects from early embryogenesis or due to a second, independent set of defects that arise later: "we addressed whether defects observed in late CI embryos such as chromosome segregation errors and nuclear fallout are the result of first division errors or a second, distinct CI-induced defect."

Using crosses, genetics, and fluorescent microscopy, the study claims that the defects at different embryonic stages are due to independent processes, and this work thus has mechanistic relevance to how bacteria inflict developmental harm to insect embryogenesis. The claim is not well supported by the weight of the evidence in this paper and the literature.

The work is technically sound and proficiently completed to an expert level with appropriate statistics, but it does not provide straight-line evidence to substantiate the primary claim of the paper that later-stage embryos die for different reasons than early-stage embryos. That is no fault of the experimental rigor but rather to the difficulty of directly answering this question. It appears the field has insufficient information on the reductionist, bacterial mechanism that induces embryonic death, namely what acutely is modified by the bacteria to cause embryonic death? As such, the authors hedge that by studying different developmental stages of the embryonic defects, the answer can be surmised. However, a simple explanation for how late and early-stage embryos could die to similar mechanisms is that host cellular conditions are more or less susceptible to the same bacterial-induced change of the insect chromosomes (e.g., new chemical marks on the DNA). It's just not possible to rule this out until the acute mechanism of killing is known. For instance, some embryos may vary in their transcriptomes, proteomes, physiology, etc within a single family of fly offspring, and as such these varying embryos may be more or less susceptible to the same proximal cause of the bacteria-mediated defects. The difference is just when do they take place in development. Without knowing the bacterial mechanism of death (e.g. changes in chemical marks of the fly DNA), the study here can characterize broad strokes of chromatin biology while speculating on the weight of the evidence for whether or not different mechanisms are at play.

To evaluate the primary question of whether or not there are completely separate defects across development, the study shows several pieces of data that offer a finer resolution of the broad defects of embryos that were previously characterized by the literature. The new follow-up details are robustly supported and include percentages of embryos experiencing a defect, nuclear fallout, determination of haploidy/diploid, sequencing depths, Y chromosome tracking, and developmental-staged characterizations of the chromatin defects. However, according to the text, there is effectively a single type of data that speaks to the main question of the paper - whether or not viable embryos that escaped the first mitosis had increased mitotic errors during later developmental stages.

"Therefore, the significant increase in mitotic errors observed in diploid CI-derived embryos relative to wild-type derived embryos demonstrates the existence of a second, CI-induced defect, completely separate from the first division defect." This was already known; later-stage, chromatin defects do occur in a variety of insect species cited in the paper. In effect, the question answers itself because, in order to traverse an early lethal state that does not occur, there must be defects that ensue later in development, several of which have already been characterized, though to a lesser resolution than this study.

Moreover, the study does not link the staged chromatin errors to the CI genes using transgenic tools that are now customary in this field. That work is quite relevant to the conclusion of the paper because the authors speculate in the discussion that additional CI genes may be necessary to explain the later defects in embryogenesis versus the initial defects. This work has been completed to a degree by the papers reporting the initial discovery of the CI genes. CI transgene expression in males causes both 1st mitosis and later chromatin defects, suggesting additional genes are not necessary to explain lethality after the first mitosis. This to me is perhaps the most significant counterpoint of the narrative of the paper's claim because the acute genetic cause of CI can lead to differently timed chromatin errors.

This is solid work and a strong effort to refine the stages and types of embryonic lethality induced by bacteria, however, the claim that there are different acute mechanisms of death during embryogenesis is not well supported.

Reviewer #3 (Public Review):

Fuchsberger et al. demonstrate that an otherwise LTD-inducing STDP protocol can produce LTP if followed by burst reactivation of post-synaptic neurons in the presence of dopamine. Using computational modeling and single-photon imaging in the CA1 in mice, they propose these findings are relevant to spatial over-representation at a reward location.

This is a follow-up of the two previous studies from the same group (Brzosko et al., 2015 and Andrade-Talavera et al., 2016) where they showed a post-before-pre STDP protocol, which by default induces a (pre-synaptic) LTD, will induce synaptic potentiation in the presence of dopamine and continuous synaptic activity. The main conceptual difference between this manuscript and these previous studies is that continuous synaptic activity can be replaced by post-synaptic burst. This means that reactivation of post-synaptic neurons without any further pre-synaptic instruction is sufficient for successful LTP induction.

Mechanistically, the two protocols (continuous vs burst activation) appear to be similar (but not identical). For example, both require the activation of post-synaptic NMDAr during STDP pairing, and both depend on the AC/PKA pathways. Additionally, there are two new observations here: The activity of voltage-gated calcium channels during bursting is required for potentiation; the burst-induced potentiation also requires protein synthesis.

The evidence provided at this stage is strong.

Major point:

It is not clear to me how the STDP studied here relates to the next part of the study, the reward-based navigation task. My interpretation is that the authors consider the activity before reaching the reward location (approaching time) as resembling the STDP priming protocol, the activity at the reward location as equivalent to the bursting protocol, and consumption of the reward as similar to dopamine application. If so, what is the circumvential evidence that the activity during the approach induces any form of plasticity? The link between the two is not obvious and I see the manuscript as two interesting but not naturally linked stories.

Reviewer #3 (Public Review):

This manuscript identifies specific dominant-negative mutations in the CRMP1 gene encoding Collapsing response mediator protein 1 involved in cytoskeletal remodeling. The authors identified 3 independent probands, each with a de novo CRMP1 mutation-based upon unbiased exome or genome sequencing. Family 1 showed p.P589L/p.P475L, family 2 showed p.T427M/p.T313M and family 3 showed p.A351S/p.A237S. CRIMP1 is known to homo-oligomerize, and the paper attempts to show defects in this ability with the incorporation of patient mutations. Finally, forced expression of patient mutations into neuronal cells show defects in the length of the longest neurite.

Major weakness:

The major weakness is Figure 2, as it is not performed up to high standards like the rest of the paper. Panel A does not show any loading control and does not confirm. Panel B at 720 kDa band is not convincing. Results should be repeated with size exclusion chromatography and/or another method to determine molecular weight and should be quantified from triplicate experiments. Panel C is also not convincing and should be repeated to more carefully show results, and quantified.

Reviewer #3 (Public Review):

Gupta and colleagues investigate the function of the PgfA (MSMEG_0317) protein in Mycobacterium smegmatis (Msmeg). This protein was of interest due to previous work showing that it interacts with the LamA protein involved in the asymmetric polar elongation of mycobacteria. Evidence is presented that PgfA is essential for the growth of Msmeg and that it localizes primarily to the old cell pole. This asymmetric localization as well as the asymmetric localization of the trehalose monomycolate (TMM) flippase MmpL3 was shown to be dependent on LamA. Co-immunoprecipitation was used to show the MmpL3 and PgfA interact. Moreover, cells depleted of PgfA and MmpL3 were shown to have similar terminal phenotypes - the depleted cells lost cell envelope material from their surface and lysed. PgfA depleted cells were also shown to have defective outer membrane by cryo-electron tomography. Crosslinking studies were also used to show that PgfA interacts directly with TMM. Together, these data make a strong case for the involvement of PgfA in the process of mycolic acid transport to the mycomembrane, which is a significant advance in the field of mycobacterial envelope assembly.

Less convincing were results showing the depletion of PgfA affects the levels of TMM and its derivative TDM (trehalose dimycolate) in cells and that overexpression of PgfA can restore asymmetric polar growth to cells lacking LamA. I was also not convinced by the argument that PgfA and its homolog from related corynebacteria (NCgl2760) have different functions. There are many explanations for the failure of NCgl2760 to complement PgfA inactivation in Msmeg that do not require invoking different functions for the two proteins. Specific protein-protein interactions required for PgfA function could have diverged in the two organisms such that NCgl2760 is unable to interact with its required mycobacterial counterparts. Additionally, the lengths of mycolic acids differ between corynebacteria and mycobacteria, which may make the transporters incompatible across organisms.

Reviewer #3 (Public Review):

The authors perform a wide range of molecular, cellular tissue, and animal model studies that demonstrate clearly that GCN2 activity impacts amino acid transporter activity and essential amino acid uptake, which is needed for PCa tumor growth in a variety of model systems. As a whole the data are convincing, and the authors have achieved their aims. One potentially translatable finding is that a small molecule inhibitor of GCN2 may be a useful candidate therapeutic tool for certain PCa patients.

Reviewer #3 (Public Review):

In this manuscript, the authors demonstrate mice can use monocular cues to estimate distance in a new task they developed. They developed an ethologically relevant task in freely moving mice where the animals must estimate the distance of a platform to complete a jump to be rewarded. The task can be coupled to eye tracking and optogenetics. The authors provide evidence that the eye movement compensates the head movement in maintaining gaze and the initiation of the jump depends on V1. The task is in freely moving mice and offers the possibility of genetics and/or electrophysiological interrogation of the brain circuitry in the future.<br /> Strengths:

The authors achieved their aims of demonstrating mice can use monocular cues to estimate distance, and the results are simple and convincing. Regarding the specific claims in the accuracy of mice estimating the distance and whether the monocular condition caused more head movement I have a few specific comments below.

Most of mice behavior is systems neuroscience has been in head-fixed behavior. The electrophysiology and/or imaging equipment do not move with the animals. There has been recent advances in electrophysiological and imaging techniques that allows them to be tethered to the animals. This calls for ethologically relevant behavior in rodents. The authors demonstrated that they can combine eye tracking and optogenetic with the task. As freely moving electrophysiological recording techniques improve in the future. Researchers will be able to combine this with their task to further elucidate the circuitry underlying behavior.

Weaknesses:

Although the paper has a simple message, most of systems neuroscience is interested in how sensory evidence, in this case, monocular cues, are encoded in the brain, and the process in which it is transformed into action. Falling short of the goal to address the circuitry underlying the behavior, we can only judge the merit of how likely the task will be adapted by the community to elucidate insights into the neural circuitry. The behavior in its current form is impossible to speculate which monocular cue the mice used to solve the task, e.g. relative size, occlusion, motion parallax etc., therefore it will be difficult to pinpoint the relevant area of interest to start the interrogation. If the interest is in motor control, the jump has many degrees of freedom and muscles involved than the classical eye movement or arm reaching tasks. It is unclear the advantages this task has. Furthermore the timing of choice and reward is poorly controlled in the trial structure of the task, so it is unclear the additional insights it can offer regarding decision making and motivation.

An important use of mice in system neuroscience is for invasive monitoring of brain activity with electrophysiology and/or imaging. The equipment for electrophysiology and imaging often require the animals to be head fixed. This study does not attempt to expand on the behavior observed, and this will be a limitation for adaptation of the task that the authors presented.

The authors also provide an insufficient amount of details on the task. For example, how were the platform and distance manually changed by the experimenter for each trial? This is an important manual step that limits the number trials and potentially the animals' engagement in the task. In its current form, the task will unlikely be adapted by the community. Head-free behavior and the low trial number might limit the utility of the task to systems neuroscience.

Reviewer #3 (Public Review):

In this manuscript it has been found that there is a deeply diverged ribonucleotide reductase class that can potentially be the ancestor of both class I and class II ribonucleotide reductases. Furthermore, the structure of a representative member of the new class was characterized with cryo-EM and SAXS. I found the manuscript very interesting and of high relevance. A weakness though was that I did not see anything written about enzyme activity and if the small subunit contains any free radical in the manuscript, which means that we cannot be sure that it really is a ribonucleotide reductase although the homologies and the ability of dTTP to induce dimerization is a strong indicator of that.

Another conclusion in the manuscript was that the last common ancestor of the ribonucleotride reductase classes had the ATP cone-mediated allosteric regulation that we see in approximately half to the ribonucleotide reductase today. However, although the analysis presented is interesting, I think that it is still an open question whether the last common ancestor had an ATP cone or not. Many species contain more than one class of ribonucleotide reductase and because it is a mobile element, it can easily jump from one class to another.

Reviewer #3 (Public Review):

In this manuscript, Mapps et al. report on the very interesting finding that satellite glia deletion significantly impacts sympathetic neuron function and survival. Specifically, loss of the glia results in reduced mTOR signaling, norepinephrine production, and a loss of neurons. Surprisingly, there was an increase in neuronal activity, leading to increased physiological effects such as increased heart rate and pupil dilation. The authors also demonstrate that many of these effects can be mimicked by glial K+ channel, Kir4.1, deletion, indicating that loss of the glia disrupts K+ buffering around the neurons. This is a very novel finding that reveals an important role for satellite glia in sympathetic physiology. It is comprehensive and well controlled. There are just a few issues that the authors should consider.

In Fig. 1C-D, how many dpi was the TUNEL assay performed? It would be helpful to know how quickly the neurons die after glial depletion and if the cell death continues or plateaus. The authors should also co-label using neuronal and glial markers to evaluate whether the apoptotic cells are primarily neurons or glia. They report a loss of neurons, but how much of that is reflected in the TUNEL labeling is not clear.

In Figs. 1C and 5C TUNEK analysis, there are quite a few TUNEL+ puncta outside of the ganglia, suggesting that there may be apoptosis in other adjacent tissues when the glia removed or Kir4.1 is deleted. The authors should comment on that if it were something consistently observed.

The loss of neurons upon glial cell loss or Kir4.1 deletion is interesting. The authors discuss how neuron death could occur, but did they observe TUNEL+ cells in regions where the glia had been deleted? Given that the diphtheria toxin did not ablate all glia, were the neurons left with little or no surrounding glia more likely to die? This may be difficult to tell, but from the images in 1E, it looks like some neurons lack nearby glia. This would be a potential explanation for why only a fraction of the neurons died; those neurons with associated glia may be more protected.

It would be helpful to clarify a bit more what the control mice used for comparison were. From the text, it seems as if they were the same mice but not treated with tamoxifen. Were they given diphtheria toxin? In addition, did the authors check for any effects of tamoxifen alone? Given that estrogen can affect many physiological parameters, including cardiac function, tamoxifen alone could have some effect, e.g., Kuo et al., PMID: 20392827.

Interestingly, TH levels in BLBP:iDTA mutant axons appeared to be similar to that in controls, despite the marked reduction in TH mRNA and protein levels in neuronal cell bodies (Figure S2A). The Kaplan lab (PMC7164330) showed that TH mRNA trafficking and local synthesis play an important role in synthesizing catecholamines in the axon and presynaptic terminal. Although a bit beyond the scope of this study, it would be interesting to determine whether TH mRNA transport is altered by deletion of the glia. The authors might check to see if TH transcripts are reduced in axons by something like RNAscope.

Reviewer #3 (Public Review):

This study aims to address the important question of whether working memory can hold multiple conjunctive task representations. The authors combined a retro-cue working memory paradigm with their previous task design that cleverly constructed multiple conjunctive tasks with the same set of stimuli, rules, and responses. They used advanced EEG analytical skills to provide the temporal dynamics of concurrent working memory representation of multiple task representations and task features (e.g., stimulus and responses) and how their representation strength changes as a function of priority and task relevance. The results generally support the authors' conclusion that multiple task representations can be simultaneously manipulated in working memory.

My only concern is that in Figure 4, the strongest priority by task-relevance interaction occurred earlier in the response than the conjunction representation, which seems to be opposite to the assumption that the conjunction representation produces the response and thus requires more discussion on why this is the case. This study expands the working memory research by showing that working memory can simultaneously hold and manipulate multiple task representations. It also provides solid foundation for future work to investigate the control mechanisms on working memory representations of task conjunctions.

Reviewer #3 (Public Review):

The manuscript examines the neural bases of the exploration/exploitation tradeoff - a crucial component of decision-making, that determines whether we choose the best option or explore less beneficial, but perhaps more informative alternatives. The authors specifically focus on the role of a substructure of the basal ganglia (the globus pallidus internus, or GPi) in modulating the amount of exploration in a simple learning task. This is a straightforward, well-designed study - albeit with a small patient sample, as is often the case in clinical data involving deep brain stimulation - and the computational modelling is rigorous. The presented work convincingly argues for the role of the GPi in suppressing exploration and enhancing exploitative choices.

Strengths of the present work<br /> 1) Testing DBS patients is a somewhat rare opportunity to directly observe the impact of stimulating or inactivating specific neural areas on human behavior. The present task's pallidal-DPS cohort and the ON/OFF stimulation manipulation make for a strong argument that the observed differences in behavior and model parameters are indeed due to the GPi, and the author's proposed neural framework for how the GPi modulates exploration is well-supported and convincing.

2) The computational modelling is rigorous; the authors have shown how their selected model complements the data and model-free analyses, as well as conducted posterior predictive checks to test the extent to which recovered model parameters are actually informative.

3) This line of investigation is always relevant and timely, as most daily decisions from small-scale human decisions to large-scale AI machines involve calibrating exploration and exploitation in some form. Further insight into the neural mechanisms of this tradeoff, therefore, holds significance and countless potential applications.

Other Comments<br /> 1) While historically, 'exploration' was simply defined - as in the present work - as simply choosing the non-greedy/non-maximizing option, in the past decade or so more recent work has crucially distinguished between types of exploration that are explicitly aimed at seeking new information (i.e. directed exploration - specifically choosing the options that are less well-known, in order to build a more accurate world representation) and those that are independent of the informativeness or other properties of the other choice options (i.e. decision noise). Existing literature provides evidence for separate neural substrates for the two, and any model that will enrich our understanding of how the brain calibrates the explore/exploit tradeoff should at least touch on how these separate types of exploration fit into the proposed framework. It would therefore help contextualize and strengthen the presented work to include more discussion on precisely which type of exploration the GPi is modulating.

2) While the proposed model is well-presented and checked, some further clarification for readers who are not familiar with RLDDM might improve clarity. Furthermore, the model-free performance analyses as well as the brain connectivity analyses, while they clearly show a link between GPi stimulation and the overall amount of exploration, do not delve too deeply into the specific patterns of the exploratory behavior (e.g. by showing within-task fluctuations through a moving window of average exploration, or by describing further the differences in decision time between explore and exploit trials, etc.). The basic performance analyses are consistent with the authors' hypotheses and support the conclusions, but a more in-depth check of specific exploration patterns might help clarify the mechanism better.

Reviewer #3 (Public Review):

In this study, the authors aimed to provide evidence of a novel developmental mechanism regulating brachial arch formation in the little skate. More specifically, the authors leveraged previous studies establishing the role of Hedgehog signaling in early little skate brachial arch development and built upon these studies by discovering the embryonic identity of Shh-expressing cells and the role of canonical Wnt signaling in regulating proper anterior brachial arch formation. The authors nicely combined the use of the spatiotemporal expression of various Hedgehog and Fgf signaling members with transcriptomic analysis and pharmacologic experiments to assess genetic relationships. In general, this manuscript is of high quality and will appeal to a diverse array of scientific disciplines. Moreover, the relationship between Shh-Fgf8 and the importance of Wnt signaling in the context of brachial arch formation in the little skate may be more broadly applied to other cartilaginous fishes or other aquatic vertebrate species in general. As the little skate is largely an unexplored model organism, this study exemplifies the utility of the little skate and emphasizes the wide array of methods that can be implored to further identify this species' development on a molecular basis. Future studies should consider the generation of genetically modified skate species, as current functional interrogation is limited to pharmacological approaches. Although this study has been eloquently conducted, there is some extraneous information that takes away from the major conclusions of the story in addition to some gaps in experimental data that are required to clarify their findings.

Reviewer #3 (Public Review):

"Obesogenic diet induces circuit-specific memory deficits in mice" by Bakoyiannis et al., investigates the role of specific ventral hippocampal circuits (specifically to nucleus accumbens and mPFC) in high-fat diet-induced memory deficits. The authors had previously shown that increases in activity in the ventral hippocampus accompany high-fat diet-induced memory deficits, and that inhibition of activity thereby normalizes those memory deficits. In this manuscript, the authors extend these findings to specific projections, showing that they normalize different types of memories by inhibiting the two different pathways.

The strengths of the paper include the pathway-specific manipulations that reveal a difference between the two types of memory. The results are a modest step forward for the field of feeding and learning and memory and would be of interest to that subgroup of neuroscientists. However, the paper also has a number of weaknesses which I detail below.

1. First, the authors show an effect of cfos from both pathways in Figure 2 on object learning. However, the inactivation studies show a pathway-specific effect on object recognition and object location, with no experiments to delineate how this divergence occurs. The authors do not specify whether they compared cfos in the control group between NAcc and mPFC projections (presumably they did some controls with each injection), which might reveal differences.

2. Related to this, it is unclear how the pathways end up diverging for memory if they do not show any differences in cfos during training. Perhaps there are pathway-specific differences in cfos following the ORM and OLM tests? It is difficult to support the claim that there are pathway differences in memory following inactivation if we do not see any pathway-specific change in activity.

3. Figure 2 and Figure 3 are also hard to interpret because of the usage of a 1-way ANOVA which is not the appropriate statistical test when there are two independent variables (HFD and DREADD manipulation). Indeed, noticing the statistical test also reveals that a critical control missing: HFD -, hM4di+CNO +. It is possible that inactivation simply brings down cfos levels regardless of diet. While this might benefit memory in the case of HFD, it is critical to know whether the manipulation is specific to the overactivation caused by HFD or just provides a general decrease in activity.

Reviewer #3 (Public Review):

In this study, Dr Tamai et al. investigated the association between bile acid level and skeletal muscle mass using a rat model and patients with HCCs. The authors found that LCA level was closely associated with skeletal muscle mass in both CLD rats and human patients with HCCs.

Reviewer #3 (Public Review):

Galdos, et al., have developed a novel lineage tracing technique using genetically encoded fluorophores in human-induced pluripotent stem cells to identify first heart field cells and ventricular cardiomyocytes during differentiation. To label the FHF lineage, the authors use a CRISPR/Cas9 strategy to express a floxed TurboGfp and add a P2A-Cre recombinase sequence at the stop codon of Tbx5 in two well-characterized hiPSC lines. In these same lines, they then added a P2A-tdTomato construct at the stop codon of the ventricular cardiomyocyte-specific sarcomeric protein Myl2. They expected this strategy to allow them to identify cells as they commit to the first heart field lineage and ultimately FHF cells that differentiate into ventricular CMs, which should therefore represent LV CMs by virtue of their lineage. RT-qPCR confirms that over the course of the differentiation protocol cells begin to express well-studied markers of the FHF lineage and eventually markers of ventricular CMs. This matches the flow analysis of their lineage-tracing technique which is suggestive though not conclusive that their technique is identifying the cells it claims to identify.

The authors found, however, that their flow data showed that the differentiation protocol they used gave rise to >90 % FHF lineage cells, most of which were also Tnnt2+ or tdTomato+ by day 30 of differentiation. None of the cells were positive for markers of the second heart field lineage. To confirm this, the authors used scRNAseq data from multiple differentiation time points to identify the paths cells follow through their Wnt-signaling-based small molecule 2D differentiation protocol. What they find suggests there are two distinct path bifurcations using this protocol. The first is between a mesodermal lineage and an endodermal lineage, and the second is, within the mesodermal cells, a bifurcation between myocardial and epicardial lineages. They compare these results to previously published datasets from murine heart field development and see that the mesodermal pathway matches murine FHF lineage development and that there is no good match for SHF lineages. They hypothesize that a 3D differentiation protocol might lead to a subset of cells developing SHF hallmarks and test this by combining the CMs from their own scRNAseq results with those from a group that developed a novel 3D differentiation protocol to form heart organoids. They identify a cluster in the 3D differentiated cells that does not appear in their own dataset and which is enriched for cells expressing SHF markers and markers of outflow tract CMs.

Strengths:<br /> 1. The use of a Cre/lox system to permanently label putative FHF lineage cells with TurboGFP even after reduction of Tbx5 expression will make it possible to both follow the same cells over time to better understand early human heart development and to evaluate novel differentiation protocols for which cell lineages are likely to predominate. This can then be paired with fluorophores tagged to markers of later progenitors or terminally differentiated cell types (as the authors do here with Myl2) allowing isolation of distinct cell types with known lineages at distinct stages of models of human heart development. This is a potentially quite powerful tool given the limited availability of human fetal tissue and the ethical concerns inherent to using it to study development.<br /> 2. The authors have identified a clear weakness of using 2D differentiation protocols based on Wnt-signaling as models of human heart development. They show convincingly for two separate hiPSC lines that while the cells progress through the primitive streak and the emergence of the first heart field cells, the second heart field does not arise in this protocol. This homogeneity of the terminally differentiated cells may be beneficial in regenerative medicine contexts, but it is clear that for studying development and for pushing cells to OFT or RV CM fates, new techniques are required. They then demonstrate the promise of 3D organoid differentiation techniques in overcoming this hurdle.<br /> 3. This manuscript also sets up a powerful workflow for evaluating cell fate decisions over pseudotime in early heart development. The authors used well-published packages to set up their datasets to meaningfully compare scRNAseq results from their own 2D differentiation experiments with those from previously published scRNAseq results of murine heart development and 3D differentiation. For the latter, they were able to combine the datasets to identify a new cluster of cells from the 3D protocol. This workflow will prove extremely beneficial in comparing cell fate outcomes arising from disparate cardiac differentiation protocols.

Weaknesses:<br /> 1. While demonstrating that 2D differentiation of hiPSCs is an imperfect model of development is a valuable outcome of this work, this also makes it an imperfect model in which to test the robustness of their lineage tracing technique. Nearly all of the cells are shown to progress through the FHF lineages using their fluorescent techniques. This is confirmed using scRNAseq, but this means that they are unable to give a proof of principle that their method will distinguish FHF cells from SHF cells since none of the latter arises.<br /> 2. The authors validate their lineage tracing technique with bulk gene expression by RT-qPCR at different time points during differentiation. However, they never directly confirm that isolated TurboGFP+ cells show higher expression or protein levels of their target FHF markers nor that the TurboGFP+tdTomato+ cells are enriched for LV CMs. While their validation as it stands is highly suggestive that their lineage tracing technique works as advertised, the evidence is still only circumstantial.<br /> 3. The section of the paper devoted to the development and validation of their lineage tracing technique is connected to the section analyzing their scRNAseq results only loosely. Having shown by their new technique and its validation that no populations positive for SHF markers are arising during their differentiation, they turn to scRNAseq to confirm this observation. The issue here is that it requires a bit of circular reasoning. Having established that better techniques are required to study human heart development to move away from relying so heavily on our understanding of murine heart development, the authors then draw their conclusion that no SHF lineages arise during the differentiation of their hiPSC lines in part by comparing them to murine heart development. This is in no way a fatal flaw to the work but it limits the ability to use the authors' techniques to draw novel distinctions between human and murine heart development.

Reviewer #3 (Public Review):

The study by Grone and colleagues proposes to understand how APOE4 contributes to Alzheimer's disease risk by understanding how different cell types within the brain are affected at the level of the transcriptome across the lifespan. There are several strengths of the study, including the concept of profiling different cell types across the lifespan using advanced sequencing methods and the use of a model incorporating neuron-specific deletion of APOE to understand how distinct pools of APOE affect the networks identified according to the form of APOE allele being expressed. There are a number of pathways identified that may inform the field in terms of the elusive role of neuronal APOE in shaping brain function. There are a number of issues in this work that limit many of the conclusions made. For example, the ages chosen to study how APOE alleles affect gene expression in different cell types are limiting and do not unfortunately include earlier ages representing developmental or young adult ages or very advanced age, two ends of lifespan where many functional changes occur in the brain that may be regulated by APOE. Additionally, sex is not studied as a biological variable in the study, leaving the results in question as to whether the findings are limited to one sex. There are a number of other methodological issues, including a lack of clarity on how variance from different sequencing datasets generated at different times for ages within the same comparisons has been handled. In terms of the impact of the study, there is a missing functional validation of key networks that have been identified. We do not know if any of the gene expression differences identified here translate to changes in brain function, limiting our ability to know whether neuronal APOE regulates the brain and may play a role in AD as claimed. Finally, constitutive deletion of APOE within neurons may result in changes in gene expression that are shaped by developmental changes mediated by APOE. Overall, this is an interesting resource that may be useful for scientists seeking to understand the non-canonical roles of APOE in shaping gene expression in the hippocampus.

Reviewer #3 (Public Review):

The manuscript by Toraason et al investigates the role of BRC-1/BRCA1 and the SMC-5/6 complex in repair pathway choice during C. elegans meiosis. The authors use a recently developed system to detect crossover and non-crossover repair events that use the sister chromatid or the same chromosome for repair of a site-specific induced DSB, a related system to look at repair outcomes using the homolog as a repair template, and a cytological approach to detect inter-sister exchanges. The authors show that BRC-1 and SMC-5 both function during meiosis to limit the formation of inter-sister crossovers but are not essential for interhomolog recombination. BRC-1 also suppresses error-prone DNA repair processes during mid-pachytene and promotes the formation of long non-crossover conversion tracts, functions that may not be reliant on SMC-5/6. Finally, the authors show genetic interactions consistent with a role of BRC-1 regulating theta-mediated end joining in smc-5 mutants; however, BRC-1 and SMC-5 do not appear to regulate one-another's localization.

The manuscript is focused on examining the consequences of brc-1 and smc-5 mutations on repair pathway choice in C. elegans meiosis. It achieves that goal. The experiments are generally well done, and the results will be of interest to investigators studying DNA repair and meiotic recombination in C. elegans.

Reviewer #3 (Public Review):

This excellent paper is of interest to developmental brain scientists in general and especially those interested in the development of the vital brainstem circuitry that is necessary for postnatal life. The manuscript provides substantial new insight into the crucial role of microglial in the formation of functional neural circuits. Overall, the data are properly controlled, analysed, and presented although other potential functional deficits in the microglia deficient mice (Pu.1-/-) could be discussed.

Microglia, brain-resident macrophages, play key roles during prenatal development in defining neural circuitry function, ensuring proper synaptic wiring, and maintaining homeostasis.

Strengths;<br /> The thorough and well-designed experiments, analysis, and presentation of the results from wild-type and microglia-deficient embryonic and early postnatal mice are convincing. The authors clearly show how microglia deficient mice exhibit lower respiratory activity fewer embryonic active respiration-related neurons as well as less connectivity. Thus their claim that microglia are crucial for vital respiration-related neural networks to function properly is convincing.

Impact:<br /> Further understanding of the role of microglia in brain and brainstem development is important, since environmental pathogens that affect microglia function, may contribute to susceptibility to developmental disorders associated with altered synapse numbers and dysfunctional neural networks.

Weakness:<br /> The paper does not describe any other malformations, that might contribute to the immediate or close to immediate postnatal death of newborn pups.<br /> Please add some more references/discussion or data to underline that the deficits that you show are a major contributor to immediate postnatal death.<br /> Are there any signs of Peripheral deficits; eg upper airway, heart, or lung anatomical /functional abnormalities that might contribute to the immediate postnatal death?

Reviewer #3 (Public Review):

Blake et al. describe a comprehensive analysis of alternative splicing changes that accompany the activation of primary human T cells with anti-CD3 and anti-CD3/CD28 antibodies. They then focused their attention on 3 genes involved in the regulation of apoptosis that exhibited anti-CD28 enhanced alternative splicing, culminating in functional studies suggesting that the 3 splicing changes make important contributions to T-cell apoptosis/cell survival. They further document a role for JNK signaling in activating the splicing changes. These results should be of considerable interest to both the alternative splicing and T-cell activation fields.

Despite the substantial merits of both the initial comprehensive analysis and the subsequent targeted analysis of genes involved in the regulation of T cell apoptosis and survival, the manuscript has one major limitation (#4 below) and a few lesser limitations. The major limitation makes it difficult to accurately assess the CRISPR-based functional experiments included in the manuscript.

1. The initial analysis in Figure 1D could have been strengthened by the inclusion of additional quantitative information about the distribution of alternative splicing changes. For example, the authors set a threshold of >10% dPSI to be considered a significant event. To appreciate the findings, it would have been helpful to know how many of these start at 0-10 PSI prior to stimulation, how many start at 10-20 PSI, 20-30 PSI, etc. In addition, the distribution of dPSI magnitudes would have been of interest (the scatter plots in Figures 2A and 2B are difficult to evaluate quantitatively).

2. Similar to the above, an evaluation of the data in Figures 2E and 2F would have benefited from a closer look. For example, only a subset of the "significant alternative splicing" events will have the potential to be enhanced 2-fold by CD28 stimulation because the dPSI value with CD3 alone may be in the range of 40 or 50 or more at some genes. It therefore would have been of interest to know the extent to which the distributions shown in Figures 2E and 2F are influenced by the CD3 dPSI. (One thought would be to examine dPSI ratio distributions after separating the splicing events into a few different bins based on CD3 dPSI.)

3. An evaluation of the data in Figure 3 would have benefited from the inclusion of the PSI value from unstimulated cells for each gene.

4. My most significant concern about the results is that, from the data in Figures 5A, 5D, and S5, it isn't clear that the remaining wild-type allele in the CASP9 and BIM heterozygous clones is generating full-length transcripts and protein (unless I'm misunderstanding the experiment). In the images shown, the full-length mRNAs and proteins appear to be entirely absent, despite the genetic evidence that an undeleted allele remains. One possibility is that a CRISPR guide RNA damaged the second wild-type clone without resulting in a large deletion. The strategy employed to create heterozygous clones to examine the impact of moderate changes in protein ratio is admirable, but the results appear to show dramatic changes (rather than moderate changes) in protein ratio due to the absence of transcripts and protein from the undeleted alleles.

Reviewer #3 (Public Review):

The authors combine comparative genomics and functional approaches to show that wtf are old genes that may drive other Schizosaccharomyces species. Their varied approaches convincingly demonstrate that wtfs exist in S. octosporus, S. osmophilus, and S. cryophilis. While the wtfs are highly diverged in sequence, some of their structural features are conserved across species. One interesting finding is that while in S. pombe wtfs are associated with LTRs, in the other species they associate with a different repetitive DNA locus, the 5S rRDNA. This is interesting, as it suggests that wtfs may have spread through non-allelic gene conversion events within lineages. They have evidence that some of the wtfs in S. octosporus are poison-antidote systems with several parallels to the wtfs in S. pombe.

Overall, this paper makes an exciting contribution to the poison-antidote killers in yeasts and the drive field more generally. The discovery that wtfs are old and are likely to be spore killers in other species, and likely their common ancestor, is interesting as most drive systems are short-lived. Their proposed mechanism for the spread of wtf-like genes through non-allelic recombination shows parallels to repetitive sequences in other taxa, including some other independent drive systems. The tests for a drive phenotype in S. octoporus are especially interesting.

The author's investigation is thorough and the results are sound, with the combination of approaches being the main strength of the study. The functional assays in S. cerevisiae complement the comparative genomic work and suggest that at least a subset of the non-pombe wtfs are poisons/antidotes. It is not clear that examining patterns of protein localization helps the authors understand if there is functional conservation between wtfs in S. pombe and non-pombe species, however. The interpretation of these analyses is unclear in the current manuscript. The paper is generally well organized and reasoned; however, simplifying the discussion to just communicate the main points would strengthen the paper.

Reviewer #3 (Public Review):

The manuscript by Woods et al. describes a highly interesting study on signalling between the alpha-crystallin domain (ACD) and the disordered N-terminal domain (NTD) in the small heat shock protein HSPB5 (alphaB-crystallin). The authors show that distinct regions in the NTD interact with specific grooves in the ACD. The data are supported with aggregation assays, SEC, HDX, NMR, and X-linking MS experiments. This is a very timely and valuable contribution that will be well received by the community.

Reviewer #3 (Public Review):

The manuscript by Jera and coworkers describes an internal long-range interaction within the dynein intermediate chain, which can be relieved by light chain binding to provide access for additional protein ligands, and partly by binding of specific protein ligands. The work uses a suite of biophysical methods including AUC, SEC-MALS, and NMR spectroscopy, and a palette of protein constructs and complexes to assess complex sizes and stoichiometries, pinpointing by NMR the molecular details. The molecular auto-inhibition is supported by the data and is likely to be of general interest. The strength of the manuscript is the use of full-length proteins/longer regions and thus the investigation of higher-order complexes within context, which have been crucial to elucidate an important and likely biologically relevant autoinhibitory state in dynein as well as its modulation.

The manuscript by Jera et al is in general very well written, the experiments have been thoroughly conducted and analyzed, and the conclusions are generally well supported by data. The work delivers important new insight into a case where disordered linkers may enable molecular functions. However, the significance of the finding for the biological function of dynein is not clear. How is it anticipated that the observed differential autoinhibition of dynein will affect the biological outcomes?

Below are some recommendations that I find may improve the manuscript.

As a non-dynein expert, I found the introduction into the protein system to be too superficial and the model shown in Fig. 1B, did not help much (e.g. the light chains were hard to acknowledge as they appear to be rather small compared to the IC chain and was at first overlooked at just the binding sites; where is the heavy chain of dynein, why is there no coiled coil of p150, etc?). The biological role of dynein is not explained particularly well in the introduction and the biological relevance of the findings is too briefly addressed. I suggest a much more detailed description of the system at the beginning of the introduction including the biological relevance of the different ligands, which should include an upgrade of figure 1b, with more detail on domains, etc. Also, the abstract would benefit from a more precise description of the biological question and why this study is relevant, and the title is also very broad. Finally, how autoinhibition plays a role in the biological function of dynein should be more clearly discussed in the discussion, e.g., what is the relevance of the differential binding of the two ligands and their differential effects on the autoinhibited state. Which biological outcomes are to be expected?

One of the conclusions is that the internal contacts occur between the C-terminal of the IC and SAH/H2, which is seen from the intensity changes in the HSQC spectra upon addition of the 160-240 construct to the 1-88 construct. However, adding the linker part from 100-160 produces a much more pronounced effect (Fig. 5C, bottom), suggesting that residues in this region, which includes the Tctex and LC8 binding motifs play additional roles. Is the binding of the light chains to IC of higher affinity to the 100-160 protein than to the 1-260? In that case, this could suggest that also inhibitory access to these two sites occurs in the autoinhibited state. The additional effect of the 100-160 residues should be addressed.

Can H3 be excluded as a player in the internal interactions, just because you see binding to the LC7 site when studied in isolation? Once the LC7 regions is bound, H3 may also participate, as also clearly indicated from the data shown in Fig. 4A. Using an H3 peptide would be relevant.

Fig6B and associated text: there is a clear although weak loss in intensity/peak volume in the H2 region for the interaction with NudE. Why assume that there is no interaction? The affinity for NudE is lower, so the concentration of the complex will also be lower at similar conditions compared to that of p150, and this would give rise to the smaller effects in the spectra. In the lower panel, there is a clear indication of binding to H2 as well, and SAH and H2 binding may very well be cooperative as they are sequentially close. What are the relative concentrations of NudE and p150 in the cell? Would they be competitive despite the difference in affinities? Can a mechanism for p150 ability to relieve autoinhibition be proposed - from Fig6B, could it be able to bind first to the H2 region even though SAH is involved in the autoinhibitory interaction?

Reviewer #3 (Public Review):

The authors have used GWAS summary results for WHR adj. BMI and T2D-risk adj. BMI to identify genome-wide significant loci that show a discordant pattern of association with the traits: higher WHRadjBMI and lower risk of T2Dadj.BMI. They identify 6 discordant loci, for which they perform a series of follow up analyses to connect the genetic variants to their causal genes and their target tissues. They find evidence that THADA-AS and GIN1/PAM may be causal genes in two of these discordant loci.

The strength of the study is the extensive work done by the authors to ensure that the discordant associations between WHRadjBMI and T2DadjBMI are colocalized, to fine-map the genetic loci, and to link the genetic variants to their target genes and tissues. The main weakness is the lack of clear biological and clinical rationale for the analyses that have been performed. Furthermore, there are some remaining concerns about the possibility of allele mismatching, as well as specific gaps in the analysis pipeline and unclear statements in the text, which will require clarification. The paper could be of interest to human geneticists and molecular biologists interested in understanding the function of genetic risk variants of cardiometabolic disease.

Reviewer #3 (Public Review):

The authors revealed the novel role of the DLL-4-Notch1-NICD signaling axis played in platelet activation, aggregation, and thrombus formation. They firstly confirmed the expression of Notch1 and DLL-4 in human platelets and demonstrated both Notch1 and DLL-4 were upregulated in response to thrombin stimulation. Further, they confirmed the exposure of human platelets with DLL-4 would lead to γ-secretase mediated NICD (a calpain substrate) release. Stimulating platelets with DLL-4 alone triggered platelet activation measured by integrin αIIbβ3 activation, P-selectin translocation, granule release, enhanced platelet-neutrophil and platelet-monocyte interactions, intracellular calcium mobilization, PEVs release, phosphorylation of cytosolic proteins, and PI3K and PKC activation. In addition, Susheel N. Chaurasia et al. showed that when platelets were stimulated with DLL-4 and low-dose thrombin, the Notch1 signaling can operate in a juxtacrine manner to potentiate low dose thrombin mediate platelet activation. When the DLL-4-Notch1-NICD signaling axis was blocked by γ-secretase inhibitors, the platelets responding to stimulation were attenuated, and the arterial thrombosis in mice was impaired.

This study by Susheel N. Chaurasia et al. was carefully designed and used multiple approaches to test their hypothesis. Their research raised the potential of targeting the DLL-4-Notch1-NICD signaling axis for anti-platelet and anti-thrombotic therapies. Interestingly, compared to thrombin, a potent physiological platelet agonist, the signaling cascade triggered by DLL-4 was relatively weak. Given that the upregulation of DLL-4 and Notch1 happened in response to thrombin stimulation, how much DLL-4 mediated signaling could contribute to in vivo platelet activation in the presence of thrombin is questionable. This could potentially limit the application of targeting Notch1 as an anti-thrombotic therapy. Further, the authors showed that Notch1 signaling could operate in a juxtacrine manner to potentiate low dose thrombin mediate platelet activation, which means the DLL-4 mediated platelet signaling can act as an accelerator of early-stage hemostasis. Again, inhibition of Notch1 may slow down the hemostasis process. But given the fact that there are other platelet agonists (ADP, collagen...) existing simultaneously, blocking Notch1 signaling may not have a strong anti-platelet effect.

Reviewer #3 (Public Review):

Single-molecule tracking is a powerful technique to uncover the dynamic properties of biomolecules at the single-molecule level. However, interpretation of the data is challenged by technical limitations of the fluorophores and image acquisition, such as photobleaching and limited depth of view. Several approaches have been proposed to overcome these challenges and to improve quantitative analysis of single-molecule data. Heckert et al. present in this manuscript novel methods that make use of Bayesian inference to uncover present diffusive states more accurately than common methods such as mean-square-displacement analysis. The advantage of their method compared to existing developed methods such as Spot-On and vbSPT is that it is possible to obtain an estimated diffusion coefficient per tracked molecule. This allows for spatial analysis of diffusion patterns within the cell and to correlate the mobility of molecules directly with underlying cellular organization.

The major strength of this work lies in their presentation of the current technical challenges (limited focus depth, photo bleaching, localization error) in single-molecule tracking and propose useful solutions to these limitations of single-molecule tracking. As an experimental biologist it is difficult for me to assess the analytical approaches entirely, but I do think that they extensively describe the methodology in the main text and in the additional computational methods. Their presentation of several simulations with relevant variables to validate their methods help to appreciate the validity of their approach.

Although I think their methods could be very useful to more accurately describing biological processes, the novel biological insights presented in this paper are limited. While in their simulations it is clear that their methods are more accurate I would suggest the authors to compare the results from their biological experiments with existing methods, such as MSD analysis. I think this could help to convince possible users of this analysis methods to apply these methods in their experiments.

Reviewer #3 (Public Review):

Yang et al have undertaken a single cell transcriptomic analysis of circulating immune cells from the shrimp, Penes vannamei. They set out to characterize transcriptional differences between circulating immune cell subsets following immune stimulation. Their investigation reveals that shrimp immune cells can be classified into a number of specific subsets defined by unique transcriptional profiles. Using specific marker genes for each cell subset, the authors provide evidence suggesting that shrimp immune cells share transcription factors that define myeloid cell development in mammalian (human) systems.

This study follows an investigative path that is shared by numerous single-cell transcriptomic studies. The authors do an admirable job of synthesizing a complex single-cell transcriptomic analysis into a focused report that highlights important transcripts that define the hemocyte subsets of the shrimp. While I disagree with some of the claims being made related to the evolutionary connection between shrimp hemocytes and mammalian myeloid cells, this dataset will undoubtedly contribute to our understanding of invertebrate immune cell complexity and the relationships these cells have to other invertebrate hemocytes and immune cell evolution.

Reviewer #3 (Public Review):

This manuscript is using an inducible and skeletal muscle specific Bmal1 knockout mouse model (iMSBmal1-/-) that was published previously by the same group. In this study, they utilized the same mouse model and further investigated the effect of a core molecular clock gene Bmal1 on isoform switching of a giant sarcomeric protein titin and sarcomere length change resulted from titin isoform switching. Lance A. Riley et al found that iMSBmal1-/- mouse TA muscle expressed more longer titin due to additional exon inclusion of Ttn mRNA compared to iMSBmal+/+ mice. They observed that sarcomere length did not significantly change but more variable in iMSBmal1-/- muscle compared to iMSBmal+/+ muscle. In addition, they identified significant exon inclusion in the proximal Ig region, so they measured the proximal Ig length domain and confirmed that proximal Ig domain was significantly longer in iMSBmal1-/- muscle. Subsequently, they experimentally generated a shorter titin in C2C12 myotubes and observed that the shorter titin led to the shorter sarcomere length. Since RBM20 is a major regulator of Ttn splicing, they determined RBM20 expression level, and found that RBM20 expression was significantly lower in iMSBmal1-/- muscle. The reduced RBM20 expression was regulated by the molecular clock controlled transcriptional factor MyoD1. By performing a rescue experiment in vivo, the authors found that rescue of RBM20 in iMSBmal1-/- TA muscle restored titin isoform expression, however, they did not measure whether sarcomere length was restored. These data provide new information that the molecular cascades in the circadian clock mechanism regulate RBM20 expression and downstream titin isoform switching and sarcomere length change. Although the conclusion of this manuscript is mostly supported by the data, some aspects of experimental design and data analysis need be clarified and extended.

Strengths:

This paper links the circadian rhythms to skeletal muscle structure and function through a new molecular cascade: the core clock component Bmal1-transcription factor MyoD1-RBM20 expression-titin isoform switching-sarcomere length change.

Utilization of muscle specific bmal1 knockout mice could rule out the confounding factors from the molecular clock in other cell types

The authors performed the RNA sequencing and label free LC-MS analyses to determine the exon inclusion and exclusion through a side-by-side comparison which is a new approach to identify individual alternative spliced exons via both mRNA level and protein level.

Weaknesses:

Both RBM20 expression and titin isoform expression varies in different skeletal muscles. The authors only detected their expression in TA muscle. It is not clear why the authors only chose TA muscle.

The sarcomere length data are self-contradictory. The authors stated that sarcomere length was not significantly changed in muscle specific KO mice in Line 149, however, in Line 163, the measurements showed significantly longer in muscle specific KO muscle. The significance is also indicated in Figures 2C and 3B.

Manipulating titin size using U7 snRNPs linking to the changes in sarcomere length and overexpressing RBM20 to switch titin size are the concepts that have been proved. These data do not directly support the impact of muscle specific Bmal1 KO on ttn splicing and RBM20 expression

There is no evidence to show if interrupted circadian rhythms in mice change RBM20 expression and ttn splicing, which is critical to validate the concept that circadian rhythms are linked to Ttn splicing through RBM20.

Reviewer #3 (Public Review):

The authors here describe that the PMO domain of CWR-1 is active on chitin, which is demonstrated with beautiful and solid biochemistry data. Furthermore, they show that the catalytic activity of the PMO domain is dispensable for allorecognition in N. crassa. More specifically, they showed that the side loops of the PMO domain of CWR-1 are important for allorecognition and cell fusion. The chitin catalytic activity of the PMO domain of CWR-1 is not surprising, as other LPMOs from the same family (AA11) had already been characterized. This paper highlights the discovery that LPMOs are involved in cell wall remodeling of filamentous fungi and cell fusion. These findings certainly strengthen the emerging biological roles that LPMOs play in microorganisms, which are still limited.

The strengths of the paper are the interdisciplinary approach, whereby microscopy is combined with genetics and biochemistry.

There are no major weaknesses in the paper.

Reviewer #3 (Public Review):

Liu et al. investigated the role of Epac2, the "other" less studied cAMP effector (compared to the classical PKA) in dopamine release and cocaine reinforcement using slice electrochemistry, behavior, and in vivo imaging in dopamine neuron-specific Epac2 conditional knockout mice (confirmed by elegant single-cell RT-PCR). Epac2 genetic deletion (Epac2 cKO) or pharmacological inhibition (using the Epac2 antagonist ESI-05, i.p.) reduced cocaine (under both fixed and progressive ratio schedules) but not sucrose, self-administration, supporting an essential role for Epac2 in cocaine reinforcement but not natural reward. Cyclic voltammetry on striatal slices demonstrated that evoked DA release was reduced in Epac2 cKO mice and enhanced by the Epac2 activator S-220 or the PKA activator 6-Bnz independently. Using in vivo chemogenetics and fiber photometry (with the DA fluorescent sensor GRABDA2M), authors showed that DCZ activation of VTA DA neurons expressing rM3D(Gs) increased NAc DA release and cocaine SA in Epac2 cKO mice (rescuing), whereas inhibition of VTA DA neurons expressing hM4D(Gi) decreased DA release and cocaine SA in WT mice (mimicking). Based on these experiments, the authors concluded that Epac2 in midbrain DA neurons contributes to cocaine reinforcement via enhancement of DA release.

The experiments are generally rigorous and the conclusions are mostly well supported by data, but some aspects of behavioral experiments and data analysis need to be clarified or extended.

1. The chemogenetic rescue experiments in Fig. 7 suggested that enhancing DA release in Epac2 cKO mice rescued cocaine SA in mutant mice, but did not necessarily demonstrate that Epac2 mediates this process, thus a causal mechanistic link is missing. This is an important point to clarify because the central theme of the work is that Epac2 regulates cocaine SA via DA release. In addition, it's unclear if chemogenetic activation of DA neurons also enhances sucrose reward. A potentially positive result would not affect the conclusion that enhancing DA release can rescue cocaine SA in mutant mice, but will affect the interpretation and specificity of the rescue data.<br /> 2. Relatedly, chemogenetic inhibition experiments in Fig 8 showed that inhibiting DA neurons reduced DA release and cocaine SA in WT mice, which suggested that the strength of DA transmission was a regulator of cocaine SA. This is expected given the essential role of DA transmission in reward in general, but it did not provide strong insights regarding the specific roles of Epac2 in the process.<br /> 3. Fig 7B. DCZ-induced DA releases enhancement in the fiber photometry recording seems to only last for ~30 min, well short of the duration of a cocaine SA session (3 hrs). It's unclear how this transient DA release enhancement could cause the prolonged cocaine SA behavior.<br /> 4. Fig. 9. working hypothesis: hM4D(Gi) and hM3D(Gs) are shown to inhibit and enhance synaptic vesicle docking, which is not accurate. These DREADDS presumably regulate neuronal excitability, which in turn affects SV release.

Reviewer #3 (Public Review):

Employing primary myometrial cells, this study investigates molecular actions and cellular pathways regulated by interleukin-33 (IL33) a ubiquitous immune modulator that shapes type 1, type 2 and regulatory immune responses. The rational for this study is the notion that Inflammation is one of the major causes of premature delivery, a hypothesis that is not universally accepted as many investigators suggest that inflammation is a consequence of labour.

The manuscript contains mostly appropriate methodologies, although there are some areas that at present are weak and require additional or more refined approaches.<br /> For example, studies on IL33 expression in human tissues have employed a small number of biopsies of limited potential. I would expect the author to use a substantive number of biopsies and calculate H-scores alongside other parameters of inflammatory pathways and develop various regression models.<br /> Without this crucial evidence once is left to wonder what is the rational for all follow-up studies described.<br /> Also, the authors need to be aware that modern approaches to quantitative PCR require multiple 'housekeeping genes and calculation of geometric means.

Reviewer #3 (Public Review):

The present study aims to elucidate posterior cingulate cortex (PCC) function with both single-unit and population-level depth electrodes. The results clearly show that the dorsal PCC (dPCC) is involved in executive functions (search and add), but that it also contains neurons that are selective for episodic memory (past and future) and rest conditions. With this impressive study design, the authors are able to reconcile discrepancies between human and primate studies. Furthermore, the derived conclusion that PCC function is more diverse than merely its participation in the DMN is of great importance for the field. Thus, I believe that this work will have a great impact on how we think about the PCC, by (1) emphasizing its participation in executive processes and (2) providing evidence of distinct single-unit response profiles that do not manifest on a population level.

The main strength of this work is the combination of population-level measurements that clearly show the participation of dPCC in executive processes with microelectrode single-unit measurements and an unsupervised hierarchical clustering approach that allows for the identification of 4 distinct SU response profiles within the dPCC. In addition, the population-level electrodes mostly engaged in executive function cluster around an fMRI meta-analysis peak related to executive processing derived from neurosynth, providing a bridge to human fMRI research.

Nevertheless, there is one concern regarding the data collected within the ventral PCC (vPCC) in this study and the way the authors integrated it into their conclusions.

Specifically, the conclusion that "Together, they [the findings] inform a view of PCC as a heterogeneous region composed of dorsal and ventral subregions specializing in executive and episodic processing respectively" may not be completely supported by the data. The dPCC macroelectrode data does clearly show a functional specialization in executive processing, but does the data from vPCC presented in this manuscript also support the claim? While taking a closer look at the vPCC data, several inconsistencies stood out: First, the total number of vPCC electrodes was much smaller (6 vs 29 microelectrodes and one microwire probe that was not analyzed). Second, it is not clear which of the presented electrodes in figure 3 were considered to be ventral. From comparing figure 3 with the dorsal/ventral split displayed in figure 1B, it seems as if only one electrode was unambiguously placed in vPCC. Third, BBG statistics of these 6 electrodes are not presented, thus the claim that they show vPCC functional specialization is not statistically supported.

Reviewer #3 (Public Review):

The primary goal of this work is to link scale free dynamics, as measured by the distributions of event sizes and durations, of behavioral events and neuronal populations. The work uses recordings from Stringer et al. and focus on identifying scale-free models by fitting the log-log distribution of event sizes. Specifically, the authors take averages of correlated neural sub-populations and compute the scale-free characterization. Importantly, neither the full population average nor random uncorrelated subsets exhibited scaling free dynamics, only correlated subsets. The authors then work to relate the characterization of the neuronal activity to specific behavioral variables by testing the scale-free characteristics as a function of correlation with behavior. To explain their experimental observation, the authors turn to classic e-i network constructions as models of activity that could produce the observed data. The authors hypothesize that a winner-take-all e-i network can reproduce the activity profiles and therefore might be a viable candidate for further study. While well written, I find that there are a significant number of potential issues that should be clarified. Primarily I have main concerns: 1) The data processing seems to have the potential to distort features that may be important for this analysis (including missed detections and dynamic range), 2) The analysis jumps right to e-i network interactions, while there seems to be a much simpler, and more general explanation that seems like it could describe their observations (which has to do with the way they are averaging neurons), and 3) that the relationship between the neural and behavioral data could be further clarified by accounting for the lop-sidedness of the data statistics. I have included more details below about my concerns below.

Main points:<br /> 1)Limits of calcium imaging: There is a large uncertainty that is not accounted for in dealing with smaller events. In particular there are a number of studies now, both using paired electro-physiology and imaging [R1] and biophysical simulations [R2] that show that for small neural events are often not visible in the calcium signal. Moreover, this problem may be exacerbated by the fact that the imaging is at 3Hz, much lower than the more typical 10-30Hz imaging speeds. The effects of this missing data should be accounted for as could be a potential source of large errors in estimating the neural activity distributions.

2) Correlations and power-laws in subsets. I have a number of concerns with how neurons are selected and partitioned to achieve scale-free dynamics.<br /> 2a) First, it's unclear why the averaging is required in the first place. This operation projects the entire population down in an incredibly lossy way and removes much of the complexity of the population activity.<br /> 2b) Second, the authors state that it is highly curious that subsets of the population exhibit power laws while the entire population does not. While the discussion and hypothesizing about different e-i interactions is interesting I believe that there's a discussion to be had on a much more basic level of whether there are topology independent explanations, such as basic distributions of correlations between neurons that can explain the subnetwork averaging. Specifically, if the correlation to any given neuron falls off, e.g., with an exponential falloff (i.e., a Gaussian Process type covariance between neurons), it seems that similar effects should hold. This type of effect can be easily tested by generating null distributions using code bases such as [R3]. I believe that this is an important point, since local (broadly defined) correlations of neurons implying the observed subnetwork behavior means that many mechanisms that have local correlations but don't cluster in any meaningful way could also be responsible for the local averaging effect.<br /> 2c) In general, the discussion of "two networks" seems like it relies on the correlation plot of Figure~7B. The decay away from the peak correlation is sharp, but there does not seem to be significant clustering in the anti-correlation population, instead a very slow decay away from zero. The authors do not show evidence of clustering in the neurons, nor any biophysical reason why e and i neurons are present in the imaging data. The alternative explanation (as mentioned in (b)) is that the there is a more continuous set of correlations among the neurons with the same result. In fact I tested this myself using [R3] to generate some data with the desired statistics, and the distribution of events seems to also describe this same observation. Obviously, the full test would need to use the same event identification code, and so I believe that it is quite important that the authors consider the much more generic explanation for the sub-network averaging effect.<br /> 2d) Another important aspect here is how single neurons behave. I didn't catch if single neurons were stated to exhibit a power law. If they do, then that would help in that there are different limiting behaviors to the averaging that pass through the observed stated numbers. If not, then there is an additional oddity that one must average neurons at all to obtain a power law.

3) There is something that seems off about the range of \beta values inferred with the ranges of \tau and $\alpha$. With \tau in [0.9,1.1], then the denominator 1-\tau is in [-0.1, 0.1], which the authors state means that \beta (found to be in [2,2.4]) is not near \beta_{crackling} = (\alpha-1)/(1-\tau). It seems as this is the opposite, as the possible values of the \beta_{crackling} is huge due to the denominator, and so \beta is in the range of possible \beta_{crackling} almost vacuously. Was this statement just poorly worded?

4) Connection between brain and behavior:<br /> 4a) It is not clear if there is more to what the authors are trying to say with the specifics of the scale free fits for behavior. From what I can see those results are used to motivate the neural studies, but aside from that the details of those ranges don't seem to come up again.<br /> 4b) Given that the primary connection between neuronal and behavioral activity seems to be Figure~4. The distribution of points in these plots seem to be very lopsided, in that some plots have large ranges of few-to-no data points. It would be very helpful to get a sense of the distribution of points which are a bit hard to see given the overlapping points and super-imposed lines.<br /> 4c) Neural activity correlated with some behavior variables can sometimes be the most active subset of neurons. This could potentially skew the maximum sizes of events and give behaviorally correlated subsets an unfair advantage in terms of the scale-free range.

Reviewer #3 (Public Review):

Williamson et al. have investigated the role of cells derived from a neural stem cell (NSC) region of the adult mouse brain called the subventricular zone (SVZ) in a model of stroke. The authors labeled SVZ cells with Nestin-CreER and the Ai14 (tdTomato) reporter, induced cortical infarcts 4 weeks later, then analyzed brains 2 weeks thereafter. Most of the tdTomato+ cells in the peri-infarct regions were not neurons but less differentiated neural precursor cells. They then ablated proliferating NSCs in the SVZ with GFAP-TK mice and ganciclovir (GCV) administration, and this reduced SVZ-derived peri-stroke cells and impaired motor recovery. Older mice have less proliferation in the SVZ, and these older mice have fewer peri-infarct SVZ-derived cells and worse recovery than younger mice. Using multi-exposure speckle imaging (MESI) and 2 photon imaging, the authors found that ablation of proliferating SVZ cells reduced vascular remodeling and synaptic turnover in peri-infarct areas. Immunohistochemical analysis revealed the expression of VEGF, BDNF, GDNF, and FGF2. The authors selected VEGF for functional studies, conditionally knocking out VEGF in SVZ cells and finding that this reduced recovery and neuronal spine density. Finally, the authors expressed VEGF by AAV vectors in mice with ablated SVZ, finding that VEGF could improve repair and recovery after stroke.

The results presented in the paper support some of the authors' general conclusions and may be of interest to investigators of adult mouse SVZ. The use of genetic labels for lineage analysis and studies of VEGF conditional knockout in SVZ cells are technical strengths of the study. The results support the idea that VEGF in SVZ cells is important for recovery from stroke in younger adult mice. However, the impact of the work may be somewhat limited, as outlined below.

1. It is already well known that VEGF is an important aspect of stroke recovery (at least in rodent models), and that ectopic expression of VEGF can be beneficial. Showing that some of the VEGF in peri-stroke regions might come from SVZ-derived cells would be a relatively incremental discovery.<br /> 2. Furthermore, while it seems clear that the VEGF conditional knockout (VEGF-cKO) in SVZ cells reduces behavioral recovery and certain histological measures, it is not clear that these impairments are due to a lack of VEGF delivery from the SVZ cells. It is possible that VEGF-cKO changed the proportion of SVZ cells that arrive in the peri-stroke region. It is also possible that VEGF-cKO makes these cells impaired in the expression of other trophic factors.<br /> 3. The cytogenic response to stroke was not characterized in much detail at the cellular level. Essentially only one time point (2 weeks) was selected for immunohistochemistry (Fig. 1), and so the dynamics of this response cannot be evaluated. Does the proportion of cell types change over time? Are migratory cells more homogeneous and then diversify after arrival to the peri-stroke region? At longer time points, do these SVZ-derived cells still exist? Such an analysis is important to the story since the behavior was evaluated at a range of time points (3-28 days after stroke), and recovery was noted as early as 7 days. Are SVZ-derived cells already at the peri-stroke area after 7 days? If they are not already there, then how would the recovery be explained? The behavioral recovery also continues to improve at 28 days; are SVZ-derived cells still present in large numbers at that time? How would the authors explain continued recovery if the SVZ-derived cell population drops away after 2 weeks?<br /> 4. The SVZ-derived peri-stroke cells were not characterized in much detail at the molecular/transcriptomic level. The authors studied 4 trophic factors by antibody staining, but there are many other potential genes that may contribute to the effect. Transcriptomic analyses of SVZ-derived peri-stroke cells (e.g., by single-cell RNA-seq) may provide deeper insights into potential mechanisms.<br /> 5. The significance of this work for understanding stroke in human patients is unclear since the adult human brain SVZ is essentially devoid of neurogenic stem cells. Thus, although some of the observations in this paper are interesting, the cytogenic response to stroke described here may not occur in human patients.

Reviewer #3 (Public Review):

In the manuscript by Scalabrino et al. a rigorous characterization of the functionality of retinal ganglion cells in a mouse model of rod photoreceptor degeneration is presented. The authors analyzed the degeneration of cone photoreceptors, which is known to be linked to rod degeneration. Based on the time course of cone degeneration they investigated the functional properties of retinal ganglion cells aged between 1 month and seven months.

The most interesting finding is robust preservation of functional properties, as reflected in little changes of the receptive fields (spatial and temporal characteristics) or signaling fidelity/information rate. In contrast to other mouse models, the present one shows no oscillatory activity until a complete loss of cone photoreceptors occurred at an age of nine months.

Although the receptive fields of retinal ganglion cells remain nearly intact, the number of ganglion cells with identifiable receptive fields decreases significantly with age (Fig.2F). Could the authors comment, if this might imply a "patchy" vision?

Reviewer #3 (Public Review):

Meechan et al. describe a technical modification of a standard ultramicrotome that allows, in combination with software solutions provided, both, the precise orientation and the depth of the cutting plane according to sample features pre-defined by X-ray imaging. Accurate targeting of specific structures in heavy-metal¬-impregnated volume EM samples is challenging and time-consuming and good reproducibility across samples is difficult. Since the applications for volume EM are rapidly increasing during the last years, improved workflows can have an important impact in the field.

A great strength of the workflow described here is the easy access to the required components. Once X-ray data acquisition at a micron-resolution has been achieved, no further expensive, sophisticated equipment is required for its application. Motors and controllers are assembled from common electronics or mechanical parts. The microtomes used are standard microtomes as they are available in most electron microscopy laboratories. No major modification to the microtome is required. However, a statement on whether a dedicated microtome is recommended, or how fast the system can be disassembled would have been useful.

The comparative data collection on two different microtome setups, regarding both microtome brand and users, provides a big credit to the study. The design and calibration steps for the microtome motorization are well documented. The success of reaching the targeting plane with an average of below 2 microns in the RMC setup is an amazing result when considering cellular dimensions, and even the 4.5-micron precision achieved on the Leica system is in the range of a single cell.<br /> In this regard, however, the correlation of the targeting precision with user skills remains an open question that has not been addressed. Prior to the automated cutting, the initial manual alignment of the block surface to the knife is of crucial importance (as stated as a potential explanation for differences in the RMC and Leica setup performance). A comparison of the precision reached by different users on one setup could have further completed the study.

Pre-selection of the precise cutting orientation can challenge the users' 3D imagination. Here, the authors have modified modules of existing software solutions (mostly Fiji plugins) for the visualization of the X-ray data and presumptive cutting views. The resulting Crosshair Fiji plugin can be used on a standard computer and is provided with detailed and clear documentation. The implementation within a standard software (Fiji) with existing modules, will ease the use of this plugin.

The choice of Platynereis larvae for targeting the imaging plane allows very clear visualization of the whole procedure. Both the general workflow as well as the specific cases of 10 test samples are well-illustrated by this example tissue. In the future, this proof of principle documented here for the simple larvae should be further validated by a structure embedded in the context of a dense tissue, which can be more challenging.

Further applications will reveal whether this semi-automated workflow can be expanded to correlative light and electron microscopy, with or instead of X-ray imaging. A rapid, precise trimming of fluorescent structures will be of great impact on the volume EM community. For the correlation between X-ray and EM data, the workflow documented by the authors here is already offering an elegant improvement to the time-consuming sample approach with a standard setup.

Reviewer #3 (Public Review):

Wong et al. developed a new versatile approach with a robust signal to track protein dynamics by inserting a tag into the endogenous loci and different properties of fluorescent dyes for conjugation. Using this approach, the authors monitor the trafficking of Fluorescent dye and Halo-tagged GluA1 with time-lapse imaging and found that neuronal stimulation induces GluA1 accumulation surrounding stimulated synapses on dendritic shafts and actin polymerization at synapses and dendrites. Furthermore, combining with pharmacological manipulations of actin polymerization or myosin activity, the authors found that actin polymerization facilitates exocytosis of GluA1 near activated synapses. The new approach may provide broad impacts upon appropriate control experiments, and the practical application of this approach to GluA1 trafficking upon neuronal activation is significant. However, there are several weaknesses, including confirmation of activity of the tagged receptors and receptor specificity mimicking endogenous LTP machinery. If the receptor tagged by the new robust approach reflects endogenous machinery, this approach will provide a big opportunity to the community as a versatile method to visualize a protein not visualized previously.

Reviewer #3 (Public Review):

In this manuscript by Ardiel et al, the authors develop a novel automated approach to behavioral classification of C elegans embryos. They provide detailed validation of this system, and in exploiting it, identity a previously unknown period of behavioral quiescence in the late embryo that is likely dependent on synaptic transmission. Then shifting to a high throughput assay to focus on this specific period, they provide evidence for a sleep/quiescent like state. The highly technical approaches they develop can potentially be used by many labs, and the rich behavioral dataset can likewise serve as a foundation for numerous future studies. However, I have major concerns. Foremost is that at its core, there are very limited new biological conclusions to come out of this work, which will dampen impact of the techniques described. Other major issues:

1. The period of quiescence/SWT is intriguing, though I believe the authors are premature in their conclusions. SWT shares molecular features of worm sleep, but the work does not go far enough to prove quiescence. Are the animals paralyzed? Does SWT have features of sleep homeostasis? I do not think the authors need to prove every feature exhaustively, but at a minimum, should demonstrate that it is a reversible state. Moreover, the authors convert midway through the work to calling this slow wave twitch (SWT). These are all words that are likely chosen specifically to evoke a sense of "sleeping" from readers, but the behavior does not really seem like twitching, and are these really slow waves?

2. For the high throughput portion, the authors find some mutants that disrupt SWT. they should also test to see whether earlier embryonic behaviors are affected (as was tested with unc13), as this would very much alter the interpretation

3. The Discussion really overreaches. There is a heavy focus on sleep and autism, despite no clear evidence that SWT is sleep. I certainly agree discussions can be speculative, but the tone here seems to make claims that are absolutely not supported by the data. I would suggest ending the manuscript with "Together, these similarities suggest that SWT may be akin to the developmentally timed sleep associated with each larval molt" which underscores to readers that the data really ends short of showing SWT is indeed sleep.

4. The manuscript feels disjointed as a whole in some respects, as the authors put huge effort into the methodology of Figures 1-4, and then completely shift approaches. Perhaps they can reframe the work to better emphasize how MHHT led to an important biological discovery, and then better justify why moving to a new system was necessary. Also important - the manuscript portion describing Figs 1-4 is so technical that most readers will not be able to follow. Perhaps there are ways to better hand hold for a broad audience.

5. Fig 6g attempts to show that the correlation between RIS calcium transients and motion is reduced in FLP-11 mutants. While this reduction is evident, it still seems like a very strong correlation, undercutting the idea that FLP-11 is required for SWT, as it is for sleep. This further calls into question whether SWT is the same at lethargus.

Reviewer #3 (Public Review):

Argenty et al. investigated the role of Lissencephaly gene 1 (LIS1), a dynein-binding protein, in thymic development and T cell proliferation. They find that LIS1 is essential for the early stages of T and B cell development, and demonstrate that loss of LIS1 has a negative impact on the transition from DN3 to DN4 thymocytes and on the maturation of pre-pro-B cells into pro-B cells in the bone marrow. Using a CD2Cre Lis1fl/fl murine model, they observe that in thymocytes LIS1 is critical for DN3 proliferation and completion of cell division. Then, using a CD4Cre Lisfl/fl model (Cd4 promoter is up-regulated just in later stages of thymic development and, thus, does not impact DN3 thymocytes) they show that LIS1-deficient CD4 T cells have proliferation defects following both TCR-dependent or -independent stimulation, which results in apoptosis. They also confirm previous reports that show that LIS1-deficient CD8 T cells do not have their proliferation impaired upon TCR stimulation, which suggests that these two cell types rely on different mechanisms to regulate the cell cycle. Finally, the authors make efforts to determine how LIS1 regulates proliferation in thymocytes and CD4 T cells. Interestingly, they show that LIS1 is important for chromosome alignment and centrosome integrity and provide data that support a model where LIS1 would facilitate the assembly of active dynein-dynactin complexes. These data provide interesting insights into how different cell types use distinct strategies to undergo mitosis and how this can impact on their proliferation and fate decisions. The conclusions of the manuscript are mostly supported by the provided data, although certain aspects can be further investigated and clarified.

Strengths of the paper:

By combining a re-assessment of previous reports with new findings, the data from this manuscript convincingly demonstrates that LIS1 is crucial for cell proliferation in certain development steps/cell types. Furthermore, the manuscript provides clear evidence of how LIS1 loss causes proliferation defects by disrupting centrosome integrity and chromosome alignment both in CD4+ T cells and thymocytes.

Weakness of the paper:

Although authors successfully address the mechanistic role of LIS in thymocyte and CD4+ T cell division, the manuscript would be strengthened by both providing further evidence to support some of their conclusions and a review of some speculations raised in the discussion.

In Figure 1, the authors claim that LIS1 is not required for pre-TCR assembly, but for expansion/proliferation of DN3 thymocytes as a step prior to reaching the DN4 stage. However, authors indeed observe increased expression of CD5 (which is a downstream event of Notch and IL-7R signalling). Thus, from the data provided it is not clear whether signalling through Notch or IL-7R is definitely not affected, which could be clarified by assessing the expression of other downstream targets of these molecules.

In Figure 3, the authors mostly confirm previous data from Ngoi, Lopez, Chang, Journal of Immunology, 2016 (reference 34), but also provide evidence of a role of LIS1 in CD4+ T cell proliferation in more physiological setups, using OT2-CD4-Cre Lis1flox/flox (or OT2 Lisflox/flox as controls) in adoptive transfer experiments followed by antigen-specific immunization. However, the evidence provided by the authors about proliferation defects in LIS1-deficient cells in this context is limited by the early timepoint chosen: day 3 post-immunization.

In the discussion, the authors speculate about the differences observed between CD4 and CD8 T cells, as the latter do now show proliferative defects upon TCR-triggered stimulation, and come up with the hypothesis that LIS1 might be important for symmetric cell divisions, but not for asymmetric cell divisions. However, the arguments used by the authors have few caveats, especially because CD4+ T cells can also undergo asymmetric cell division following TCR-triggered stimulation upon the first cognate antigen encounter (Chat et al., Science, 2007, Ref. 8).

Finally, the authors discuss that mono-allelic LIS1 defects might contribute to malignancies. Certainly not all points raised in the discussion need to be experimentally addressed, but for this particular hypothesis the authors would likely have the tools to achieve that, which would broaden the relevance of understanding LIS1 function.

Reviewer #3 (Public Review):

This study of U1 snRNP interaction with the 5'ss is an interesting and exciting piece of work. In particular, the data support two important conclusions of general importance to the field: 1) the association of the U1 snRNP with the 5'ss is largely determined by the snRNP itself and does not require other splicing factors and 2) the ability to form "productive" (i.e. long-lived) interactions between the U1 snRNP and the 5'ss cannot be accurately predicted by base-pairing potential alone. This second point is particularly important as many algorithms for predicting splicing efficiency are based on base-pairing strength between the U1 snRNA and the 5'ss sequence. The data immediately suggest two additional questions.

1. The authors repeatedly speculate that the benefit of basepairing toward the 3' end is due to the activity of Yhc1. If this model is true, these 3' end basepairs should not influence binding for a U1 snRNP with a mutant Yhc1. Since the authors have used mutant Yhc1 in other studies it seems possible to test this prediction.

2. Since splice sites are often "found" in the context of alternative or pseudo/near-cognate splice sites, it would be interesting to know how the "rules" identified in the experiments presented in this study influence splice site competition and whether both the short- and long-lived states are subject to competition or, rather, only the short-lived complexes. Is it possible to repeat the CoSMoS experiment with two oligomer sequences of different colors?

3. Finally, the authors should say more about the particular requirement for basepairing at position 6, especially in the context of the experiments in Figure 5. This is particularly striking as this position is not well conserved in natural 5'ss, at least compared to position 5.

Reviewer #3 (Public Review):

By use of in vivo fluorescence imaging and image analysis tools, Blanc et al. have established an automatic pipeline to build a digital 3D-temporal atlas of zebrafish hindbrain. Based on the common fluorescence labelling with HuCD the authors first established a pipeline and a reference atlas of the hindbrain. The pipeline is based on the already established tools in Fiji for registration of multi-modal data, such as Fijiyama plugin, and automatic segmentation of the data, in particular Weka 3D segmentation. By use of this pipeline, the authors then mapped rhombomeres markers Mu4127, precursor cell populations by nestin, Neural basic helix-loop-helix (bHLH) transcription factor neurog1 expressed in proliferating cells, motoneurons by isl1, and glutamatergic and GABAergic neurons via vglut2 and gad1b correspondingly. All these cell populations were mapped precisely from 24 to 72 hpf of zebrafish brain development. By comparison of fluorescent marker expression in a temporal manner, the authors demonstrate that one can approximate the birthdate of cells for which reporter expression is delayed and becomes present only later.

Strengths:<br /> Free and easy access to Fiji plugins used and developed in this work makes the building of digital 3D atlases accessible for many labs, potentially also in other settings. The analysis of marker expressions in space, that is anterior-posterior and mediolateral is simple (without the need for high computational power or specialized and expensive software) and at the same time biologically relevant.

Weaknesses:<br /> Due to the use of fluorescence imaging, the pipeline is limited to easily accessible and rather transparent tissues. Additionally need for one channel as a common reference is time and labour extensive in terms of experimental work. In terms of the 3D digital atlas maker, the use of user supervised training limits the "easiness" and widespread use of the pipeline in the future.

Reviewer #3 (Public Review):

The manuscript by Wang et al. investigates the role of actin and an associated capping protein in cytoadherence and motility of T. vaginalis and represents a substantial amount of work. The authors first demonstrate the adherent lines and clinical isolates express high levels of actin than non-adherent lines, and that a higher percentage of actin is found in the filamentous form in these isolates. FACP was subsequently identified as an actin-binding protein in immunoprecipitation experiments. Overexpression of FACP-WT, but not overexpression of FACP lacking a putative actin-binding domain, resulted in a decreased amount of F-actin in cells, suggesting a role for FACP in limiting actin polymerization by presumably capping the barbed (+) end of filaments. Phosphorylation of FACP at serine 2, mitigates this effect demonstrating that phosphorylation is important for the actin-binding ability of FACP. Phosphorylation also leads to lower adherence to epithelial cells.

However, a major conclusion of this paper, namely that FACP acts via a novel mechanism and binds both G and F-actin, is not supported by the data. This conclusion is based on experiments with recombinant TvActin expressed in bacteria and co-immunoprecipitation of FACP with actin. The execution of these experiments is problematic for a number of reasons:

1) The authors state in the methods that the majority of GST-actin is found in inclusion bodies in E. coli. The protein was solubilized in 8M urea, which will denature the protein and the authors then attempted to refold actin by dialysis in G-buffer. F-actin buffer was then added to induce polymerization. The authors provide no evidence that actin folds correctly upon renaturation with G-buffer. It is quite possible that the proteins that pellet upon the addition of the F-buffer are not filaments but insoluble aggregates. I say this because (1) the assay is done at 80 picomoles, which is well below the critical concentration for most actins (typically the Cc is ~0.1-0.5uM range), and (2) the authors provide no evidence by EM or light microscopy to demonstrate that actin filaments are formed under these conditions. Inclusion of these controls in the manuscript is critical to the interpretation of all experiments which utilized the recombinant actin, including the elisa-based assay which is offered as evidence for an interaction with G-actin.

2) In a number of experiments, the authors performed His-tagged immunoprecipitation of FACP to identify interacting proteins. Actin is found to co-IP with FACP, however, it is not clear if the immunoprecipitated actin represents an interaction with FACP with the F or G isoform. The interpretation of this data is critical for the conclusions of the paper, where the authors argue that FACP has an "atypical" mode of action (title) and the authors' conclusion (line 608) that FACP binds directly to G or F-actin.

Reviewer #3 (Public Review):

This paper examines the relative performance of linear mixed models (LMMs), principal components (PCA), and their combination (PCA-LMM) for genetic association studies in human populations. The authors claim that previous papers examining this question are inadequate and that: (i) there remains confusion on which method is best and in which context, (ii) that the metrics used in previous evaluations were insufficient, and (iii) that the simulation settings used in previous papers were not comprehensive. To fix these problems the authors perform an extensive set of simulations within several frameworks and suggest two new metrics for evaluating performance.

Strengths:

The simulation framework used in this paper and the extensive number of simulations provide an opportunity to examine the relative properties of the three approaches (LMM, PCA, PCA-LMM) in a variety of contexts.

The parameters of the simulation framework are based on highly diverged populations, which is an increasingly common analysis choice that has not been examined in detail via simulation previously.

The evaluation metrics used in this paper are AUC and a test of the uniformity of the p-value distribution under the null. This is an improvement over some previous analyses which did not examine power and relied on less sensitive tests of type I error.

Weaknesses:

This paper has a limited set of population frameworks just like all papers before it. The breakdown of which method is best (LMM, PCA, PCA-LMM) will be a function of the simulation framework chosen.

The frameworks chosen for this paper are certainly not comprehensive in contemporary human genetic studies. In fact, the authors make a number of unusual choices. For example, the populations in the simulated study have extremely large Fsts. While this is also a strength, the lack of more standard study designs is a weakness. More importantly, there is no simulation of family effects, which is the basis of many of the PCA-LMM papers reported in Table 1.

The discussion (and simulations) of LMM vs PCA, particularly LMMs with PCs as fixed effects misses the critical distinction of whether PCs are in-sample (in which case including PCs as fixed effects effectively serves as a preconditioner for the kinship matrix, speeding up iterative methods such as BOLT), or projections of individuals onto out-of-sample principal axes. There is also no discussion of LOO methods to address "proximal contamination", also quite relevant in evaluating power as a function of the number of PCs.

There is no discussion/simulation of spatial/environmental effects or rare vs common PCs as raised in Zaidi et al 2020. There are some open questions here regarding relative performance the authors could have looked at. Same for LMMs with multiple GRMs corresponding to maf/ld bins and thresholded GRMs. For example, it would be helpful to know if multiple-GRM LMMs mitigate some of the problems raised in the Zaidi paper.

Reviewer #3 (Public Review):

To investigate the action of Ism1 and reveal the difference from insulin, the authors performed a non-biased phosphorylation proteome analysis of pre-adipocytes (3T3-F442A cells). They found that Ism1-induced signaling pathways are related to unexpected GO terms, including "protein anabolism" and "muscle." Furthermore, Ism1 enhanced Akt-mediated protein synthesis in C2C2 myotubes, and Ism1 KO mice showed weakness and decreased muscle size. Based on these data, the authors claimed that Ism1 is a novel factor in governing muscle hypertrophy and atrophy via protein synthesis.

The new role of Ism1 in protein synthesis discovered using non-biased exhaustive analysis is a unique finding. However, they analyzed the phosphorylation cascade of Ism1 only in 3T3-F442A cells and did not compare the difference between Ism1 and the insulin signal in skeletal muscle cells. In Fig.3C, the actions of Ism1 and Igf1 are compared in C2C12 myotubes, but it is unclear whether these pathways are different. The authors did not analyze whether the protein synthesis action of Ism1 belongs to the same pathway as insulin or IGF1 or to a different pathway in skeletal muscle cells.

As the author states in the Discussion, it is important to clarify which phase of the skeletal muscle regeneration process Ism1 influences. Single-cell RNAseq cannot analyze skeletal muscle fibers, which are large, multinucleated, terminally differentiated cells. Therefore, it is unclear whether Ism1 acts on satellite cells, myoblasts, myotube cells, or skeletal muscle fibers.

Reviewer #3 (Public Review):

The authors have performed a transcriptional analysis of young/aged hematopoietic stem/progenitor cells which were obtained from normal individuals and those with MDS.

The authors generated an important and valuable dataset that will be of considerable benefit to the field. However, the data appear to be over-interpreted at times (for example, GSEA analysis does not have "functionality", as the authors claim). On the other hand, a comparison between normal-aged HSC and HSC from MDS patients appears to be under-explored in trying to understand how this disease (which is more common in the elderly) disrupts HSC function.

A more extensive cross-referencing of other normal HSPC/MDS HSCP datasets from aged humans would have been helpful to highlight the usefulness of the analytical tools that the authors have generated.

Major points

1. The authors detail methodology for identification of cell types from single-cell data - GLMnet. This portion of the text needs to be clarified as it is not immediately clear what it is or how it's being used. It also needs to be explained by what metric the classifier "performed better among progenitor cell types" and why this apparent advantage was sufficient to use it for the subsequent analysis. This is critical since interpretation of the data that follows depends on the validation of GLMnet as a reliable tool.

2. The finding of an increased number of erythroid progenitors and decreased number of myeloid cells in aged HPSC is surprising since aging is known to be associated with anemia and myeloid bias. Given that the initial validation of GLMnet is insufficiently described, this result raises concerns about the method. Along the same lines, the authors report that their tool detects a reduced frequency of monocyte progenitors. How does this finding correlate with the published data on aging humans? Is monocytopenia a feature of normal aging?

3. The use of terminology requires more clarity in order to better understand what kind of comparison has been performed, i.e. whether global transcriptional profiles are being compared, or those of specific subset populations. Also, the young/aged comparisons are often unclear, i.e. it's not evident whether the authors are referring to genes upregulated in aged HSC and downregulated in young HSC or vice versa. A more consistent data description would make the paper much easier to read.

4. The link between aging and MDS is not explored but could be an informative use of the data that the authors have generated. For example, anemia is a feature of both aging and MDS whereas neutropenia and thrombocytopenia only occur in MDS. Are there any specific pathways governing myeloid/platelet development that are only affected in MDS?

5. MDS is a very heterogeneous disorder and while the authors did specify that they were using samples from MDS with multilineage dysplasia, more clinical details (blood counts, cytogenetics, mutational status) are needed to be able to interpret the data.

Reviewer #3 (Public Review):

The study uses a mouse animal model of sensorineural hearing loss after sound overexposure at high frequencies that mimics ageing sensorineural hearing loss in humans. Those mice present behavioural hypersensitivity to mid-frequency tones stimuli that can be recreated with optogenetic stimulation of thalamocortical terminals in the auditory cortex. Calcium chronic imaging in pyramidal neurons in layers 2-3 of the auditory cortex shows reorganization of the tonotopic maps and changes in sound intensity coding in line with the loudness hypersensitivity showed behaviourally. After an initial state of neural diffuse hyperactivity and high correlation between cells in the auditory cortex, changes concentrate in the deafferented high-frequency edge by day 3, especially when using mid-frequency tones as sound stimuli. Those neurons can show homeostatic gain control or non-homeostatic excess gain depending on their previous baseline spontaneous activity, suggesting a specific set of cortical neurons prompt to develop hyperactivity following acoustic trauma.

This study is excellent in the combination of techniques, especially behaviour and calcium chronic imaging. Neural hyperactivity, increase in synchrony, and reorganization of the tonotopic maps in the auditory cortex following peripheral insult in the cochlea has been shown in seminal papers by Jos Eggermont or Dexter Irvine among others, although intensity level changes are a new addition. More importantly, the authors show data that suggest a close association between loudness hypersensitivity perception and an excess of cortical gain after cochlear sensorineural damage, which is the main message of the study.

The problem is that not all the high-frequency sensorineural hearing loss in humans present hyperacusis and/or tinnitus as co-morbidities, in the same manner that not all animal models of sensorineural hearing loss present combined tinnitus and/or hyperacusis. In fact, among different studies on the topic, there is a consensus that about 2/3rds or 70% of animals with hearing loss develop tinnitus too, but not all of them. A similar scenario may happen with hearing loss and hyperacusis. Therefore, we need to ask whether all the animals in this study develop hyperacusis and tinnitus with the hearing loss or not, and if not, what are the differences in the neural activity between the cases that presented only hearing loss and the cases that presented hearing loss and hyperacusis and/or tinnitus. It could be possible that the proportion of cells showing non-homeostatic excess gain were higher in those cases where tinnitus and hyperacusis were combined with hearing loss.

Reviewer #3 (Public Review):

Fernandez et al. report results from a multi-day fMRI experiment in which participants learned to locate fractal stimuli along three oval-shaped tracks. The results suggest the concurrent emergence of a local, differentiated within-track representation and a global, integrated cross-track representation. More specifically, the authors report decreases in pattern similarity for stimuli encountered on the same track in the entorhinal cortex and hippocampus relative to a pre-task baseline scan. Intriguingly, following navigation on the individual tracks, but prior to global navigation requiring track-switching, pattern similarity in the hippocampus correlated with link distances between landmark stimuli. This effect was only observed in participants who navigated less efficiently in the global navigation task and was absent after global navigation.

Overall, the study is of high quality in my view and addresses relevant questions regarding the differentiation and integration of memories and the formation of so-called cognitive maps. The results reported by the authors are interesting and are based upon a well-designed experiment and thorough data analysis using appropriate techniques. A more detailed assessment of strengths and weaknesses can be found below.

Strengths

1. The authors address an interesting question at the intersection of memory differentiation and integration. The study is further relevant for researchers interested in the question of how we form cognitive maps of space.

2. The study is well-designed. In particular, the pre-learning baseline scan and the random-order presentation of stimuli during MR scanning allow the authors to track the emergence of representations in a well-controlled fashion. Further, the authors include an adequate control region and report direct comparisons of their effects against the patterns observed in this control region.

3. The manuscript is well-written. The introduction provides a good overview of the research field and the discussion does a good job of summarizing the findings of the present study and positioning them in the literature.

Weaknesses

1. Despite these distinct strengths, the present study also has some weaknesses. On the behavioral level, I am wondering about the use of path inefficiency as a metric for global navigation performance. Because it is quantified based on the local response, it conflates the contributions of local and global errors.

2. For the distance-based analysis in the hippocampus, the authors choose to only analyze landmark images and do not include fractal stimuli. There seems to be little reason to expect that distances between the fractal stimuli, on which the memory task was based, would be represented differently relative to distances between the landmarks.

3. Related to the aforementioned analysis, I am wondering why the authors chose the link distance between landmarks as their distance metric for the analysis and why they limit their analysis to pairs of stimuli with distance 1 or 2 and do not include pairs separated by the highest possible distance (3).

4. Surprisingly, the authors report that across-track distances can be observed in the hippocampus after local navigation, but that this effect cannot be detected after global, cross-track navigation. Relatedly, the cross-track distance effect was detected only in the half of participants that performed relatively badly in the cross-track navigation task. In the results and discussion, the authors suggest that the effect of cross-track distances cannot be detected because participants formed a "more fully integrated global map". I do not find this a convincing explanation for why the effect the authors are testing would be absent after global navigation and for why the effect was only present in those participants who navigated less efficiently.

5. The authors report differences in the hippocampal representational similarity between participants who navigated along inefficient vs. efficient paths. These are based on a median split of the sample, resulting in a comparison of groups including 11 and 10 individuals, respectively. The median split (see e.g. MacCallum et al., Psychological Methods, 2002) and the low sample size mandate cautionary interpretation of the resulting findings about interindividual differences.

Reviewer #3 (Public Review):

The manuscript of Birckman and colleagues tackles the link between lineage priming, lineage specification, and cell cycle in the ESCs culture. This is an interesting piece of work, with several noteworthy findings, that elegantly explain how lineage priming can be efficiently achieved during the changing cultural conditions. There are several interesting points raised by the authors, relating to lineage priming, cell specification, and cell cycle, that can be presented to the scientific community. Namely:

• Differential regulation of the cell cycle can tip the balance between populations of cells primed to different cell fate choices (here PrE and Epi).

• Different culture conditions favour acceleration/stimulation of the cell cycle of different cell populations.

• Only a small population of cells from the original culture enters a differentiation process which is followed by selected expansion and/or survival of their progeny.

• In the case of endodermal type specification (towards PrE), a shortening of the cell cycle is accompanied by the proportional relative increase of G1 phase length.

• FGF activity is responsible for cell cycle synchronisation, required for the inheritance of similar cell cycles between sisters and cousins

Unfortunately, in the current version of the manuscript, the authors try to create the impression that the relationship between cell cycle, heterogeneity and cell fate found in ESCs can be directly translated to the in vivo system. It is not clear, however, how easily and reliably the information about the cell cycle in ESCs can be translated to an in vivo setting. The timeline of PrE vs Epi specification in vivo and in vitro are completely different. In embryos, PrE is specified within 24h, whereas with in vitro it takes 6 days. I cannot see how these two timelines - and also different cell cycle lengths - can be reliably compared.

Reviewer #3 (Public Review):

Mating changes behavior of female fruit flies. Authors previously reported that putrescine-rich foods increase number of progenies per mated female and mated females detect putrescine with IR76b and IR41a and are attracted to putrescine odor (Hussain, Zhang et al., 2016). In another paper, authors reported that this change of putrescine preference is mediated by sex peptide receptor (SPR) and its ligand, myoinhibiotry peptides (MIPs; Hussain, Ucpunar et al., 2016). In yet another paper, authors reported that two types of dopaminergic neurons (DANs) which innervate alpha prime 3 (a'3) or beta prime 1 (b'1) compartment of the mushroom body (MB) show enhanced response to cVA, the male sex pheromone 11-cis-Vaccenyl acetate (Siju et al., 2020). The present study investigated neural circuits that potentially link these observations.

The authors first showed that putrescine-attraction in mated females is sustained over 7-days, which cannot be explained by SPR-MIP dependent mechanism that disappears in one week. Then they explored a factor that is transferred from males during copulation and required for putrescine-attraction in mated females. They found that blocking synaptic transmission of cVA-sensitive OR67d olfactory receptor neurons during 24 hour period of pairing with males reduces putrescine-attraction 3-5 days later (Figure 1). On the other hand, experiments with mutant flies lacking ability to generate eggs or sperms indicated that fertilization is not essential for the change in odor preference. In a proposed scenario, cVA transferred to the female during copulation activates DANs projecting to the b'1 and that in turn induces a shift in how the MB regulates the expression of polyamine odor preference, possibly by alternating activity of MB output neurons (MBONs) in the beta prime 2 (b'2) compartment.

Some data are in line with this scenario. Blocking synaptic transmissions of Kenyon cells during mating or odor preference test reduced attraction to putrescine (Figure 2). Activation of dopaminergic neurons projecting to the beta prime 1, gamma 3 and gamma 4 in virgin females promoted attraction to putrescine when tested 3-5 days later (Figure 3). Flies expressing shibire ts1 in the MBONs in the b'1 compartment showed reduced putrescine preference when females were mated at restrictive temperature (Figure 4). Using calcium imaging and EM connectome, authors also found candidate lateral horn output neurons that may mediate putrescine signals from olfactory projection neurons to the b'1 DANs.

This study utilized molecular genetic tools, behavioral experiments and calcium imaging to comprehensively investigate neural circuits from sensory neurons for cVA or putrescine to the learning circuits of the MB. Addressing points detailed below will strengthen a causal link between enhanced cVA response in beta prime 1 DANs and enhanced putrescine preference in mated females.

1) The MB is the center for olfactory associative learning. It is not so surprising that 24-hour long activation of any MB cell types have long-term consequence on fly's odor preference. As authors showed in Hussain et al., 2016 and Figure S1, mated females change preference to polyamines but not ammonium. Therefore, it is important to show odor specificity of the circuit manipulations to claim that phenomenon in mated females are recapitulated by each manipulation. Wang et al., 2003 (DOI:https://doi.org/10.1016/j.cub.2003.10.003) reported that blocking a broad set of Kenyon cells impairs innate odor attraction to fruit odors and diluted odors but not repulsion.

2) Requirement of PAM-b'1 DANs for putrescine-attraction in mated females should be demonstrated. The authors suggested existence of alternative mechanisms that may mask requirement of PAM-b'1 (Figure 3B). In a previous study, the authors reported SPR-dependent mechanism. I suggest testing the requirement of PAM-b'1 DANs in SPR mutant background or one-week after mating when SPR-dependent effect on sensory neurons disappear.

3) Activation phenotype of MB188B-split-GAL4/UAS-dTrpA1 cannot be ascribed to activation of PMA-b'1 alone because of additional expression in DANs projecting to gamam3 and gamma4 compartments. Run the same experiment with more PMA-b'1 specific driver line.

4) Some of EM connections are too low to be considered (e.g. two in Figure S3 and five in Figure 5). Although these connections could be functional, previous EM connectome analysis typically set much higher threshold (e.g. 10 in Hulse et al., 2021 DOI: 10.7554/eLife.66039) to avoid considering artifacts.

5) Data for Kenyon cells (Figure 2) and LHON (Figure 6) are interesting, but not directly related to other data regarding PAM-b'1 and MBON-b'1. Due to lack of long-term changes in MBOB's odor responses in mated females (Figure 5), it is unclear what information needs to be read out from Kenyon cells and how does it affect processing of putrescine signals potentially carried by LHAD1b2.

Reviewer #3 (Public Review):

The goal of this work is to understand the role that previously neglected, unannotated ORFs play in the evolution of gene novelty in the Drosophila melanogaster lineage. These are ORFs that mostly code for small proteins, most of them having noncanonical start codons. The authors sought to identify translated ORFs using published MS proteomics datasets, making sure to achieve a balance between false positives and false negatives; they succeed rather convincingly. They then focused on when these ORFs first appeared and how they evolved, mainly aiming to understand whether some of them have emerged de novo and the evolutionary trajectories that they have taken.

The major strengths of the manuscript lie in its scope, as it takes advantage of recently published data to exhaustively search the entire ORF catalogue of D. melanogaster for translation, in the application of rigorous methodologies for the identification of MS-supported ORFs and in the inference of the phylogenetic age of the ORF using a novel synteny-based approach. About this last point, however, I feel that some methodological details are missing. I understand that the genomic MSA of the D. melanogaster ORF and its orthologous region is extracted and that a search for the optimally aligning segment in the sequence of each species is conducted. Does that search include only ORFs in each orthologous region? I assume this is the case because the similarity cut-off of 2.5 is then calculated from protein alignments. If that is the case, why not use global alignments of entire ORFs? Furthermore, why is there no gap penalty used? Finally, I cannot see where the genomic similarity scoring part detailed in the methods is used, which adds to my confusion.

Albeit not a major one, an additional weakness comes from the use of Latent Class Analysis to identify subpopulations of ORFs within the greater set, and examine their differences. I see why the authors did it and in theory, I have no objection, but given the small number of factors (8 if I'm counting correctly), it's unclear if it's worth the added level of complexity. Plus there's some potential bias involved since it requires binning continuous variables and hence defining bins. It seems to me that the authors could have achieved more or less the same by looking for specific subgroups based on criteria that they set themselves a priori.

A crucial part of the work is the attribution of de novo origin to utORFs. Here, I find the initial analysis, wherein a single outgroup species is sufficient to invoke de novo origination, relatively unnecessary. Especially since the authors go on to state themselves that only two or more supporting outgroups can provide convincing evidence. I would add that at least two of the outgroups should be non-monophyletic. It is also unclear why an ORF needs to be present in the outgroups at all (and lacking significant similarity). Is there a limit to how small that ORF can be? If so, and if there happens to be no such ORF in a region, why would that not count as evidence?

I feel that the authors achieve most of their aims, at least the ones that I perceive as the most important.<br /> There are however some findings that are not sufficiently well supported.

Reviewer #3 (Public Review):

The paper describes an ingenious and painstakingly reported method of evaluating the informativeness of clinical trials. The authors have checked all the marks of robust, well-designed and transparently reported research: the study is registered, deviations from the protocol are clearly laid out, the method is reported with transparency and all the necessary details, code and data are shared, independent raters were used etc. The result is a methodology of assessing informativeness of clinical trials, which I look forward to use in my own content area.

My only reserve, which I submit more for discussion than for other changes, is the reliance on clinicaltrials.gov. Sadly, and despite tremendous efforts from the developers of clinicaltrials.gov (one of the founders is an author of this paper and I am well-aware of her unrelenting work to improve reporting of information on clinicaltrials.gov), this remains a resource where many trials are registered and reported in a patchy, incomplete or downright superficial and sloppy manner. For outcome reporting, the authors compensate this limitation by searching for and subsequently checking primary publications. However, for the feasibility surrogate this could be a problem. Also, for risk of bias, for the trials the authors had to rate themselves (i.e., ratings were not available in a high-quality systematic review), what did the authors use, the publication or the record from the trial registry?

In general, it seems like a problem for this sophisticated methodology might be the scarcity of publicly available information that is necessary to rate the proposed surrogates. Though the amount of work involved is already tremendous, the validity of the methodology would be improved by extracting information from a larger and more diverse pool of sources of information (e.g., protocols, regulatory documents, sponsor documents).

In that sense, maybe it would be interesting for the authors to comment on how their methodology would be improved by having access to clinical trial protocols and statistical analysis plans. Of course, one would also need to know what was prospective and what was changed in those protocols, i.e., having protocols and statistical analysis plans prospectively registered and publicly available. Having access to these documents would open interesting possibilities to assessing changes in primary outcomes, though as the authors say that evaluation would also require making a judgement as to whether the change was justified. Relatedly, perhaps registered reports could be a potential candidate for clinical trials that would also support a more accurate assessment of informativeness, per the authors' method, provided the protocol is made openly available.

Still related to protocols, were FDA documents consulted for pivotal trials, which again could give an indication of the protocol approved by the FDA and subsequent changes to it?

Reviewer #3 (Public Review):

Early life trauma is a risk factor for adult aberrant aggressive behavior but this important public health issue remains under examined in the neurosciences. This study seeks to fill the gap with a mouse model of adolescent trauma that involves a combination of fearful and anxiety-provoking experiences and assessment on gene expression in brain region controlling aggression, the hypothalamus, and another controlling executive function, the prefrontal cortex. Mice are categorized for aggressive phenotype as being extreme or moderate, with the extreme being compared to controls for transcriptomic analyses of the hypothalamus and PFC. Females did not show increased adult aggression in the resident-intruder paradigm following adolescent fear and anxiety. Pathway analysis implicated the thyroid hormone pathway in male hypothalamus with the thyroid receptor, Ttr, being the top candidate gene. This formed the basis of an in depth analyses of thyroid hormone pathway and discovery of reduced T3 following adolescent stress which was causally linked to adult aggression. This is a novel observation with potentially important implications.

The strengths of the study are the detailed behavioral analyses, inclusion of both sexes and down regulation of Ttr specifically in hypothalamus, reducing T3 and increasing aggression. The weaknesses are a lack of mechanistic explanations for how reduced T3 and T4 leads to pathological aggression in males, weakly supported claims of transgenerational inheritance, lack of consideration of other pathways and no explanation for the profound sex difference.

Specific Comments

1) The KEGG analyses does implicate the thyroid hormone pathway but the more consistent changes seem to be in drug addiction pathways and estrogen signaling, leaving one to wonder if the emphasis on the TH pathway is truly warranted.

2) Aggression in females under normal circumstances is not evoked by a male intruder unless the female has a litter. Thus, it is not that surprising that the peripubertal stress did not evoke aggression in virgin females. Rather, the more interesting question is whether maternal aggression would become aberrant after peripubertal stress.

3) Regarding the trans-generational transmission of the PPS, since the germ cells were present in the animals that were subject to PPS and gave rise to the offspring that were then tested, this is not truly transgenerational as the germ cells were residing in the stressed body. The transmission needs to be to at least the F2 generation with no stress in the F1 for this to be considered transgenerational.

4) Regarding the methylation status of the Ttr, confidence in this result requires consideration of other targets as well in order to understand whether the epigenetic modifications are specific to just Ttr or are more widespread.

5) The statistical analysis rests on unpaired t-tests but in most experiments a 2-way ANOVA is warranted with treatment and brain region as factors.

6) The word "trauma" in the context used here connotes an emotional interpretation of stressful or fearful events. We do not know if the mice are experiencing trauma, instead we know they are being subject to fearful and stress-inducing experiences. It is suggested that the word trauma be removed throughout and replaced with more precise terminology.

Reviewer #3 (Public Review):

This is a very interesting and impressive manuscript. It is complex in its multiple components, and in some ways that makes it a difficult manuscript to evaluate. There is a lot in it, including empirical analyses of a face dataset and of behavioral association data, combined with a theoretical model.

The three main findings are: 1) Paternal siblings look alike (similar to, and building on, a recent manuscript the authors published elsewhere); 2) Infants that are more facially similar tend to associate; and 3) mothers tend to be found in association with other unrelated infants that look more like their own infants. Such results are interesting, and indeed one potential interpretation, perhaps even the most likely, is that mothers are behaving in such a way that promotes association between their own infants and the paternal kin of their infants.

Nonetheless, the evidence provided is logically only consistent with the authors' hypothesis, rather than being strong direct evidence for it. As such, the current framing and indeed the title, "Primate mothers promote proximity between their offspring and infants who look like them", are both problematic. (In addition, the title should be about mandrills, not "primates", since this manuscript does not provide evidence from any other species.) The evidence provided is consistent with the hypothesis, but also consistent with other potential hypotheses. The evidence given to dismiss other potential hypotheses is not strong, and rests on the fact that many males are not around all year to influence things, and that "males that were present during a given reproductive cycle are not responsible for maintaining proximity with either infants or their mothers (MJEC and BRT, pers. obs.)".

My opinion is that these are really interesting analyses and data, which are being somewhat undermined by the insistence that only one hypothesis can explain the observed association patterns. It could easily be presented differently, as a demonstration that paternal siblings look alike and that they associate. The authors could then go on to explore different possible explanations for this using their association data, make the case that maternal behavior is the most plausible (but not the only) explanation, and present their model of how such behavior could bring fitness benefits.

In my view, such a presentation would be both more cautious and more appropriate, without in any way reducing the impact or importance of the data. In the current iteration, I think there are issues because the data do not provide sufficient support for the surety of the title and conclusion, as presented.

Reviewer #3 (Public Review):

The authors critically assessed a widespread assumption that paternal biases in the number of germline mutations passed to offspring and the number of germline cell divisions have a causal link. They gather a diverse set of previously published findings that are inconsistent with this assumption, including the accumulation of maternal DNMs with age, the consistent ratio of paternal-to-maternal germline mutation (α) in humans, the range of α in mammals, and the dominance of mutational processes that are uncorrelated to cell division in human germline and somatic tissues. They then generate estimates of α based on evolutionary rates at sex chromosomes vs autosomes. They find αevo of 1-4 across the species considered, which are robust to changes/exclusion of a number of potentially confounding factors. They find an increase in αevo with generation time in mammals but not in birds. The authors consider and evaluate a model with a fixed number of early mutations for both sexes followed by post sexual differentiation stage with a paternal mutation bias.

Reviewer #3 (Public Review):

In this manuscript, Baumgartner et al investigated how cells control Rhino specific deposition on only a subset of the H3K9me3 chromatin domains to specify piRNA source loci. They identified a previously unknown protein, Kipferl, which by interacting with the chromodomain of Rhino guides and stabilizes its specific recruitment to selected piRNA source loci. Kipferl would be preferentially recruited to Guanine-rich DNA motifs. They show that in Kipferl mutant flies, Rhino nuclear subcellular localization and Rhino's chromatin occupancy changes dramatically. Then, they dissect all the domains of the Kipferl protein and show that the Rhino- and DNA-binding activities can be separated and that the 4th ZnF of Kipferl is required to interact with Rhino.

It is a very elegant genetic work (CRISPR-edited, rescue, KD, overexpression fly lines). In addition, the authors used a combination of yeast two hybrid screen, ChIP, small-RNA-seq and imaging to dissect the function of this new protein. The data in this paper are compelling. Some conclusions might be more moderate. Even if the effect of Kipfler on 80F (Rhino binding, piRNA production) is very obvious, this study also clearly demonstrates that other protagonists are required for the specific binding of Rhino to other piRNA source loci (including 42AB and 38C).

- Is Kipferl expressed early during oogenesis development? If Kipferl starts to be expressed only after the GSCs and cystoblast stage, Kipferl is probably not required to determine the specification of piRNA source loci identity but probably more for the maintenance of the specification. Could the authors discuss or comment on that?

- To perform most of their ChIP-seq analysis, the authors have divided the genome into pericentromeric heterochromatin and euchromatin based on H3K9me3 ChIP-seq data performed on ovaries. With this classification the 42AB (2R:6,256,844-6,499,214) and the 38C (2L:20148259-20227581) piRNA clusters known to be heterochromatic fall in the euchromatic part of the genome. Was there a problem with the annotation?

- Some regions exist in euchromatin that are strongly enriched in Rhino, in Kipferl and in H3K9me3 but are not producing piRNA. Does this type of region exist in heterochromatin?

- Kipferl has been identified to interact with Rhino by a yeast two-hybrid screen (Figure 2). A co-IP which is the classical method for confirming the occurrence of this intracellular Rhino-Kipferl interaction should be provided.

- Rhino is known to homodimerize and it has been reported that this homodimerization is important for its binding to H3K9me3 (Yu et al, Cell Res 2015). It is surprising not to find Rhino among the interactors that were picked up from the screen. Do the authors have any explanations or at least comments on these results?

- In Kip mutants, the delocalization of Rhino to a very large structure at the nuclear periphery is a very clear phenotype (Figure 3). All the very elegant genetic controls are provided. This particular localization of Rhino is correlated with an increase in 1.688 Satellite expression and a colocalization of Rhino and the 1.688 RNAs in the nucleus. The authors propose that this increase is consistent with an elevated Rhino occupancy at 1.688 satellites. The authors should moderate their statements in the light of the results of ChIP experiments. Rhino is maintained on these loci in Kip mutants but an increase is not very clearly observed. Couldn't it be the RNA and not the DNA of this 1.688 region traps Rhino? The same in situ experiment should be performed after an RNAse treatment. The delocalization of Rhino is lost in the Kipferl, nxf3 double mutant flies. What is the chromosomal Rhino distribution in this context? Is the increase in nascent transcripts of 1.688 satellites lost?

- The level of some Rhino dependent germline TE piRNAs is affected in Kipferl GLKD. Is there a direct correlation between TEs which lost piRNAs and those for which the level of transcripts increases (Diver, 3S18, Chimpo, HMS Beagle, flea, hobo) ?

- Figure 5E, it seems that Kipferl binding is also dependent on Rhino. All the presented loci have much less binding of Kip in Rhino -/- (The scale for the 42AB locus should be the same between the Rhino -/- and the control MTD w-sh). In addition, the distribution of Rhino in the Kipferl-sh on the 42AB is maintained but seems to be different. Could the authors discuss these points?

- It is not clear why the authors focus only on Kipferl binding sites in a Rhino mutant in the Figure 5D? Even if the authors mention in the text that "Kipferl binding sites in Rhino mutants ... often coincided with regions bound by Kipferl and Rhino in wildtype ovaries" it should be added the same analysis presented in figure 5D centered on Kipferl peaks detected in ChIP experiments in WT condition in the different genotypes.

- There is a discrepancy between the results found Figure 3A and Supp figure 3B. In the Rhino mutant the level of Kipferl protein does not seem to be affected whereas in the Rhino GLKD, there is a strong decrease of Kipferl protein. The authors completely elude this point.

- Comparing the figure 5E and the figure 6G presenting both the 80F piRNA cluster, depending of the scale and the control line that was chosen to illustrate the results we can draw different conclusions. In the figure 5E we can conclude that le level of Kipferl decreases on the 80F locus in Rhino (-/-) compared to the control MTD w-sh, whereas in the figure 6G we can conclude that the level of Kipferl is similar in the Rhino (-/-) compared to the control w1118.

- gypsy8 or RT1b are enriched in GRGG motifs and are also the ones that among Rhino-independent Kipferl enrichment are the most Rhino enriched. Are these 2 elements present in the 80F cluster? Are these two elements derepressed upon Kipferl GLKD ? Where are these two elements in the figure presenting the change in TE transcript level upon Kipferl GLKD?

Reviewer #3 (Public Review):

This is an exciting new cryoEM structure of the HOPS tethering complex, which is necessary for membrane fusion at the vacuole/lysosome in eukaryotic cells. Finally, we can visualize, at moderate resolution, the positioning of HOPS subunits with respect to each other, and predict how HOPS and its various binding partners, such as Rab GTPases and SNAREs, can interact and control fusion. A conceptual advance put forward by this structure seems to be a rigid central core of HOPS that may contribute to helping drive the efficiency of the SNARE-mediated fusion mechanism.

As exciting as this new structure is, however, the study seems to fall a bit short of its promise to explain "why tethering complexes are an essential part of the membrane fusion machinery, or how HOPS "catalyzes fusion." As such, the title is also misleading with regard to HOPS being the "lysosomal membrane fusion machinery."

Overall, the manuscript could benefit greatly, especially for a non-HOPS specialist reader, in providing more introduction and context to the complex and tethering/fusion mechanisms in general. Additionally, the examination of the structure, in light of decades of biochemistry and cell biology studies of HOPS (and homologous proteins that regulate fusion), seems superficial and suggests that deeper analyses may reveal additional insights and lead to a more detailed and impactful model for HOPS function. Moreover, are the insights gained here applicable to other tethering complexes, why or why not?

Reviewer #3 (Public Review):

PME-1 catalyzes the removal of carboxyl methylation of the PP2A catalytic subunit and negatively regulates PP2A activity. Like the PP2A methyltransferase LCMT-1, PME-1 was previously thought to act only on the PP2A core enzyme. However, in this study, the authors show that PME-1 can interact and demethylate different families of PP2A holoenzymes in vitro. They also report the cryo-EM structure of the PP2A-B56 holoenzyme in complex with PME-1. Their structure reveals that the substrate-mimicking motif of PME-1 binds to the substrate-binding pocket of B56 subunit, which tethers PME-1 to PP2A, blocks substrate-binding to PP2A, and promotes PME-1 activation and demethylation of PP2A holoenzyme. Their further mutagenesis and functional analyses indicate that cellular PME-1 function in p53 signaling is mediated by PME-1 activity towards PP2A-B56 holoenzyme. In summary, this study has provided significant insights into our understanding of PP2A regulation by PME-1, demonstrating that PME-1 not only demethylates the PP2A core enzyme, but also the holoenzyme to control cellular PP2A homeostasis.

Reviewer #3 (Public Review):

The number of identified anti-phage defense systems is increasing. However, the general understanding of how phages can overcome such bacterial defense mechanisms is a black box. Srikant et al. apply an experimental evolution approach to identify mechanisms of how phages can overcome anti-phage defense systems. As a model system, the bacteriophage T4 and its host Escherichia coli are applied to understand genome dynamics resulting in the deactivation of phage-defensive toxin-antitoxin systems.

Strengths:<br /> The application of a coevolutionary experimental design resulted in the discovery of a gene-operon: dmd-tifA. Using immunoprecipitation experiments, the interaction of TifA with ToxN was demonstrated. This interaction results in the inactivation of ToxN, which enables the phage to overcome the anti-phage defense system ToxIN.<br /> The characterization of the genomes of T4 phages that overcome the phage-defensive ToxIN revealed that the T4 genome can undergo large genomic changes. As a driving force to manipulate the T4 phage genome, the authors identified recombination events between short homologous sequences that flank the dmd-tifA operon.<br /> The discovery of TifA is well supported by data. The authors prepared several mutant strains to start the functional characterization of TifA and can show that TifA is present in several T4-like phages.

In addition, they describe T4 head protein IPIII as another antagonist of a so far unknown defense system.

In summary, the application of a coevolutionary approach to discover anti-phage defense systems is a promising technique that might be helpful to study a variety of virus-host interactions and to predict phage evolution techniques.

Weaknesses:<br /> The authors apply Illumina sequencing to characterize genome dynamics. This NGS method has the advantage of identifying point mutations in the genome. However, the identification of repetitive elements, especially their absolute quantification in the T4 genome, cannot be achieved using this method. Thus, the authors should combine Illumina Sequencing with a long-read sequencing technology to characterize the genome of T4 in more detail.

To characterize the influence of TifA during infection, T4 phage mutants are generated using a CRISPR-Cas-based technique. The preparation of these phages is unclearly described in the methods section. The authors should describe in detail whether a b-gt deficient strain was applied to prepare the mutants. Information about the used primers and cloning schemes of the Cas9 plasmid would allow the community to repeat such experiments successfully.

The discovery of TifA would benefit from additional data, e.g. structure-based predictions, that describe the protein-protein interaction TifA/ToxN in more detail.

Several publications have described that antitoxins can arise rapidly during a phage attack. The authors should address that this concept has been described before as well by citing appropriate publications.

The authors propose that accessory genomes of viruses reflect the integrated evolutionary history of the hosts they infected. However, the experimental data do not support such a claim.

Reviewer #3 (Public Review):

In their study "Membrane-mediated dimerization potentiates PIP5K lipid kinase activity", Hansen et al. aim to deepen their biochemical understanding of a fascinating self-organizing system the authors have previously been reporting on (Hansen et al., PNAS 2019), in particular, the regulation of PI(4,5)P2 lipids by the kinase PIP5K, which is itself recruited to the membrane by the PI(4,5)P2. From reconstitution studies on supported membranes investigated by TIRF microscopy, following elegant assays that have they previously developed, they conclude that PIPK5 activity is regulated by cooperative binding to and membrane-mediated dimerization of the kinase domain. Dimerization enhances the catalytic efficiency of PIP5K through a mechanism consistent with allosteric regulation and amplifies stochastic variation in the kinase reaction velocity, leading to stochastic geometry sensing that has been reported earlier.

Overall, this is a beautiful biochemical system of great general interest. Also, the findings are plausible in the light of other pattern forming systems. However, the quality of both, the writing (with partly confusing annotations, inconsistencies, and missing clarity of what is actually reported on) and the data is extremely variable, giving the whole paper a somehow immature "patchwork" impression. Not the least, error bars are missing throughout the paper, and although both the protein/membrane system and the instrumental setup seem to be sufficiently well controlled, the quantitative aspect of this study could be greatly improved.

Reviewer #3 (Public Review):

The authors describe the crystal structure of a large fragment of PKG Ib in an autoinhibited state. The structure includes both the regulatory (R) and catalytic (C) kinase domains, and shows in atomic detail how the regulatory cGMP binding domains and autoinhibitory segment bind the kinase to block its activity. A crystal structure of one of the cGMP binding domains bearing a disease-associated mutation (TAAD, Thoracic aortic aneurysms and dissections) provides an understanding of the mechanism by which the mutation leads to constitutive activation of PKG by inducing a conformation that resembles the cyclic nucleotide bound state. This interpretation is further supported by an NMR study of the mutant that reveals chemical shifts consistent with the "open" (nucleotide-bound) conformation. A structure-function study in which variants with mutations in one or both of the active sites and regulatory domain are co-expressed shows that autoinhibition occurs in cis; that is, in an intra-chain manner, rather than as part of a dimer as is likely present in the crystal. A SAXS experiment further supports this model. The authors propose a model for PKG activation, referencing the structures described here as well as prior crystal structures of the isolated kinase and regulatory domains as "snapshots" of distinct states in the autoinhibition-activation pathway. This is a careful and technically sound study that provides a first structural view of PKG autoinhibition. It also enables comparison to the related mechanism of regulation of protein kinase A, but this aspect of the manuscript could be much better developed.

Reviewer #3 (Public Review):

Carraro et al utilize systems biology approaches to decode the mechanism of action of 3-chloropiperidines (a novel class of cancer therapeutics) in cancer cell lines and build a drug-sensitivity model from the data that they evaluate using samples from The Cancer Genome Atlas and cancer cell lines. The approach provides a framework for integrating transcriptomic and open-chromatin data to better understand the mechanism of action of drugs on cancer cell types. The author's approach is of sound design, is clearly explained, and is bolstered by validation via holdout sets and analysis in new cell lines which lends the findings and approach credibility.

The major strength of this approach is the depth of information provided by performing RNA-seq and ATAC-seq on cells treated with 3-CePs at various time points, and the author's utilization of this data to perform pairwise and crosswise analyses. Their approach identified gene modules that were indicative of why one cell type was more sensitive to a particular drug compared to another. The data was then used to build a sensitivity model which could be applied to samples from The Cancer Genome Atlas, and the authors evaluated their sensitivity predictions on a set of cancer cell lines which validated the predictions.

The major drawback to this type of approach is that it relies on next-generation sequencing (somewhat costly) and requires intricate bioinformatics analyses. While I agree with the author's perspective that this approach can be applied to additional classes of drugs and cancer samples, I disagree with their view that it is efficient and versatile. However, for research teams with the means to perform both transcriptomic and open-chromatin studies, I think this integrated approach has promise for evaluating novel classes of drugs, particularly in cancer cell lines that are easy to manipulate in vitro.

While there are examples of similar frameworks being applied to drug development, this work will add to the body of literature utilizing an integrated systems biology approach for pairing drugs with specific tumor or cancer types and understanding their mechanism of action on an epigenetic level.

Reviewer #3 (Public Review):

The authors sought to identify transcriptional changes that occur in the various somatic cell populations of the adult mouse ovary during different reproductive states using single-cell RNA sequencing. The ovaries for the analysis were harvested from mice during the four stages of the normal estrus cycle (proestrus, estrus, metestrus and diestrus), from lactating or non-lactating 10 days postpartum mice, and from randomly cycling mice. They identified the major cell subtypes of the adult ovary but focused their analysis on the mesenchyme (stromal and theca) and granulosa cells. They identified novel markers for stromal, theca and granulosa cell subpopulations and validated these by RNA in situ hybridization. They used trajectory analysis to infer differentiation lineages within the stromal and granulosa cell subtypes. Finally, from their data set they identify four secreted factors that could serve as biomarkers for staging estrus cycle progression.

Strengths - This is the first study to profile ovarian somatic gonad cells at different stages of the reproductive cycle.

Weaknesses - Enthusiasm for the current manuscript is lessened because it does not employ state-of-the-art scRNA-seq analysis. For example, once general cell populations have been determined by clustering with all cells, it is best to individually re-cluster these cell populations to identify more refined and accurate subpopulations. The PC used for the initial clustering is very useful for distinguishing different general cell populations (e.g. mesenchyme vs. granulosa vs. endothelial) but may not be as useful for distinguishing biologically relevant subpopulations (e.g. stromal subpopulations). Finally, certain cell subpopulations were excluded from the trajectory analysis without justification - specifically, the mitotic and atretic granulosa cells - calling into question what conclusions can be drawn from this analysis.

Reviewer #3 (Public Review):

The authors reanalyze an existing dataset of single-cell Sperm-seq data to search for signals of transmission distortion. They develop an improved genotype imputation method and use this approach to phase donors and characterize the landscape of ancestry across each sperm genome. Using these data, the authors determined that there are no regions in any of the male donors' genomes that display a significant excess of TD. The main biological claim of the paper is that there is a strict adherence to Mendelian transmission ratios in human males.

The computational approaches for accurately phasing and reconstructing haplotypes in individually lightly sequenced gametes is a potentially useful advance that I expect may be valuable for geneticists analyzing similar datasets. The quality of software documentation and usability is high. I have concerns about the appropriateness of the comparisons selected for this approach and the algorithm does not appear particularly novel.

I have no doubt about the authors' basic conclusion that there are no strong male TD loci in the male donors examined. However, I find their statements about "strict adherence to Mendelian ratios" and many references to strong statistical power to be oversold. The power of this study is still quite limited relative to the strength of TD that we would expect to find in human populations.

Major Concerns:

There are really two distinct papers here. One is about improved imputation and crossover analysis from sperm-seq data and one is about TD. The bulk of the methodological development is a rework of the approach for genotype imputation and haplotype phasing in Sperm-seq. Yet, the major conclusions are focused on a scan for TD. I am left wondering if analyzing these data using the original method in the Bell et al paper would have produced different conclusions about either? If not, is there a systematic bias such that one would find an excess of false detections of TD? Phasing slightly more markers is not a particularly compelling link between these sections because even fairly sparsely distributed markers that are correctly phased would certainly be fine in a scan for TD within a single individual due to linkage. If this cannot be shown I wonder if this work would be better split into two manuscripts with one more technical paper describing the differences in recombination maps associated with rhapsodi and the other as a brief report stating that strong TD is probably uncommon in human males.

It is not surprising that rhapsodi outperforms Hapi since Hapi was designed for a very different quantity of samples and sequencing depths. I appreciate the authors' point that Hapi performed better than other methods in comparisons run by the Hapi authors. However, they were looking at very few gametes (10 or so, I believe). For that reason, this comparison is not appropriate to address the application to the datasets used in this paper. The authors should include an analysis comparing rhapsodi against hapcut2, PHMM and other methods that are appropriate for the full scale and sequencing depth of the data. Additionally, the original Bell paper used a phasing + HMM approach of some kind for exactly this data. Why wasn't that approach considered as a point of comparison?

With respect to the method for imputation, no comparison is made to known recombination maps nor do the authors make any comparison across the maps derived from each donor. Reporting an improved method without it motivating novel biological conclusions is not compelling in itself. I suggest the authors expand that analysis to consider these are related questions. E.g., are there males whose recombination maps differ in specific regions? Are those associated with known major chromosomal abnormalities? Is this map consistent with estimates from LD, pedigrees, Bell et al?

Most of the validations presented are based on simulated data. This is fine and has some advantages, but real data imposes challenges that these analyses do not address. My understanding is that the Bell et al. (2020) paper includes a donor with a phased diploid genome. A comparison of rhapsodi's phasing accuracy against that genome should be included.

The main biological conclusion about a "strict adherence to Mendelian expectations across sperm genomes" is an overstatement. Statistical power of this study is still limited relative to the strength of TD that would be expected within human populations. One reason is the multiple testing correction. Another is that 1000-3000 draws from a binomial distribution with expected p = 0.5 is just not sufficient to overcome binomial sampling variance. In light of this concern and the central conclusion of this paper, the authors' discussion of power is inadequate. The main text really should contain explicit discussion of the required genotype ratio skew for TD in each donor to be detected with good power. Given previous pedigree studies, it is not surprising that no significant TD was discovered that exceeded the necessary ~10% effect sizes to be detectable. Recent, much more powerful analyses in mice, Drosophila and plants, indicate that strong TD is probably uncommon and even weak effects can be detected but are uncommon.

This manuscript would benefit from a much clearer examination of statistical power and a detailed comparison of the power of this approach vs pedigree-based analyses as well as bulk gamete sequencing approaches. Although the authors are correct that all scans for TD in human genomes have been pedigree or single-cell based, more powerful alternatives are known. These are based on sequencing pools of individuals or gametes (e.g., Wei et al. 2017, Corbett-Detig et al. 2019). Each of those studies has been able to identify signatures of segregation distortion below the thresholds required for significance in this study. These and related works should be acknowledged in both the introduction and discussion. Although I appreciate that the ability to phase the genome in a single experiment may be appealing, phasing diploid genomes via hi-c omni-c is straightforward and the advantages in statistical power suggest that approaches using pools of gametes are preferable for well-powered scans for TD.

Reviewer #3 (Public Review):

The manuscript by Bae et al describes the role of a point mutation in the PH domain of Akt that changes the inhibition by the PH domain. The data underlying the manuscript appear to be done at a high technical level. The discovery that the R86A mutant has an enhanced inhibitory interface with the kinase domain is intriguing. Although this residue is not at the putative interface, it forms an electrostatic interaction with the Glu17 in the PH domain and causes a reorientation of the loop including the Y18. Analysis of Y18 and E17 mutants can reverse this effect, revealing a molecular mechanism of R86 increased inhibition.

My main concern with the manuscript is that the conclusions as currently written do not appear to be fully supported by the data. Mainly on the role of the pi-pi stacking of the 309-18 interface. This paper requires a major rewrite. There also could be additional validation data included to verify the stability and phosphorylation state of the different proteins purified.

Major concerns

1. There are concerns about the validation of the proteins used.

2. The authors note on page 9 that they analyzed the alphafold structure to look at the PhH-kinase interface.

From the analysis of the alphafold model, it does not seem appropriate for this analysis, as the alphafold predicted aligned error (taken from alphafold protein structure database, https://www.alphafold.ebi.ac.uk/entry/P31749) validation clearly shows that there is only limited predictive value of the inter-domain interfaces. I am not sure the mutant data on the predicted pi stacking interaction can be supported by alphafold here as strongly as the authors describe, as these mutants may be working through a separate mechanism. The alphafold model also appears to be templated on the 4ekk phosphorylated structure/mutant of 308 and 473, which seems to go against the authors' hypothesis that 473 phosphorylation disrupts the PH domain interface.

The best model for interpreting the Ph-kinase interface seems to be the nanobody-bound X-ray structure, and this region is disordered at F309 in this structure. While the authors' data clearly shows a role for the Y18 reorientation in changing Ph domain binding, and they also show that mutation of F309L also changes binding, they are basing their molecular model on an alphafold model with limited predictive ability for inter-domain contacts.

Reviewer #3 (Public Review):

The main goals of this study by Guan, Aflalo and colleagues were to examine the encoding scheme of populations of neurons in the posterior parietal cortex (PPC) of a person with paralysis while she attempted individual finger movements as part of a brain-computer interface task (BCI). They used these data to answer several questions:

1) Could they decode attempted finger movements from these data (building on this group's prior work decoding a variety of movements, including arm movements, from PPC)?

2) Is there evidence that the encoding scheme for these movements is similar to that of able-bodied individuals, which would argue that even after paralysis, this area is not reorganized and that the motor representations remain more or less stable after the injury?

3) Related to #2: is there beneficial remapping, such that neural correlates of attempted movements change to improve BCI performance over time?

4) Can looking at the interrelationship between different fingers' population firing rate patterns (one aspect of the encoding scheme) indicate whether the representation structure is similar to the statistics of natural finger use, a somatotopic organization (how close the fingers are to each other), or be uniformly different from one another (which would be advantageous for the BCI and connects to question #3)? Furthermore, does the best fit amongst these choices to the data change over the course of a movement, indicating a time-varying neural encoding structure or multiple overlapping processes?

The study is well-conducted and uses sound analysis methods, and is able to contribute some new knowledge related to all of the above questions. These are rare and precious data, given the relatively few people implanted with multielectrode arrays like the Utah arrays used in this study. Even more so when considering that to this reviewer's knowledge, no other group is recording from PPC, and this manuscript thus is the first look at the attempted finger moving encoding scheme in this part of human cortex .

An important caveat is that the representational similarity analysis (RDA) method and resulting representational dissimilarity matrix (RDM) that is the workhorse analysis/metric throughout the study is capturing a fairly specific question: which pairs of finger movements' neural correlates are more/less similar, and how does that pattern across the pairings compare to other datasets. There are other questions that one could ask with these data (and perhaps this group will in subsequent studies), which will provide additional information about the encoding; for example, how well does the population activity correlate with the kinematics, kinetics, and predicted sensory feedback that would accompany such movements in an able-bodied person?

What this study shows is that the RDMs from these PPC Utah array data are most similar to motor cortical RDMs based on a prior fMRI study. It's innovative to compare effectors' representational similarity across different recording modalities, but this apparent similarity should be interpreted in light of several limitations: 1) the vastly different spatial scales (voxels spanning cm that average activity of millions of neurons each versus a few mm of cortex with sparse sampling of individual neurons, 2) the vastly different temporal scales (firing rates versus blood flow), 3) that dramatically different encoding schemes and dynamics could still result in the same RDMs. As currently written, the study does not adequately caveat the relatively superficial and narrow similarity being made between these data and the prior Ejaz et al (2015) sensorimotor cortex fMRI results before except for (some) exposition in the Discussion.

Relatedly, the study would benefit from additional explanation for why the comparison is being made to able-bodied fMRI data, rather than similar intracortical neural recordings made in homologous areas of non-human primates (NHPs), which have been traditionally used as an animal model for vision-guided forelimb reaching. This group has an illustrious history of such macaque studies, which makes this omission more surprising.

A second area in which the manuscript in its current form could better set the context for its reader is in how it introduces their motivating question of "do paralyzed BCI users need to learn a fundamentally new skillset, or can they leverage their pre-injury motor repertoire". Until the Discussion, there is almost no mention of the many previous human BCI studies where high performance movement decoding was possible based on asking participants to attempt to make arm or hand movements (to just list a small number of the many such studies: Hochberg et al 2006 and 2012, Collinger et al 2013, Gilja et al 2015, Bouton et al 2016, Ajiboye*, Willett* et al 2017; Brandman et al 2018; Willett et al 2020; Flesher et al 2021). This is important; while most of these past studies examined motor (and somatosensory) cortex and not PPC (though this group's prior Aflalo*, Kellis* et al 2015 study did!), they all did show that motor representations remain at least distinct enough between movements to allow for decoding; were qualitatively similar to the able-bodied animal studies upon which that body of work was build; and could be readily engaged by the user just by attempting/imagining a movement. Thus, there was a very strong expectation going into this present study that the result would be that there would be a resemblance to able-bodied motor representational similarity. While explicitly making this connection is a meaningful contribution to the literature by the present study (and so is comparing it to different areas' representational similarity), care should be taken not to overstate the novelty of retained motor encoding schemes in people with paralysis, given the extensive prior work.

The final analyses in the manuscript are particularly interesting: they examine the representational structure as a function of a short sliding analysis window, which indicates that there is a more motoric representational structure at the start of the movement, followed by a more somatotopic structure. These analyses are a welcome expansion of the study scope to include the population dynamics, and provides clues as to the role of this activity / the computations this area is involved in throughout movement (e.g., the authors speculate the initial activity is an efference copy from motor cortex, and the later activity is a sensory-consequence model).

An interesting result in this study is that the participant did not improve performance at the task (and that the neural representations of each finger did not change to become more separable by the decoder). This was despite ample room for improvement (the performance was below 90% accuracy across 5 possible choices), at least not over 4,016 trials. The authors provide several possible explanations for this in the Discussion. Another possibility is that the nature of the task impeded learning because feedback was delayed until the end of the 1.5 second attempted movement period (at which time the participant was presented with text reporting which finger's movement was decoded). This is a very different discrete-and-delayed paradigm from the continuous control used in prior NHP BCI studies that showed motor learning (e.g., Sadtler et al 2014 and follow-ups; Vyas et al 2018 and follow-up; Ganguly & Carmena 2009 and follow-ups). It is possible that having continuous visual feedback about the BCI effector is more similar to the natural motor system (where there is consistent visual, as well as proprioceptive and somatosensory feedback about movements), and thus better engages motor adaptation/learning mechanisms.

Overall the study contributes to the state of knowledge about human PPC cortex and its neurophysiology even years after injury when a person attempts movements. The methods are sound, but are unlikely (in this reviewer's view) to be widely adopted by the community. Two specific contributions of this study are 1) that it provides an additional data point that motor representations are stable after injury, lowering the risk of BCI strategies based on PPC recording; and 2) that it starts the conversation about how to make deeper comparisons between able-bodied neural dynamics and those of people unable to make overt movements.

Reviewer #3 (Public Review):

Childhood acute myeloid leukemia (AML) is a heterogeneous disease with different outcomes for different patients, making identifying patients with different prognoses for clinical management. A variety of approaches have been used to stratify AML patients' risk, including molecular and clinical measurements to build prognostic risk scores. Previously, Chaudhary et al found that mitochondrial genome copy number per AML cell could stratify patients who would have good and poor outcomes and survival. This interesting finding suggested that mitochondrial amount and/or function alter AML disease course and suggested a further in-depth study of mitochondria in AML.

Chaudhary and colleagues follow up their preliminary study on mitochondrial genome copy number in AML with this current study by looking if the expression of specific genes encoding mitochondrial components could provide further insight into AML prognosis. The authors collected childhood AML patient samples and grouped them based on mitochondrial genome copy number. They then performed transcriptomic analysis and identified a number of nuclear-encoded mitochondrial component genes whose expression was correlated or anticorrelated with mitochondrial genome copy number and this was confirmed with targeted analysis of identified transcripts in validation cohorts. Multivariate analysis was used to identify those genes whose expression was prognostic of patient outcome. This led to the identification of three mitochondrial genes (SDHC, CLIC1, SLC25A29) whose expression was used to build a multivariate risk model for childhood AML patients. The risk model based on the expression of these genes outperformed currently used ELN risk stratification and could be combined with ELN to increase prognostic power. Lastly, the authors used publically available data from adult AML patients and found that their risk score also had prognostic power in adult AML patients as well.

Altogether, the work by Chaudhary and colleagues interestingly builds on their previous work and suggests that mitochondria may influence AML outcomes, and measuring mitochondrial parameters may help assess patient risk. Numerous exciting questions remain: what outputs of the mitochondria influence AML disease course and how? Why are some mitochondrial genes but not others correlated with mitochondrial DNA copy number in AML cells and how does this influence mitochondrial properties? Outside of predicting patient risk, can the mitochondrial phenotype of AML cells predict effective therapies? How does the mitochondrial risk model perform compared to and when utilized with other transcriptional-based risk stratification models proposed in the literature?

Reviewer #3 (Public Review):

In their previous work, the authors studied the problem of clonal life cycles evolution. Here they extended the previous work by developing a model that describes such evolution under the presence of competition between groups. The model is studied using a combination of analytical methods and numerical simulations. The results obtained are more biologically justifiable than those obtained in the linear model that neglects competition between groups.

Strengths:

- As is known from previous work, in a linear model (when the competition is absent), a typical outcome is an exponential growth in the number of groups of some life cycle, which can be considered as a natural limitation of the model. Obviously, this limitation is removed in the presented paper.

- The authors provide analytical results for some special cases of the model and compare them with those obtained in the absence of competition. In the general case of the model, when analytical progress is impossible, the authors provide the results of extensive numerical simulations. All these results allow the authors to build a clear picture of the process under study.

- The authors study the evolutionary stability of various life cycles. Specifically, it was shown that only binary fragmentation life cycles can be evolutionary stable strategies. This result holds in the linear model as well. In contrast to the linear model, more complex dynamics can be observed in the general case (like the existence of several evolutionary stable strategies).

Overall, in my opinion, the model significantly contributes to our understanding of the evolution of clonal life cycles. Moreover, it illuminates to what extent are adequate the results of simple linear models in describing the processes under consideration.

Reviewer #3 (Public Review):

In this manuscript, the authors investigated the role of glutamine metabolism in chondrocytes and in the context of inflammation. Thus, they report that chondrocytes use glutamine for their energy production and anabolic functions. Moreover, they found that removal of glutamine resulted in metabolic reprogramming and decreased inflammatory response of chondrocytes. They attributed this anti-inflammatory response to decreased NF-κB activity. Moreover, the removal of glutamine promoted autophagy. This is a very interesting study and the vast majority of the conclusions are supported by strong data.

Reviewer #3 (Public Review):

The authors present a modular computational workflow for automated sample screening and collection of cryo-EM data and demonstrate its use for screening and 3D structure determination of human mitochondrial DNA polymerase as a test sample. Despite major advances in automation of microscope operation, optimising and screening sample conditions for the acquisition of high-quality data is still a laborious task that involves human input to navigate low-, medium- and high-magnification images to identify and select specimen areas amenable to high-resolution structure determination; and subjective tuning of parameters that can result in inefficient use of high-end cryo-TEM equipment. Fully automated methods for screening and data collection are therefore needed to meet the increasing demand for access and throughput of cryo-EM. Utilising deep-learning-based object detection algorithms, the authors show that their pre-trained models can effectively detect, classify, and rank regions (grid squares and holes) of interest based on established criteria such as contamination, support film integrity, and ice thickness. A challenge for any such method is the scarcity of annotated data reflecting the broad variety across the wide range of image and sample conditions in cryo-EM, and that selection of the "best" areas may vary by particle and sample preparation conditions. To mitigate this risk, the authors provide a web interface that allows re-training of the feature models and integrates on-the-fly assessment of data quality and adjustment of data collection parameters. As such, the presented pipeline and related approaches can become a useful addition to existing automation software for cryo-EM data collection, in multi-user environments such as cryo-EM facilities. Such approaches will best strive if software and models are openly available to the cryo-EM community so that annotated data can be added or customised and the quality of the prediction methods can improve over time.

Reviewer #3 (Public Review):

In general, I find this to be an experimentally and analytically sound paper. The observation that rate information is preserved in hippocampal replay is hinted at in previous work, but to my knowledge, has not yet been explicitly quantified as the authors have done here. Thus, this work is novel and, in my opinion, an important contribution to our understanding of hippocampal network function.

The large number of control analyses strongly support the core finding of this work. I feel that the authors have very convincingly demonstrated that rate information is represented along with spatial information in replay.

While I can think of many suggestions to follow up on this work, I have no major concerns regarding the experiments, analyses, or interpretation of the manuscript.

Reviewer #3 (Public Review):

The TRPV1 receptor channel is primarily localised to sensory nerves as well as other non-neuronal tissues. It has been known for some time that TRPV1 has a role in the regulation of body temperature, as TRPV1 antagonists, being developed as analgesics, cause hyperthermia. There is a need for further mechanistic information, as the present drug discovery programme has been delayed by the inability of scientists to develop TRPV1 analgesics that act without temperature-related side effects. This manuscript is designed to investigate whether sensory nerves or smooth muscle cells are included in the mechanisms, through the study of tissue specific genetically modified mice.

This is a highly readable and concise manuscript with a relatively simple and clear take home message that advances current knowledge. However, at times the information could be more fully given.

Reviewer #3 (Public Review):

This work seeks to identify a common factor governing priority effects, including mechanism, condition, evolution, and functional consequences. It is suggested that environmental pH is the main factor that explains various aspects of priority effects across levels of biological organization. Building upon this well-studied nectar microbiome system, it is suggested that pH-mediated priority effects give rise to bacterial and yeast dominance as alternative community states. Furthermore, pH determines both the strengths and limits of priority effects through rapid evolution, with functional consequences for the host plant's reproduction. These data contribute to ongoing discussions of deterministic and stochastic drivers of community assembly processes.

Strengths:

Provides multiple lines of field and laboratory evidence to show that pH is the main factor shaping priority effects in the nectar microbiome. Field surveys characterize the distribution of microbial communities with flowers frequently dominated by either bacteria or yeast, suggesting that inhibitory priority effects explain these patterns. Microcosm experiments showed that A. nectaris (bacteria) showed negative inhibitory priority effects against M. reukaffi (yeast). Furthermore, high densities of bacteria were correlated with lower pH potentially due to bacteria-induced reduction in nectar pH. Experimental evolution showed that yeast evolved in low-pH and bacteria-conditioned treatments were less affected by priority effects as compared to ancestral yeast populations. This potentially explains the variation of bacteria-dominated flowers observed in the field, as yeast rapidly evolves resistance to bacterial priority effects. Genome sequencing further reveals that phenotypic changes in low-pH and bacteria-conditioned nectar treatments corresponded to genomic variation. Lastly, a field experiment showed that low nectar pH reduced flower visitation by hummingbirds. pH not only affected microbial priority effects but also has functional consequences for host plants.

Weaknesses:

The conclusions of this paper are generally well-supported by the data, but some aspects of the experiments and analysis need to be clarified and expanded.

The authors imply that in their field surveys flowers were frequently dominated by bacteria or yeast, but rarely together. The authors argue that the distributional patterns of bacteria and yeast are therefore indicative of alternative states. In each of the 12 sites, 96 flowers were sampled for nectar microbes. However, it's unclear to what degree the spatial proximity of flowers within each of the sampled sites biased the observed distribution patterns. Furthermore, seasonal patterns may also influence microbial distribution patterns, especially in the case of co-dominated flowers. Temperature and moisture might influence the dominance patterns of bacteria and yeast.

The authors exposed yeast to nectar treatments varying in pH levels. Using experimental evolution approaches, the authors determined that yeast grown in low pH nectar treatments were more resistant to priority effects by bacteria. The metric used to determine the bacteria's priority effect strength on yeast does not seem to take into account factors that limit growth, such as the environmental carrying capacity. In addition, yeast evolves in normal (pH =6) and low pH (3) nectar treatments, but it's unclear how resistance differs across a range of pH levels (ranging from low to high pH) and affects the cost of yeast resistance to bacteria priority effects. The cost of resistance may influence yeast life-history traits.