Joint Public Review:

Although sensory neurons are thought to be the primary detectors of environmental stimuli in skin, it is more and more appreciated that non-neuronal cell types also play important roles. Previous work from the Stucky group (and others) has shown stimulation of optical excitation of keratinocytes can evoke action potentials in sensory neurons and behavioral responses suggesting functional connectivity. Earlier work from the Stucky group provided evidence that keratinocytes are thermosenstive and required for normal temperature sensation.

Here, they look into whether these cells are also important for mechanosensation. Using K14-Cre-dependent conditional KO mice, functional assays and behavioral analysis, Moehring and collaborators report that the mechanosensitive channel Piezo1 is expressed in keratinocytes in mice and humans and claim that it contributes to normal touch sensation. The in vitro data convincingly show that keratinocytes have mechanically evoked currents mediated by Piezo1. Interestingly, this work shows that recruitment of epidermal, non-neuronal Piezo1 by mechanical stimulation of keratinocytes could contribute significantly to touch through activation of cutaneous sensory fibers (mechanoreceptors). Specifically, they provide evidence that removing Piezo1 from keratinocytes reduces the frequency of spiking in select types of sensory neurons to punctate and dynamic touch stimuli. Finally, they supply quite surprising data documenting significant behavioral deficits in Krt-conditional knockout mice.

Overall, this work provides an intriguing series of observation and potentially fundamental discovery. However, concerns remain as to how the relatively subtle differences in the skin-nerve recordings result in such profound behavioral effects? Similarly, it is hard to understand how loss of the related channel Piezo2 in sensory neurons completely abolishes many touch responses if mechanosensitivity of keratinocytes is sufficient to evoke touch behaviors (as their experiments applying Yoda-1 to the hindpaw of mice would suggest). Altogether, this work suggests a novel role for epidermal Piezo1 in normal touch but the key neuro-epithelial signaling remains to be identified.

Reviewer #1 (Public Review):

The manuscript by Rosello et al., describes the application of cytosine base editing to efficiently introduce known and predictable mutations into disease genes in vivo in zebrafish, and examine signaling pathways and model disease. The majority of the data presented is analysis of editing precision and efficiency in somatically targeted embryos, with one example of a precise edited germline allele recovered. A direct comparison of the cytosine base editor BE4 and an improved version ancBE4max indicates both are highly efficient at somatic base editing. ancBE4max reduces alteration of bases outside the base editing window, and the data suggests loci for which BE4 base editing has failed can be targeted with ancBe4max. The authors demonstrate efficient base editing in embryos at multiple cancer genes (up to 91%), introducing activating mutations into oncogenes and nonsense mutations in a number of tumor suppressors. A S33L allele was introduced into the b-catenin gene ctnnb1 to activate the wnt signaling pathway as evidenced by expression of the wnt reporter Tg(tcf:GFP). Another novel aspect of this study is that the authors have expanded base editing target site selection by switching out the ancBe4max SpCas9 PAM-interacting motif domain with the domain from Spymac, which recognizes an NAA PAM. ancBe4maxSpymac editing efficiency was modest (16-19%). The method reported here has strong potential for effective combinatorial mutagenesis to map complex genetic interactions that underly disease pathogenesis. Overall, this study demonstrates cytosine base editing is an efficient and powerful method for introducing precise in vivo edits into the zebrafish genome.

Reviewer #1 (Public Review):

While I am not sufficiently qualified to comprehensively assess the molecular dynamics simulations, all interpretations seem careful and remain within the described limitations of the various metrics that the authors report.

The experiments are well executed; the results are presented clearly and interpreted carefully. This is a rigorous and important biophysical study that provides a solid foundation for the investigation of archaeal genome biology. The authors' new findings raise interesting questions, and although addressing them is outside the scope of this study, the article would perhaps benefit from a more detailed discussion of the biological implications of the results. The manuscript does not indicate whether the cryo-EM maps and atomic models were deposited in the EMDB and PDB. I strongly encourage the authors to do that: it would add a lot of value not only for the readers of this study, but also for the wider structural biology community.

Reviewer #1 (Public Review):

Previous research showed a close link between sub-clinical AFib (Atrial Fibrillation) and ESUS (Embolic Stroke of Undetermined Source). As such, current established clinical care for ESUS patients is long-term monitoring for evidence of AFib and anticoagulant treatment for an individual with high risk for AFib. Nevertheless, questions are still unanswered about who the individuals with high-risk for ESUS are and how to properly identify this population.

This research tries to identify the fibrotic properties of ESUS patients and its pro-arrhythmic potential using computational modeling of patient's left atria reconstructed from cardiac LGE-MRI (Late-Gadolinium Enhanced Magnetic Resonance Imaging). Ultimately, their results of the comparison between left atria of ESUS and AFib patients revealed that the fibrotic substrate that could induce arrhythmia in ESUS and AFib patients are indistinguishable, raising more questions that would need to be addressed in further studies.

This study uses a sophisticated personalized computational modeling approach that has been validated in previously published papers. This study is also well designed, clearly written, with robust data and proper statistical analysis.

What is left unclear is what is unique about the fibrotic substrate in ESUS patients in comparison to AFib patients in the future.

Reviewer #1 (Public Review):

In the days of the COVID-19 pandemic vaccines, mechanisms of vaccine administration are important and of broad interest. Vaccines are most often given into the skin. Antigen-presenting cells of the skin are responsible for eliciting the immune response in draining lymph nodes. Langerhans cells, the dendritic cell variant of the epidermis, are one of these cutaneous antigen presenting cells that are believed to do this job. They migrate from the skin, the site of antigen/vaccine uptake to the draining lymph nodes, where lymphocytes are located and where the immune reaction will be initiated. With their sophisticated experiments, the authors challenge this view. They use leading edge methodology (mouse models) that strongly suggest that there may be yet another subset of skin antigen presenting cells, that is responsible for carrying antigen from skin to lymph — at least in the steady-state skin. This population resides in the dermis (the connective tissue part of skin), as opposed to the classical Langerhans cells, which sit in the epidermis. This may be relevant to the maintenance of immunologic tolerance to innocuous substances in the absence of an overt inflammation. The data suggest that Langerhans cells may not play the crucial role they were thought to play. This is certainly a conceptual advance that — like always in science, especially when experimental systems are complex, as they are here — needs to be underpinned by future studies. In the long run, it will be very interesting (but much more difficult to study) to see whether this also holds true for human skin.

Reviewer #1:

The manuscript by Rae et al. reports the development of a new protocol for labeling genetically-tagged proteins of interest with heavy atom particles for visualization by electron microscopy. The optimized protocol builds on the established use of the enzyme APEX fused to the target of interest. APEX oxidizes diaminobenzidine, DAB, which in turn converts silver and gold metal salts to particulates in close proximity to the APEX-fused protein of interest. The optimized protocol is related to the contrast-enhancement method reported by Sedmak et al., 2009 and Mavlyutov et al., 2017. The changes to the method may improve the proportionality of the signal such that the number of APEX tags present in a sample is better correlated with the number of heavy atom particles. While the study appears to be sound, it is an extension of an established labeling method.

Reviewer #1:

This study takes on the question of the roles of the many pathways leading to ERK activation in long-term potentiation. This is an advance: few models consider more than a couple of input pathways. The authors consider two aspects: how pathways sum to give strong responses, and distinct temporal pattern selectivity. They show that both summation linearity, and pattern selectivity, are strongly governed by which pathways are engaged in driving the response.

The model and analysis is potentially interesting, but the paper would be much strengthened if there were more convincing validation of the properties of the model by way of simulations to compare with experiments. Further, the pathways chosen are already one step into the synapse. Thus the actual combination of pathway activations would not be quite as cleanly separated if they were driven by synaptic input.

Reviewer #1:

Hofmann et al. investigate the link between two phenomena, emotional arousal and oscillatory alpha activity in the cerebral cortex, which is of central interest in their respective fields. Although alpha activity is tightly linked to the first reports of electric activity in the brain nearly 100 years ago, a comprehensive characterization of this phenomenon is elusive. One of the reasons is that EEG, the major method to investigate electric activity in the human brain, is susceptible to motion artifacts and, thus, mostly used in laboratory settings. Here, the authors combine EEG with a virtual reality setup to give experimental participants a roller-coaster ride with high immersion. The ride, literally, leads to large ups and downs in emotional arousal, which is quantified by the subjects during a later rerun. Next, the authors decode the degree of emotional arousal as stated in the rerun based on the EEG signals recorded during the VR session. They demonstrate convincingly a negative dependence of alpha activity with the degree of emotional arousal. Further, they demonstrate the differential involvement of parietal and occipital regions in this process. The sequencing of the description of methods and results could be improved upon, is, however, as such perfectly ok. This investigation comes timely, makes an important contribution to our understanding of the relation of emotions and sensory processing.

Reviewer #1:

The manuscript entitled "An evolutionary model identifies the main selective pressures for the evolution of genome-replication profiles" is an examination of the principles shaping evolution of replication origin placement. Overall I found the manuscript to be engaging and interesting, and the topic of general importance. It is quite compelling that with just two parameters, origin efficiency and distance between origins, a good model can be built to describe the dynamics of origin birth and death. While this work on its own is sufficiently important for publication, it would be very interesting to see whether the model can be updated in the future to address whether there are fork-stalling or origin-generating mechanisms that shape evolution of specific inter-origin spaces. This work provides a very good foundation for such efforts.

I have a few major, general concerns I would like the authors to address.

If I'm interpreting the methods correctly, it seems the parameters used in these simulations, such as mean birth rate, mean death rate, gamma, and beta, were fit to the data once, and used as point estimates during simulation. If true, I expect the simulations to be yielding estimates of birth and death rates with a much narrower distribution of outcomes than is likely to be realistic given what an appropriate level of confidence in those parameter estimates would be. Could the parameters be fit to data in such a way that we attain an estimate of confidence in the parameter values, from which a distribution could be generated and sampled from during simulation?

Closely related to my prior concern, I would like the authors to demonstrate the general predictive value of their model on out-of-sample data. Can the model be applied to other data on replication timing? Without such an attempt to demonstrate the model's applicability to out-of-sample prediction, the reader cannot ascertain whether the model is overfit to the Lachancea data from Agier et al, 2018. Also, keeps the parameter estimates here from being overfit to better predict origin birth and death events in closely related branches of the Lachancea tree in Figure S1? Are gamma and beta inferred in a way that accounts for the higher correlation in birth and death events in closer-related branches than in distal branches, or has the fit ignored those correlations?

The authors state that their model identifies selective pressures. The authors imply, and specifically state in lines 238-242, that increased death rate of origins which happen to be nearby highly efficient origins represents selective pressure against the less efficient origins. It isn't until the discussion that the authors raise the possibility that there may simply be a lack of selective pressure to retain inefficient origins that are near highly efficient origins. In my view, it's more likely that selection for the existence of an inefficient origin is simply lower than the drift barrier, so mutagenesis and drift can passively remove such origins over time without the need to invoke selection against inefficient origins.

Figure 3 is intended to show that the stall-aversion and interference model performs better at predicting correlations between efficiency of lost origins and their nearest neighbor. I agree, but I do not think Figure 3 presents a strong case for this conclusion. Fig S6 presents stronger evidence to me. While fig 3 does qualitatively suggest that the joint model may predict the correlation between neighboring origin efficiency and origin loss better than the double-stall model alone, it almost appears to me that the model with fork stalling and interference has significantly overestimated the correlation. Is there a quantitative way, perhaps using information criteria, though I admittedly am not sure how one would go about doing that with simulations such as these, to demonstrate that the model with both effects has better predictive value than the one with only fork stalling?

There are a couple of assumptions of the model that I would like the authors to examine in further detail. First, that origin birth events occur in the middle of an inter-origin space. I am not aware of evidence pointing to this being a good a priori assumption. Can you re-run the simulations, allowing origins to arise at a random site within the inter-origin space into which it is born? Second, is it reasonable to expect origin firing rates to reshuffle to a new value randomly, without any dependence on their prior rate? Perhaps I'm mistaken, but it seems to me that an origin's firing rate should evolve more gradually, and should have a higher probability of sampling from values near its current value than from values very far from its current value.

Reviewer #1:

In this manuscript, Bakken et al use single cell and single nucleus RNA-sequencing to conduct comparative analysis of dLGN in humans, macaques and mice. dLGN exhibits a dramatic reorganization and lamination in primates relative to mice. Other components of the visual system (retina, V1) have previously been explored with cross-species transcriptomic analyses to reveal species-specific or evolutionary modifications. How dLGN fits in this picture, and the extent to which differences amongst previously identified cell types can be discerned from transcriptomic data, is an important question.

The conclusions are supported by the data, but the paper could better motivate what the main questions or debates are.

Strengths:

The authors use highly sensitive SMART-seq v4 to collect and analyze thousands of cells from dLGN and some adjacent nuclei. The gene detection rate using this method is impressive, and the plate/strip-based workflow has distinct advantages in terms of lower ambient contamination and risk of doublets compared to microfluidics-based single cell platforms. Cells or nuclei are sorted to enrich for neurons, which are the main focus of this paper. Key results are validated by smFISH or by examining publicly available Allen Brain Atlas ISH data. By examining conservation and divergence of cell types and evolutionarily conserved thalamic nucleus that has nonetheless undergone dramatic anatomical reorganization, these data and analyses add to our understanding of how cell types evolve in mammalian brains. They also contribute nuance to the ongoing debate of the extent to which transcriptomic data alone can be used to identify and discriminate cell types that have been described using other methodologies.

Weaknesses:

The Introduction does a nice job of describing what is known about the anatomy and cell types of the dLGN in each species, but it is less obvious what the motivating cross-species question is. Similarly, the Discussion focuses on technical details but the take-away is not clear.

dLGN is collected from all species, but in some species (macaque, mouse), additional thalamic nuclei are also collected. These are useful for examining cell type correspondences across regions or shifts between species, but their inclusion in cross-species integrations can also distort results (e.g. with some integration approaches, inclusion of very different, dataset-specific cell types can distort integration of more similar types). Analyses could be done to better distinguish the evolutionary comparisons within dLGN itself vs. what is additionally learned from inclusion of extra-dLGN nuclei.

One major evolutionary difference can involve differences in cell type proportions. Some proportion results are described but mainly for individual species (some of which include extra-dLGN regions) rather than in the integrated maps, so they can't be compared across species. The FISH results could also be used to corroborate proportion changes when such data are available.

Parameters for clustering analysis (using CCA/Seurat) are not described. Often changes in parameters can change the clusters, and it would be important to know if species integration results robust across a range of parameters and inclusion of extra-dLGN regions.

Some expected genes (PVALB) are barely detected in the macaque neurons, raising the question of whether this is due to tissue or annotation/alignment quality.

Reviewer #1:

This is an interesting manuscript which covers an important topic in the field of computational neuroscience - the 'temporal signatures' of individual neurons. The authors set out to address several important questions using a single-neuron electrophysiology dataset, recorded from monkeys, which has previously been published. The behavioural paradigm is well designed, and particularly well suited to investigating the functional importance of different temporal signatures - as it simultaneously requires the subjects to monitor feedback across a short timescale, as well as integrate multiple outcomes across a longer timescale. The neural data are of high quality, and include recordings from lateral prefrontal cortex (LPFC) and mid-cingulate cortex (MCC). First, the authors modify an existing method to quantify the temporal signatures of individual neurons. This modification appears helpful, and an improvement on similar previously published methods, as the authors are able to capture the temporal signatures of the vast majority of neurons they recorded from. The temporal signatures differ across brain regions, and according to the neurons' spike width. The authors argue that the temporal signatures of a subset of neurons are modified by the subjects' degree of task engagement, and that neurons with different temporal signatures play dissociable roles in task-related encoding. However, I have several concerns about these conclusions which I will outline below. The authors then present a biophysical network model, and show that by varying certain parameters in their model (AHP and GABAB conductances) the temporal signatures of the monkey data can be reproduced. Although I cannot comment on the technical specifics of their models, this seems to be an important advance. Finally, they perform a Hidden-Markov Model analysis to investigate the metastability of activity in MCC, LPFC, and their network model. However, there are a few important differences between the model and experimental data (e.g. neurons recorded asynchronously, and the network model not performing a task) that limit the interpretation of these analyses. Overall, I found the manuscript interesting - and the insights from the biophysical modelling are exciting. However, in its current form, the conclusions drawn from the experimental data are not supported by sufficient evidence.

Major Comments:

1) The authors use a hierarchical clustering algorithm to divide neurons into separate groups according to their spike width and amplitude (Fig 1C). There are three groups: FS, RS1, and RS2. The authors ultimately pool RS1 and RS2 groups to form a single 'RS' category. They then go on to suggest that RS neurons may correspond to pyramidal neurons, and FS neurons to interneurons. I have a few concerns about this. Firstly, the suggestion that spike width determined from extracellular recordings in macaques can be used as an indicator of cell type is controversial. A few studies have presented evidence against this idea (e.g. Vigneswaran et al. 2011 JNeuro; Casale et al. 2015 JNeuro). The authors should at least acknowledge the limitation of the inference they are making in the discussion section. Secondly, visualising the data alone in Fig 1C, it is far from clear that there are three (or two) relatively distinct clusters of neurons to warrant treating them differently in subsequent analyses. In the methods section, the authors mention some analyses they performed to justify the cluster boundaries. However, this data is not presented. A recent study approached this problem by fitting one gaussian to the spike waveform distribution, then performing a model comparison to a 2-gaussian model (Torres-Gomez et al. 2020 Cer Cortex). Including an analysis such as this would provide a stronger justification for their decision to divide cells based on spike waveform.

2) The authors conclude that the results in Fig 3 show that MCC temporal signatures are modulated by current behavioural state. However, this conclusion seems a bit of a stretch from the data currently presented. I can understand why the authors used the 'pause' periods as a proxy for a different behavioural state, but the experiment clearly was not designed for this purpose. As the authors acknowledge, there is only a very limited amount (e.g. a few minutes) of 'pause' data available for the fitting process compared with 'engage' data. Do the authors observe the same results if they constrain the amount of included 'engage' data to match the length of the 'pause' data? Also, presumably the subjects are more likely to 'pause' later on in the behavioural session once they are tired/sated. Could this difference between 'pause' and 'engage' data be responsible for the difference in taus? For instance, there may have been more across-session drift in the electrode position by the time the 'pause' data is acquired, and this could possibly account for the difference with the 'engage' data. Is the firing rate different between 'pause' and 'engage' periods - if so, this should be controlled for as a covariate in the analyses. Finally it is not really clear to me, or more importantly addressed by the authors, as to why they would expect/explain this effect only being present in MCC RS neurons (but not FS or LPFC neurons).

3) At many points in the manuscript, the authors seem to be suggesting that the results of Fig 4 demonstrate that neurons with longer (shorter) timescales are more involved in encoding the task information which is used across longer (shorter) behavioural timescales (e.g. "long TAU were mostly involved in encoding gauge information", and "population of MCC RS units with short TAU was mostly involved in encoding feedback information"). However, I disagree that this conclusion can be reached based on the way the analysis has currently been performed. A high coefficient simply indicates that the population is biased to be more responsive depending on a particular trial type / condition - i.e. the valence of encoding. This does not necessarily tell us how much information the population of neurons is encoding, as the authors suggest. For instance, every neuron in the population could be extremely selective to a particular parameter (i.e. positive feedback), but if half the neurons encode this attribute by increasing their firing but the other half of neurons encode it by decreasing their firing, the effects will be lost in the authors' regression model (i.e. the beta coefficient would equal 0). I would suggest that the authors consider using an alternative analysis method (e.g. a percentage of explained variance or coefficient of partial determination statistic for each neuron) to quantify coding strength - then compare this metric between the high and low tau neurons.

4) Similarly, in Fig 4 the authors suggest that the information is coded differently in the short and long tau neurons. However, they do not perform any statistical test to directly compare these two populations. One option would be to perform a permutation test, where the neurons are randomly allocated into the 'High TAU' or 'Low TAU' group. A similar comment applies to the different groups of neurons qualitatively compared in panel Fig 4C.

5) The authors make an interesting and well-supported case for why changing the AHP and GABA-B parameters in their model may be one mechanism which is sufficient to explain the differences in temporal signatures they observed between MCC and LPFC experimentally. However, I think in places the conclusions they draw from this are overstated (e.g. "This suggests that GABA-B inhibitory - rather than excitatory - transmission is the causal determinant of longer spiking timescales, at least in the LPFC and MCC."). There are many other biophysical differences between different cortical regions - which are not explored in the authors' modelling - which could also account for the differences in their temporal signatures. These could include differences in extra-regional input, the position of the region in an anatomical hierarchy, proportion of excitatory to inhibitory neurons, neurotransmitter receptor/receptor subunit expression, connectivity architecture etc. I think the authors should tone down the conclusions a little, and address some more of these possibilities in more detail in their discussion.

6) For the Hidden Markov Model, I think there are a couple of really important limitations that the authors only touch upon very briefly. Firstly, the authors are performing a population-level analysis on neurons which were not simultaneously recorded during the experiment (only mentioned in the methods). This really affects the interpretation of their results, as presumably the number of states and their duration is greatly influenced by the overall pattern of population activity which the authors are not able to capture. At this stage of the study, I am not sure how the authors can address this point. Secondly, the experimental data is compared to the network model which is not performing any specific task (i.e. without temporal structure). The authors suggest this may be the reason why their predictions for the state durations (Fig 7B) are roughly an order of magnitude out. Presumably, the authors could consider designing a network model which could perform the same task (or a simplified version with a similar temporal structure) as the subjects perform. This would be very helpful in helping to relate the experimental data to the model, and may also provide a better understanding of the functional importance of the metastability they have identified in behaviour.

7) It is not clear to me how many neurons the authors included in their dataset, as there appear to be inconsistencies throughout the manuscript (Line 73, Fig 1A-B: MCC = 140, LPFC = 159; line 97-98: MCC = 294, LPFC=276; Fig2: MCC = 266, LPFC = 258; Methods section line 734-735 and Fig 2S2: MCC = 298, LPFC = 272). While this is likely a combination of typos and excluding some neurons from certain analyses, this will need to be resolved. It will be important for the authors to check their analyses, and also add a bit more clarity in the text as to which neurons are being included/excluded in each analysis, and justify this.

Reviewer #1:

Astrocyte glutamate transporter, GLT1, plays a crucial role in confining the levels of extrasynaptic glutamate, and therefore, understanding the cellular basis by which surface dynamics of GLT1 is regulated has implications in regulating glutamatergic transmission. Here, Michaluk et al. perform FRAP experiments using pHluorin (SEP)-tagged GLT1, and present a careful quantitative characterization of GLT1 surface dynamics that takes into account both lateral diffusion and exocytic delivery. The authors report that 25-30% of surface GLT1 represent immobile fraction which may be subject to slower exchange via exocytic delivery from intracellular compartments. In addition, the cytoplasmic domain of GLT1 plays a role in regulating GLT1 subcellular localization patterns and its activity-dependent dynamics. While the roles for mGluR and calcium-signaling mechanisms are explored, given the drugs have been applied under conditions in which neurons are equally affected, whether mGluR and calcium signaling involving calcineurin are engaged in astrocytes to impact GLT1 remains to be established. In addition, the super-resolution imaging, which does not discriminate between surface and intracellular pool of GLT1, is not well connected to the FRAP results, which is performed blind to the location of synapses.

Reviewer #1:

The strength of this paper is its coupling of careful phylogenetic work with genomics to demonstrate the take-home message: all afrotherians are equal, but some are more equal than others with respect to mechanisms that reduce cancer risk. This is a significant advance in our understanding of the evolution of cancer risk with body size, and in so doing it considerably lengthens the list of genes of interest. It also has interesting examples illustrating the logical criteria of consistency, necessity, and sufficiency that will make it quite useful in teaching critical thinking to students.

Reviewer #1:

Vandaele et al. probe the mechanisms of decision making in rats when making a forced choice between drug and non-drug reward. The authors have led the field in this domain. In this manuscript, a retrospective analysis of choice response times from many rats in their past work is used to tease out potential decision-making mechanisms. We know already from decades of work that choice response times are almost always log-normally distributed (humans, non-human primates, rodents). The question here is whether differences in the mean and dispersion of these distributions can be used to derive insights into nature of the decision-making mechanism - a deliberative comparison versus a race model - and how this may differ for rats that prefer cocaine over saccharin and how this might be altered by more extended training. These questions are framed in terms of the differences between goal-directed and habitual behavior which, to be frank, I found less compelling (these response time data are of significant interest in their own right). I enjoyed reading this manuscript. It was thoughtful and well presented. I have only two comments.

First, much, if not all, of the absolute differences between latencies in sample and choice phases appear to be carried by the sample rather than the choice phase. Choice latencies for cocaine preferring rats, saccharin preferring rats, and the indifferent rats are all very similar. In contrast, the sampling latencies for cocaine preferring rats and the indifferent rats are longer. I am not sure why this should be. My reading was that the authors were more concerned with the choice side of the experiment being different, not the sample phase. Is this predicted by the models being tested? I struggled to understand why an SCM-like model would predict the difference being in the sample phase. Either way, the authors could be clearer about where the difference is expected to lie and why the sample phase is so obviously different in some conditions and the choice phase so similar.

Second, the main and real issue for me is whether the differences between response latencies in the sample versus choice phases plausibly reflect operation of different decision making mechanisms (race model versus deliberative processing) or different operation of the same decision-making mechanism. I don't know the answer, but I could not really derive the answer from the data and modelling provided. The authors frame the differences in response time as being uniquely predicted or explained by different forms of choice. The models that the authors are using are closely linked to, and intellectually derived from, models of human choice reaction time. The most successful of these models are the diffusion model (DDM) (Ratcliff, R., Smith, P.L., Brown, S.D., and McKoon, G. (2016). Diffusion Decision Model: Current Issues and History. Trends in Cognitive Sciences 20, 260-281) and the linear ballistic accumulator (LBA) (Brown, S.D., and Heathcote, A. (2008). The simplest complete model of choice response time: linear ballistic accumulation. Cognitive Psychology 57, 153-178.2008).

Even though the DDM and LBA adopt different architectures to each other (but the same architectures as those in Supp Fig 1A), they are intended to explain the same data. Of relevance, the same model (a DDM or an LBA) can explain differences in both the response distribution and the mean response time via changes in the starting point of evidence accumulation, rate of evidence accumulation, and/or the boundary or threshold at which evidence is translated into choice behavior. So, for either a difference accumulator model (DDM) or a race model (LBA), the difference between sampling and choice performance could reflect changes in how the model is operating between these two phases, including a change in the starting point of the decision [bias], a change in rate of accumulation [evidence], a change in threshold [caution] or collapsing boundary scenario, rather than reflecting operation of a completely different decision-making mechanism.

In thinking of a way forward I readily concede I could be wrong and the authors may effectively rebut this point. Another option could be to acknowledge this possibility and discuss it. E.g., does it really matter if it is a qualitatively different decision-making process or different operation of the same decision-making mechanism? I don't really think the action-habit distinction lives or dies by reaction/response time data, this distinction is almost certainly far less absolute than often portrayed in the addiction literature, and it is generally intended as an account of what is learned rather than an account of how that learning is translated into behaviour (even if an S-R mechanism provides an account of both). Response time data tell me, at least, something different about how what has been learned is translated into behaviour. The third, marginally more difficult but more interesting option, would be to explore these issues formally and to move beyond simple descriptive or LDA analyses of response time distributions. The LBA has a full analytical solution and there are reasonable approximations for the DDM. Formal modelling of choice response times (e.g., Bayesian parameter estimation for a race model or DDM) could indicate whether a single decision-making mechanism (LBA or DDM or something else) can explain response times under both sample and choice conditions or not. This is a standard approach in cognitive modelling. This would be compelling if it showed the dissociation the authors argue - i.e. one model cannot be fit to both sample and choice datasets for all animals. However, if one model can be fit to both, then formal modelling would show which decision making parameters change between the sample and choice conditions for cocaine v saccharin v individual animals to putatively cause the differences in response times observed. Either way, more formal modelling would provide a platform towards identification of those specific features of the decision-making mechanisms that are being affected.

Reviewer #1:

The authors report the analysis of a Mga deletion and provide convincing evidence that Mga functions as a tumor suppressor during lung carcinogenesis. The data shown are clear, the message is important and the discussion is very careful. There is a certain overlap with a recent study by Llabata et al., but there is sufficient novelty in the current study.

Comments:

It seems that the investigation of publicly available datasets is essentially identical to the Schaub et al . analysis and not new data. If the authors want to maintain this, they would need to better explain what is new. One important piece of information that seems to be missing is whether the mutations are homozygous or heterozygous. So data on MGA and MYC protein expression in human tumors would greatly strengthen this part.

Conceptually, one would to know whether tumor development in an MGA-delete situation depends on MYC. One would also like to know whether the polycomb complex that is assembled by MGA is tumor-suppressive. Therefore,the authors should perform a similar analysis as they did for MGA (introduce sgRNAs into the lung models) and score the phenotypes they get. Both experiments could be done in cell lines established from this model and either in vitro (that would allow a mechanistic analysis, e.g. RNA seq) or upon re-transplantation. This would also prevent simply reporting negative results.

The interpretation of the VENN diagram and the heatmaps in Figure 5A,B is somewhat uncertain. If one plots these for MYC, occupancy often simply parallels occupancy by RNAPII, so essentially being bound by MYC simply says the promoter is open/active. Is this the case for MGA and its complex partners? Or is there a specificity in binding? The authors should do RNAPII ChipSeqs in these cells, preferentially +/- MGA, and then show these alongside (and plot a correlation between MYC, RNAPII and MGA occupancy).

Along these lines, it is hard to understand how one obtains the extreme p-values shown in figure 5E and 5H, I would challenge this. If the authors want to maintain this, they should not use ENCODe data, but simply determine what genes are active in the cells (e.g. what promoters are bound by RNAPII) and then use those as background list and calculate P-values for overlap between MYC, MAX and E2F6.

Based on the description, the ChIPSeq analyses are not spike-normalized and I could not find information about the number of repeats. If it is n=1, the authors need to find a way to exclude that the differences are due to experimental variation.

I think the Llabata reference is missing in the list.

Reviewer #1:

Bacterial chemotaxis is a well-studied process at many levels, from the chemical networks that control the rotation of the flagella to the fluid dynamics of the motility itself. In the present paper the authors address the widely held view that ligand sensing is responsible only for changing the rotational bias of the motor driving flagellar motion, and not its speed. Using a well-established method of quantifying motor activity by monitoring the rotation of the cell body when the flagella are stuck to a surface, a fluorescent labelling technique to determine the membrane potential, a mutant with fluorescently labelled stator units, and direct measurements of swimming speed, the authors show that the sensing of a non-metabolizable analogue of glucose leads to a momentary increase in motor speed and stator unit numbers. At the same time, control experiments make it clear that this is purely as a consequence of ligand sensing. This behaviour is indeed contrary to the accepted view, and although the fundamental mechanism is as yet unclear, this is an important result.

On the whole I am very supportive of this work, which has been done with great care and clear logic. My only suggestion for improvement would be to make quantitative the changes in chemotactic behaviour that would be expected as a consequence of the motor speed changes revealed in this research. That is, can the authors put some numbers into a standard analysis of run-and-tumble dynamics to quantify any improvement in chemotactic efficiency or speed under such changes?

The definition of the term WEIRD by delineating what each letter stands for makes sense. It was an interesting expreince of for me earlier in the day when one of my clients whose parents are Nigerian immigrants and who was born and brought up here was reacting to her previous therapists suggestion that she is possibly suffering from PTSD. My client said that African parents tend to be stricter and relatively more punitive, however, she does not think that her childhood was truamatic. This conversation came alive for me reading this article that tries to communicate that human experiences vary widely and it is hard to make generalizations about a lot of topics.

Reviewer #1:

The current study by Ohara et al. describes differences in the connectivity patterns between LVb to LVa. The study builds on the authors previous study (Ohara et al., 2018) where they showed the intrinsic connectivity of LVb neurons in the MEC and LEC. The focus of the current study is the difference the authors observed in the strengths of connectivity between LVb and LVa in the MEC and LEC. The authors suggest that the in MEC Vb neurons do not provide substantial direct input to LVa neurons. The manuscript emphasizes the functional importance of difference as the authors suggest that "...hippocampal -cortex output circuit is present only in LEC, suggesting that episodic systems consolidation predominantly uses LEC-derived information and not allocentric spatial information from MEC." The study uses a newly developed mouse line to investigate connectivity differences, this is a nice technical approach and the experimental data is of high quality. While the data is solid, the authors tend to over-interpret their findings from the functional point of view. While the observed difference is quite interesting, it is unclear what the impact is on information flow in the MEC and LEC and to which degree they differ functionally. The authors assume major differences and their work is framed based on these expected differences, but the manuscript does not provide data that would demonstrate functionally distinct features.

Major Comments:

1) Throughout the text the authors treat their findings as if it was 'all-or-none' i.e the LEC has a direct connection between LVb and LVa while the MEC does not. This does not seem to be the case based on their data, the data shows that connectivity in the MEC is less robust but it is definitely there. The difference seems to be quantitative and not qualitative.

2) Due to this problem, the authors seem to be over-interpreting their data by suggesting that the information flow must be significantly different conceptually in the LEC and the MEC and this would have important implications for memory consolidation. It is impossible to draw these conclusions based on the data presented, as there are no experiments investigating the functional, network level consequences of these connectivity differences.

3) The electrophysiology experiments provide information about the basic parameters of the investigated cells, but these lack a physiological context that would allow the authors to evaluate the consequences of these differences on information flow and/or processing in the MEC and the LEC.

4) The study is using a novel transgenic mouse line to differentiate between LVb and LVa neurons, while this is definitely a strength of the study, this strategy allows the authors to visualize ~50% of LEC and ~30% MEC neurons. Since the authors aim to prove a negative (MEC does not have direct connection) the fact that ~70% of the neurons are not labelled could be problematic.

Reviewer #1:

In the article "Glypicans specifically regulate Hedgehog signalling through their interaction with Ihog in cytonemes" Simon et al. elucidate the function of Glypicans in Hh transport via cytonemes. The manuscript describes convincingly that the fly glypicans Dally and Dally-like are required to maintain the expression of the Hh co-receptor Ihog. Ihog - in turns - stabilises Hh cytonemes through its interaction with Glypicans to establish the Hh gradient in the wing imaginal disc. The authors further carried out an extensive molecular analysis of Ihog and identified the relevant domains within the protein required for interactions with Glypicans, Patched, and Hh. In general, this is a very thorough, detailed analysis of Ihog function. The images and videos are excellent. However, prior publication, there are two major criticisms, which needs to be addressed, in my opinion.

Firstly, the first part of the manuscript, the molecular analysis of Ihog (Fig.1-4) seems to be detached from the second cytoneme-focussed part (Fig. 5, 6). Independent evidence is needed to show support for the idea that the Ihog-Gly mediated stabilisation of cytonemes is responsible for the expansion of the signalling gradient. Are the static cytonemes involved in a flattened gradient or are the receiving cells just sensitised for Hh? Can cytonemes be (de-) stabilised w/o interfering with Hh components to untangle these observations? The authors write "Intriguingly, the same Ihog domains that regulate cytoneme dynamics are those also involved in the recruitment of Hh ligand, glypicans and the reception complex."

My concern is that cytoneme dynamics and Hh gradient formation could be two parallel, independent events -> one needs to show this interdependency in a clear way. I could imagine an analysis of the consequences when Ihog is overexpressed, and cytoneme formation is inhibited (by other means). Consistently, could one stabilise cytonemes in an Ihog-reduced background and analyse gradient formation?

Secondly, the authors demonstrate an effect of Ihog alterations on the formation of the gradient. However, what is the physiological relevance? What are the consequences of Ihog/Gly-mediated cytoneme stabilisation and gradient formation on tissue patterning and wing formation? If this is not possible to show experimentally, this needs to be discussed.

AQ:1 Please check whether the hierarchy of the section head levels is correct throughout the article.

ML: Seems like the font for the “big” section such as the intro should be bigger than the subsections.

Reviewer #1:

The authors present a novel framework for running CPM simulations in the web browser. The CPM framework is a well-established model methodology for cells and tissues. Several well established other simulation platforms exist, however they do not run in the web browser, and require varying amounts of setup. This often presents an insurmountable roadblock since many researchers do not have the required software packages or expertise to read, execute, and run models in different formats. Artistoo on the other hand promises a zero-install experience for end-users, and ease of model construction for modellers.

The unique feature of Artistoo is that it runs in the web browser. This allows users to execute simulations in a zero-install setting. In the web browser users can change model parameters, and observe resulting effects instantly. Extending or modifying models requires the user to know JS. Artistoo implements core modern CPM features. Artistoo is successfully benchmarked against the existing software of Morpheus. The source code is available on github. A wiki with an apparent complete and extensive documentation is available.

The authors argue for three main avenues of impact: (1) accelerated feedback loops on models with experimental collaborators, (2) science communication, and (3) in teaching.

The authors' points have merit, point (1) in particular. Installation and execution of tissue modelling software by non-experts is a well known challenge. Artistoo elegantly avoids this issue, by allowing models to be shared via the web browser. The non-expert is able to gain insights into model dynamics, and can explore the model's parameter space at ease. This approach has the potential of stimulating more frequent feedback between experimentalists and modellers, and maybe even the adoption of such a model by experimentalists.

There is no markup language support. The software package Morpheus describes simulations using a markup language, allowing non-expert users to assemble complex models without writing a single line of C++, while at the same time preserving exact details of each simulation run. Morpheus is (as far as I know) the only based on a markup language. It would be fantastic if Artistoo could read and execute Morpheus ML files. From a technical point of view this should be possible. This would mean, all `Morpheus' models become "Artistoo' models, meaning that Artistoo would become the standard for sharing CPM models with collaborators. Finally the markup language would allow novices to implement new models without being discouraged by the JS requirement. Adopting a common markup language between projects would be the first example of standardization across open-source CPM software packages.

I can see Artistoo being adopted by ``CPM modellers', who want to share models with collaborators, a wider audience (science communication). It may also find adoption in teaching. At the same time, the adoption of Artistoo faces some challenges: (1) Among modellers existing platforms have more features, are familiar, and have similar computational efficiency; (2) existing models are to be rewritten in the Artistoo framework.

DOI: 10.7554/eLife.60432

Resource: (ZFIN Cat# ZDB-GENO-100615-1,RRID:ZFIN_ZDB-GENO-100615-1)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-GENO-100615-1

What is this?

Reviewer #1:

The authors devised clinical criteria for identifying Zebrafish larvae with high or low T. cassari infections in order to track. Using transgenic fish line marking macrophages and neutrophils, the authors showed that both groups of larvae increase macrophage (and to lesser extent neutrophil) levels in response to infection. However, the macrophages in high parasitaemia animals migrated into the capillaries and had elevated levels of inflammatory markers (TNF, IL-1) and lipids, indicative of a foamy phenotype. The authors conclude that a measured inflammatory response allows animals to control the initial infection, while an exaggerated inflammatory response leads to an environment in which the bloodstream trypanosomes can proliferate. The findings support and extend data from murine models of infection, by allowing direct visualization of host immune response.

Reviewer #1:

These investigators examine how lactic acid producing E. coli impact age-related decline in neurological function through the use of temperature-food associative learning or thermotaxis. In particular, they screen a panel of different lactate producing E. coli and identify a particular clade of bacteria, Lactobacilli, that are able to suppress age-dependent decline in thermotaxis in a daf-16 dependent manner. Moreover, they uncouple improvement in neurological function from lifespan determination and locomotion. Overall, this group presents an interesting phenomenon regarding the effects of the lactic acid producing bacteria. However, it is not clear what is happening in the worm to elicit this neurological response and much work remains to determine this mechanism of action.

While I can appreciate the careful nature of these worm behavioral assays including a host of different controls, these studies lack cellular and molecular details, which reduce my overall excitement for the story. It is interesting that a clade of lactic acid bacteria (LAB) can improve associative learning in C. elegans. However, I was very underwhelmed when I got to the final figure, which very briefly touched on molecular mechanism (only to give DAF-16 dependence). Since it has previously been shown that daf-16 mutant animals impact taste avoidance learning (Nagashima et al. PLOS Genetics, 2019), the dependence of DAF-16 and its role in associative learning seemed predictable. For future submissions, this previous study on DAF-16 should be referenced in the manuscript. Moreover, data regarding dietary restriction and the eat-2 mutation appear to be misinterpreted. Thus, more attention and analysis should be dedicated to the effects of dietary restriction on their paradigm. I thought that it was interesting that a clade of LAB consistently reduced expression of PHA-4 transcription factor and the authors might benefit for expanding upon this observation.

In addition to molecular characterization, the manuscript provides little explanation at the cellular level. It is unclear what neurons or neuronal circuit are responsible for this phenomenon. Although mentioned in the discussion, this manuscript would benefit by close examination of the thermosensory circuit including the AFD and AIY neurons. How are these lactic acid producing E. coli ultimately signaling to the neurons? Do the LAB slow the rate of degeneration of either neuron? Is this phenomenon the result of lactic acid production or something else in the bacteria? Would it be possible to supplement lactic acid to worm media and produce the same result?

This is an interesting phenomenon and requires more in-depth cellular and molecular characterization.

Reviewer #1:

This manuscript describes a longitudinal study of the adolescent structural connectome. The authors find strong effects of expansion of structural connectomes in transmodal brain regions during adolescence. They also report findings centered on the caudate and thalamus, and supplement the structural connectivity analyses with transcriptome association analyses revealing genes enriched in specific brain regions. Finally, intelligence measures are predicted from baseline structural measures. This is an interesting and comprehensive set of analyses on an important topic. Overall, the figures are lovely. The sensitivity analyses are particularly commendable. Some suggestions and points for clarification are below.

There is not much in the introduction about why co-localized gene sets are of interest to explore. What is already known about brain development using this approach, and how does the current work fill a gap in our knowledge?

Similarly, the introduction states that the study aims to "predict future measures of cognitive function". What cognitive functions specifically were of interest in this study, and why? No rationale or background is provided for conducting these analyses.

The authors claim that their study examines "the entire adolescent time period", however some would argue that age 14 does not represent the earliest age at which adolescence onsets. I think it would be more accurate to say the study covers the mid to late adolescent period.

In the results (page 4) it is stated that three eigenvector explained approximately 50% of the variance in the template affinity matrix. Here it would be helpful to report exactly how much of the variance was explained by each (E1, E2, E3).

Pubertal development occurs across the age range investigated, and affects brain structure and function. Was information on pubertal stage of participants available? Did some participants undergo changes in pubertal status from timepoint 1 to timepoint 2?

The introduction does not mention cortical thickness much, therefore these analyses come as a bit of surprise in the results.

As in the introduction, there is not much interpretation of the transcriptome findings in the discussion.

For constructing the structural connectome, the Schaefer 7-network atlas was utilized. Can the authors comment on why a functional atlas (rather than a structural atlas) was used here?

Reviewer #1:

The authors provide interesting evidence on the properties of CSF-contacting neurons, referred to as 'CSFcNs' in their manuscript, using 2 photon calcium imaging in mice.

Their work relies on calcium imaging using 2 photon microscopy in slices of the mouse spinal cord. The authors observed calcium transients with two different amplitudes and propose that these transients reflect the activation of different voltage dependent calcium channels (T and L).

Although the work is of interest, there are throughout the manuscript numerous issues: -shortcuts and oversimplified assumptions (calcium transients do not equal spikes!) (see title of Figure 2, 3) -the massive ignorance of the relevant literature for this small field on CSF-cNs in mice. In particular, but not only, the authors should know and refer to the work of Orts Dell'Immagine, Wanaverbecq, Trouslard who have shown since 2012 that CSF-cN in mice are chemosensory cells whose spontaneous activity is driven by the channel PKD2L1.

Major comments

1) The authors assume that calcium transients equal to firing (Figure 2) or calcium spikes (Figure 3) but these are far from being the same. No deconvolution algorithm can use calcium transients to infer spiking with better than 70% accuracy.

In the recordings of the Wanaverbecq group, spontaneous firing in slices was 0.4Hz in control and 0.1Hz in PKD2L1 KO. The authors find here calcium transients occurring at 0.16Hz (n = 63 cells), suggesting that some of the sparse firing activity is missed by the authors.

Since calcium transients reflect spiking but not in a linear manner, a calibration is necessary via cell attached or loose patch recordings in order to infer on CSF-cN spiking, or perforated patch to validate the evidence for calcium spikes.

2) This assumption of calcium = firing does not hold in cells that have an input resistance of GOhms and whose activity has been shown to be driven by the opening of the channel PKD2L1 (Orts Del Immagine et al Neuropharmacology 2016). In particular, observations of the TTX insensitive calcium transients may be due to the PKD2L1 channel.

=> The authors need to combine recordings with perforated Patch Clamp together with the 2P calcium imaging in order to tackle the question of the role of the channel openings in the generation of the different calcium transients observed in WT or KO for PKD2L1.

From introduction to discussion, the authors should properly cite the work of the Wanaverbecq group as well as other groups in the field, whose contributions were relevant and ignored.

3) Activation leading to calcium spikes (K+, ATPergic, Cholinergic inputs, ...) was done without blockage of the neurotransmission in the slices and could therefore originate from indirect sources, including activation of metabotropic receptors presynaptically.

The authors need to solve these issues.

4) In Figure 7, there are diverse responses that the authors should better illustrate. Many cells appear to not respond for multiple stimuli tested : what is the rational criterion to define that a cell responded or not? Can the authors quantify the proportion of cells responding? Did the author take into account the high level of spontaneous activity? Can the negative dip in response possibly from a motion artifact in panel G and H?

Reviewer #1:

I'm quite enthusiastic about the care the authors have taken in designing this cutting edge hybrid environment, and the effort they've gone through to describe it in detail. I believe that this endeavor has great merit, and that seeing the advancements in animal welfare and experimentation should be of interest to the general reader. However, at present, the stated interpretations are not fully justified by the results, and this must be addressed.

The manuscript should be amended and updated in one of two possible ways: the interpretations of the scientific result here should be tamped down significantly, or additional evidence should be presented for some of the claims in the originally submitted manuscript. I am confident that the authors should be able to carry out either of these to a satisfying degreen.

Major issues:

1) Throughout the manuscript, stating that the third monkey learned the task "merely by observing two other trained monkeys" is misleading. The naive monkey may have learned very important details about the cognitive testing set-up from observation. But the third monkey learned the task of a unique behavioural shaping paradigm that included -but was not limited to- watching trained monkeys. The authors trained the third monkey on the cognitive task in the absence of the other monkeys, and do not show that the third monkey learned the specific cognitive task from watching other monkeys. Over-interpreting the anecdotal observations here hinders obfuscates what is novel and notable in this manuscript.

2) The authors repeatedly state that the third monkey learned the task faster than the previous two monkeys. It is quite difficult to parse exactly what the authors mean by this, and exactly what the data is that supports that claim.

The authors go on to state that M2 learned the "task structure" faster than M1/M3. However, "task structure" is not defined, so it is difficult for a reader to know precisely what was learned faster under social observation. Furthermore, the data showing that M2 learned the task structure faster than M1/M3 is not clear, and it is not known how M1/M3 learned the task structure in isolation. Description of which training steps may be aided by observation of trained monkeys must be clarified. The authors allowed M2 to observe M1 and M3 during initial familiarization of the experimental set-up, but it seems that observation may not have aided M2 in learning the complex same-different task at all.

Even though M2 may have learned the task structure faster than M1/M3, these observations are anecdotal and should not be over-interpreted. If there is a clear difference in the time to learn basic task structure, it may be due to social observation, but the authors should not favor that interpretation without considering alternatives as well. E.g., monkeys have widely varying personalities (see e.g. Capitanio 1999, Am J Primatology), and this has important implications for the curiosity, exploration behavior, and likelihood to accept and complete new challenges in training. To what extent could the differences in learning rate also be explained by these differences across these 3 monkeys? To what extent does the different training regimen in the task explain differences in learning rate across monkeys (e.g. M2 got two days of repeating correction trials, which significantly alters learning rates)?

3) There is a vast literature in ethological settings where the gaze of nonhuman primates has been tracked using noninvasive methods that the authors do not acknowledge. Instead, authors state that most infrared eye trackers require head restraint (line 32), though this is demonstrably not the case. For review, see Hopper et al. 2020, Behav Res Methods.

4) Some important details for introducing monkeys to the testing apparatus during Tailored Automated Training should be described. For example, were animals water-restricted, or on any sort of fluid restriction when TAT began? How did the authors entice the animals to initially explore the testing apparatus?

Reviewer #1:

General Assessment:

The work presented by Knight et al. in "Major genetic discontinuity and novel toxigenic species in Clostridioides difficile taxonomy" is of excellent quality and spans several of the themes of eLife. The manuscript provides a thorough and robust examination of publicly available C. difficile genomes, to deliver a much-needed update of C. difficile phylogeny, in particular the cryptic clades of C. difficile. However, there are some further clarifications could be included to confirm if the cryptic clades of C. difficile, and the 26 unclassified STs (which seemingly form 4 distinct clusters) should indeed be assigned to the Clostridioides genus, distinct from both C. mangenotii and C. difficile.

Specific comments:

Lines 96-97 and Figure 2: Figure 2 suggests the 26 unclassified STs form at least 4 distinct clusters, yet these STs are classified as outliers. Could you please comment on why these are considered outliers? Or do these STs represent new cryptic clades? C-IV, C-V etc.? And do these unclassified STs also fit into the criteria for the novel independent Clostridioides genomospecies?

Lines 161-162; Table 1: C. mangenotii is referred to as Clostridioides mangenotii on lines 161-162, but has been listed as Clostridium mangenotii in table 1. Was this intentional? Or should this be Clostridioides mangenotii as C. difficile is also listed as Clostridioides difficile?

Figure 6: Many of the numbers and symbols on the figure are difficult to see e.g. Figure 6A the values listed above each data point are extremely small. Can these values/symbols be increased?

Lines 224-225: Given that C. difficile strains lacking tcdA and tcdB can still cause infections, consider rephrasing "indicating their ability to cause CDI".

Figure 7: As with Figure 6, many of the numbers and symbols on the figure are difficult to see. Can these values/symbols be increased?

General comments:

Were the unclassified STs included in the species wide ANI analyses in Figure 3? If similar analyses were performed for these STs and given the clusters that are presented in Figure 2 would this support the idea that they may also fit into the criteria for the novel independent Clostridioides genomospecies?

Similarly, were these same unclassified STs included in the BactDating and BEAST analyses? Or the pairwise ANI and 16S rRNA value comparisons in Figure 5? Or the pangenome and toxin gene analysis also presented in Figures 6 and 7? And would this add further strength to the idea that these "outliers" could be the first typed representatives of additional genomospecies?

Lastly, your conclusions are a little too on the fence. You have presented sufficient evidence to suggest that the cryptic clades of C. difficile likely represent novel independent Clostridioides genomospecies, but dilute out the importance of this throughout the discussion and conclusions. Although controversial, the evidence provided gives credence to these claims, and the text should be changed to reflect this.

Reviewer #1:

In the study by Buus et al., the authors set out to address an important need to understand how oligo-conjugated antibodies should be optimally utilized in droplet-based scRNA-seq studies. These techniques, often referred to as CITE-seq, complement techniques such as flow cytometry and mass cytometry yet also further extend them by the ability to jointly measure intra-cellular RNA-based cell states together with antibody-based measurements. As is the case with flow cytometry, manufacturers provide staining recommendations, yet encourage users to titrate antibodies on their specific samples in order to derive a final staining panel. Based on the ability to stain with hundreds of antibodies jointly, few studies to date have assessed how the antibodies present in these pre-made staining panels respond to a standard titration curve. In order to address this point, this study tests two dilution factors, staining volume, cell count, and tissue of origin to understand the relationships between signal and background for a commercially available antibody panel. They arrive at the general recommendation that these panels could be improved, grouping various antibodies into distinct categories.

This study is of general interest to the scRNA-seq and CITE-seq communities as it draws attention to this important aspect of CITE-seq panel design. However, it would stand to be substantially improved by not only providing suggestions but also testing at least one, if not more, of their suggestions from Supplementary Table 2, and preferably performing experiments using more technical replicates or biological replicates. As it stands now, the study is largely based on one PBMC and one lung sample, that were stained once with each manipulation as far as can be gathered from the Methods.

Major comments:

1) Given the title is improving oligo-conjugated antibody... it would be important to functionally test one of the suggestions. We would suggest a full titration curve of selected antibodies, perhaps one from each of the categories, but if cost is a concern at least two or three antibodies, to identify how titration impacts antibodies, and especially those in categories labeled as in need of improvement. Relatedly, if the idea is that if antibodies (such as gD-TCR) do not have a cognate receptor leading to general background spread, does spiking in a cell that is a known positive in increasing ratios remedy this issue by acting as a target for the antibodies? Does adding extra washes help to remedy these issues of background?

2) Another way of improving these panels is through reducing the costs spent on both staining but perhaps more importantly the sequencing-based readouts. Several times in the manuscript (at line 77 for example or line 277) it is alluded to that the background signal of antibodies can make up a substantial cost of sequencing these libraries. However, no formal data on cost is presented, which would be important to formalize the author's points. It would be important to provide cost calculations and recommendations on sequencing depth of ADT libraries based on variation of staining concentration. Relatedly, in the methods, sequencing platform and read depth for ADT libraries was not discussed, nor is the RNA-seq quality control metrics provided other than a mention of ~5,000 reads/cell targeted. This is important to report in all transcriptomic studies, and especially a methods development study.

3) One of the powerful elements of joint multi-modal profiling, as mentioned in the title, is to be able to measure protein and RNA from a single cell. This study does not formally look at correlation of protein and RNA levels, and whether a decrease in concentration of antibody either improves or diminishes this correlation. This would be important to test within this study to ensure that decreasing antibody levels does not then adversely affect the power of correlating protein with RNA, and whether it may even improve it.

4) How was the lack of antibody binding determined for Category E? CD56 is frequently detected on NK cells in peripheral blood, CD117 should be detected on mast cells in the lung, and CD127 should be found on T cells, particularly CD8+ T cells. From inspecting Figure 1E, it appears as if all three of these markers are detected on small but consistent cell subsets. As the clusters are only numbered and no supplementary table is provided to help the reader in their interpretation, it is difficult to determine if these represent rare but specific binding, or have not bound with any specificity.

5) References: At 14 references, the paper overall could benefit from a more comprehensive citation of related literature including flow cytometry and/or CyTOF best practices for antibody staining and dealing with background, and joint RNA and protein measurement from single cells.

Reviewer #2:

In this paper, Numssen and co-workers focus on the functional differences between hemispheres to investigate the "domain-role" of IPL in different types of mental processes. They employ multivariate pattern-learning algorithms to assess the specific involvement of two IPL subregions in three tasks: an attentional task (Attention), a semantic task (Semantics) and a social task (Social cognition). The authors describe how, when involved in different tasks, each right and left IPL subregion recruits a different pattern of connected areas.

The employed tasks are "well established", and the results confirm previous findings. However, the novelty of the paper lies in the fact that the authors use these results as a tool to observe IPL activity when involved in different domains of cognition.

The methodology is sound, well explained in the method section, the analyses are appropriate, and the results clear and well explained in the text and in graphic format.

However, a solid experimental design is required to provide strong results. To the reviewer's view, the employed design can provide interesting results about functional connectivity, but not about the functional role of IPL in the investigated functions.

I think the study would be correct and much more interesting if only based on functional connectivity data. Note that rewriting the paper accordingly would lead to a thorough discussion about how anatomical circuits are differently recruited based on different cognitive demands and about the variable role of cortical regions in functional tasks. This issue is neglected in the present discussion, and this concept is in disagreement with the main results, suggesting (probably beyond the intention of the authors) that different parts of the right and left IPL are the areas responsible for the studied functions.

Major points:

1) The 3 chosen tasks explore functions that are widespread in the brain, and are not specifically aimed at investigating IPL. The results (see. e.g. fig 1) confirm this idea, but the authors specifically focus on IPL. This seems a rather arbitrary and not justified choice. If they want to explore the lateralization issue, they should consider the whole set of involved areas or use tasks showing all their maximal activation in IPL.

2) The authors aims to study lateralization using an attentional task, considering the violation of a prevision (invalid>valid), a linguistic task, looking for an activation related to word identification (word>pseudoword) and a social task, considering correct perspective taking (false belief>true belief), but they do not consider that in all cases a movement (key press) is required. It is well known that IPL is a key area also for creating motor commands and guiding movements. Accordingly, the lateralization bias observed could be due more to the unbalance between effectors while issuing the motor command, than to a different involvement of IPL regions in the specific tasks functions.

3) Like point 2, the position of keys is also crucial if the authors want to explore lateralization. This is especially important if one considers that IPL plays a major role in spatial attention (e.g. Neglect syndrome). In the Methods, the authors simply say "Button assignments were randomized across subjects and kept identical across sessions", this should be explained in more detail.

4) The authors show to know well the anatomical complexity of IPL, however their results are referred to two large-multiareal-regions. This seems to the reader at odds with all the descriptions related to fig.2. If they don't find any more subtle distinction within these 2 macro-regions, they should at least discuss this discrepancy.

5) The part about Task-specific network connectivity is indeed very interesting, I would suggest to the authors to focus exclusively on this part. (Note that the results of this part seems to confirm that only the linguistic task is able to show a clear lateralization).

Reviewer #1:

The authors have performed a rare feat in the study of the posterior parietal cortex, which is to achieve a functional parcellation of this crucial area on the basis of its response during a diverse set of tasks. The variety of tasks and the analytical approach married to it are very strong and lead to a division that agrees well with data from patients with lesions and studies in homologous areas of non-human primates.

Readers are encouraged to note the analytical approach, with particular regard for the permutation testing that establishes the differences between the tasks in the functional connectivity of the area.

Conceptually, this paper is another strong argument for understanding the broad role of the posterior parietal across tasks and point at the flexibility of its functional response in supporting those roles.

This manuscript lays out a series of fMRI investigations and analyses centered on examining the response of the IPL during three different tasks (attention, semantics, social cognition). The analyses are largely data-driven and examine functional response and connectivity, to make the argument for a functional parcellation of the IPL into at least two distinct subregions. The manuscript is well-written and the analyses well described. There are some concerns about the analyses that dampen enthusiasm slightly and a lack of consideration of the associated literature in non-human primates, but these problems seem imminently correctable.

The analyses begin with a data-driven cluster analysis across an anatomically constrained IPL ROI, searching for cluster solutions that efficiently parcellate IPL on the basis of the response of voxels across the three tasks. This analysis is fine, but does constrain the average activity in the identified clusters to differ across the tasks. That makes the univariate activation in 3b a bit circular and hard to interpret. Either the error bars should be removed and a note added that the univariate activity is purely descriptive or the univariate data should be displayed from a slice of the data that did not contribute to the derivation of the clusters. The strongest version of this analysis would hold out entire participants.

The predictive coding analysis is potentially informative but the details were a bit unclear. In the one versus rest analysis the strongest test would be to build the model on the data from n-1 participants and then test it on the trials of the held-out participant. If this was not done, some justification for not doing it would be in order.

Finally, the authors should also consider integrating some of the non-human primate literature as it only strengthens their case. In the human literature the IPL has proved a tough nut to crack, but the single unit physiology has revealed strong differences in the homologous areas of macaque, some of which directly map onto the division argued for here.

Reviewer #1:

Xiao et al describes a kinetic model of enhance-promoter interactions, which the authors use to explain the changes in transcription levels upon disruption of genomic contacts within topologically associated domains (TADs). The model uses the law of mass action to describe activity of promoters and enhancers, which are proposed to be able to accommodate multiple transcription activation tags. The authors use the model to explain the nonlinear relationship between the genomic contact frequencies within TADs and their corresponding transcription rates. They recapitulate the superlinear relationship between the changes in genomic contact probabilities and transcription rates within TADs observed in their recent experiments (Mateo et al, 2019). Inspired by the futile cycle of cell signaling, their model incorporates multiple tagging of promoters allowing for transient amplification of transcription rates.

Conceptually, this work is interesting and the model suggests possible reconciliation of seemingly contradictory experimental observations reported earlier.

However, the manuscript in its current form fails to substantiate many of its claims.

Here are my major concerns:

1) The presentation of the model is unclear. It is currently present in the text, lines 110-122, in pure qualitative description. Authors define only rates in the text; definitions of other model parameters are not present. For example, E and a are not specifically defined in the text or Methods section. Since both terms "enzyme" and "enhancer" are being used and in fact "enzyme tagging" and "enhancer tagging" occur simultaneously in the model, it is not possible to say for sure when do authors call which one in the model and thus the methods section can be interpreted in different ways. Moreover, the cartoon is missing a legend confirming, which molecular player is which. The figure caption mentions only green triangles being the tags, but no other parts of the cartoon are being explained. Taken together, this makes it very difficult to verify the mechanics of the model.

• The authors should provide a detailed technical description of their model directly in the text, including description of their parameters, list their constitutive equations and identify all parameters in their cartoon Fig. 1C.
• Axes labels in all figures should be expressed in the parameters/variables of the model (as in Fig. 6C-D) directly connecting to inputs/outputs of the model.

2) Due to the lack of description, in many sections it is not clear what are the specific inputs and outputs of the model (e.g. Fig. 2).

3) The Methods section describes the chemical kinetics of the suggested reactions and the insulation score calculations. But it is not clear how do these inform each other, how are contact-frequency maps chosen/computed and cross-referenced with the local E-P kinetics?

4) In the Methods section, it appears that in lines 577-580 of the model description, the mass is not conserved.

5) In 587-588, the index of k is 2(n+1), which equals to 2n+2, but then in the next line the following assumption is made 2n+1 → n+1

6) The authors make assumptions that their kinetic considerations hold for n>2. What is the evidence?

7) The authors observe hysteresis in median transcription rate as a function of enhancer contact frequency. However, the presented violin plots suggest a presence of two states, one with low and one with high transcription rates. In the intermediate regime of enhancer contact frequency, where authors report hysteresis, the violin plots show bimodal distributions suggesting coexistence of these two states. This would suggest that the system exists in and switches between two distinct states with a discontinuous transition, instead of a continuous hysteretic behavior as suggested by the median behavior.

8) The language of the paper is often not technically precise with qualifiers missing, which could lead to ambiguities and misinterpretations. Here are some examples:

• *p. 1, line 10, "difference in contact across TAD borders is usually less than twofold"
• *p. 1, line 17, "results from recent cohesion disruption"
• *p. 2, line 71, "A simple model of hypersensitivity to changes in contact frequency"

9) On p. 13, line 483, authors define Ostwald ripening as given by weak multivalent interactions; however, Ostwald ripening is a thermodynamic process. In addition, they propose that liquid condensates become larger due to Ostwald ripening, but there are also other processes that may occur, such as coalescence of condensates, which would also lead to larger condensates.

10) At the beginning of the Discussion section authors state they will propose future experiments in each section. However, in some of the sections it is not clear what specifically authors are proposing. These suggestions should be made clearer.

Reviewer #1:

This paper investigates the modulation of spatial signals in higher order visual areas. A number of the findings are novel and interesting, including that signals in higher visual areas are not more influenced by spatial position that signals in V1, that this modulation is not a general feature of the entire visual circuit (i.e. LGN boutons in L4 of V1, as well as LGN units, show very little spatial modulation, and that spatial modulation decreases when mice are watching a replay of tunnel traversals. Overall, I think this paper provides new insight regarding position coding in visual systems. However, there are some points that should be addressed.

1) The imaging data is from mice with different genetic backgrounds, as well as a mixture of gcamp6f and 6s. In addition, different reward protocols were used for different mice. Although the authors state in the methods that none of these factors impact their results, it would be good to include some quantifications to this effect (e.g. they could show the distribution of SMI for 6f data vs 6s data). While I don't expect the major observations to change if it turns out that some of these factors have as systematic effect, it could affect portions of the results where the dataset is split up - for example in the comparison between different higher visual areas, and the observation that spatial modulation appears to vary with receptive field location.

2) The authors state that it is to be expected that LGN neurons respond more strongly in the first half of the corridor due to contrast adaption mechanisms. However, I did not see any quantification that could support this statement?

3) When looking at the spatial modulation index, the authors switch between using median (e.g. Fig 1 and 2) and mean (Fig 4), t-test and rank-sum - and sometimes there is missing information regarding which (mean or median) they are reporting. The authors need to include more detail regarding these statistics.

4) It was not clear to me if the authors are only imaging from layer 2/3 or if they also attempted to image deeper layers.

5) Throughout the paper, the authors use 'firing rate' to refer to deconvolved calcium signal. Although this is stated in the methods, this wording can be misleading, especially since the paper also contains extracellular recordings of spiking activity.

6) It was not clear to me how the dotted lines (e.g. Fig 1 b) were calculated.

Reviewer #1:

This manuscript reports the results of two timing experiments. The experimental paradigm asks participants to judge the time of target items in an unfilled interval between two landmark stimuli. In experiment 1, there is one item that must be judged. In experiment 2, there are two items to be judged. The basic empirical result is that relative order judgments in experiment 2 are more accurate than one might expect from the absolute timing judgments of experiment 1. A model is presented.

My overall reaction is that this paper does not present a sufficiently noteworthy empirical result. I can't imagine that there is a cognitive psychologist studying memory who would be surprised by the finding that relative order judgments in the second experiment are more accurate than one might expect from the absolute judgments in experiment 1. On the encoding side, in these really short lists (with no secondary task), there is nothing preventing the participant from noting and encoding the order as the items are presented (not unlike the recursive reminding). On the retrieval side, we've known for a very long time that judgments of serial position use temporal landmarks (see for instance a series of remarkable studies by Hintzman and colleagues circa 1970).

Methodologically, this paper falls short of the standards one would expect for a cognitive psychology paper. There are basically no statistics or description of the distribution of the effect across participants. Although I'm pretty well-convinced that the basic finding (distributions in experiment 2 are different from experiment 1), I could not begin to guess at an effect size. The model is not seriously evaluated. The bimodal distributions are a large qualitative discrepancy that is not really discussed.

Although the title of the paper invites us to understand these results as telling us something about episodic memory, the empirical burden of this claim is not carried. Amnesia patients (and animals with hippocampal lesions) show relatively subtle differences in timing tasks. There is no evidence presented here, nor literature review, to convince the reader of this point.

Reviewer #1:

This study examines MEG activity in a picture categorization task (decide living or non-living) in a sample of 18 patients with semantic variant PPA, compared to 18 controls. As svPPA is a rare (but scientifically informative) disorder, the sample size is impressive, and given that relatively few MEG studies exist in PPA at all, this is an interesting dataset. The authors show differences in engagement of oscillatory activity, specifically increased low-gamma ERS in occipital cortex and increased beta ERD in the superior temporal gyrus. The authors interpret this as reflecting increased engagement of / reliance on early perceptual mechanisms for completing the task, as opposed to semantic identification of the picture.

Major concerns:

1) My biggest methodological issue with this paper relates to a very old debate in neuroimaging that still comes up all the time: the choice of statistical threshold. Using a high threshold prevents false positives, but may also lead to false negatives, and I fear that is the case here, with the high threshold contributing to an unrealistic impression of spatial specificity in MEG. It is obvious from the average responses in both groups that these oscillatory responses are widespread through the brain. Indeed the alpha and beta responses are significant in the majority of cortical voxels. This basic property of the responses should be presented clearly and prominently in the paper - I don't think it's appropriate to put it in supplementary information where only a minority of readers will even see it. The authors then use what I think is an extremely high and conservative statistical threshold to contrast differences between the two groups. P<.005 uncorrected is a highly conservative threshold already, even before cluster-thresholding is added (although with data as smooth as MEG beamforming solutions, cluster-thresholding is unlikely to change anything). Basically this makes the only the strongest part of the activation survive, and it is valid to conclude that a significant group difference exists there (protected from Type 1 error), but this can give a false impression of the difference is specific to that region. I think a more realistic characterization of the results would involve measuring differences in the strength of the responses between groups on a broader level, possibly the sensors or in large ROIs - and not ROIs pre-selected to show a dramatic difference by first searching the whole brain for the most significant effects - that is the classic "double-dipping" fallacy in neuroimaging.

2) Similarly, the ERD/ERS in each frequency band is treated as a separate entity, ignoring the fact that these bands are arbitrary and frequency is a continuous quantity. This matters because much is made of the fact that PPA participants exhibited greater ERS in the low-gamma range, and that this was correlated with reaction time. Supplementary figure 1 shows that both groups had strong occipital ERS in the high-gamma range, but only PPA showed it in the low gamma range as well. This suggests that the ERS in the PPA group may simply have been shifted to a lower frequency range. A more fulsome characterization of these group differences via time-frequency analysis and/or power spectral analysis would help clarify what is going on here.

3) It is surprising that PPA participants only exhibited increased MEG responses compared to controls - assuming that both gamma ERS and beta ERD can be interpreted as increased neural activation, which is a reasonable assumption based on the literature. No decreases in the PPA group are found, and thus the observed increases can be plausibly attributed to compensatory processes as framed by the authors. However, I am concerned about the role of certain analysis choices in producing this data pattern. In particular, the authors state (line 611): "To remove potential artifacts due to neurodegeneration or eye movement (lacking electrooculograms), we masked statistical maps using patients' ATL atrophy maps (see section MRI protocol and analyses), as well as a ventromedial frontal mask."

It is not clear whether this masking was done in group space from average atrophy maps, or on an individual level. In either case, I don't think this is well justified. I don't know any physical mechanism by which tissue undergoing neurodegeneration can be said to generate an artifactual signal. Atrophied tissue still contains living neurons with ionic currents; these are real signals not artifacts, and furthermore, atrophy is a continuous process with tissue further from the epicenter also undergoing similar neurodegenerative mechanisms. Atrophied tissue may well generate electromagnetic signals that are different from healthy tissue, and such differences should be included in this paper. I think that there may be regions of hypoactivation as well as hyperactivation in this PPA group. If the hypoactivation localizes to atrophied tissue and the hyperactivation to other regions, that will bolster the case that we are seeing compensatory processes, but it isn't certain with half the story masked. I also don't really see statistical masking of the frontal region as a valid solution to eye movement artifacts. The authors would have to present evidence that the region that they masked corresponds to the region potentially affected by eye movements. However, many studies have found that beamforming already does a pretty good job of removing ocular artifacts from estimated brain signals, except for very close to the eyes.

4) The correlation with reaction time in the occipital cortex is consistent with the idea that the ERS there may reflect compensatory overreliance on perceptual information, but it isn't conclusive. The authors suggest that PPA patients are able to categorize the stimuli correctly based on visual features, but are unable to name them. What about testing for correlations with the out-of-scanner behavioural measures that established that the patients have a naming deficit? It would strengthen the case if atrophy or hypoactivation (see comment above) correlated with the naming deficit.

Reviewer #1:

Perez-Ortega and colleagues performed rigorous experiments to determine if the activity of neurons in the visual cortex is similar across days, in particular comparing spontaneous activity in the absence of visual stimuli across days, which was previously not examined to my knowledge. The paper claims that evoked ensembles are more stable than spontaneous ensembles, but more convincing quantitative analyses are required to support these claims.

Major Comments:

1) There is only one mention of prior work with multi-day imaging in the visual cortex (Ranson 2017). Another related study to cite and compare your results to would be Jeon, ..., Kuhlman 2018 (and I think a comment about how similar/different your results are from this study + Ranson would be useful for the reader). I would also recommend mentioning that there are studies that have observed differences in evoked activity across learning in V1 (e.g. Poort, Khan et al 2015; Henschke, Dylda et al 2020). Do you think there was adaptation across days to the stimulus that you repeated?

2) Some GCaMP6f mice have aberrant cortical activity (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5604087/). In the raw data (Fig 1F) it doesn't look present, but it would be useful to show more time and sort the neurons by their first PC weights perhaps to see the activity structure.

3) The approach of 3 plane imaging taking the maximum projection seems useful for tracking cells across days. There is a claim that some cells are no longer found / no longer active. Based on Fig 1G it appears there may have been some Z-movement from day 10 to day 46. This Z movement may explain some of the lost active cells. As a sanity check I would recommend plotting the Z-plane on which the cells were maximally active on day 1 vs the Z-plane on which the cells were maximally active on day n.

4) There is an emphasis on analyzing the data as ensembles but I think this may be missing other slow, gradual changes. The definition of stable is at least 50% of neurons were preserved across days. However, the fitting procedure of finding ensembles may produce different ensembles even if those neurons are still correlated to each other. I would recommend two possible additional analyses: 1) compare the correlation matrices for common neurons across days (unless there are too few neurons for this); 2) look at changes in single neuron statistics across days. For 2) this may include reliability of neural responses to the visual stimuli, the weights of the neuron onto the first principal component of spontaneous activity, or the correlation of a neuron with running speed. I think these results may solidify your ensemble result (evoked-related statistics change less across time).

Reviewer #1:

This study presents a detailed and focused study of the structural basis for a regulation strategy used by a human iron-sulfur cluster biosynthesis system, elucidated by artificial installation of new amino acids into a bacterial system that lacks the allosteric elements of the human enzyme. The work includes quaternary structure analysis and activity assays of variant bacterial proteins. It is performed competently and supports the conclusions. But the focus may be too narrow for a general audience. To bring the work over the bar, the authors could test whether installing the bacterial residues into human NFS1 restores activity without frataxin (inactivated in the human genetic disorder Friedrich's Ataxia). Furthermore, some elements of the study could be presented more clearly/rigorously to communicate the significance of the work to a general audience. These suggestions are listed below.

1) It would be useful for an unfamiliar reader to include a diagram of the bacterial and human iron-sulfur cluster biogenesis pathway. It would also be helpful to depict the mechanism of the IscS/NFS1 cysteine desulfurase reaction - essentially a picture to go along with the description of the PLP-dependent transformations described in paragraph 2.

2) In the first paragraph of the results section - I would be interested to see more details about the selection of the three residues targeted for mutagenesis. For example, did the authors inspect the interfaces of existing crystal structures of these complexes? Did they create sequence alignments for multiple eukaryotic/prokaryotic cysteine desulfurases and select sites conserved in bacterial proteins but not eukaryotic ones? More description of the experimental or bioinformatics basis for selecting these three sites would be important for convincing the reader that the basis for this work is sound.

3) The structural basis for the dimer interaction and the enhanced activity isn't completely clear - how do the changed interactions enhance the enzyme activity? A good description of the different quaternary forms and why they are more/less active is given on page 4-5 - but perhaps another link could be made between the exact residues targeted for substitution and the features of the system important for catalysis.

4) On page 10, the authors describe changes in IscS quaternary structure as a function of concentration. What is the estimated copy number or concentration inside the cell? Which concentration ranges would be most physiologically relevant?

5) Addition of any helper protein appears to increase the proportion of variant IscS dimer and activity. Is there any reason to believe that this phenomenon is simply a crowding effect? If the same amount of an unrelated protein is added - does the activity/dimer fraction change compared to variant IscS alone?

6) I found the color scheme in Figure 1 hard to follow - could the authors keep the subunit colors consistent and use text labels directly on the figure panels for the subunits and forms (open, ready, etc). I also don't think the "Clash!!" labels are necessary. A more effective approach might be to use zoomed-in insets for each clash.

7) In Figures 4-6 - could the authors include a more complete description of the error bars? What kind of error is shown? Are the replicates different experiments done on different days? These presentations might also benefit from showing the actual data points on top of the bars/error bars.

Reviewer #1:

Dingens et al. report a timely complementary study to map neutralizing and binding responses in polyclonal rabbit sera induced by immunization with the BG505 SOSIP Env trimer. Neutralizing responses are mapped using libraries of replication-competent HIV expressing all mutants of the BG505 Env, an approach developed in the Bloom laboratory. Binding responses were mapped using an EM-based method, EMPEM, developed in the Ward laboratory. The Env mutations that affect neutralization of the autologous BG505 strain in the BG505-SOSIP-immunized animals were largely known from other studies, as were the binding (not necessarily neutralizing) responses - the strength of this study is the combination of the two approaches. It is especially useful that the complex datasets have been deposited on-line where they can be interactively explored, including mapping onto Env trimer and monomer structures. Although results were anticipated, it is very nice to directly compare the neutralization epitopes to the binding epitopes determined by EMPEM. This is a well-written and beautifully illustrated paper.

Reviewer #1:

The manuscript by Badhiwala et al. is an interesting study using the emerging model system Hydra, which has many advantages for studying the entire nervous system of an animal during simple behavior. Some of the foundational neuroscience papers in this field have only come out in the past few years, and new studies such as the one here, might have the potential to contribute to an important early literature. Despite clear reasons for enthusiasm, the many shortcomings in this work greatly diminished my enthusiasm and support for this study. Although I appreciate building the microfluidic devise with simultaneous pan-neuronal imaging, the nature of the new biological insights provided here seems quite limited and easily predicted based on prior studies in hydra and other model systems. Moreover, the crude nature of some experiments inhibits my ability to make fair judgement of potential findings.

Major concerns:

1) The pressurized stimulation of the hydra appeared to be specific to the center of the body. The authors don't mention why this region was chosen, which seems critical to this study. Relatedly, why didn't they test multiple areas across the hydra with this system? Might we expect to see different sensorimotor behaviors, and thus different neural outputs?

2) The authors reference a recent single cell study characterizing multiple neuronal cell types in hydra. This work would greatly benefit by using some cell-type resolution studies to determine the functional nature of the neurons being activated as opposed to solely using pan-neuronal GCAMP imaging. If they can put GCAMP in all neurons, why not put it in specific subsets of neurons based on cellular identity? This point becomes more salient because a major take-home from this paper is that the spontaneous behavior and firing patterns is nearly identical to the stimulus evoked patterns, except for an apparent increase in firing rate. The true nature of the mechanosensory response might be revealed with cell-type specific experiments.

3) Although the authors reference whole animal imaging, they focus imaging analysis on peduncle and hypostomal nerve rings, despite the videos showing calcium activity in other areas throughout the body. Moreover, are the authors certain their pan-neuronal genetic strategy equally samples neurons throughout the body? In other words, is the apparent increase in activity in the nerve ring over other areas driven by a technical artifact of these neurons being labeled better?

4) While I appreciate the resection studies to get at "loss-of-function" experiments, this approach seems rather crude, and potentially confounding to clear interpretation. Exactly which neurons are killed and to what extent, and how many, if any began to regenerate throughout this process? My alarm here is raised especially in light of the author's surprising finding that "footless" animals show that the aboral nerve ring is not required for spontaneous or mechanosensory responses. What if residual activity from neurons not ablated is driving this response?

Reviewer #1:

The authors investigated the importance of visual and vestibular sensory cues and the underlying motion dynamics to the accuracy of spatial navigation by human subjects. A virtual environment coupled with a 6-degrees of motion platform, as described in prior studies, allowed precise control over sensory cues and motion dynamics. The research builds on previous work in several important ways: 1) the authors demonstrate that reliance on vestibular cues leads to an undershooting of trajectories to hidden goal locations, 2) manipulation of the underlying motion dynamics (the time constant) during navigation alters the accuracy of trajectories particularly when subjects are reliant on vestibular cues, 3) probabilistic models were used to demonstrate that path integration errors can be explained by mis-estimates of the underlying motion time constants, and 4) time constant estimates were improved when visual cues were available. Overall, the analyses are appropriate, the conclusions are judicious, and the authors provide an important contribution to understanding the sensory mechanisms underlying human spatial navigation.

1) Some minor methodological clarifications: how many trials were performed per subject? How many of the trials were performed in each condition (visual, vestibular, combined)?

2) The study tested performance by both male and female subjects. Could the authors comment as to whether sex differences were observed across performance measures? Perhaps sex can be indicated in some of the scatter plots.

3) Figure 2A. It would be helpful if the authors identified the start-point of the trajectory and also provided more explanation of the schematic in the caption.

4) Figure 2B-C. It would be helpful if the authors could expand this section to show some example trajectories and the relationship between examples and plotted data points. This could be done by presenting measures (radial distance, angular eccentricity, grain) for each example trajectory.

5) Because the range of sampled time-constants can vary across subjects, it would be nice to show plots as in Figure 3B for each subject (i.e., in supplementary material).

6) Discussion. The broader implications of the findings from the models are not sufficiently discussed. In addition, some comparison could also be made to other recent efforts to model path integration error (e.g., PMC7250899).

Reviewer #1:

This manuscript by Gould et al presents highly novel data which is logically presented and is likely to have both clinical and fundamental implications. Of relevance to the bone field, it defines a new mechanism by which one of the most important clinical targets for the treatment of osteoporosis is endogenously regulated. Beyond bone, I am not aware of any other examples of stimulus-directed acute lysosomal degradation of a secreted canonical Wnt antagonist as a mechanism to provide rapid de-repression. What seems lacking is a careful analysis of the physiological consequences of the acute degradation of sclerostin.

1) A landmark paper which convinced many in the field that sclerostin down-regulation is necessary for osteoanabolic responses to loading was based on a transgenic model from the Bellido lab (Tu et al, Bone, 2012). In that study, expression of Sost from the DMP-1 promoter precluded its transcriptional and protein-level down-regulation at late time points. That was sufficient to largely prevent bone gain following loading. Several other groups interpreted this as indicating Sost transcript regulation is required for bone's adaptation to loading, calling into question the physiological relevance of transient post-translational degradation described here. Can the authors reconcile that study with their own?

2) One way the authors attempt to demonstrate in vivo relevance is through western blotting of mechanically loaded mouse ulnas, showing previously-undocumented acute reductions in lysate sclerostin levels. It is standard practice in the field to quantify sclerostin positive osteocytes histologically, rather than by western blotting. This is because mechanical loading can rapidly increase blood flow to the limb (even in this study, the authors implicate the vasodilator NO) as well as having inflammatory effects, diluting the proportion of osteocyte-specific proteins in the lysate. Demonstrating protein-level sclerostin down-regulation specifically in osteocytes rapidly following loading would be a major addition to this study.

3) A long-stranding, reproducible finding which has always been very perplexing is that the largest transcriptomic responses to osteogenic mechanical loading occur very quickly, within an hour of loading, before Sost is down-regulated. Even in UMR106 cells in vitro, B-catenin is stabilised before Sost is down-regulated following exposure to substrate strain. The current findings may explain this temporal discrepancy. The authors should responses to sclerostin degradation such as quantifying Wnt target genes to provide physiologically-relevant readouts of their findings.

4) Figure 3 shows co-localisation of endogenous or ever-expressed sclerostin with lysosomal markers. Does this co-localisation change following FSS or PTH?

5) It is not clear whether early lysosomal degradation which transiently decreases sclerostin is triggered by the same mechanoresponsive pathways which subsequently down-regulate its RNA levels, or whether the two responses are distinct. Can the authors clarify this? For example, does Sost decrease in the BafA1-treated cells 8 hours after FSS or PTH treatment?

6) Discussion "that the rapid and transient nature of sclerostin degradation may be critical to the precise anatomical positioning of new bone formation following an anabolic stimulus" is very unclear. How do the authors propose that lysosomal sclerostin degradation produces regionalised responses to a greater degree than the previously-reported transcriptional mechanisms?

7) The evidence of lysosomal involvement in sclerostin down-regulation is largely based on pharmacological compounds of limited selectivity. A degree of genetic evidence is indirectly provided by the Gaucher cell line, but this is based on a single patient line. Can the authors provide direct genetic evidence that lysosomal function is necessary for sclerostin down-regulation, and ideally for bone formation?

8) References to previous studies which described mechanisms and relevance of Sost down-regulation are sparse. For example, see previous implications of NO signalling from the Vanderschueren lab (Callewaert et al, JBMR, 2010), protein-level down-regulation of sclerostin in the context of ageing from the Price lab (Meakin et al, JBMR, 2014) relevant to the discussion in the current manuscript, as well as work from the Ferrari lab on sclerostin regulation following both PTH and mechanical loading (e.g. Bonnet et al, JBC 2009; Bonnet et al, PNAS 2012).

Reviewer #1:

In this study, Feilong and colleagues showed that hyper-aligned fine-grained cortical connectivity profiles can be used to strongly predict general intelligence in individual participants. This is an important study demonstrating the utility of previously developed connectivity hyperalignment and highlighting the behavioral importance of fine-grained connectivity which is typically ignored in more standard functional connectivity analysis.

1) How does the bootstrapping handle the family structure in the data? More details are needed.

2) The authors mentioned that "the code for performing hyperalignment and nuisance regression was adapted from PyMVPA". One of the most important contributions of this study is the impressive demonstration of prediction performance improvement using hyperalignment and fine-grained connectivity profiles. Therefore, it is important that the adapted code and code utilized for the current study be made publicly available. While connectivity hyperalignment code from the previous study is available in PyMVPA, my experience is that it is not easy to use. If no code from the current study is made available, I believe it will be very difficult to replicate this study.

Reviewer #1:

The manuscript by Allio et al. tries to justify that roadkill can be a useful source for genomic sequencing and even genome assembly level data. The authors cover all aspects of using this resource, from a new protocol to extract DNA, through generating a hybrid short- and long-read genome assembly and to various applications, showing that this data can be used in phylogenomic and population genomic analyses. Although I think that the manuscript is useful in highlighting how this resource can be analysed, it covers a lot of different topics and covers them in varying depth, which makes it difficult to follow and understand the real importance of the different sections.

Major comments:

-Overall, this manuscript left me a bit confused about what is the main scope. It covers a lot of different topics from the laboratory-end of the spectrum, e.g. protocol used to get good DNA out of roadkills and how to assemble these genomes with a hybrid genome assembly, and crossing into a phylogenomic analyses making taxonomic suggestions and an analyses of the complete carnivora group, plus a demographic analyses showing the changes of population size over time.

I was left with an impression that the authors tried to cover a lot of different topics but did not go deep enough in any of those. As a consequence, the results section ends up sounding somewhat shallow, while the discussion takes up a lot of space.

What I would suggest if this manuscript is indeed to serve as a roadmap to roadkill genomics, is to add a figure showing the pipeline and then adjust the structure of the manuscript accordingly. For example, one box in the figure would correspond to one heading in the results/methods, where the DNA analyses is explained - reasoning why a special protocol is needed, what is the main difference to existing methods, how does the yield compare to other methods, etc.

And then the different topics explored in this manuscript could be shown as different examples of the application - taxonomic questions on intra-/inter-species level, higher level taxonomic analyses (of the whole Carnivora), population genomic analyses, etc. Highlighting this as examples of the potential use of the roadkill genomes would make it understandable why this paper is trying to cover aardwolf and bat-eared fox genomics from so many ends.

-Even though showing that roadkill samples can be useful for analysing particular species for which obtaining samples is difficult in other ways, I'm missing a discussion of how difficult it is to obtain roadkill samples and what are the ramifications. Can this approach be generally applied due to legislation reasons, do you need permits, do you find enough roadkill to rely on this source or do you only see it as an opportunistic sampling scheme?

-Genome assembly is not exactly my field of expertise; therefore, I would like the authors to better explain how their hybrid, short- and long-read genome assembly approach is novel. My impression was that such hybrid assemblies are now a rather common and well-established practice. But a lot of space is dedicated to explaining this topic in the introduction and again in the discussion, which to me is something obvious and reads more like a review than a research article. But maybe I'm missing something obvious here, in which case I'd like the authors to make it clearer.

Reviewer #1:

The evidence that sumoylation of K68 in the PIE-1 zinc finger protein is important for HDA-1 type 1 histone deacetylase association and sumoylation seems reasonable, and, is important because as shown in the co-submitted paper HDA-1 sumoylation leads to its association with MEP-1 and LET-418/NuRD complex thus accelerating H3K9ac deacetylation, and silencing gene expression.

The evidence that PIE-1 is needed for sumoylation of HDA-1, presumably through association of PIE-1 with the UBC-9 SUMO E2, is reasonable. However, several aspects of the authors' model remain unclear, and there is an absence of biochemical assays to establish the role of sumoylated PIE-1 in HDA-1 sumoylation, and the effects of sumoylation on HDA-1 HDAC activity.

1) How sumoylation of K68 in PIE1 affects its function was not worked out. Can the deleterious effect of the K68R mutation on PIE-1 function be reversed by generating a SUMO-PIE-1 fusion, as was done for HDA-1 in the co-submitted paper? K68 maps to the N-terminal side of ZF1 in the PIE-1 protein in what appears to be an unstructured region. Does the SUMO residue play a role in the interaction of PIE-1 with HDA-1? Are the zinc fingers required for PIE-1 interaction with HDA-1 or UBC-9? No zinc finger mutations were tested. Does HDA-1 have a SIM that would allow it to interact selectively with sumoylated PIE-1? Another possibility is that the PIE-1 SUMO moiety is important because it interacts with the non-covalent SUMO-binding site on the backside of UBC-9 (Capill and Lima, JMB 369:606, 2007), which might stabilize the interaction. The backside interaction of SUMO with UBC-9 is proposed to promote UBC-9-mediated sumoylation of target proteins with SUMO consensus sites that are directly recognized by UBC-9. In this scenario, SUMO-PIE-1 would in effect be acting as an E3 SUMO ligase for HDA-1 by serving as a recruitment "factor". In this regard, the authors could test biochemically whether recombinant PIE-1 or K68SUMO-PIE-1 stimulates sumoylation of HDA-1 by UBC-9, using recombinant WT and KKRR mutant HDA-1 as substrates. These issues deserve discussion.

2) What is the SUMO E3 ligase that sumoylates PIE-1? Is it possible that through association with UBC-9, perhaps through its zinc fingers, PIE-1 is sumoylated in cis within a PIE-1/UBC-9 complex?

3) In many places, including the title, the authors make the claim that PIE-1 promotes sumoylation and activation of HDA-1. While it is clear that PIE-1 does increase sumoylation of HDA-1, in a manner requiring K68, and that H3K9ac levels are decreased as a result, the authors do not provide any direct evidence that this process increases HDA-1 catalytic activity, as is implied in the title and elsewhere. As indicated in the review of the co-submitted paper, this would need to be established by carrying out an HDAC assay on control and sumoylated HDA-1 in vitro. Instead of enzymatic activation, it is possible that the PIE-1 interaction and HDA-1 sumoylation results in relocalization of HDA-1 within the nucleus to facilitate more efficient H3K9ac deacetylation.

Reviewer #1:

The evidence that sumoylation of HDA-1, a type 1 HDAC, plays a key role in establishing transcriptional silencing of piRNA-regulated genes in C. elegans is quite convincing. The genetic analysis demonstrating that the SUMO pathway is involved in piRNA silencing is strong, and the mutational evidence that this involves sumoylation of two Lys in the tail of HDA-1 is reasonable. Likewise, the finding that HDA-1 sumoylation promotes association with NuRD complex components and association of MEP-1, an HDA-1 interactor, with chromatin regulators is convincing. In addition, the evidence that HDA-1 sumoylation increases H3K9ac deacetylation in vivo, leading to negative regulation of hundreds of target genes, and plays a role in the inherited RNAi pathway is solid.

While the overall conclusion provides an interesting advance in understanding mechanisms of piRNA-mediated gene silencing in C. elegans, the paper is lacking any biochemical analysis of the effects of sumoylation on HDA-1 activity and its association with other transcriptional regulators.

1) The authors mapped two sumoylation sites close to the C terminus of HDA-1, K444 and K459, based on extremely weak homology with two established sumoylation sites in human HDAC1 that are reported to be important for transcriptional repression (N.B. the authors should indicate here that David et al. reported that K444/476R HDAC1 had reduced transcriptional repression activity in reporter assays.). While the two human sites conform to the sumoylation site consensus, ψKXE, neither K444 nor K459 in HDA-1 fits this consensus (possibly one could argue that K444 is in an inverted motif). The fact that the KKRR mutant HDA-1 is no longer sumoylated is consistent with these two Lys being sumoylated, but it would be reassuring to have direct MS evidence that K444 and K459 are indeed sumoylated, which could be achieved using a SUMO Thr91Arg mutant that generates a GlyGly stub upon trypsin digestion, among other methods.

2) It remains unclear how sumoylated HDA-1 is recognized by MEP-1 for assembly into the NuRD complex. Does MEP-1, or another NuRD subunit, have a SIM that could facilitate direct interaction of MEP-1 and sumoylated HDA-1?

3) As the authors discuss, it is surprising that the HDA-1(KKRR)::SUMO protein, which in effect is a constitutively sumoylated form of HDA-1 that will interact constitutively with MEP-1/NuRD, does not have more deleterious effects on the organism, since according to the data in Figure 2B, the stoichiometry of endogenous HDA-1 sumoylation was extremely low. Of course, low sumoylation stoichiometry, which is a general issue with sumoylation studies, means that only a very small fraction of the HDA-1 endogenous population will be able to engage with the silencing complexes at any one time. This point is also worth discussion.

4) Page 5: Here, and elsewhere, the authors claim that sumoylation of the two C-terminal Lys activates HDA-1 histone deacetylase activity, but provide no direct evidence for this statement. There are no HDAC assays, and it is unclear how C-terminal SUMO residues distant from the catalytic domain would alter its enzymatic activity, unless there is a SIM motif in HDA-1 that might allow for intramolecular interaction with SUMO residues at the tail leading to a conformation change. Did the authors check for a SIM motif in HDA-1? The fact that adding SUMO to the C-terminus rather than one or both of the two Lys would also have to be taken into account in determining bow sumoylation might "activate" HDA-1. To demonstrate that sumoylation activated HDA-1 in vitro deacetylation assays would need to be carried out comparing the activities of unmodified and sumoylated HDA-1. Instead of enzymatic activation, it is possible that the PIE-1 interaction and HDA-1 sumoylation results in relocalization of HDA-1 within the nucleus to facilitate more efficient H3K9ac deacetylation.

Reviewer #1:

In this manuscript, Soucy and colleagues present a novel innervated system which they use to model the effects of prenatal nicotine and opioid exposure. Using the system they provide potentially interesting insights on how prenatal nicotine and opioid exposure could impact release of catecholamines. However, following careful review of the manuscript,I recommend that the authors provide substantial additional data and evidence to support the biological relevance of their findings.

Major points:

1) A main pillar of this manuscript is the assumption that the adrenal medulla is innervated. To substantiate their claims the authors cite books/book chapters, rather than citing convincing primary evidence. In fact, other than old EM images showing vesicular densities akin to synapses, I have not found published images of convincing axonal arborization in the adrenal medulla - if such images exist the authors should at least try to reproduce them for internal consistency of their study. This is particularly relevant if they wish to draw parallels between in vitro and in vivo systems. As this is a major pillar upon which this research stands, the lack of supporting histological evidence, which could be easily done, undermines the validity of this manuscript. Presenting primary evidence (i.e. not a textbook diagram) is essentia.

2) Multiple experiments lack appropriate controls. See comments on Figure 2B, 2D, Supplementary Figure 2.

Reviewer #1:

The authors report a model about the confidence-effort tradeoff; explaining how subjects invest effort depending on how confident they want to be in their decision (and how costly this is). They fit their model to behavioural data and report qualitative similarities between model and data.

I find this an interesting model, with interesting links between timely topics of interest, such as confidence, effort, and cost optimisation. But I have several requests for clarification.

Major Comments:

Line 274: Without loss of generality: what does it mean here? I guess that with a different cost function, not all conclusions remain the same?

The model assumes that it is "rewarding" to choose the correct (highest-value) option (B = R*P). But is this realistic? If the two options have approx the same value, then R should be small (it doesn't matter which one you choose); if the options have different values, it is important to choose the correct one. Of course, the probability P_c continuously differentiates between the two options, but that is not the same as the reward. Can the predictions generalise toward a more general R that depends on value difference?

In Figure 2, I guess that the important quantity to decide is a standardised delta-mu (similar to d' in signal detection theory). It might be useful to also plot that (essentially combining the current two plots). Or alternatively, plot P_c(z), which relates more directly to the theory.

The section Probabilistic model fit is unclear. Are the MCD variables y the 5 variables mentioned above? Do different y's share the same alpha, beta, gamma? Are different transformation parameters a and b fitted for each y? Is estimation done per subject? It is mentioned that VBA is used, but what distribution is approximated exactly using VBA? Is it a mean-field approximation, optimised with gradient descent? Is the goal function a posterior across the 5 parameters? It would also be good then to have an intuition on the estimated model parameters (e.g., their standard error or Bayesian equivalent). Is there an estimate of model fit (in addition to checking qualitative predictions)? Figure S3 is a good start (and I think it is worth putting in the main MS), but it would be nice, for example, to see model comparisons where one or more parameters are restricted.

Figure 4, 5, 6 should be better annotated. I have a hard time trying to fill in what is plotted exactly (eg, scale of the color bar). Why are the data grouped in percentiles? Also in Figure 4 legend, I guess that "beta" is not used as the MCD model parameter? Please avoid overloading definitions.

Figure 7: It seems that "spreading" of alternatives occurs in the model only for alternatives that are initially close together? Is this consistent with their discussion around equation (14)? (I may be overlooking something; if so, consider making this more explicit.)

I find it a really interesting feature of the model that it can explain spreading of alternatives from a statistical perspective. So I think it's worth commenting on it in the Discussion. For example, does the current model capture trends in the literature? To what extent is the effect (also in empirical data) dependent on initial value differences?

Reviewer #1:

This paper attempts to explain perinatal risk factors and the associated risk of developing pediatric asthma in the mid-childhood and early teenage years. The authors found that some maternal characteristics such as atopy, BMI, race/ethnicity and demographics such as newborn sex, and birth characteristics such as birthweight, gestational age, and mode of delivery were associated with risks of subsequent asthma development in the pediatric population. The paper then goes on to demonstrate the differences in immune response during the different time frames of pregnancy. Throughout the majority of the pregnancy, fetal hematopoiesis generates mostly lymphoid and erythroid lineages. Towards term, the immune cells are predominantly neutrophils and monocytes. Pre-term is characterized primarily by lymphocytes. It was seen during term deliveries that the myeloid response produces several cytokines that shift CD4+ T- cells away from the Th2 response. Enhanced production of IFN gamma by leukocytes stimulation early in life is associated with reduced susceptibility to infections. However, the author states that these findings do not extend to asthma diagnosis in childhood.

Major comments:

I would have liked the paper to readjust the introduction; a lot of emphasis is placed on IFN/infection/asthma, but after this fact, it seems neglected going forward and the paper explores another topic. Instead, the paper's focus was on determining the biological nature, serologically, with a granulocytic luminal marker (PGLYRP-1) and a membrane-bound marker (sIL6Ra) and its association to pediatric asthma.

The take-home message for the paper - that there appears to be an inverse relationship between serum levels of PGLYRP-1 and overall risk for pediatric asthma - should be explored in relation to the whether a therapeutic role for such proteins is possible since they can accurately predict risk factors for disease and assess pulmonary function. Other proteins, like the sIL6Ra, have no association with disease predictability and have no association with predicting pulmonary outcomes. This should be explored/explained in greater detail.

Minor comments:

As part of the validation efforts of the study - the rationale for using three different cohorts to assess pediatric asthma risk was not clearly explained.

One of the main findings of the analysis was the conclusion that patients with higher levels of myeloid cells in their CBMCs are at lower risk of developing pediatric asthma, and vice versa. Furthermore, CBMC neutrophil abundance was negatively associated with the number of risk factors. (patients with more risk factors, as mentioned above, were found to have lower levels of neutrophils in their CBMC, and more at risk of pediatric asthma). This was further elucidated with measuring CBMC plasma levels of PGLYRP-1 with levels of mRNA and correlating it with risk of developing pediatric asthma. Increased levels of mRNA for the PGLYRP-1 protein was associated with an increased serum concentration of the protein. However, this was inversely correlated with risk factors. Patients with reduced risk factors for development of pediatric asthma were found to have increased levels of the protein and its mRNA.

The measurement and correlation of PGLYRP-1 (present in neutrophil specific granules) and sIL6Ra (derived from neutrophils, but not present in granules) to pediatric asthma at mid-childhood and early-teen years was determined. There were two follow-up points where asthma outcomes and pulmonary function by way of the FEV1/FVC ratio was determined. It was found that increased levels of PGLYRP-1 were significantly associated with current asthma at mid-childhood. However, there was no association between levels at the early-teen follow-up.

In terms of correlations between each protein level and pulmonary function - the sIL6Ra protein was NOT associated with the FEV1/FVC ratio or a bronchodilator response at either age group. However, it was found that increased levels of PGLYRP-1 were associated with an INCREASED FEV1/FVC ratio (not indicative of asthma) and reduced odds of developing pediatric asthma at each age group.

This analysis makes sense as increased production of neutrophil granules, PGLYRP-1, serves a protective effect against infection, reducing incidence of disease states. The paper, however, should explore the rationale behind the no-response to the sIL6Ra protein. In terms of understanding, since this protein is NOT associated with neutrophilic granules, it can be inferred, that is it may not have a role in protecting against infection. However, this could have been explored in more detail in the paper.

Reviewer #1:

A very interesting paper testing the biological embedding model in a wild long-lived mammal using an impressive dataset. However, the results for immature orphans are not entirely straight forward. The effect on the HPA axis is in the opposite direction to humans and there seems to be no significant increase in cortisol compared to non-orphans overall - it depends on time since maternal loss. The paper would be improved by communicating this more clearly and discussing exactly why this pattern may be different to that in humans. Some of the evolutionary ideas discussed in the paper also need to be more clearly conveyed or thought through.

Substantive concerns:

1) There are important sections in the introduction (L125-128 particularly) and discussion (L403-409) about the evolution of the HPA response and differences between humans and other mammals that are unclear. Greater detail on the evolutionary logic being used, the precise hypotheses being suggested and references to back the ideas up are required (further details in minor comments).

2) Table2/Model 1a doesn't directly test whether orphans have higher cortisol than non-orphans (or no p-value reported in table 2) and CIs in table 1 suggest that there is not a significant difference. Therefore, categorical statements that orphans have higher cortisol levels don't seem to be entirely justified. However, model 1B demonstrates that cortisol declines with years since maternal loss and figure 3 supports the idea that orphans do have higher cortisol than non-orphans in the first 2 years following maternal loss but that this declines to levels similar to those of non-orphans after 2 years. Could a statistical test be run to back this up? Perhaps instead of using a binary variable for orphan status (yes/no) it could be analysed as categories (orphaned within 2 years, orphaned more than 2 years ago, not orphaned as an immature) which could be used to directly test this and back up statements e.g. recently orphaned immatures had higher cortisol levels than non-orphans. A broader concern is why likelihood ratio tests have been used to calculate p values (and for only some of the predictors) rather than reporting the output from the models themselves. Could you explain what the benefit of this is over reporting values from the actual models and/or also provide the model outputs?

3) The effect on cortisol slopes found in this study is in the opposite direction to that in humans. This is discussed in some detail but is lacking clarity in places and I think it would help to make this difference more obvious - it is really a key finding of the paper not a secondary point. The expected pattern is very nicely set out in the introduction so it would be good to format the discussion so there is a paragraph that outlines exactly how the results differ from hypothesized:

(a) that the effect on cortisol slopes is in the opposite direction

(b) that only the cortisol levels of recently orphaned immatures are significantly different to non-orphan immatures and then brings in the ideas discussed about why these differences may be present. I think this would really help communicate the findings more clearly, bringing the discussion more inline with what is set out in the introduction.

Reviewer #1:

This is overall a well written and methodologically sound study researching how educational achievement can be predicted using genomic data when the sample is stratified to those without and those with diagnoses of common psychiatric disorders. I think that it is a very important study area, the study is well powered using a fantastic representative sample and offers some insights into aetiology of associations between psychiatric traits and educational achievement.

I suggest some minor adjustments for the authors to consider, mainly addressing the conclusions and implications of the findings. I also recommend some clarifications in the methods and the results sections; these suggestions might require some very modest additional analyses and rethinking/rewording some of the conclusions.

• The major issue I have is that you discuss family SES as a purely environmental factor throughout the manuscript. However, we know that this is not the case and that there is substantial heritability for SES. It follows from what SES composite is made out of, in your case parental education and occupation, both of which are highly heritable (as you rightly note in the manuscript yourself). This needs to be addressed and discussed throughout the manuscript.

• The major conclusion in the manuscript, even if you acknowledge that this is speculation, is that the attenuation of the association between EA-PGS and school grades after correcting for SES can be explained by genetic nurture. I agree, this can be one of the explanations, however, here you also control (partially) for transmitted genes, that is educationally related genetic variants present in both generations (so without genotyped trios here you cannot distinguish between direct and indirect genetic effects). In addition, this attenuation can also be explained by gene and environment correlation (not only passive which is addressed by genetic nurture hypothesis) but also active and evocative rGE. In addition, in your design, you need to consider assortative mating. I suggest directly addressing this in the manuscript.

• I also think that you should address that you are dealing with diagnosed disorders only. It is a great strength of the paper, and you are using a fantastic resource, but we know that these disorders are quantitative traits and your study does not allow to take that into account, so there are possibly individuals with high ADHD symptoms are included in the control group; similarly, you cannot take into account the symptom severity. In terms of symptom level data, I see you have referenced Selzam et al., 2019 paper that, among other things, related EA-PGS to ADHD symptoms and vice versa, and also controlled for SES.

• In the introduction, you rightly state that individual differences are explained by genetic and environmental factors and the interplay between them, however, I suggest rephrasing it, because "much of the variance can be explained" is incorrect, all of the individual differences can be explained by the combination of these factors.

• You report low rG between schizophrenia and E1, can you specify how this was calculated

• You state that your prediction in the control sample is lower than the other studies and offer a possible solution of the inclusion or exclusion of 23andMe data in the summary statistics, please note that other studies have not used 23ndme statistics either (for example TEDS publications). You also discuss genetic heterogeneity; I think that the difference can be explained by both genetic and environmental heterogeneity. What is the rG between EA in your sample and GWAS sample?

• I think that the conclusion that the impact of low EA-PGS is comparable to the impact of ADHD is too strong, your data does not support this strong conclusion. I suggest rephrasing it, especially as we're not aware of the associated mechanisms. Note that people with ADHD in your sample also have lower EA-PGS compared to control conditions. In addition, symptom severity of ADHD varies greatly.

• I also do not agree with the statement that having wealthy parents does not boost the performance as much for children with ADHD as compared to children without for the reasons mentioned above.

• I think that you have fantastic data, and you have data available about how many of your participants have multiple diagnoses. I suggest adding a stratified group with multiple diagnoses to the analyses, that is adding groups with 2, 3 or 4 and more psychiatric diagnoses and checking their polygenic score prediction to EA.

• I suggest making it clearer what covariates were used in every analysis (you say first that you added psychiatric diagnoses as covariate among the usual covariates, but later only that covariates were included 'as before', I assume you did not include diagnoses in later analyses, but this is not clear). In addition, it is not clear to me why you control for psychiatric diagnoses in the first set of analyses, I would have wanted to see full results without this covariate.

Overall, this is a beautiful study and it was a pleasure to read/review it.

Terapi Oksigen Hiperbarik (HBOT) semakin sering digunakan di berbagai bidang medis, perawatan, dan praktik kesehatan. Menjadi intervensi penting dengan mekanisme tindakan yang tidak dipahami dengan baik. Terapi Oksigen Hiperbarik adalah salah satu metode p engobatan yang dilakukan dengan menyediakan 100% oksigen murni yang dihirup oleh pasien di ruangan khusus dengan udara bertekanan tinggi. Tekanan udara yang meningkat pada ruang Hiperbarik menyebabkan paru pasien menyerap lebih banyak oksigen daripada bias anya, yang dapat membantu menyembuhkan berbagai penyakit. Diharapkan adanya kajian ilmiah, ulasan dan diskusi tentang terapi heperbaric dan pencarian literatur tentang penggunaannya dapat bermanfaat bagi tim medis baik perawat, dokter, pekerja kesehatan la innya dan masyarakat, sehingga mereka dapat meningkatkan pengetahuan, berdasarkan fisiologi, patologi, fisika, farmakologi, manfaat, indikasi dan perawatan tentang terapi hiperbarik sehingga dapat diterapkan dalam berbagai bidang yang diperlukan.

Ruang Hiperbarik Ruang hiperbarik dapat terdiri dari dua jenis: tunggal atau ganda. Sementara tekanan terjadi di tempat duduk tunggal melalui oksigen dan peningkatan tekanan bersifat sistemik, ruang multiplace diberi tekanan dengan udara dan oksigen disuplai kepada pasien melalui masker, helm, atau tabung endotrakeal, tergantung kasusnya. (Gill & Bell, 2004)

Indikasi Terapi Oksigen Hiperbarik Penting untuk mengetahui indikasi terapi hiperbarik. Indikasi meliputi penyakit dekompresi, emboli udara, keracunan karbon monoksida, cedera, anemia kehilangan darah akut, abses intrakranial, luka bakar termal, fasciitis nekrotikans, gas gangren, dan kehilangan pendengaran akut.

Indikasi Terapi Oksigen Hiperbarik menurut (Mathieu et al., 2017) Keracunan karbon monoksida (CO) Keracunan karbon monoksida dapat terjadi ketika seseorang menghirup gas karbon monoksida yang menyebabkan penyerapan oksigen oleh darah terganggu. Terapi oksigen hiperbarik dapat mengatasi kondisi ini dengan cara menghilangkan karbon monoksida dari dalam darah dengan pemberian oksigen murni bertekanan tinggi. • Merekomendasikan HBOT dalam pengobatan keracunan CO (rekomendasi Tipe 1, bukti Level B). • Merekomendasikan 100% oksigen segera diterapkan pada orang yang keracunan CO sebagai pengobatan pertolongan pertama (rekomendasi Tipe 1, bukti Level C). • Merekomendasikan HBOT untuk setiap orang yang keracunan CO yang disertai dengan adanya perubahan kesadaran, tanda- tanda klinis gangguan neurologis, jantung, pernapasan atau psikologis dan tingkat karbokshaemoglobin pada saat masuk rumah sakit • Merekomendasikan HBOT pada wanita hamil yang keracunan CO apa pun gejala klinis mereka dan tingkat karboksihemoglobin saat masuk rumah sakit (rekomendasi Tipe 1, bukti Level B). • Sebaiknya merawat pasien dengan keracunan CO minor baik dengan oksigen normobarik 12 jam atau HBOT (rekomendasi Tipe 3, bukti Level B). • Tdak merekomendasikan perawatan dengan pasien tanpa gejala HBOT yang terlihat lebih dari 24 jam setelah akhir paparan CO (rekomendasi Tipe 1, bukti Level C).

Penyakit Dekompresi (DCI) Penyakit dekompresi merupakan kondisi yang terjadi pada saat aliran darah di dalam tubuh terhambat, dikarenakan perubahan tekanan udara. Perubahan tekanan ini dapat terjadi akibat penerbangan, menyelam, atau hal lain yang mengakibatkan terjadinya perubahan tekanan udara secara drastis. Perubahan tekanan udara di luar tubuh yang tiba-tiba dapat menyebabkan timbulnya gelembung udara di dalam pembuluh darah atau emboli. Terapi oksigen hiperbarik dapat mengecilkan gelembung di dalam pembuluh darah akibat perubahan tekanan. 186 p-ISSN: 2083-0840: E-ISSN: 2622-5905 Volume 11, Nomor 2, Desember 2019 • Merekomendasikan HBOT dalam pengobatan DCI (Rekomendasi Tipe 1, bukti Level C). • Merekomendasikan 100% normobarik oksigen pertolongan pertama (rekomendasi Tipe 1, bukti Level C). • Merekomendasikan resusitasi cairan intravena dengan larutan kristaloid yang tidak mengandung glukosa (Rekomendasi Tipe 1, bukti Level C). • Merekomendasikan tabel terapi kompresi HBOT⁄ (Tabel Perawatan Angkatan Laut AS 6 atau helium / oksigen (Heliox) Comex Cx30 atau yang setara) untuk pengobatan awal DCI (Rekomendasi Tipe 1, bukti Level C). • Merekomendasikan tabel pengobatan HBOT yang sesuai untuk manifestasi residual DCI (rekomendasi Tipe 1, bukti Level C). • Merekomendasikan penggunaan heparin dengan berat molekul rendah untuk profilaksis trombosis vena dalam untuk kasus DCI yang lumpuh atau lumpuh (rekomendasi Tipe 1, bukti Level C). • Menyarankan penggunaan tabel lignocaine (lidocaine) dan rekompresi Heliox untuk DCI neurologis yang serius (rekomendasi Tipe 2, bukti Level C). • Menyarankan tenoxicam oral (atau NSAID serupa) untuk kasus DCI yang dipilih dengan tepat (Rekomendasi Tipe 2, bukti Level B).

FISIOLOGIS, DAN FARMAKOLOGIS GAS HIPERBARIK 194 Di permukaan oksigen plasma adalah 3 ml/l. Jaringan saat istirahat membutuhkan sekitar 60 ml oksigen per liter aliran darah (dengan asumsi perfusi normal) untuk mempertahankan metabolisme seluler yang normal, meskipun persyaratan bervariasi di antara jaringan. Pada tekanan 3 atmosfer (304 kPa) oksigen terlarut mendekati 60 ml/l plasma, yang hampir mencukupi untuk memasok kebutuhan oksigen total istirahat dari banyak jaringan tanpa kontribusi dari oksigen yang terikat dengan hemoglobin. Ini memiliki keuntungan dalam situasi seperti keracunan karbon monoksida atau anemia berat di mana sulit melakukan crossmatching atau keyakinan agama mencegah transfusi darah (Leach et al., 1998).

Fungsi HBOT Secara umum dapat dibagi menjadi dua jenis efek, fisiologis dan farmakologis, kadang terjadi tumpang tindih. Oksigen dapat dianggap sebagai unsur alami yang penting untuk kehidupan, dan sebagai obat yang digunakan untuk mengubah patologi penyakit. HBOT menggunakan oksigen sebagai obat dan oleh karena itu, memiliki protokol dosis yang tepat, indeks terapi, dan efek samping yang perlu dipahami agar dapat digunakan dengan aman dan efektif

Efek Fisiologis Dalam kondisi normal di permukaan laut, udara terdiri dari sekitar 21% oksigen yang menghasilkan tekanan oksigen alveolar (PAO2) sekitar 100 mmHg. Dalam kondisi ini, hemoglobin plasma hampir seluruhnya jenuh, dan oksigen plasma terlarut minimal.Oleh karena itu, dengan asumsi konsentrasi hemoglobin 12 g/dL, kandungan oksigen darah gabungan dalam seluruh darah adalah sekitar 16,2 mL O2/dL. Dalam kondisi hiperbarik yang menghirup oksigen 100% pada 3 atmospheric absolut (ATA), nilai PAO2 meningkat menjadi sekitar 2280 mmHg; dan menurut hukum Henry, kandungan oksigen gabungan dalam darah lengkap meningkat menjadi 23,0 mL O2 / dL. Peningkatan 42% dari baseline ini hampir seluruhnya berasal dari peningkatan oksigen yang terlarut dalam plasma. Peningkatan pasokan oksigen dan tekanan oksigen arteri membentuk dasar HBOT (Lambertsen, 1988; Oh et al., 2008; Rothfuss & Speit, 2002)

Terapi Oksigen hiperbarik fungsi HBOT sangat kompleks. Akan mengurangi ukuran gelembung gas dalam cairan (darah). Sehingga meningkatkan kapasitas pembawa oksigen darah melalui peningkatan konsentrasi oksigen plasma menjadi sekitar 7%. Adanya bakteriostatik dan bakteriosidal pada tekanan dan oksigenasi yang lebih tinggi. Oksigen hiperbarik akan meningkatkan neovaskularisasi arteri dan mengurangi edema jaringan, yang akan menghambat berbagai eksotoksin seperti racun alfa dan beta yang terkait dengan infeksi nekrotikans. Pengobatan hiperbarik akan meningkatkan difusi oksigen lebih lanjut dalam jaringan dengan jarak sekitar empat kali jarak perfusi normal. Sehingga akan menyebabkan terjadi difusi oksigen dari lingkungan yang kaya oksigen ke lingkungan oksigen yang buruk seperti dengan luka iskemik dan anggota badan. Hukum Boyle adalah dasar untuk efektivitas dalam penyakit dekompresi dan emboli udara. Permukaan terlalu cepat dari penyelaman bawah laut yang dalam akan menghasilkan presipitasi gelembung nitrogen dalam darah. Ini akan menghasilkan persendian yang sangat menyakitkan, tikungan, dan bahkan kematian. (Fife et al., 2016; Jones & Wyatt, 2019). Tujuan pengobatan adalah untuk mencegah pembentukan gelembung nitrogen sehingga berkurang ukurannya dan kembali larut. Hal yang sama berlaku untuk perawatan emboli udara. Peningkatan tekanan yang diberikan oleh terapi medis hiperbarik akan mengurangi gelembung gas tersebut.

BAHAYA DAN KONTRAINDIKASI Terapi oksigen hiperbarik kontraindikasi mutlak untuk perawatan hiperbarik adalah pneumotoraks yang tidak diobati. Kontraindikasi relatif lainnya adalah jika pasien menggunakan agen kemoterapi tertentu seperti Adriamycin dan Cisplatinum atau Antabuse. Masalah lain yang menjadi perhatian adalah pasien berventilasi, pasien dengan hipertensi yang tidak terkontrol, dan penderita diabetes. Masalah dengan pasien berventilasi adalah varian dalam volume udara dan tekanan dan masalah barotrauma. Gula darah serum sering jatuh saat menyelam. Oleh karena itu, disarankan untuk memastikan glukosa serum pada pasien dengan diabetes berada pada sisi yang tinggi sebelum menyelam (Cho et al., 2018). Efek samping negatif dari menerima O2 bertekanan, akan terjadi cedera stres oksidatif, kerusakan DNA, metabolisme seluler, pengaktifan koagulasi, disfungsi endotel, neurotoksisitas akut, dan toksisitas paru, gangguan metabolisme sel, mengaktifkan koagulasi, disfungsi endotel

Definisi Terapi Oksigen Hiperbarik (HBOT)

Terapi Oksigen hiperbarik adalah suatu terapi dengan pemberian oksigen konsentrasi 100% dan tekanan lebih dari 1 atmosfer absolut (ATA), yang di lakukan di ruang udara bertekanan tinggi/ruang hiperbarik dengan tekanan lebih dari 1 atmosfer. Tujuan terapi oksigen hiperbarik untuk perawatan dan pengobatan beberapa penyakit seperti emboli intravaskuler, dekompresi, infeksi aneorob, dan keracunan CO.

Ruang Hiperbarik

Ruang hiperbarik dapat terdiri dari dua jenis : tunggal atau ganda, perbedaan tunggal dan ganda

Indikasi Terapi Oksigen Hiperbarik

Pada umumnya pusat hiperbarik merawat pasien dengan kondisi non alergi seperti penyembuhan luka yang buruk , cedera radiasi yang tertunda, osteomielitis kronis dan flap. Menurut UHMS indikasi terapi oksigen hiperbarik adalah: emboli atau keracunan gas karbon monoksida, keracunan sianida, inhalasi asap myostitis, dan mionekrosis klostridial. Cedera;sindrom kompartemen, dan iskemia perifer akut lainnya. Penyakit dekompresi; Peningkatan penyembuhan pada luka; Anemia kehilangan darah yang banyak; Abses intrakranial; Infeksi jaringan lunak nekrotikans; Osteomielitis refraktori; Flap dan cangkok kulit (terganggu); Cedera radiasi (jaringan lunak dan nekrosis tulang); Luka bakar termal.

Dasar Fisiologis Terapi Oksigen Hiperbarik

Efek HBOTdidasarkan pada regulasi gas, dan efek fisiologis dan biokimia dari hiperoksia.Hukum Boyle menyatakan bahwa pada suhu konstan, tekanan dan volume gas berbanding terbalik. Ini adalah dasar untuk semua terapi hiperbarik. Hukum Boyle menjelaskan tentang hubungan tekanan gas dan volume gas. Tekanan gas berbanding terbalik dengan volume gas. Bila tekanan semakin besar maka volume akan semakin kecil. Prinsip ini digunakan pada kasus-kasus penyakit dekompresi dan emboli gas. Pada penyakit dekompresi, terjadi gelembung-gelembung nitrogen (nitrogen bubbles) sehingga terjadi penyumbatan pembuluh darah akibat gelembung ini.

Mekanism HBOT

Prinsip dari terapi oksigen hiperbarik adalah membantu tubuh untuk memperbaiki jaringan yang rusak dengan meningkatkan aliran oksigen ke jaringan tubuh. Terapi oksigen hiperbarik akan menyebabkan darah menyerap oksigen lebih banyak akibat peningkatan tekanan oksigen di dalam paru-paru yang dimanipulasi oleh ruangan hiperbarik. Dengan konsentrasi oksigen yang lebih tinggi dari normal, tubuh akan terpicu untuk memperbaiki jaringan yang rusak lebih cepat dari biasanya. Terapi oksigen hiperbarik (HBOT) memberikan oksigen di bawah tekanan untuk meningkatkan kadar oksigen jaringan.Oksigen diberikan 2-3 kali lebih tinggi dari tekanan atmosfer, dan didistribusikan di sekitar area yang terinfeksi;sehingga memungkinkan terjadinya proses penyembuhan alami tubuh dan memperbaiki fungsi jaringan.

Fisiologis Dan Farmakologis Gas Hiperbarik

Dalam kondisi normal di permukaan laut, udara terdiri dari sekitar 21% oksigen yang menghasilkan tekanan oksigen alveolar (PAO2) sekitar 100 mmHg.Dalam kondisi ini, hemoglobin plasma hampir seluruhnya jenuh, dan oksigen plasma terlarut minimal.Oleh karena itu, dengan asumsi konsentrasi hemoglobin 12g/dL, kandungan oksigen darah gabungan dalam seluruh darah adalah sekitar 16,2 mL O2/dL.Dalam kondisi hiperbarik yang menghirup oksigen 100% pada 3 atmospheric absolut (ATA), nilai PAO2 meningkat menjadi sekitar 2280 mmHg;dan menurut hukum Henry, kandungan oksigen gabungan dalam darah lengkap meningkat menjadi 23,0 mL O2 / dL.Peningkatan 42% dari baseline ini hampir seluruhnya berasal dari peningkatan oksigen yang terlarut dalam plasma.Peningkatan pasokan oksigen dan tekanan oksigen arteri membentuk dasar HBOT. HBOT telah terbukti meningkatkan produksi faktor pertumbuhan endotel vaskular (VEGF), varian faktor pertumbuhan turunan trombosit (PDGF), dan faktor pertumbuhan fibroblast (FGF) sebagian melalui modulasi nitrat oksida.VEGF dan PDGF bertanggung jawab untuk merangsang pertumbuhan kapiler dan granulasi luka, dan melakukannya dengan mengubah jalur pensinyalan yang mengarah pada proliferasi dan migrasi sel.FGF memainkan peran yang sama dalam angiogenesis, tetapi juga menginduksi perkembangan saraf, organisasi keratinosit, dan proliferasi fibroblast di lokasi luka yang mengarah ke granulasi dan epitelisasi.Oksigen juga memiliki efek antibakteri di lokasi luka.Ketika neutrofil dan makrofag memasuki lingkungan ini untuk membunuh bakteri dan menghilangkan bahan nekrotik, mereka mengkonsumsi oksigen dalam jumlah besar.Oksigen kemudian digunakan oleh sel-sel ini untuk membuat hidrogen peroksida, anion superoksida, asam hidroklorat, dan radikal hidroksil.

Kontraindikasi Hiperbarik

Efek samping negatif dari menerima O2 bertekanan, akan terjadi cedera stres oksidatif, kerusakan DNA, metabolisme seluler, pengaktifan koagulasi, disfungsi endotel, neurotoksisitas akut,dan toksisitas paru, gangguan metabolisme sel, mengaktifkan koagulasi, disfungsi endotel, neurotoksisitas akut dan toksisitas paru. Risiko potensial dan rasio risiko-manfaat dari oksigen hiperbarik sering kurang ditekankan dalam uji coba terapeutik.Efek sampingnya sering ringan dan reversibel tetapi bisa parah dan mengancam nyawa.Secara umum, jika tekanan tidak melebihi 300 kPa dan lamanya pengobatan kurang dari 120 menit, terapi oksigen hiperbarik aman.

A.Definisi Terapi oksigen hiperbarik (HBOT) Terapi Oksigen Hiperbarik (HBOT) adalah suatu terapi dengan pemberian oksigen konsentrasi 100% dan tekanan lebih dari 1 atmosfer absolut (ATA), yang dilakukan di ruang udara bertekanan tinggi/ruang hiperbarik dengan tekanan lebih dari 1 atmosfer (Atm). Regimen HBO (hiperbarik oksigen) menggunakan tekanan 1,5 hingga 2,5 Atm untuk durasi 30 hingga 90 menit, yang dapat diulang beberapa kali. Waktu antara dan jumlah total sesi berulang sangat bervariasi. Tujuan terapi oksigen hiperbarik untuk perawatan dan pengobatan beberapa penyakit seperti emboli intravaskular, penyakit dekompresi, infeksi anaerob, keracunan CO (Shahriari, Khooshideh, & Heidari, 2014).

B.Ruang Hiperbarik Ruang hiperbarik dapat terdiri dari dua jenis: tunggal atau ganda. Sementara tekanan terjadi di tempat duduk tunggal melalui oksigen dan peningkatan tekanan bersifat sistemik, ruang multiplace diberi tekanan dengan udara dan oksigen disuplai kepada pasien melalui masker, helm, atau tabung endotrakeal, tergantung kasusnya. (Gill & Bell, 2004)

C. Indikasi Terapi Oksigen Hiperbarik Penting untuk mengetahui indikasi untuk terapi hiperbarik. Indikasi meliputi penyakit dekompresi, emboli udara, keracunan karbon monoksida, cedera, anemia kehilangan darah akut, abses intrakranial, luka bakar termal, fasciitis nekrotikans, gas gangren, dan kehilangan pendengaran akut. Kondisi tersebut perlu mendapat perawatan terapi oksigen hiperbarik. Pada umunya pusat hiperbarik merawat pasien dengan dengan kondisi non- alergi seperti penyembuhan luka yang buruk, cedera radiasi yang tertunda, osteomielitis kronis dan flap. Sangat penting bagi tim medis yang merawat untuk mengenali indikasi hiperbarik yang muncul. (Chen et al., 2019)

D. Indikasi Terapi Oksigen Hiperbarik menurut (Mathieu et al., 2017) Keracunan karbon monoksida (CO) Merekomendasikan HBOT dalam pengobatan keracunan CO (rekomendasi Tipe 1, bukti Level B). • Merekomendasikan 100% oksigen segera diterapkan pada orang yang keracunan CO sebagai pengobatan pertolongan pertama (rekomendasi Tipe 1, bukti Level C). • Merekomendasikan HBOT untuk setiap orang yang keracunan CO yang disertai dengan adanya perubahan kesadaran, tanda- tanda klinis gangguan neurologis, jantung, pernapasan atau psikologis dan tingkat karbokshaemoglobin pada saat masuk rumah sakit (rekomendasi Tipe 1, bukti Level B). • Merekomendasikan HBOT pada wanita hamil yang keracunan CO apa pun gejala klinis mereka dan tingkat karboksihemoglobin saat masuk rumah sakit (rekomendasi Tipe 1, bukti Level B). • Sebaiknya merawat pasien dengan keracunan CO minor baik dengan oksigen normobarik 12 jam atau HBOT (rekomendasi Tipe 3, bukti Level B). • Tdak merekomendasikan perawatan dengan pasien tanpa gejala HBOT yang terlihat lebih dari 24 jam setelah akhir paparan CO (rekomendasi Tipe 1, bukti Level C)

Fraktur terbuka dengan crush injury

• Merekomendasikan HBOT dalam pengobatan fraktur terbuka dan/ atau dengan crush injury (Rekomendasi Tipe 1, bukti Level B). • Merekomendasikan aplikasi awal HBOT setelah fraktur terbuka parah karena dapat mengurangi komplikasi seperti nekrosis jaringan dan infeksi. Cedera Gustilo 3B dan 3C dianggap sebagai indikasi untuk HBOT dan cedera yang kurang parah harus dipertimbangkan untuk perawatan ketika terdapat faktor risiko terkait host atau cedera (rekomendasi Tipe 1, bukti Level B). • Menyarankan bahwa HBOT dapat memberikan manfaat pada crush injury dengan luka terbuka tanpa fraktur, di mana viabilitas jaringan berisiko atau di mana ada risiko infeksi yang signifikan (rekomendasi Tipe 2, bukti Level C). • Sebaiknya memberikan HBOT untuk crush injury/ cedera tertutup di mana viabilitas jaringan secara klinis dinilai berisiko (rekomendasi Tipe 3, bukti Level C). • Sebaiknya memberikan HBOT untuk crush injury/ cedera tertutup di mana ada potensi sindrom kompartemen, tetapi yang tidak memerlukan fasciotomi dan dimungkinkan untuk memantau kemajuan dan respons terhadap pengobatan baik secara klinis atau melalui tekanan kompartemen atau pemantauan oksigenasi (Rekomendasi Tipe 3, bukti Level C). • Merekomendasikan bahwa pusat HBOT yang merawat crush injury harus memiliki peralatan untuk pengukuran oksimetri transkutan (TCOM) di bawah tekanan karena ini memiliki nilai prediksi dalam beberapa situasi (Rekomendasi Tipe 1, bukti Level B

D. Radionekrosis/lesi yang disebabkan oleh radiasi • Merekomendasikan HBOT dalam pengobatan osteoradionekrosis mandibula (Rekomendasi Tipe 1, bukti Level B). • Merekomendasikan HBOT untuk pencegahan osteoradionekrosis mandibula setelah pencabutan gigi (rekomendasi Tipe 1, bukti Level B). • Merekomendasikan HBOT dalam pengobatan sistitis radiasi hemoragik (Rekomendasi Tipe 1, bukti Level B). • Merekomendasikan HBOT dalam pengobatan proktitis radiasi (rekomendasi Tipe 1, bukti Level A). • Menyarankan HBOT dalam pengobatan osteoradionekrosis tulang selain mandibula (Rekomendasi Tipe 2, bukti Level C). • Menyarankan HBOT untuk mencegah kehilangan implan osseointegrasi pada tulang yang diradiasi (Rekomendasi Tipe 2, bukti Level C). • Menyarankan HBOT dalam pengobatan radionekrosis jaringan lunak (selain sistitis dan proktitis), khususnya di daerah kepala dan leher (rekomendasi Tipe 2, bukti Level C). • Sebaiknya menggunakan HBOT untuk mengobati atau mencegah lesi yang diinduksi radio dari laring (Rekomendasi Tipe 3, bukti Level C). • Sebaiknya menggunakan HBOT dalam pengobatan lesi yang diinduksi radio dari sistem saraf pusat (Rekomendasi Tipe 3, bukti Level C).

E. Penyakit Dekompresi (DCI)

Penyakit dekompresi merupakan kondisi yang terjadi pada saat aliran darah di dalam tubuh terhambat, dikarenakan perubahan tekanan udara. Perubahan tekanan ini dapat terjadi akibat penerbangan, menyelam, atau hal lain yang mengakibatkan terjadinya perubahan tekanan udara secara drastis. Perubahan tekanan udara di luar tubuh yang tiba-tiba dapat menyebabkan timbulnya gelembung udara di dalam pembuluh darah atau emboli.

F. DASAR FISIOLOGIS TERAPI OKSIGEN HIPERBARIK

Efek HBOT didasarkan pada regulasi gas, dan efek fisiologis dan biokimia dari hiperoksia. Hukum Boyle menyatakan bahwa pada suhu konstan, tekanan dan volume gas berbanding terbalik. Ini adalah dasar untuk semua terapi hiperbarik. Hukum Boyle menjelaskan tentang hubungan tekanan gas dan volume gas. Tekanan gas berbanding terbalik dengan volume gas. Bila tekanan semakin besar maka volume akan semakin kecil. Prinsip ini digunakan pada kasus-kasus penyakit dekompresi dan emboli gas. Pada penyakit dekompresi, terjadi gelembung-gelembung nitrogen (nitrogen bubbles) sehingga terjadi penyumbatan pembuluh darah akibat gelembung ini.

Oksigen yang ditangkap dengan bernapas juga bervariasi selama perawatan di ruang hiperbarik, karena, selama penurunan ke kedalaman, tekanan oksigen intraalveolar meningkat; Ini terjadi sebagai respons fisiologis yang merespons hukum Boyle dan hukum Dalton. Hukum Boyle menyatakan bahwa, pada suhu konstan, tekanan gas berbanding terbalik dengan volumenya, sementara hukum Dalton menyatakan bahwa dalam campuran gas setiap elemen memberikan tekanan yang sebanding dengan fraksinya dari total volume; hukum-hukum ini menjelaskan efek dari tekanan parsial oksigen dan intraalveolar tersedia. (Balestra et al., 2016).

G. MEKANISME HBOT

Reactive Oxygen Species (ROS) Prinsip dari terapi oksigen hiperbarik adalah membantu tubuh untuk memperbaiki jaringan yang rusak dengan meningkatkan aliran oksigen ke jaringan tubuh. Terapi oksigen hiperbarik akan menyebabkan darah menyerap oksigen lebih banyak akibat peningkatan tekanan oksigen di dalam paru￾paru yang dimanipulasi oleh ruangan hiperbarik. Dengan konsentrasi oksigen yang lebih tinggi dari normal, tubuh akan terpicu untuk memperbaiki jaringan yang rusak lebih cepat dari biasanya. Terapi oksigen hiperbarik (HBOT) memberikan oksigen di bawah tekanan untuk meningkatkan kadar oksigen jaringan. Oksigen diberikan 2-3 kali lebih tinggi dari tekanan atmosfer, dan didistribusikan di sekitar area yang terinfeksi; sehingga memungkinkan terjadinya proses penyembuhan alami tubuh dan memperbaiki fungsi jaringan. HBOT juga merangsang kaskade transduksi sinyal dengan meningkatkan oksigen reaktif dan spesies nitrogen, maka jaringan akan melepaskan prostaglandin, oksida nitrat, dan sitokin yang menunjukkan respons patofisiologis terhadap luka, pembedahan, dan infeksi. HBOT diketahui sebagai terapi untuk mengobati penyakit dekompresi, gangren, atau keracunan karbon monoksida. (Al-Waili & Butler, 2006; Gandhi et al., 2018).

ROS dipandang berbahaya karena potensinya menyebabkan kerusakan pada lipid, protein, dan DNA (Alfadda & Sallam, 2012), tetapi secara ilmiah ROS sangat penting dalam pensinyalan dan pengaturan sel, contoh, dalam sel endotel, ROS adalah penyebab utama dari banyak patologi vaskular, seperti disfungsi endotel diabetes dan hiperpietik, di sisi lain, angiogenesis dan vasorelaxasi yang bergantung pada endotelium berada di bawah kendali redoks. Karena sifatnya yang aktif dan berumur pendek, ROS harus dihasilkan di kompartemen subseluler yang tepat yang dekat dengan molekul yang dimodifikasi dalam proses pensinyalan dan pengaturan sel yang bergantung pada ROS. (Craige, Kant, & Keaney Jr, 2015).

H. FISIOLOGIS, DAN FARMAKOLOGIS GAS HIPERBARIK

Di permukaan laut konsentrasi oksigen plasma adalah 3 ml/l. Jaringan saat istirahat membutuhkan sekitar 60 ml oksigen per liter aliran darah (dengan asumsi perfusi normal) untuk mempertahankan metabolisme seluler yang normal, meskipun persyaratan bervariasi di antara jaringan. Pada tekanan 3 atmosfer (304 kPa) oksigen terlarut mendekati 60 ml/l plasma, yang hampir mencukupi untuk memasok kebutuhan oksigen total istirahat dari banyak jaringan tanpa kontribusi dari oksigen yang terikat dengan hemoglobin. Ini memiliki keuntungan dalam situasi seperti keracunan karbon monoksida atau anemia berat di mana sulit melakukan crossmatching atau keyakinan agama mencegah transfusi darah (Leach et al., 1998).

Fungsi HBOT Secara umum dapat dibagi menjadi dua jenis efek, fisiologis dan farmakologis, kadang terjadi tumpang tindih. Oksigen dapat dianggap sebagai unsur alami yang penting untuk kehidupan, dan sebagai obat yang digunakan untuk mengubah patologi penyakit. HBOT menggunakan oksigen sebagai obat dan oleh karena itu, memiliki protokol dosis yang tepat, indeks terapi, dan efek samping yang perlu dipahami agar dapat digunakan dengan aman dan efektif. (Kahle & Cooper, 2019; Maslova & Klimova, 2012)

I.FUNGSI/MANFAAT (HBOT)

Terapi Oksigen hiperbarik fungsi HBOT sangat kompleks. Akan mengurangi ukuran gelembung gas dalam cairan (darah). Sehingga meningkatkan kapasitas pembawa oksigen darah melalui peningkatan konsentrasi oksigen plasma menjadi sekitar 7%. Adanya bakteriostatik dan bakteriosidal pada tekanan dan oksigenasi yang lebih tinggi. Oksigen hiperbarik akan meningkatkan neovaskularisasi arteri dan mengurangi edema jaringan, yang akan menghambat berbagai eksotoksin seperti racun alfa dan beta yang terkait dengan infeksi nekrotikans. Pengobatan hiperbarik akan meningkatkan difusi oksigen lebih lanjut dalam jaringan dengan jarak sekitar empat kali jarak perfusi normal. Sehingga akan menyebabkan terjadi difusi oksigen dari lingkungan yang kaya oksigen ke lingkungan oksigen yang buruk seperti dengan luka iskemik dan anggota badan. Hukum Boyle adalah dasar untuk efektivitas dalam penyakit dekompresi dan emboli udara. Permukaan terlalu cepat dari penyelaman bawah laut yang dalam akan menghasilkan presipitasi gelembung nitrogen dalam darah. Ini akan menghasilkan persendian yang sangat menyakitkan, tikungan, dan bahkan kematian. (Fife et al., 2016; Jones & Wyatt, 2019)

J.BAHAYA DAN KONTRAINDIKASI Terapi oksigen hiperbarik

kontraindikasi mutlak untuk perawatan hiperbarik adalah pneumotoraks yang tidak diobati. Kontraindikasi relatif lainnya adalah jika pasien menggunakan agen kemoterapi tertentu seperti Adriamycin dan Cisplatinum atau Antabuse. Masalah lain yang menjadi perhatian adalah pasien berventilasi, pasien dengan hipertensi yang tidak terkontrol, dan penderita diabetes. Masalah dengan pasien berventilasi adalah varian dalam volume udara dan tekanan dan masalah barotrauma. Gula darah serum sering jatuh saat menyelam. Oleh karena itu, disarankan untuk memastikan glukosa serum pada pasien dengan diabetes berada pada sisi yang tinggi sebelum menyelam (Cho et al., 2018). Efek samping negatif dari menerima O2 bertekanan, akan terjadi cedera stres oksidatif, kerusakan DNA, metabolisme seluler, pengaktifan koagulasi, disfungsi endotel, neurotoksisitas akut, dan toksisitas paru, gangguan metabolisme sel, mengaktifkan koagulasi, disfungsi endotel, neurotoksisitas akut dan toksisitas paru. (Chen et al., 2019).

Alasan mengapa HBOT tidak boleh diberikan pada interval yang jauh lebih sering dan dalam sesi yang lebih lama adalah potensi risiko keracunan oksigen (Körpınar & Uzun, 2019) Risiko oksigen hiperbarik; Bahaya kebakaran; Komplikasi fatal yang paling umum. Fitur umum; Claustrophobi, Miopia yang dapat dibalik, Kelelahan, Sakit kepala, Muntah. Barotrauma; Kerusakan telinga, Kerusakan sinus, Telinga tengah yang pecah, Kerusakan paru-paru. Toksisitas oksigen; Otak, Kejang, Psikologi. Paru-paru, Edema paru, perdarahan, Toksisitas paru, Gagal pernafasan (mungkin tidak dapat dikembalikan ketika terjadi fibrosis topulmoner). Penyakit dekompresi; Penyakit dekompresi, Pneumotoraks, Emboli gas. (Chen et al., 2019; Leach et al., 1998; Thom, 2009; Stephen R Thom, 2011).

Reviewer #1:

General Assessment:

I found the studies to be well motivated and thoughtfully designed to disentangle competing interpretations in the extant literature on visual perception in ASD. The first two experiments provided compelling evidence that prior choices affect perceptual decision making in ASD, but the outcome of the response invariant condition suggests that the authors' interpretation goes beyond the data.

Substantive Concerns:

"In summary, we found here that individuals with ASD demonstrated an increased influence of recent prior choices on perceptual decisions (vs. controls),..." is the major finding in the paper, quoted here in the concluding paragraph. It seems, however that the data support a narrower (and potentially less interesting) conclusion that individuals with ASD demonstrated an increased influence of recent button presses/motor responses, as the finding which forms the basis of the summary went away when different keys were used to report prior vs. test responses (i.e., in the response invariant condition). I understand that the authors present these data as challenges to theories of attenuated priors in ASD, but they seem to sidestep the issue that these data make their general conclusion more complicated.

For completeness, it would be helpful to present some information on the stimulus values for the test stimuli, as these were set individually using a staircase. Where did these staircases converge? Were there group differences?

Reviewer #1:

In this manuscript, the authors show they can accomplish imaging in complex specimens using 3- and 4-photon excitation, deeper in the specimen than comparable optics can accomplish with 2-photon excitation laser scanning microscopy. This is a clear advantage for imaging optically hostile specimens such as cultured organoids or spheroids, or in challenging in vivo settings. I am excited about these findings, but I am not at all supportive of the current version of the manuscript being used to present these lovely findings.

There are two strong reasons for my opinion:

i. The manuscript presents the findings in a manner that will only be understandable by the readers who are familiar with the topic, and who are likely to already have heard of the capabilities of 3- and 4-photon excitation to image deeper into specimens.

ii. The results are not presented in a way that the large body of potential readers can understand. They will be unable to grasp the way that the experiments were performed, or understand what the figures are showing, or critically evaluate the results that are presented.

Thus, there is a disconnect between the quality of the work and the quality of the presentation. There are many areas of quantitative imaging and intravital imaging that are well known to those that know about them (or use them), and that are a complete mystery to the vast majority of those that don't know about the tools or use them. The authors must take this as an opportunity to reach the many workers that could benefit from this powerful approach, rather than writing for the group that already knows (and even uses) the approaches presented.

1) Provide needed background and present important things first. The authors should give the reader a clear view into the issues in imaging biological tissues with the longer wavelengths that are used for confocal laser scanning microscopy (CLSM) and for two-photon laser scanning microscopy (TPLSM). There are several factoids presented, all seemingly true, but not presented in an accessible manner. Rather than starting with a mention of the expected temperature rise due to the dramatically higher absorbance by water of 1300nm and 1700nm light, the paper first presents the major absorbance of the light (~2/3 loss) and that this isn't a problem because there is sufficient laser power. For most readers, the need for a larger laser won't be their first question; instead it will be the viability after/during the imaging session. The expected temperature rise, and an indirect mention of burn marks (!), comes at the end of the section.

2) Explain and perform cell viability tests. Calcium imaging for assessing tissue viability is not the technique of choice for most readers, and is presented in a way that assumes general knowledge that simply does not exist. Membrane patency assays using membrane-impermeant DNA dyes, or other live-dead assays are far more common, but not presented in this study. I am not insistent that the authors use any particular assay, but I am insistent that the authors present the need for viability assay(s), teach the reader the principles of the assay(s) used, and present the results in an understandable manner.

3) Present the finding and the figures in an accessible manner. The figures are simply not digestible by the readers who do not perform this sort of work, and the legends do not help sufficiently. For those of us who do perform work of this sort, the figures are not as convincing as they should be, or presented in a way that they can be critically evaluated.

Consider the legend for Figure 1: "Microscopy with simultaneous 2-, 3- and 4 photon processes excited in fluorescent skin tumor xenografts in vivo. Representative images were selected from median-filtered (1 pixel) z-stacks, which were taken in the center of fluorescent tumors through a dermis imaging window. a) Excitation at 1300nm (OPA) in day-10 tumor at 145 μm imaging depth with a calculated 3.3 nJ pulse energy at the sample surface, 24 μs pixel integration time and 0.36 μm pixel size. For calculation of pulse energy at the sample surface see Figure S3. b) Excitation at 1650 nm (OPA) in day-13 tumor at 30 μm depth with a calculated 6.3 nJ pulse energy at the sample surface, 12 μs pixel integration time and 0.46 μm pixel size. c) Excitation at 1650 nm (OPA) in day-14 tumor at 85 μm depth, with a calculated 5.4 nJ pulse energy at the sample surface, 12 μs pixel integration time and 0.46 μm pixel size. Cell nuclei containing a mixture of mCherry and Hoechst appear as green."

If I gave any of the figures and legends to the people in my lab, the half that don't do multiphoton imaging (but that have sat through many lab meetings) would just hand them back to me with quizzical expressions on their faces.

The figures are not as compelling as the results, and defer to the body of the paper to explain what was done or what was shown, and assumes that the average reader remembers the differences between OPO and OPA , for example (which they won't). The power plots showing nJ and mW in Figure 3 are inaccessible to most readers, and not well described.

I should mention that the figures, legends and text are not satisfying for the readers who are familiar with 2-, 3- and 4-photon imaging either. These are fantastic findings, and deserve figures that are as lovely as the results, and are compelling. Some of these issues are due to typos: "Consistently, multiparameter recordings were achieved inside the tumor at 350 μm depth using excitation at 1650 nm and 1300 nm, but 1180 nm (Figure 3b). "

However, the greater problem is that the text doesn't present the findings in a straightforward, convincing fashion and then interpret them. Instead, the conclusion often leads the evidence: "In line with an improved depth range, the signal-to-noise ratio (SNR) of 3PE TagRFP outperformed the SNR of 2PE TagRFP at depths beyond 150 μm (Figure 3c). Because H2B-eGFP expression in HT1080 tumors was very high, 3PE eGFP emission reached the highest SNR."

The legend and figure that it describes should be able to stand on their own, and convince a skeptical reader with the help of the text in the body of the manuscript.

In summary, these are lovely and important results that I am excited about. They are presented in a fashion that will make it difficult for most to appreciate because the body of the paper is not fashioned to teach the reader, and the figures themselves are challenging, and the legends inadequately present what is shown in the figures. Careful expansion and editing should resolve all of these issues and make the manuscript into the presentation these excellent findings deserve.

Reviewer #1:

The manuscript describes the characterization of in total 85 Cryptococcus spp. clinical isolates with regard to virulence phenotypes including a Galleria mellonella infection model for cryptococcosis. The authors determined the melanization kinetics of all strains, measured the whole-cell and extracellular laccase activity, the capsule thickness, and the concentration of the cell wall polysaccharide glucuronoxylomannan. In addition, during macrophage interaction the proportion of Cryptococcus-containing LC3-positive phagosomes for each strain was determined as well as the survival of G. mellonella after infection with selected Cryptococcus strains. Finally, regression analyses were performed to estimate the relationship between the risk of death in crytptococcosis patients and the phenotypes of the isolated Cryptococcus strains. A major finding was that the risk of death in patients with disseminated cryptococcosis increased with the level of extracellular laccase activity and the time for half-maximum melanization in the Cryptococcus isolates. This suggests that the melanization rate, more than the total amount of melanin, impacts the outcome of a Cryptococcus infection.

General assessment:

The study is based on carefully performed experiments. However, the scientific significance of this work is moderate. Melanin and the laccases that are involved in its synthesis are known virulence factors of Cryptococcus spp. for many years and similar studies have already been published elsewhere (e.g. Samarasinghe et al. 2018). The major new finding of the presented work is that the speed of melanization has an impact on the virulence of Cryptococcus spp. rather than total amount of melanin. The shortcoming of the manuscript is that the author's hypothesis is mainly based on regression analyses, but the final proof based on a genetically well-defined background is missing. Therefore, the study only provides little new insight into fundamental mechanisms of Cryptococcus virulence but includes associations with patients and therefore might be more suited for a journal specialized in pathogenic fungi.

Following points should be considered:

1) The authors show the association between faster Cryptococcus melanization and more effective evasion from host immunity. However, the author cannot totally exclude other factors that are associated with host evasion. It would be more appropriate to either create a mutant (e.g. overexpression of LAC1), which showed faster melanization in comparison to a wildtype strain or to perform multilocus sequence typing (including the LAC1 locus) to capture the genetic variation of the clinical isolates and to find come correlations with the speed of melanization. The interesting question is which genetic factors contribute to the difference in the melanization rate.

2) The authors should critically discuss the suitability of their Galleria mallonella infection model. It is a known fact that temperature has an influence on the melanization in Cryptococcus spp.. Laccasse activity is significantly inhibited at temperatures of 37°C and higher. The Galleria model can only be used at lower temperatures.

Reviewer #1:

The present paper addresses the very topical problem of understanding of dynamic switching in central pattern generators. The paper investigates switching between bursting and spiking modes in spinal cord neurons. This is modelled using a multichannel HCO that identifies narrow regions in parameters where the system is bistable. It is argued that neurotransmitters drive invertebrate CPGs to favourable bistable regimes that allow rapid switching from one oscillatory state to another (e.g. foraging to escape) to be enacted by fast electrical stimuli. The paper is generally well-written and does a good job at interpreting observations.

I have two major comments:

1) The authors seem to ignore the switching between phasic and antiphasic oscillatory states, even though this is shown in Fig.1, and more generally between the polyrhythms that would occur in larger inhibitory networks. The latter switching may be at least as relevant to gait generation as the switching from bursting to spiking. Polyrhythms have also been shown experimentally and theoretically to produce robust multistable states that overlap over a wide parameter space. It would therefore be useful if the authors could comment on the relative robustness of spiking/bursting multistability vs polyrhythm multistability.

2) It is argued that an hyperpolarizing Ip pulse will induce a transition from continuous spiking to bursting and conversely a depolarizing pulse induces the reverse transition from bursting to continuous spiking. Transitions are a dynamic process which will depend, among other things, on the timing when the pulse is applied during the heteroclinic cycle. In the absence of more information on the dynamics of the system such claims look over-simplistic.

Reviewer #1:

In this study, the authors investigated whether the morphological and electrophysiological properties of glycinergic interneurons in the spinal ventral horn of GlyT2eGFP SOD1 G93A mice are altered compared with GlyT2eGFP WT mice at P6-P10 (the SOD1 G93A mice is the classic mouse model of amyotrophic lateral sclerosis). Such an investigation has never previously been done. The main body of results relies on a sample of 34 WT and 25 SOD1 patched interneurons located throughout the ventral horn. The authors found that soma sizes of patched interneurons are not significantly different in SOD1 animals than in WT animals but their dendrites are larger. The onset and the peak of persistent inward currents (PICs) are more depolarized in SOD1 interneurons suggesting that they are less excitable than in WT. Immunohistochemistry for Calbindin was performed in a subset of the patched interneurons to identify Renshaw cells (7 cells in WT animals and 6 cells in SOD1 animals). Calbindin positive cells display more depolarized PICs onset and peak in SOD1 than in WT animals. A predictive statistical analysis was then performed in order to include in the Renshaw cells sample cells that were not tested for calbindin. This analysis suggested that the predicted Renshaw cells are less excitable in SOD1 mice than in WT mice whereas the predicted non-Renshaw cells are more excitable. The implications of these findings for the ALS pathophysiology are discussed.

However, a number of major concerns substantially weaken the findings:

1) Morphological properties Texas red allowed the authors to localize the patched cells in the ventral horn, to measure the soma and the dendrites and to investigate whether the patched cells were immunopositive to Calbindin. It appears that the soma volumes of the patched neurons are on average 2-3 times larger than the soma of the general population of GlyT2-GFP neurons in the ventral horn or in lamina IX (Table 1). No explanation is provided for this discrepancy. Does it mean that there is a systematic recording bias towards the largest interneurons ? Alternatively, is there a systematic swelling of the patched cells or a shrinking in the fixed spinal sections? Also, it is not clear what the dendritic parameters are? It is necessary in Figure 2 to show a reconstruction of dendrites in order to figure out which dendritic length, surface and volume are reported in Table 1.

2) Electrophysiological properties The shift in the onset of the persistent inward currents onset is taken as an important indicator of a reduced excitability in SOD1 interneurons. However the measurement of the PIC onset is problematic. It is claimed in the Material and Methods section that "PIC onset was defined as the voltage at which the current began to deviate from the horizontal, leak substracted trace" (lines 374-375), which seems reasonable. However, in Figure 3A, the arrow for the PIC in the SOD1 motor neuron (red trace) does not point to the initial deviation from the horizontal which actually occurred at about -60mV, i.e. close to the PIC onset for the WT motoneuron (blue trace), in contradiction with the authors claim. The arrow points to a second component whose onset appears at a more depolarized voltage. Then the net current is likely to be complex and a pharmacological dissection of the currents at work is required both in WT and SOD1 neurons. Indeed, the net inward current might result from the summation of inward and outward currents. Are they outward currents at work? Are the inward currents Na+ or Ca++ currents? In the absence of such a pharmacological "dissection" it is difficult to fully interpret the data.

3) Identification of the Renshaw cells The authors identified a subset of GlyT2 neurons as Renshaw cells because they expressed Calbindin-D-28K. This sole criteria does not allow a proper identification of Renshaw cells, particularly in P6-P10 mice. Indeed, many non-Renshaw cells in the ventral horn are calbindin-immunopositive during this post-natal maturation period in addition to the Renshaw cells (Siembab et al, J Comp Neurol, 2010). One distinguishing feature of Renshaw cells is that they are excited by recurrent motor axon collaterals. Then, the presence of VACht boutons on the GlyT2 cells would have been an interesting additional identification criteria. However, there is another source of VACht boutons than motor axon terminals in the spinal cord (Zagoraiou et al, Neuron 2009). Since this is an electrophysiological work, the authors had the possibility to unambiguously identify Renshaw cells: the presence of synaptic excitations in response to the stimulation of motor axons in a ventral rootlet (using oblique spinal cord slices, see for instance: Lamotte d'Incamps and Ascher, J Neurosci 2008; Bhumbra et al, J Neurosci 2014). The authors are advised to perform such an electrophysiological identification of Renshaw cells.

4) Statistics and predictive model The number of patched cells identified as "Renshaw cells" on the basis of their Calbindin immunopositivity is low (7 WT and 6 SOD1). Indeed, I do not see any reason why the authors did not repeat the experiments in order to gather a more reasonable number of cells. Statistical analysis was performed on this low cell samples, in order to investigate whether each property under investigation differs or not in WT and SOD1 animals as reported in Table 3 (normality of the distribution was tested for each property and either ANOVA analysis or Kruskall Wallis analysis was performed). The validity of statistics on such low cell samples is questionable. The analysis was then extended to all patched cells using sophisticated random forest and principal components analysis in order to check whether some cells among those not tested for calbindin display enough similarities with the calbindin-positive cells to be considered as putative Renshaw cells. The model predicted that 80% of the 59 patched cells were "Renshaw cells", a percentage astonishingly larger than the percentage of calbindin-positive cells in the ventral horn (65%). This prediction is doubtful since the number of calbindin-positive cells is already higher at P6-P10 than the number of Renshaw cells (see bullet point 3). Nevertheless, the authors made statistics (not shown on the paper) on the basis of this prediction, and they found that the predicted Renshaw cells are less excitable in SOD1 mice than in WT mice whereas the predicted non-Renshaw cells are more excitable.

+1.

Reviewer #1:

This paper shows that Cas 9 mediated homology directed repair can be used to insert a synthetic rescue gene into an essential gene, here mitochondrial Pol-gamma35 was chosen. The insertion is marked by an eyeless-GFP reporter and also contains the gRNA (gene drive) but not the Cas9 (considered as a safe split gene drive). 'Homing' of the eye-GFP is assayed to detect insertion at the homologous locus when Cas9 is present by HDR. The authors show that this works well in the female germline with various tested Cas9 lines (vas, nos, Act5C and ubiq-Cas9). In all cases close to 100% transmission to the homologous locus on the homologous chromosome is achieved when an effective guide RNA is used. Hence, eye GFP transmits ('homes') in a 'super-Mendelian' ratio at the chosen target. A male specific transmission works less well (exuL-Cas9). The reason why it works well appears to be that the chosen target is an essential gene (Pol-gamma35) in which small changes caused by NHEJ that result in homing 'resistant' alleles will be loss of function alleles and hence will not spread in the population. Unfortunately, the authors did not test how the drive could spread in a wild type population (no Cas9 expression). I am also missing a test relevant for pest studies that would achieve the spread of a potentially deleterious or beneficial insertion that could kill a population or make it resistant to a disease.

1) This paper is very hard to read. Sentences are excessively long and complicated. References to the Figures appear not always correct.

2) Figure 1. Genotypes in Figure 1A are unreadable in the print version because of the small font. Are the 2 crossing schemes required that only differ in gRNA1 or gRNA2? The surviving progeny should be quantified as in Fig 1B. Figure 1B shows nos-Cas9 and not act-Cas9 results (several typos in line 148-155). Figure 1C: the incidence of heterozygous, homozygous and 'resistant' cells is schematic and not supported by data, hence questionable if Figure 1C should be shown in results.

3) Figure 2. Genotypes not readable in print. Is it necessary to show schemes of the procedure how transgenic flies were generated and how the Pol-gamma 35 HomeR were made with all chromosomes detailed (Fig 1D)? This could move to the methods as it is standard and we learn not much new.

4) More typos: line 286: Fig2B is the wrong reference; line 295 should read Actin 5C. Figure 4B GGG codes for Gly (not Gla). lines 576 to 592 should refer to Figure 6?

5) Figure 5 - as figures 1+ 2, only readable on the computer.

6) It would be interesting to see how the gene drive would spread if Home R and Cas9 would be introduced in a competitive way into wild type populations. This is similar to Fig 4C, but the only the Home R males or females would carry the Cas9. This would be a more realistic test how the gene drive could spread in a wild population that obviously does not express Cas9.

Reviewer #1:

This paper uses a variety of mouse lines to investigate what retinal circuits control the pupillary light reflex (PLR). Recordings from rods and bipolar cells confirm that the manipulations work as expected - at least at the level of the bipolars. Measurements of the PLR in these mice then are used to draw inferences about the relevant pathways. The main conclusions are that cones contribute little to the PLR across light levels, that signaling in Off retinal circuits contributes little, and that both primary and secondary rod pathways contribute.

I have several concerns about the work as presented:

1) Use of mouse lines. The mouse lines are interpreted as cleanly dissecting different retinal pathways, but this may not be the case. For example, deletion of one pathway may alter signaling in another pathway - either through compensatory effects, or from interactions between the pathways that are missing when one is removed. One way to address this concern would be to record from RGCs to test for such effects. For example, the cone sensitivity in the RGCs in Cx36-/- mice should not be altered. The bipolar recordings are helpful in this regard, but they do not represent the circuit output and hence could miss key interactions or compensation.

2) Interpretation. The results are interpreted in the context of a standard model of retinal circuitry. Yet several aspects of the results suggest that such a model is incomplete. One example mentioned in the text is the possibility of direct RBC to RGC connections. A specific concern in this regard is that it is unclear how the secondary pathway could control the PLR but cones could not - since rod and cone signals are mixed in the secondary pathway. Accounting for the results in the paper would appear to require revisiting our understanding of retinal circuits - but more direct tests of the circuits are needed for such a conclusion.

3) Relation with past work. The paper is short and suffers from short or missing descriptions of related past work. For example, a good deal is known about how signals from the primary and secondary pathways modulate cone bipolar and RGC responses. This is directly relevant to what is expected and unexpected in the present work. Recent work (Lee et al., 2019) also shows a contribution of melanopsin to ipRGC responses at low light levels - but this is mentioned only in passing in the present paper. This work appears highly relevant to the present study.

Reviewer #1:

My general assessment of this work is that it is full of good ideas and presents a novel and general approach to examine lipid remodeling in cells and perhaps subsequent transport of lipids, mainly to mitochondria, but it lacks the scientific rigor necessary to be fully confident that their conclusions firmly support their claims. Often, insufficient information about the methods are provided and the manuscript is hard to follow critically.

More specific comments:

1) I am surprised that acyl-CoAs are transported into cells. I don't know of any precedent for this. Usually fatty acids are imported into cells and then converted to acyl-CoAs as part of the mechanism of import. Could it be that the acyl--CoAs are hydrolysed before uptake only to be reformed inside the cells? I would suggest feeding the NBD-palmitate plus the lysolipids to the cells as a control to see whether this is the case.

2) In fig 1 as an example they choose a region to blow up. As one can see there is a large variation, even in the blowups of mitochondrial labeling and if one looks at the originals the variation is confirmed. How have they chosen these areas? Furthermore, in figure 1 there is quite a bit of label with MLCL outside of the mitochondria, in particular in regions that they did not choose to blow up. What are these structures? Remodeling of MLCL is thought to take place in mitochondria.

3) They speak of transport of lipids from ER to mitochondria, but in fact the demonstration of this is very weak from what they show in the time course in supp fig 1. I am also disturbed by the difference in patterns of the NBD-PA patterns in a and b. They should be the same, but there are problems, maybe focus? I would say anyway that there is no clear evidence that the NBD PA first appears in the ER then goes to mitos. It could be synthesized in both compartments from their data.

4) The product characterization by TLC is insufficient. There are no standards, no characterization. Would they have seen the free NBD-palm by their methods?

5) When they use mutants and find less "transport" the mitochondrial signal as seen by mitotracker is always more diffuse. This indicates to me that there is another problem.

6) In fig 3 the fluorescent pictures do not correspond to what is seen in the quantification. There is more yellow in e than in h.

7) How did they add cholesterol at 50 or 100 micromolar? It is soluble at less than 1 micromolar in aqueous solution. The cholesterol experiments are puzzling. From what we know about StAR protein it recognizes cholesterol not esters. There is no precedent for cholesterol ester transport into mitochondria. Can they rule out that the esters are transported to the surface of the mitochondria and the NBD-Palm cleaved off and transported into the mitochondria?

8) The MAG and DAG experiments are overinterpreted. It could just be a kinetic problem since the MAG gets converted to DAG before TAG

9) They compare to externally added NBD lipids, but we don't know which ones they used. Are they using short chain NBD phospholipids. I could not find this in their manuscript. If they do not have the same NBD-palm in the sn-2 position then the comparison is meaningless.

10) The excitation and emission spectra of their probes are sometimes overlapping. How did they deal with this? Are they sure that they are not seeing FRET?

Reviewer #1:

This study from the Henderson laboratory describes the identification of a hybrid secretion system involved in the acylation and trafficking of a conserved class of bacterial lipoproteins. Spurred by the serendipitous observation of posttranslational modification, Icke et al. identify the AatD protein as the factor responsible for CexE acylation. Combining alignment of conserved sequences and structural data the authors isolate the site of acylation on the CexE polypeptide and identify AatD residues responsible for catalysis Overall this is a strong manuscript, densely packed with supporting data and extremely well written.

My only significant concern is the issue of novelty. Although the authors seem to imply they are the first to report this type of system, they cite a 2020 PLOS Pathogens paper by Belmont-Monroy detailing nearly identical results in Enteroaggregative E. coli. Given the significant amount of overlap between these two manuscripts, it would seem prudent for the authors to spend some time in the introduction and discussion highlighting open questions that this paper addresses.

Reviewer #1:

This is a potentially interesting analysis, but there is a lack of framing, details, and specificity that dampens my enthusiasm for the work.

1) As far as I can tell, the authors do not really demonstrate that "markers of negative emotion and pain" can be down-regulated during self-reassurance". They simply show that regions surviving multiple comparisons change depending on condition, but they don't show data supporting their hypothesis. How much do regions activated during criticism actually change during reassurance? What is the time course of these differences?

2) Behaviorally, the neutral statements from the two "conditions" appeared to have distinct intensity levels. Specifically the "intensity" for neutral trials during criticism blocks appears significantly lower than neutral trials for reassuring blocks. Because of this behavioral effect, within their design it is difficult to identify the cause of the brain changes.

3) How were subjects trained in self-criticism vs. reassurance? Is there any way to confirm that they were in fact doing the "task"? Further, at what point in the 2-week compassion training paradigm were FMRI data collected?

4) Figure 2 is quite confusing to me: (1) the authors refer to brain maps as "neural pain"? I would strongly advise against this as it is very reverse-inferency. I would recommend against using this phrase throughout the paper. (2) How would one interpret the phrase "neural pain during self-reassurance"? Is this emotional > neutral during reassurance?

5) Figure 3 refers to "trial by trial ratings of intensity" but if I am understanding the figure, this is not an accurate description. The authors are reporting the mean across subjects for each condition. It is unclear in fact how much variability there is on a trial-by-trial level within persons for the intensity of each condition. One idea is to use an amplitude modulation analysis to scale FMRI parameter estimates by the intensity rating on a per-trial basis. That would be an interesting analysis, IMO.

6) It is unclear from this paper what was done previously. It appears that the authors examined physiological data (e.g., HRV) in their previous report but don't talk about other measures that were collected here. It would be useful to know the extent to which they buttress the authors findings (or if they do not).

Reviewer #1:

The authors utilize a novel ex vivo system to visualize granulocytes migrating within experimentally induced blood clots. Granulocytes are labeled using CD11b and CD66b and visualized over time using fluorescence microscopy as they move within the ex vivo formed clots, which have been prepared under different anticoagulant conditions to generate varied clot structure. Leukocytes are activated using various agonists and behavior classified into 2 different phenotypes based on the staining pattern of DiOC6 as either diffuse or punctate. The experimental system allows for measure of individual cell velocity and clear images of cells showing changes in cell body structure. The use of cells from WAS patients provides a nice validation of the model system being presented in the study.

1) The authors state that citrated blood results in local fibrin formation and platelet activation; would it not be relevant to compare granulocyte behavior in such a setting to the hiridin or heparin-anticoagulated samples? This could also provide a valuable setting to study platelet-granulocyte interactions.

2) The further elimination of heparin-anticoagulated blood in favor of hirudin is also not clear. How does heparin pre-activate granulocytes, and what experimental evidence is seen by the authors other than an increase in number?

3) Are type A and type B leukocytes defined only by the DiOC6 staining pattern, or also by their velocity? Please clarify in the text.

4) The choice of "leukocyte priming agents" is not clear, in particular myeloperoxidase and lactoferrin. Leukocyte activation caused by these agents should be validated and the rationale more clearly defined (i.e. by referencing previous work that provides the mechanism of binding for these leading to neutrophil activation).

5) Regarding the Annexin V stained smaller structures presented in Figure 4; have the authors ruled out that these could be procoagulant extracellular vesicles from other leukocytes, i.e. by performing a co-staining with a platelet marker for positive identification?

6) Have the fibrinogen (Sigma) and von Willebrand factor (an in-house preparation from an academic lab collaborator) been tested for endotoxin levels prior to use in this system?

Reviewer #1:

General assessment:

Since Precision Oncology is getting important these days, understanding the relationship between cancer type-specific vulnerabilities and their biomarker is a major challenge of personalized therapy. Previously, genomic signatures such as mutation and copy number variation were favorable to predicting cancer vulnerabilities. Dempster et al. presented a systematic comparison of predictions with or without gene expression features using five major screen data sets, suggesting that gene expression would better predict cancer vulnerabilities. Although suggested interpretable models in the last part of the paper are questionable, the main message and the supportive comparisons are clear.

Major comments:

1) RNA expression cannot be separated from cell lineage bias. For example, ESR1 gene is also relatively overexpressed in normal female tissues. I'm wondering how overexpression specific dependency can be separated from the tissue bias.

2) Predicting drug response by expression signature might be risky if there is no clear copy number amplification signature or reasonable causality. Is it possible to find casual features of why a gene is overexpressed?

3) In this paper, the authors presented that EDA2R expression is the top feature of predicting the TP53 dependency and MDM2 inhibitors' response as an example of interpretable models. However, many studies have confirmed MDM2 phenotype depends upon TP53 genomic status. Similarly, the response of MDM2 inhibitors can be explained by TP53 mutational status. I'm curious whether the prediction of MDM2 dependency using EDA2R expression status shows a better prediction than the prediction using TP53 mutational status in statistics.

Reviewer #1:

The study is interesting and does have potential translational relevance. There are some concerns however: (1) in Figure 1 the blood glucose drops independent of food intake is this all related to decreased hepatic glucose output or are there any effects on urine. Was urinary glucose measured? Is there increased glycosuria?; (2) In previous papers you discuss the increased lean body mass when aprosin is not present. There is no body composition data in this study. Was there any body composition differences with the antibody among the different mouse models (e.g DIO vs Nash diet)?; (3) were any changes in lean body mass with the antibodies associated with increases in strength?; (4) several mouse ages are discussed in the Methods section: 12 weeks, 16 weeks, 12 week of high fat diet or 24 week of NASH diet. Not clear from description if mice were matched for age. Please clarify; (5) In Figure 5 there are a number of inflammatory markers which can vary according to the model. What about anti inflammatory markers (cortisol, IL-10 etc) would be helpful to get a better picture of physiologic changes.

Reviewer #1:

This manuscript by Luettgau et al. describes a study of second-order conditioning in humans. The behavioral task involved visual first- (CS1) and second-order cues (CS2) and gustatory outcomes (US). Behavioral results show that subjects preferred both the CS1+ and CS2+ over the CS1- and CS2-, respectively. MVPA shows that the CS1 evokes US representations in the lateral OFC, and that US representations in the amygdala increase over second-order conditioning. This study addresses an important and novel question. However, I have several major concerns regarding the study design and data analysis:

1) I do not see how it would be possible to disentangle responses to the CS1 and CS2 in this task. The delay between the CS2 and CS1 is only 500 ms, which is not long enough to disentangle fMRI responses to the two CS.

2) For the main "reinstatement" analysis, activity was averaged across both CS2 and CS1, and so it is unclear whether reinstatement is driven by the CS1 or CS2. The authors argue that "US reinstatement during SOC could only be faithfully attributed to the respective CS1, but not to CS2, since only CS1 had been directly paired with the US, and CS2 had not previously been experienced." However, this is only strictly true for the very first trial during which the CS2 could have gained full access to the US representation.

3) In this regard, it is unclear why the authors did not use data from the first-order conditioning phase to test for US reinstatement. Although the 4-second delay between CS1 and US is still quite short, TR-wise MVPA could provide evidence that signals are related to the CS1 and not the US itself.

4) Relatedly, the authors perform analyses suggesting that, from early to late phases of second-order conditioning, representations of CS2 in the amygdala became more similar to US representations. Although here they attempt to model fMRI responses to the CS1 and CS2 separately, there is no evidence that this was indeed successful. As I see it, the delay between the two CS is just not long enough to dissociate these responses.

5) Is there evidence for a CS1 evoked reinstatement of the US in the amygdala, and a CS2 evoked reinstatement of the US in the lateral OFC? In theory these signals should exist, but independently testing for activity related to the two CS requires a task design where the two CS are presented in isolation or with long enough delay between them.

Reviewer #1:

This manuscript extends on prior work by the authors (Griffis et al, 2017), which originally reported eccentricity-dependent differences in resting state connectivity between V1 and regions brain wide. This study builds on that work by expanding the pool of participants, using the HCP dataset, as well as also investigating any eccentricity-dependent effects that may emerge with tractography. Interestingly, both measures find that foveal areas in V1 are more strongly connected to frontoparietal networks. The study is interesting, but I have a few remaining points.

1) While during the resting state scans, there was, in theory, no 'task', participants were asked to maintain fixation on the cross in the center of the screen throughout the scan. I think it would be important for the authors to note that there is a possibility that the resting state correlations observed wherein foveal areas were more correlated with frontoparietal regions (and far periphery with DMN areas) could be due to attention directed towards the fixation cross, and away from the periphery. While I acknowledge the authors have no way to test this with this data set, it is possible that if participants had been asked to covertly attend to a ring in their far periphery the entire time instead, the correlations might have been flipped, with frontoparietal connectivity highest in the periphery towards the attended eccentricity. The authors should either explain why this is not a concern, or acknowledge it in the manuscript.

2) Related to the last point, what was the size of the screen used during the connectivity data acquisition? I ask because the far eccentricity bands determined using Benson et al's technique are very eccentric. And if participants had eyes opened and were fixating, was that eccentricity outside the outer edge of the screen? Because then it would be encouraged to be 'unattended', thereby potentially influencing connectivity results.

3) Was there any attempt at replicating these results in extra striate cortex? Are these patterns still there, both in structural and functional connectivity, for V2 or V3?

Reviewer #1:

This paper looks at the mechanism of transcription regulation by the ANTAR domain protein, EutV. ANTAR domain proteins are an evolutionarily widespread family of RNA-binding regulators in bacteria. EutV has been proposed to regulate expression of target genes by binding two RNA loops in a 5' UTR, leading to a change in the RNA structure that modulates premature transcription termination. The current study determines the structure of dimeric EutV bound to an RNA target with two binding sites. Surprisingly, the interactions between the ANTAR domains in each monomer and each of the two RNA loops are incompatible with simultaneous binding of one EutV dimer to both loops. Hence, the authors propose a model in which EutV is "handed off" from one loop to the other as the RNA is transcribed.

The structural information regarding the interaction between the ANTAR domain and RNA is an important advance, although there is very little comparison to previous studies, including a study that identified many of the same residues as being required for RNA binding (reference 33). The evidence that a EutV dimer cannot bind both RNA loops simultaneously is strong, and inconsistent with a previously proposed model of regulation. However, other than the structure, there are no data that support the authors' proposed hand-off model. In fact, as it is drawn in Figure 6D, I don't think the model is possible based on the same structural constraints that prevent simultaneous binding of the EutV dimer to both RNA loops. Without further experiments, I don't think the authors can conclude much about the mechanism other than it being unlikely that a single EutV protein binds both RNA sites simultaneously.

Major comments:

1) Throughout the paper, there is insufficient description of previous work on ANTAR domain proteins. In particular, there is little comparison to published structural data, including modeled RNA-bound structures. There is also very little discussion of the mutagenesis in reference 33 that identified many of the same residues as being required for RNA binding. There is no doubt that the structural work in the current study represents a substantial advance over previous studies, but it is important to describe the similarities and differences to prior work.

2) Discussion, second paragraph. The evidence for a conformational shift in EutV upon phosphorylation is weak. This hypothesis is based on structural modeling from a homologous protein that has only 37% sequence similarity.

3) The structure does appear to rule out the possibility of EutV binding both RNA hexaloops simultaneously, but the hand-off model is still rather speculative, and not supported by any additional experimental data; binding of two EutV dimers to the same nascent RNA would seem just as likely. There is insufficient discussion of how the hand-off model fits with previous mutagenesis studies (e.g. reference 25), and no follow-up experiments designed to test the model. If EutV is unable to bind both hexaloops simultaneously due to spatial constraints, how is it able to transition from one hexaloop to the other, as depicted in Figure 6D? I would expect the same spatial constraints to apply.

permanent magnet excitation

Reviewer #1:

Buskirk et al. examined the evolution of nontransitive fitness effects in yeast. They showed that during evolution in rich glucose medium, a late clone (1000 generations) outcompeted an intermediate clone (300 generations), but lost in direct competition with the ancestor (in a frequency-dependent fashion: late clone when rare loses to ancestor and when abundant outcompetes ancestor). This is due to adaptation in the nuclear genome and intracellular killer virus. Essentially, the ancestor expresses both killing and immunity phenotypes (K+I+), the intermediate clone expresses immunity (K-I+), and the late clone expresses neither (K-I-). This trend is observed in many evolving populations. In the absence of the killing interaction, virus does not affect host fitness. That is, when killing interactions are absent, fitness changes are due to mutations in the nuclear genome. Changes in killing and immunity phenotypes are driven by intracellular competition of viruses where viruses defective in killing and/or immunity have an advantage over functional viruses.

This work demonstrates that evolution may not be a simple linear march of progress. Rather, progresses over short time scales can sometimes lead to a reduction of fitness over the longer time scale due to ecological interactions. I find the work quite interesting, although I also find it a bit incomplete.

What are the nuclear mutations that made intermediate clones more fit than ancestor and late clones more fit than intermediate clones? I think that giving one example for both cases will be helpful.

A schematic summary figure will be helpful.

Reviewer #1:

This is an interesting translational and comprehensive study which examines cellular and genetic mechanisms involved in the diversity of corpus callosum dysgenesis (CCD) phenotypes. Using mouse models and human cohorts with a spectrum CCD, it is found that the extent of aberrant interhemispheric fissure (IHF) remodeling predicts commissure dysgenesis severity. Elegant neuroanatomical experiments show that abnormal proliferation/migration of midline zipper glia (MZG) progenitors underlies aberrant IHF remodeling. Thus, in addition to genetic perturbations linked to aberrant callosal axon guidance in humans and mice (i.e. variants in DCC guidance cue receptor gene), disruption to IHF remodeling also causes CCD. Indeed, an 8-base pair deletion in the DCC receptor ligand, Draxin, which is expressed in MZG, associates with CC malformations in mice. The findings are novel and important to both basic and clinical scientists.

Below are comments and suggestions that need to be addressed:

1) Introduction:

-More detailed information about the BTBR mouse line and the rationale for using the BTBR x C57 mouse cross should be provided.

-The main question addressed in the study should be clearly stated.

2) Methods:

The Statistical analysis section needs to provide a more detailed description of the statistical tests that were used and the reason why these tests were chosen.

3) Results:

In general, the description of the statistical results lacks important details. For example:

-For figure 1, there is very little information about statistical analysis. For figure 1 C, it needs to be explained why a Welsh test was used instead of a one-way ANOVA. The errors on the bars do not seem to correspond to SEM, this needs to be clarified.

-For figures 3 G and H, if the data are presented in single graphs, it is not clear why unpaired t tests or Mann-Whitney tests were conducted (instead of ANOVAs). Why a non-parametric test was used is not explained.

-The description of the findings that prompted the authors to investigate the role of Draxin in CCD needs to be clearer.

-The references to the different panels of Figures 5 and 4 need to be revised in the Results section.

-It is not clear what is the impact of the Draxin deletion to IHF remodeling. There seems to be an effect shown in one of the supplementary figures (in BTRB mice), but there is no discussion in this regard. This is particularly important considering that Draxin is expressed by MZG.

-It seems that the Draxin deletion does not affect HC formation. However, at some point in the Results section it is stated "To investigate how DRAXIN regulates CC and HC formation...". This is confusing. It seems that the effect varies between BTRB mice and the BTRB x C57 cross, but this is not discussed clearly.

-Figure 7 should indicate the mouse genotype on the actual figure to avoid confusion.

-The study by Vosberg et al, 2019 in Annals in Neurology needs to be included when referring to studies linking DCC variance and CC dysgenesis in humans.

Minor Comments:

The organization of the manuscript could be improved to increase its clarity. The authors may want to consider moving the Draxin findings to the last part of the Results.

Reviewer #1:

The authors use a tetracycline controlled gene expression system to compare the effectiveness of two difference promoters to express channelrhodopsin in different populations of retinal neurons with the goal of rescuing visual function in mouse models of photoreceptor degeneration. The expression patterns of two promoters were compared - the first a muscarinic AChR (referred to as M4 in the manuscript) led to expression in a subset of RGCs and a subset of amacrine cells, while the second a 5-HT receptor (5B) led to expression in a subset of RGCs only. In the M4 line, the amacrine cells that were labeled were a subset of starburst amacrine cells located in the INL and did not label the SACs displaced in the GCL. Also, it was a subset of the INL-SACs. To assess the impact of these different expression patterns on vision restoration, mice expression ChR under these two different promoters were treated with MNU to induce PR degeneration. The light responses restored by ChR were assessed with a MEA recordings cortical VEPs and behavior. The M4 promoter had stronger light evoked responses. The authors used pharmacology to assess how the M4 retinal circuit might explain the enhanced light response.

There were several fundamental problems with the manuscript that need to be addressed. These problems range from experimental design, interpretation of findings, some mistakes in description of retina circuits. Moreover, there is no context given comparing these results to the multiple other studies on vision restoration impact on visual-guided behaviors. These problems are listed here:

1) The choice of promoters and expression patterns need to be further explored. The motivation for a particular subtype of mAChRs and 5-HT is not given. Though M4 and 5b drives expression in roughly the same percentage of total RGCs, there is no way to know whether they drive expression in the same subtypes of RGCs. Hence differences in firing patterns are not likely to be fully explained by the fact that M4 promoter also drives expression I a subset of INL-SACs.

2) The observation that M4 drives expression in a subset of OFF SACs was quite intriguing. Though there are ways to distinguish ON from OFF SACs, this is the first example of which I am aware that a subset of OFF-SACs is labeled. Does this mean only a subset of OFF-SACs have mAChRs? Or was this reflective of the partial express induced by Tet? It is worth the authors quantifying the percent of OFF-SACs labeled in the M4 mouse line.

3) The observation that they are able to rescue the OKR result in MNU treated mice using the M4-promoter is impressive. Again, the authors conclude that this is due to presence of ChR in INL-SACs but it could be they also have ChR expression in direction selective ganglion cells themselves. Hence the rescue is impressive, it is difficult to interpret. Also, this important behavior is confined to a supplemental figure.

4) The authors conclude that M4-driven expression of ChR rescues the OKR in MNU-treated mice and not rd-mice because the rd mice have a "thinner INL" and therefore may have a depletion of INL-SACs. This appears to be an easy test for the authors using immunofluorescence.

5) The authors do some pharmacology to test whether SACs are the basis of the larger sustained response observed in M4 vs 5B . However, the assumptions/interpretations for these experiments are based on some mistakes regarding retinal circuits. SACs release GABA and acetylcholine. However, the pharmacology they do is quite limited. Namely they use TPMPA, which blocks GABA-C receptors which are found on a subset of bipolar cell terminal and by no means represent the major source of GABAergic signaling in retina which is via GABA-A and GABA-B receptors. Similarly, the authors assess impact of ACh release by using atropine, which blocks muscarinic receptors but not nicotinic ACh receptors. Finally, the authors use MFA, a blocker of gap junctions, which does have clear impact on sustained responses. However, SACs are not thought to be gap junction coupled to anything. So, it is more likely MFA is acting via RGC-RGC gap junction coupling or having an off-target effect. Much more needs to be done to have a complete understanding of the circuits that mediate the ChR-mediated light responses.

6) 226-227 - what is the conclusion - results suggest not due entirely to gene transfer? This needs further explanation.

7) Comparison of light induced responses in MNU vs non MNU treated rather confusing. Authors should consider revising this point.

Reviewer #1:

Santos and Sirota developed a novel Tetrode-based Amperometric ChOx (TACO) sensor. This multichannel configuration can simultaneously measure the ChOX activity (COA) and O2 in the same brain spot. Using the TACO sensor in freely-moving and head-fixed rodents, they found that COA and O2 dynamics following locomotion in active state and hippocampal sharp-wave/ripple (SWR) complexes during quiescence state. It's interesting that the COA signal can be calibrated by subtraction of the pseudo-sentinel from the Ch-sensing sites signal the TACO sensor. However, the COA signal is confounded by phasic O2 fluctuations, so, the authentic changes in COA are interfered by O2-evoked enzymatic responses. This question isn't addressed in this paper.

Major concerns:

1) The author found that the COA readout is confounded by phasic O2 fluctuations in in vitro and in vivo experiments. These results cast doubt on the validity of the authentic cholinergic response in freely-moving or head-fixed rodents. These findings seem to be generalized to other oxidase-based biosensors, although the author has some discussion on how to address this question. However, we can't get authentic cholinergic dynamic in vivo by TACO biosensor if we didn't clear the biosensor O2 dependence. So, the author should try to address this question.

2) The author should demonstrate how to calibrate moving artificial signals in freely-moving rodents.

3) Concerns on the selectivity. Figure 1E shows the TACO sensor also responses to dopamine and ascorbate. The author should demonstrate the selectivity of TACO sensor on different monoamines at different concentrations.

Reviewer #1:

Using a simulation approach, the authors investigate the impact of removing group members likely to possess key social or ecological information on the topology of elephant social networks in order to better understand how poaching pressure may influence their resilience and functionality. Removals were based on three metrics thought to correlate with an individual's knowledge (age, degree, betweenness centrality) and compared to random removals for both an empirical network and virtual networks. Whereas targeted removals based on age had relatively limited impact on networks characteristics, removal of socially central individuals led to less integrated networks with potential consequences for the spread of adaptive information.

The manuscript was generally clear and well-written. The introduction nicely laid out the rationale for this study and the authors do a nice job walking the reader through the steps of the simulation (how the networks were constructed, how deletions were performed, etc.). I also appreciated the discussion given to the limitations of their approach, such as the lack of network restructuring in response to removals.

1) My main critique is that I believe the authors should be more cautious in attributing functional meaning to their network metrics, particularly given that data was unavailable to allow them to simulate a transmission process. For example, at L461-463, it is stated that targeted removal of individuals with high betweenness decreased the speed of information flow, but what was actually found was that values for weighted diameter increased. Put another way, weighted diameter provides an indication of how rapidly information could potentially flow, but not whether it in fact does so. The actual dynamics of information flow are going to depend on the nature of the information and how it is transmitted among individuals, as the authors note in the discussion (L627-640). I believe that the results should be reworded to focus more on what was actually found (i.e. changes in network metrics), with the potential functional relevance of those changes then examined in the Discussion.

2) In addition, I couldn't see if this was addressed anywhere, but is there empirical evidence to suggest that the mature elephants that possess high-quality information are those characterized by high degree or betweenness?

Thank you for the interesting read!

Reviewer #1:

The manuscript from Quiroga and colleagues reports a function for the mechanosensor Piezo 1 in myocyte fusion. The manuscript concludes via a series of in vitro experiments that Piezo 1 knockdown results in decreased myotube formation.

While overall the manuscript reports some potentially interesting observations, the main conclusion seems preliminary and the work would benefit from substantial additional validations in multiple models to strengthen the tie between myomaker and Piezo1 functions.

Major Comments:

1) siRNA reduces gene expression in a transient manner and it is unclear for how long there is significant silencing of Piezo1 RNA during differentiation. Therefore, a more consistent model that expresses consistent amounts of Piezo1 might be beneficial. Importantly, a more stable mutant form of Piezo 1 (generated with CRISPR/Cas9) was generated in a previous study (Tsuchiya et al, 2018, ref. 17). The long-term consequences of differentiation/fusion of myogenic cells following loss of Piezo 1 expression in the Tsuchiya study reached opposite conclusions to the current study. These findings raise concerns that are not clearly addressed in the present study. While the authors attempt to explain the opposite findings by the use of a different Piezo 1 silencing model, it is difficult to reconcile with the present data the very opposite findings.

2) Figure 3A and C have duplicated images showing siRNA of Piezo 1 in EDL and Soleus. The correct images need to be inserted.

3) Quantification of proteins levels downstream of Piezo silencing should be corroborated by western blot analyses. These include data presented in Figures 2 and 3.

4) In Figure 4, it would be helpful to include a graph illustrating the amount of Piezo1 silencing and the corresponding decrease in Myomaker expression.

5) In Figure 6, expression of myomaker and myomixer should be monitored following administration of Yoda1. If Yoda1 increases fusion at low concentrations, the fusion genes should be upregulated in expression.

6) In Figure 7 the myotube width should also be accompanied by quantifications of numbers of nuclei fused in the myotubes. This data will address whether cell fusion changes following Yoda1 treatment.

7) While the present work explores the function of Piezo 1 in myogenesis in vitro, no experiments address a potential parallel function of Piezo1 in vivo. Supporting data using injured/regenerating muscle should strengthen the overall message.

8) Figure 9 proposes an interesting hypothesis linking Piezo 1 to FSHD. However, the hypothesis is not supported by experimental data and remains rather exploratory in its current form.

Reviewer #1:

Taken collectively, the findings described in the manuscript provide a new perspective on how LAP2alpha influences the state of A-type lamins. By extension, one impact of the findings is that they provide a mechanism by which A-type lamin state is distinct within the nucleoplasm and at the nuclear lamina. The authors also arrive at some additional insights that are valuable. For example, the data supporting the initial peripheral localization of what is argued to be pre-lamin A during processing rather than filament assembly was interesting and, although indirect, largely convincing. I would encourage the authors to address the fact that this work drives a reinterpretation of their prior findings early in the paper. I also have some concern that the impact of the findings is somewhat narrow.

Major points:

1) Given that a major focus of the paper is to explain conflicting results with (the same group's) prior published data on the effect of LAP2alpha depletion, it would have helped to lay this out more clearly from the outset of the paper. As written, the reader is confused until arriving at Figure 3. I appreciate that resolving this conflict leads to a new perspective - namely that LAP2alpha influences the state of the lamin assembly in a way that disrupts its detection by the N18 antibody, but structuring the manuscript to get to this point as quickly as possible would improve its accessibility.

2) I found the plots in Fig. 1A and B confusing. Can the authors clarify how the measurements are achieved - through ROIs for the entire nucleoplasm/periphery? How do they capture the diffuse versus focal signal within the nucleoplasm? There is also some concern that the nucleoplasmic signal may simply be too low to detect robustly at early time points (leading to an increase at later time points as the protein accumulates). Line profiles (which are useful in Fig. 3) would be very helpful if used more broadly for assessing the data particularly for Figure 1.

3) Related to Figure 1 - the results for the deltaK32 mutant is essential for the interpretation and should be included in the primary figures.

4) The authors make no comment on the functionality of the mEos-tagged lamin A/C CRISPR lines. However, the comment suggesting that some clones could have altered nuclear morphology (line 225) raises some questions. How did the authors interpret this? Were these clones in which there were indels in some lmnA alleles affecting the levels? Or is this a consequence of the fusion? How do the authors explain the relatively low expression level of the mEos fusion relative to the untagged? If the MDFs are diploid, presumably we would expect this to be one allele tagged and one allele untagged. Given that the expression ratio is very different from this, could the tagged lamin A/C be targeted for degradation? As these cell lines are critical for the rest of the study, this information is important.

5) How does the deltaK32 mutation affect the ability to detect lamin A/C with the N18 antibody? Could this provide further insight into the impact of LAP2alpha by extension?

6) Greater explanation for the apparent paradox between the increase in immobile fraction by FRAP and the increased diffusion coefficient by FCS in the LAP2alpha-depleted condition is needed. The authors suggest that the latter is due to the loss of LAP2alpha binding (line 395), but some modeling would go a long way here. What form are the lamins thought to be in, and how does the bulk that LAP2 alpha would bring match the apparent changes in diffusivity?

7) One prediction that arises from the proposed model is that regulation of LAP2alpha levels will modulate the relative pool of A-type lamins at the nuclear interior versus the nucleoplasm. Beyond the knock-out cells, is there any other evidence of this relationship?

8) Much of the biochemical characterization seems confirmatory - e.g. the binding and gradients in Fig. 5A and B. Use of the assembly mutants of lamin here could be informative is essential to interpret the changes induced by addition of LAP2alpha.

9) With regards to the effects on chromatin mobility - over what time interval was the volume of movement observed? This is important because more fluctuations in nuclear position, for example, could influence this measure. In addition, telomeres are a confusing choice, given abundant evidence that there is crosstalk between the state of the nuclear lamina and telomere biology (e.g. lamin mutants affecting telomere homeostasis, etc.). At a minimum, acknowledging that telomeres may not reflect the effect on chromatin globally is important. Examples of the raw mean squared displacements would be more informative. Is the difference between lmna KO and lmna/Lap2alpha DKO (Fig. 6 right panel) significant?

10) How do the authors think the membrane integrated LAP2beta fits into the story?

Reviewer #1:

This is a rigorous and very interesting study on a timely topic: combining modeling traditions of (reinforcement) learning and decision-making. The central claim of the paper is that the often-used combination of reinforcement learning with the drift diffusion model does not provide an adequate model of instrumental learning, but that the recently proposed "advantage accumulation framework" does. This claim will likely be of interest for anyone studying learning and decision-making, ranging from mathematical psychologists to neuroscientists running animal labs. I have a number of concerns regarding this paper.

1) I think the basic behavior and model fit quality should be better described. The reinforcement-learning + evidence accumulation models (RL-EAM) are fitted to choices and reaction times (RTs). I find it therefore odd that we don't get to see any actual RT distributions, but only the 10th, 50th and 90th percentile thereof. What did the grand average RT distribution and model predictions look like (pooled across subjects and trials)? How much variability was there across subjects? I understand that that model was fit hierarchically, but it would be nice to (i) see a distribution of fit quality across subjects, to (ii) see RT distributions of a couple of good and bad fits, and to (iii) check whether the results hold after excluding the subjects with worst fits (if there are any outliers). Related, in the RT percentile plots (Figures 3 & 4), it would be nice to see some measure of variability across subjects.

2) The authors pit four competing RL-EAMs against one another. I have a number of issues with the way this is done:

-The qualitative model fits presented in Figure 3 are potentially misleading, as the competing models have different numbers of free parameters: DDM, 4; RL-RD, 5; RL-IARD, 5; RL-ARD: 6. RL-ARD has most free parameters, which might trivially lead to the best visual fit. For this reason, I find the BPIC results more compelling, and I think these should feature more prominently (perhaps even as bars in the main figure?).

-All three racing diffusion models implement an urgency signal. Why did the authors not consider a similar mechanism within the DDM framework? Here, urgency could be implemented either as (linearly or hyperbolically) collapsing bounds, or as self-excitation (inverse of leak); both require only one extra parameter.

3) I could imagine a scenario in which the decision-making process becomes progressively biased toward the more rewarding stimulus. In fact, this can be observed in Figure 7. Therefore, I wonder if the authors have considered RL-AEMs in which the choice boundaries do not correspond to correct vs. error, but instead to the actual choice alternatives (stimulus A vs. B). In such an implementation one can fit bias parameters like starting point and/or drift bias.

4) The authors write that RL-AEMs assume that "[...] a subject gradually accumulates evidence for each choice option by sampling from a distribution of memory representations of the subjective value (or expected reward) associated with each choice option (known as Q-values)." Sampling from a distribution of memory representations is a relatively new idea, and I think it would help if the authors would be more circumscribed in the interpretation of these results, and also provide more context and rationale both in the Introduction and Discussion. For example, an interesting Discussion paragraph would be on how such a memory-sampling process might actually be implemented in the brain.

Reviewer #1:

This article investigates how uncertainty about the value of alternatives affects the decision process through the lens of the drift diffusion model. The article proposes several models for how uncertainty might affect the drift rates or diffusion variance, and tests those models on four different food-choice datasets. The authors conclude that the best model is one in which the drift rate depends on the values of the options divided by their degree of uncertainty.

I think the article is pursuing an interesting question. The core set of results are perhaps not as surprising or as puzzling to a DDM audience as the introduction might have you believe, but from there the paper does a nice job of exploring different ways in which uncertainty might affect the choice process. This seems like a good set of models to consider, as they cover the obvious ways in which one might consider incorporating uncertainty into the DDM, and each one, except for the favored Model 4, has a clear inability to capture a facet of the data.

1) I could quibble about why the authors don't explore more variants of the favored Model 4, for example ones where the values are divided by non-linear functions of the uncertainty measure (e.g. squared or square root)? The results in Figure 4 are not a slam dunk for Model 4, as the effect of dC seems to outweigh C, while in the data it is the opposite. I don't think this is critical, but the authors might try an extra exponent parameter on uncertainty in Model 4. At minimum, the authors should discuss how they might modify Model 4 to better match the data.

2) As I alluded to above, I think the article somewhat mischaracterizes the DDM by saying that "the most straightforward way to include option-specific noise in the preferential DDM - by assuming that noise increases with value uncertainty - leads to the wrong qualitative predictions..." "Most straightforward" is subjective. The standard diffusion model sets the diffusion noise variance to a constant, and so no, adjusting the noise is not "straightforward"; in many DDM software packages it is not even an option. Instead the effect of uncertainty would show up in the drift rate (or boundaries), as it does here. So, I would urge the authors to temper their claims in the introduction and discussion about what the "straightforward" model would be. Many researchers who use the DDM think about the drift rate as a signal-to-noise ratio, and for them Model 4 would have been the straightforward model.

3) This isn't to say that what the article does isn't interesting or important. A standard DDM analysis would just fit different drift-rate and boundary parameters to high and low uncertainty conditions and then report the differences. This article takes a more elegant approach by explicitly modeling uncertainty in the DDM components. This is why I would urge the authors to do a bit more with that aspect of the paper, to try to better understand how uncertainty impacts the drift rates.

4) On Page 16 - the authors write "in line with the best fit parameters". What exactly do they mean here? Did they use the best-fitting parameters or not? Could the authors add a table to the supplements with the average best-fitting parameters for each model, for each dataset? That would greatly help in understanding the results.

5) Figure 4 - how were the experimental data and model simulations combined to generate these figures? For the data, was this one big mixed-effects regression including all datasets? How did the authors handle the random effects in this case, given the multiple datasets? The simulations are also vaguely described. How "similar" were the input values to the data; how exactly were these input values generated? Again, how were the simulations from different subjects/studies combined to generate a single plot per model? It would be useful, though not strictly necessary, to see the basic behavioral results broken down by study (in the supplements). It is unclear how consistent the patterns in Figure 2/4 are across the studies.

Reviewer #1:

This MEG study by Griffiths and colleagues used a sequence learning paradigm which separates information encoding and binding in time to investigate the role of two neural indexes - neocortical alpha/beta desynchronization and hippocampal theta/gamma oscillation - in human episodic memory formation. They employed a linear regression approach to examine the behavioral correlates of the two neural indexes in the two phases, respectively and demonstrated an interesting dissociation, i.e., decreased alpha/beta power only during the "sequence perception" epoch and increased hippocampal theta/gamma coupling only during the "mnemonic binding" phase. Based on the results, they propose that the two neural mechanisms separately mediate two processes - information representation and mnemonic binding. Overall, this is an interesting study using a state-of-art approach to address an important question. Meanwhile, I have several major concerns that need more analysis and clarifications.

Major comments:

1) The lack of theta-gamma coupling during the stimulus encoding period is possibly due to the presentation of figure stimulus, which would elicit strong sensory responses that mask the hippocampus activity. How could the author exclude the possibility? In other words, the dissociated results might derive from different sensory inputs during the two phases.

2) About the hippocampal theta/gamma phase-power coupling analysis. I understand that this hypothesis derives from previous research (e.g., Heusser et al., 2018) as well as the group itself (Griffiths et al., PNAS, 2019). Meanwhile, MEG recording, especially the gradiometer, is known to be relatively insensitive to deep sources. Therefore, the authors should provide more direct evidence to support this approach. For instance, the theta/gamma analysis relies on the presence of theta-band and gamma-band peak in each subject. Although the authors have provided two representative examples (Figure 3A), it remains unknown how stable the theta-band and gamma-band peak exist in individual subject.

3) Related to the above comment, the theta-gamma coupling is a brain-wide phenomenon including both cortical and subcortical areas and not limited to just hippocampus. Although the authors have performed a control analysis to assess the behavioral correlates of the coupling in other regions, the division of brain region is too coarse and I am not convinced that this is a fair comparison, since they differ from hippocampus at least in terms of area size in the source space. The authors could consider plotting the power-phase coupling distribution in the source space and then assessing their behavioral correlates, rather than just showing results from hippocampus. This result would be important to confirm the uniqueness of the hippocampus in this binding process.

4) About behavioral correlates. The current behavioral index confounds encoding and binding processes. Is there any way to seperate the encoding and binding performance from the overall behavioral measurements? It would be more convincing for me to find the two neural indexes at two phases predict the two behavioral indexes, respectively.

5) The author's previous works have elegantly shown the two neural indexes during fMRI and intracranial recording in episodic memory. The current work, although providing an interesting view about their possible dissociated functions, only focuses on the memory formation period (information encoding and binding). Given previous works showing an interesting relationship between encoding and retrieval (Griffith et al., PNAS, 2019), I would recommend the authors to also analyze the retrieval period and see whether the two indexes show consistent dissociated function as well.

Reviewer #1:

In this manuscript, the authors revisit DCC and NTN1 mutants in order to better define the basis for midline crossing defects. This group recently demonstrated that midline zipper glia (MZG) must migrate along the interhemispheric fissure (IHF) and intercalate across the midline while remodeling the meningeal basement membrane to provide a substrate for callosal axons to cross the midline. In this study, they show that DCC and its ligand NTN1 are required for proper midline zipper glia (MZG) distribution/morphology along the IHF, proper remodeling of the basement membrane, and subsequent corpus callosum (CC) formation. The data in figures 2 and 3 generally do a nice job of supporting the model that DCC and NTN1 are expressed in MZG and that the morphology and distribution of MZG are affected in DCC/NTN1 mutants. There appear to be some defects in MZG migration that may account for this (Figure 4). Due to technical limitations, the author's attempt to use a conditional knockout of DCC to genetically dissect whether CC formation defects are due to defects in MZG or callosal axons are a bit inconclusive (Figure 6). Finally, the paper ends with experiments showing that mutations in DCC identified in acallosal patients are loss-of-function using an in vitro cell morphology assay (Figure 7 and 8).

The authors are commended for the quality of their imaging data and for being as quantitative as possible when measuring their in vivo phenotypes, which is not often done with these types of studies. There are few issues that need to be addressed.

Major points:

1) In Figure 4, in addition to the migration defects of Sox9+ MZG, there seems to be a rather large increase in the total number of Sox9+ cells along the IHF by E16 (more than 2 fold, Figure 4G). The authors show there is no change in cell cycle or apoptosis of these cells in the supplemental data (Figure S4), so what accounts for this increase? Is this also seen with NFIA/B staining at E16?

2) Regarding the attempt to distinguish between DCC in MZG versus callosal axons (Figure 6), the incomplete deletion/loss of DCC protein (Figures 6C, I, J) is a bit concerning. It's not clear to me why this would happen, but it confounds the interpretation of the results. While the authors state "The severity of callosal agenesis was associated with the extent to which the IHF had been remodeled" (pg 15), they don't actually quantify this. It might be informative to generate scatterplots of IHF length vs. CC/HC length to determine if there is a significant correlation between the two. This might lend more evidence to a causal relationship between IHF remodeling and CC/HC formation.

3) At the end of the result section, the authors state: "mutations that affect the ability for DCC to regulate cell shape (Figure 8F), are likely to cause callosal agenesis through perturbed MZG migration and IHF remodelling." (pg. 19). While the authors nicely show that patient mutations in DCC affect the morphology of cells in cell lines (Figure 7-8), it is not clear why simply transfecting WT DCC into cell lines results in such a dramatic change in morphology, or why addition of NTN1 doesn't increase this. The authors mention that the cell lines could express NTN1 or that NTN1 is not required for the effect. This seems an important distinction. Did the authors check this? Could they use a function blocking antibody or a soluble fragment of the NTN1 binding domain of DCC to block NTN1:DCC interactions? DCC has been shown to function as a "dependence receptor" that can induce apoptosis in the absence of ligand; are the authors certain that the morphology changes they are seeing in DCC transfected cells aren't cytoskeletal changes resulting from caspase activation?

Minor points:

1) The authors should mention recent work showing Netrin localization to basement membranes during axon guidance (Varadarjan et al, Neuron 2017). The data in Figure 2 are very much in agreement with this previous work, and it should be mentioned in this context.

2) Figure S5A: Representative images from each genotype don't look comparable, even though there's no difference in quantification.

3) Did the authors check whether the cell lines they used in Figure 7-8 express DCC?

Reviewer #1:

This manuscript investigates TE diversity and variation across several clades of bdelloid rotifers, which are particularly interesting from an evolutionary perspective since they reproduce asexually. As stated by the authors, theory predicts that asexuality may lead to two opposite outcomes in terms of TEs content. In the absence of sex, TEs may not easily jump into new genomic backgrounds where they are not repressed, leading to a decline in TE content. On the other hand, there is no recombination without sex, which removes the selective pressure against TEs due to their involvement in ectopic recombination. The authors show that despite these extreme expectations, asexual rotifers do not seem to display any of these patterns, although recent insertions seem rare and possibly brought through horizontal transfers. They do not observe any clear effect of adaptation to desiccation on TEs content, which seems to exclude any effect of enhanced DNA repair mechanisms in controlling TEs. They observe less LINEs and more (recent) DNA transposons in bdelloid rotifers, which is consistent with the absence of sex (limiting LINEs spread) and horizontal transfers (more frequent for DNA transposons). The expansion of RNAi gene silencing pathways suggests that asexuality comes at a cost, such as the proliferation of TEs, the accumulation of genetic load, and the control of horizontal gene transfers that might be deleterious. I think this supports the hypothesis of strong TEs activity associated with the onset of asexuality, leading to a strong evolutionary response. This suggests that these clades survived the arms race with TEs. This work shows how intricate the coevolutionary dynamics between TEs and their hosts can be. The manuscript is well-written, analyses are sound and detailed. I have a few general comments/questions that I detail below: Horizontal gene transfer: given the abundance of recent DNA transposons in some clades (class I), it may be worth discussing a bit more this possibility (at this stage it is mostly discussed in the Conclusion).

If my understanding is correct, there is no assessment of TEs or SNPs heterozygosity for each individual. This might be interesting to explore. If TEs are deleterious recessive, one might observe more frequently at the heterozygous state. For intraspecific data, it may be interesting to look at how nucleotide diversity varies along the genome. Since variable recombination may be associated with diversity due to the effects of selection at linked sites, checking diversity along the genome may bring another layer of information about the frequency of sexual reproduction and its effects on TEs diversity. I acknowledge that this would be a rather exploratory analysis, and am not asking the authors to carry it, but I am curious to know how do methods designed to estimate effective recombination rates perform on these data (e.g. LDHat, or more recently iSMC for a single diploid genome).

Question related to demography and selection: would it be possible to obtain estimates of the effective population size for these clades? It would be interesting to have such an estimate to get an idea of the efficiency of purifying selection against TEs, and whether Muller's ratchet could explain the current abundance of TEs (in the case of moderate/small effective population sizes). I liked the idea of using the ABC to test for consistency with asexuality, but am wondering to what extent it is biased by non-constant transposition rates, which cannot be properly modeled by the coalescent simulation? I would also assume these simulations do not take into account past changes in demography (I believe this option has not been included in the software yet). This is not necessarily a major issue for me, as long as these limitations are mentioned. When presenting the ABC framework in the Methods section, you may want to give more details about the part carried with the abc package itself (e.g. which regression/rejection algorithms were used, etc.).

A few other comments linked to specific paragraphs/sentences:

• L419: why choosing LTR-Rs in particular (abundance and the fact they are not class I I guess).
• L450: Would it be possible to obtain a time in generations from, e.g., an approximate mutation rate?
• L455: Would it be possible to call heterozygote SNPs/elements?
• L550-656: do you examine the most recent elements only? It may be interesting to check these correlations for elements of different ages, since selection may have had the time to act on the most ancient TEs.
• L642: It might also be that longer elements display functional regulatory/promoter regions, and have a stronger impact on fitness.
• L725: I liked this part, but wondered if a slightly more detailed discussion was possible. As the authors state, the expansion of RNAi pathways is consistent with a control mechanism against TEs. It is important to detail alternative explanations since there is no functional evidence in this model that this expansion actually controls TEs proliferation (unless I missed something). Given the rather unique properties of these organisms, it may be worth discussing.

Reviewer #1:

This paper presents a very interesting set of techniques (monocular and binocular visuomotor tracking) to evaluate subtle differences in visual processing as a function of luminance.

Despite some technical caveats I'll explain below, the paper fairly convincing demonstrates that the monocular visuomotor tracking task can be used to identify millisecond-scale differences in visual processing lags, e.g. caused by different levels of luminance. The basic experimental analysis and comparison to traditional approaches were fairly thorough and convincing.

The binocular tracking component was less convincing, and the data were messy (which the authors acknowledge). Unfortunately, the very small sample size (N=5), lack of attention to trial order effects and learning of this new task, etc, reduce enthusiasm about this part of the paper.

While this seems like a solid paper in most respects, it seems it’s primary focus is to demonstrate that a 'new' technique visuomotor tracking (which is not new per se, but may be new in this field), gives results on delay estimation that are indistinguishable from traditional psychophysical techniques. This new approach requires fewer experiments and uses the richness of the full time series for analysis. The basic approach is near and dear to my heart in that it uses continuous-time system identification to really extract rich information.

However, while I think the technique (which I quite like) is promising, I do not know what the new finding is. The analysis also only scratches the surface. I think this is a solid, field specific paper that verifies a new method and, despite its technical contributions, may be suitable for a field-specific readership, with modest effort to address or at least acknowledge the technical limitations.

Technical Limitations:

1) The visuomotor behavior is not new; continuous tracking moving stimuli is an age-old process. What is potentially new here is the use of this behavior for identifying subtle differences in delay. For a fairly old review with several papers cited in this area, see:

Roth, S. Sponberg, and N. J. Cowan, "A Comparative Approach to Closed-Loop Computation," Curr Opin Neurobiol, vol. 25, pp. 54-62, 2014

But there are many (much older) papers dating back for example to McRuer on visuomotor tracking tasks for identifying control systems in human visumotor control, including careful analysis of visuomotor delay.

For a recent paper (in a non-human system) for detecting differences in delay, see:

Luminance-dependent visual processing enables moth flight in low light Sponberg et al, 2015, SCIENCE 12 JUN 2015 : 1245-1248

2) There are no error bars. With 40 trials per condition, a simple SEM may be sufficient.

3) The binocular data highlights a general problem which is that people need to learn this task, and if you are doing system identification during learning, you are doing system ID on a time varying system. This sounds like a confusing task and I agree with the authors that "higher level cognitive processes" are probably taking place but more importantly the learning system is not in steady state even after that many trials.

4) Very importantly, unlike the traditional psychophysics trials (which are based on perception not motor output), this data must be analyzed as a closed-loop system. There are now two pieces of visual information: exogenous reference and self-movement feedback. It is extremely likely that these are processed differently, via feedforward and feedback controllers. See these papers ... These are very new, so I wouldn't have expected the authors to know about them, but they will still be useful for understanding this concept and improving your analyses:

Yamagami, M., Howell, D., Roth, E., & Burden, S. A. (2019). Contributions of feedforward and feedback control in a manual trajectory-tracking task. IFAC-PapersOnLine, 51(34), 61-66.

Yamagami, Momona, et al. "Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking." bioRxiv (2020). https://www.biorxiv.org/content/10.1101/2020.08.01.232454v1

That said, the highest-frequency responses - those picked up in the earliest moments of the impulse response function - are largely "open-loop", a fact that can be verified by noting that in the frequency domain, there is a very low gain (which is almost surely true with this data as it is in all other visuomotor tracking data across species that I am aware of, and that fundamentally must be true to ensure stable tracking!). So, the observations about short-time-scale (i.e, high frequency) differences being attributed to differences in the visual processing, are likely substantiated. But a more nuanced and accurate description of the theoretical basis for this is warranted.

5) One second is not steady state in human visuomotor tasks. Tracking bandwidth for visuomotor behavior is in the ballpark of around 0.5-2Hz, which means there is still significant phase lag at 1 Hz. So the 11 second trials, with the first second thrown away does not necessarily "erase" initial conditions. As one example, see a recent paper (again I wouldn't have expected you to know this, but it still shows 1 second is not long enough):

Zimmet, A. M., Cao, D., Bastian, A. J., & Cowan, N. J. (2020). Cerebellar patients have intact feedback control that can be leveraged to improve reaching. eLife, 9, e53246.

In Fig 4S2 in that paper you see that the phase lag at 1Hz is well over 90 degrees. Always wait 10 seconds to be certain, since at 0.1Hz, the phase lag is very low.

6) Perhaps most fundamentally, lag and delay are not the same thing. Delay induces a very specific time shift, but it should be noted that in a closed-loop system one can NOT just shift the closed-loop cross-correlation function (equivalent to the impulse response in this case due to the noise input). If the delay were only on the measured target signal, and not on the feedback of self-motion, then indeed a simple time shift would be adequate; but there is a complex and subtle "compounding" of the feedback delay in closed-loop that leads to a distortion, not a simple shift, of the impulse response function. These papers show different ways on how to estimate delay differences in closed loop correctly:

Luminance-dependent visual processing enables moth flight in low light Sponberg et al, 2015, SCIENCE 12 JUN 2015 : 1245-1248

Zimmet, A. M., Cao, D., Bastian, A. J., & Cowan, N. J. (2020). Cerebellar patients have intact feedback control that can be leveraged to improve reaching. eLife, 9, e53246.

I love the first paper's method, but it is not always applicable. I think it may be applicable in this case where one may be able to assume nothing changes but the delay.

Reviewer #1:

The manuscript describes analyses of genomic data to study the population structure and demographic history of Brachypodium distachyon - a selfing Mediterranean grass species. Major findings include the existence of large-scale population structure (3 lineages), discordance between geographical occurrence and genetic relatedness (clades within the lineages), and at shorter scales, signs of dispersal without interbreeding. These patterns are explained by a combination of near-complete selfing and seed dispersal. The methods are appropriate, results well reported, and writing is good. As such, the paper provides interesting insights into the evolutionary history of B. distachyon, but due to its descriptive nature, I somewhat question the paper's value for a wider audience (i.e. people not directly working with B. distachyon). At points, the authors also engage in speculation (not supported by data) where I feel that more simpler population genetic processes are ignored.

In my opinion, the biggest weakness is the descriptive nature of the paper: it describes the genetic structure and demographic history of B. distachyon, but potential processes giving rise to the structure are only speculated. In particular, the authors invoke pre- and post-zygotic reproductive isolation (lines 384 - 387) and pathogen-driven frequency-dependent selection (lines 431 - 435) as potential causes for the observed structure. However, as the paper provides no evidence for such processes, it's not clear to me why they need to be invoked in the first place? Evidence for seed dispersal over relatively short spatial scales is shown (within populations in Italy, Fig 4), but to my reading the results suggest little dispersal/gene flow over long distances (only few individuals with increased heterozygosity or signs of admixture). Therefore, I believe that the simplest explanation for the genetic structure is founder effects (perhaps human-induces, given the peculiar differences within the A and B lineages) combined with the near-complete selfing. This would explain the emergence of the genetic lineages and the lack of interbreeding. Furthermore, I would imagine that the genetic groups are locally adapted (e.g. there's extensive local adaptation among the selfing populations of A. thaliana), which would ensure that one lineage/accession doesn't take over when otherwise feasible (e.g. within the B lineage). If the authors argue otherwise, I would like to see more convincing evidence and/or discussion supporting the invoked processes.

Below I list a few more specific comments:

Lines 26 - 27: "[our study] identifies adaptive phenotypic plasticity and frequency-dependent selection as key themes to be addressed with this model system". While reading the abstract this sentence got me interested and I expected at least some analyses addressing these topics. However, the only place where they are mentioned again are two highly speculative sentences at the end of the discussion (lines 427 - 435). Although the authors write "themes to be addressed", I think that the complete lack of evidence for adaptive plasticity or pathogen-driven frequency-dependent selection in the current study makes this sentence too misleading to be left in the abstract.

Lines 51 - 53: "For plants, genome-wide coalescence approaches have therefore been largely restricted to domesticated species and Arabidopsis thaliana". This might have been true some years ago, but not anymore. Just to highlight a few wild plant species (and studies) where demographic history has been studied using whole-genome data: A. lyrata (Mattila et al. 2017 MBE), A. arenosa (Monnahan et al. 2019 Nat Ecol Evol), Capsella genus (Douglas et al. 2015 PNAS, Koenig et al. 2019 eLife), Boechera stricta (Wang et al. 2019 Genome Biol), Populus genus (Wang et al. 2016 MBE, Hou & Li 2020 Front Plant Sci), Coclearia genus (Bray et al. 2020 bioRxiv), and many more.

Lines 383 - 387: "Flowering time differences are at best part of an explanation for genetic structure. In the scenario of subsequent lineage expansions we propose here, reproductive isolation might have evolved when the lineages were geographically isolated; and it might include other pre- and post-zygotic barriers in addition to flowering time, namely niche differentiation or genomic incompatibilities". These sentences kind of come out of nowhere. First, I don't fully understand the distinction between genetic structure and lineage expansions. If the latter is a process beyond population structure (i.e. incipient speciation), the paper shows no evidence of that. In fact, as I outlined above, I would imagine that founder effects and near-complete selfing is enough to cause and maintain population differentiation without reproductive isolation?

Lines 389 - 390: "Furthermore, differences observed in the greenhouse are most likely exaggerated through artificially short vernalization times. As our outdoors experiment shows, all accessions produced flowers within two weeks when they went through prolonged vernalization during winter". How representative are these vernalization times of the natural growing conditions? Large differences were observed in the greenhouse experiment, but the authors argue that these are not meaningful because the outdoor experiment showed little differences. However, a single experiment conducted in Zurich certainly does not capture environmental variation existing across the Mediterranean, so I'm not convinced that the role of flowering time can be ruled out so strongly based on these results. That said, the near-complete selfing suggests to me that flowering time is likely not a major factor underlying the genetic structure, and founder effects are a better explanation for it.

Line 548: Only one species (B. stacei) was used to define ancestral alleles in the fastsimcoal2 analysis. There are multiple studies showing that the use of a single outgroup, especially based on parsimony, leads to unreliable inferences of ancestral and derived alleles (e.g. Keightley et al. 2016 Genetics, Keightley & Jackson 2018 Genetics). In particular, this leads to overestimation of high-frequency derived variants, distorting the shape of the unfolded SFS. As the observed SFS has more shared high-frequency variants than predicted by the demography model (Fig S5), I imagine that this is an issue. FSC2 also works with the folded SFS, so I wonder why the authors chose to use the unfolded SFS? Unless there is a compelling reason, I suggest to either add more outgroups or to simply fold the SFS.

Reviewer #1:

This is an excellent study of centrosome polarization in the process of establishing immunological synapse and the effect of kinesin-4 on this process. The authors use a variety of microscopy techniques and controlled perturbations of the cell to obtain beautiful images that clearly suggest that kinesin-4, by increasing frequency of pauses and subsequent MT catastrophes, limits MT length, which assists dynein pulling in polarizing the centrosome. They complement the experiments with modeling based on Cytosim; the model supports the conclusions from the data, and suggests some interesting ideas.

I am not an expert in experimental techniques, though I understand what's been done, and in my limited opinion, the results are first-rate. The paper is well written and accurate. Modeling, which I know intimately, is done very well.. I have just a few minor comments:

1) I was not quite clear what does the modeling say about the centrosome sometimes being in apical position, and sometimes half-way between apical and basal positions.

2) I understand that 2d modeling cannot address this issue explicitly, but can the authors speculate about the apparent ring of MTs along the periphery of the synapse in the non-polarized case?

3) My perhaps most significant comment: the model nicely integrates and explains the data, but is it predictive? A detailed model like that clearly can generate some nontrivial prediction that could be experimentally tested.

4) "Interestingly, in our simulations, a small number of KIF21B motors was sufficient to prevent the overgrowth of the MT network." - this is a bit counter-intuitive: if the motor number is less than MT number, how would this work? Or, by a "small number of KIF21B motors" you mean still greater than ~ 100?

Reviewer #1:

In this work the authors measure the activity of the octopaminergic VUM neurons that arborize throughout the somatic body wall muscles in the Drosophila larva. They use three different larval preparations: isolated CNS (no sensory afferents), semi-intact (CNS exposed while maintaining sensory input), and intact. They find that isolated CNS has rhythmic waves of activity in the VUM neurons, but that semi-intact preparations do not show rhythmic VUM activity. They also show that "harsh" or "gentle" touch elicits different responses in VUM neurons.

There are several interesting findings. The ability of VUM neurons to show rhythmic activity in the isolated CNS is a novel finding. It would be even more interesting to register these waves to that of the glutamatergic body wall motor neurons that drive locomotion. It is also interesting that touch applied to an anterior segment results in elevated VUM activity in a posterior segment, and conversely posterior touch leads to elevated VUM activity in an anterior segment, suggesting that sensory input dampens VUM activity.

There are also issues that need to be addressed, which are listed below.

1) The function of the VUM neurons in locomotion was not tested, e.g. by silencing or activating them. These experiments would greatly strengthen the paper.

2) The three larval preparations are poorly described. (a) The fictive preparation is clearest but still should have a citation to Pulver 2015 at first use, as that paper provides a detailed description of the isolated CNS prep. (b) The semi-intact prep is not well described: is the CNS pulled from the body? How can this be done without ripping the nerves? How can the intactness of the nerves be validated? (c) The intact prep sounds simple, but how is VUM GCaMP3 fluorescence measured in an intact larva as shown in Figure 4? Is the "intact" prep the same as the "in vivo" prep? One name should be used throughout for clarity.

3) The semi-intact prep showed Ca++ signals in only 5% of the preps. This makes me worried that the prep is unhealthy, and that the data from the 5% are not physiological.

4) Experiment 1 shows four individuals, but population data for all larvae were not shown. Selecting only a subset of the analyzed larvae is not appropriate; data from all should be shown.

5) Experiment 2 shows low resolution data (left) that is not interpretable. The data highlighted in the right panel is much better but again, only three examples are presented; no population data or statistics are shown.

6) It is also unclear how many larvae were analyzed in Experiment 2. Line 163 says "...~5% of the in vivo preparations (n=27)..." but is that 1/27 or 27/540? In addition, are the different stimulation patterns done sequentially on the same larva, or independently on different larvae?

7) The prep used for Experiment 3 is not mentioned. Not in the text, not in the figure legend.

8) The prep for Experiment 4 appears to be the intact larva, but if so, how were GCaMP signals measured? How were movement artifacts handled?

9) In Experiment 4, the term "crawling frequency" is not defined. Is it frequency that locomotion is initiated?

10) How do the authors standardize harsh and gentle touches?

11) It says "in very rare cases..." on line 246. Please give actual numbers.

12) The figures are cited out of order (1, 3, 2, 4).

13) Many references are missing in the first part of the Introduction, e.g. lines 64. 65, 73, 78, and 83.

Reviewer #1:

The authors have used hydrogen deuterium exchange mass spectrometry and molecular dynamics simulations to study the interaction between the sars-cov-2 spike protein and the ace2 protein. The results suggest that the protein-protein interaction induces extremely long-range allosteric effects on the spike protein, triggering the proteolysis of the spike protein. The results of this work have implications for the development of small molecule inhibitors.

In general, the manuscript is written extremely well. The work is timely, and the results will be of interest to many. The major conclusions of the work are generally supported by the results. However, there are several key - generally minor - details, enumerated below, the authors should provide in order to strengthen the manuscript and validity of the results.

1) The authors should provide more technical details of the molecular dynamics simulations in the supplementary materials. Could the authors provide more details on the equilibration protocol? Was there any analysis done or metric used to assess whether the system was properly equilibrated? How often were snapshots of the trajectory saved for analysis? How many Na+ and Cl- ions were added to achieve 0.15 M of salt concentration? Also, how many water molecules were added? These details are relevant to the non-casual readers.

2) The authors should probably include the techniques used to study the systems in the abstract section of the manuscript.

3) Also, the authors should probably also include the fact that they performed molecular dynamics simulations in the last paragraph of the introduction. This is not apparent until toward the end of the first paragraph of the results and discussion sections.

4) Page 7; line 147: Figure 4 is introduced before Figure 3. The authors should switch the order or modify accordingly.

5) Figure S1: Could the authors elaborate on Figure S1B in the figure legend? Is (i) measuring the binding of ace2 to the S protein? Is (ii) measuring the binding of RBD to the ace2 protein? The distinction between (i) and (ii) is not made in the figure legend.

In summary, the work is interesting and timely, and the manuscript will be of interest to many in the field. The authors should address the aforementioned points.

DOI: 10.1016/j.celrep.2020.108404

Resource: (ZFIN Cat# ZDB-ALT-071127-1,RRID:ZFIN_ZDB-ALT-071127-1)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-071127-1

Curator comments: allele name: li1Tg Danio rerio ZFIN Cat# ZDB-ALT-071127-1

What is this?

DOI: 10.1016/j.celrep.2020.108404

Resource: (ZFIN Cat# ZDB-ALT-070316-1,RRID:ZFIN_ZDB-ALT-070316-1)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-070316-1

Curator comments: allele name: c264Tg Danio rerio ZFIN Cat# ZDB-ALT-070316-1

What is this?

DOI: 10.1016/j.celrep.2020.108404

Resource: (ZFIN Cat# ZDB-ALT-071120-1,RRID:ZFIN_ZDB-ALT-071120-1)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-071120-1

Curator comments: allele name: zf52Tg Danio rerio ZFIN Cat# ZDB-ALT-071120-1

What is this?

DOI: 10.1016/j.celrep.2020.108404

Resource: (ZFIN Cat# ZDB-ALT-120320-1,RRID:ZFIN_ZDB-ALT-120320-1)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-120320-1

Curator comments: allele name: sd11Tg Danio rerio ZFIN Cat# ZDB-ALT-120320-1

What is this?

DOI: 10.1016/j.celrep.2020.108404

Resource: (ZFIN Cat# ZDB-ALT-110307-1,RRID:ZFIN_ZDB-ALT-110307-1)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-110307-1

Curator comments: allele name: pd12Tg Danio rerio ZFIN Cat# ZDB-ALT-110307-1

What is this?

Reviewer #1:

This study investigates asymmetry in functional gradients in human subcortical structures (thalamus, striatum and cerebellum). The authors found that the 1st principal gradient of thalamus and palladium are asymmetric, while that's not the case for caudate, putamen and the cerebellum. In the case of the caudate and cerebellum, their 2nd and 3rd gradients were asymmetric. Further analyses suggest that these differences arise based on connectivity between subcortical structures and the cerebral cortex. In the case of the thalamus and lenticular nuclei, asymmetry is stronger in regions with no direct or driver cerebral cortical afferent connections. In the case of the cerebellum and caudate, asymmetry is stronger in regions linked to cortical regions with higher inter-hemispheric asymmetry. The writing style of this paper is quite different from the usual papers. I actually quite enjoy this conversational/didactic style. Please see my major and minor concerns below.

1) The computation of the laterality index is not clear to me. In the methods section, it's defined as "(left_score - right_score) / (left_score + right_score), where left_score and right_score correspond to the sum of all functional connectivity values for each left and right structure (for example, in the case of thalamus, functional connectivity values in left and right thalamus)". This sounded like they were averaging across all voxels within for example across all thalamic voxels. But in Figure 2, I assume each dot represents a thalamic voxel. So what are the authors averaging over? Indeed, in the results section, the authors said "We then computed a laterality index that quantified the degree of asymmetry in each functional connectivity map from each seed (see methods), and plotted laterality index scores for each voxel in thalami and lenticular nuclei against their corresponding functional gradient value." So for each thalamic voxel, the authors computed the correlation of the voxel's time course to all brain voxels or something else? This was also not clear. After obtaining the correlation map for a thalamic voxel, how do the authors then compress the correlation map of the thalamic voxel into either "left_score" or "right_score". That was not really explained. Furthermore, in order to compute the laterality index, the authors need to define a homologous thalamic voxel on the other hemisphere. How was this done? Did the authors use a symmetric MNI template? Which one? This was also not explained.

2) "Projection of subcortical functional gradients to cerebral cortex" does not quite make sense to me. According to the authors, basically FC maps of voxels are weighted by the absolute gradient values of the voxels. Essentially this means that voxels with extreme gradient values are weighted more. In the case of the thalamus, lenticular nuclei and caudate, voxels with extreme gradient values are indeed voxels with high inter-hemispheric functional asymmetry (IHFaS), so this is ok. However, in the case of the cerebellum, motor regions in lobules I-IV have extreme gradient values as well. As such, these regions would also be weighted more. Thus the resulting projected subcortical gradients might not simply reflect gradient asymmetry. Perhaps it would make more sense to compute a laterality index based on the gradient scores (i.e., left score and right scores are gradient values), and then use the absolute value of the laterality index as the weight rather than the absolute gradient values.

3) The analysis level in Figure 5 is too coarse. By performing a weighted average of thalamic voxels' FC maps (or caudate or lenticular or cerebellum), the authors are ignoring variation in functional connectivity patterns across thalamic (or cerebellar or caudate or lenticular) voxels. A more direct test of the authors' hypothesis should be as follows. According to the authors' hypothesis, cerebellar/caudate voxels that exhibited greater gradient asymmetry should be more strongly correlated with cortical vertices with strong absolute laterality index. Then there should be strong positive correlations between the absolute laterality index of cerebellar/caudate voxels and the absolute laterality index of the cortical locations mostly strongly correlated with the corresponding cerebellar/caudate voxels. On the other hand, there should be weak correlations for thalamic and lenticular nuclei.

4) The authors suggest that no p value is necessary with a 1000-subject dataset. That might be true for certain things like functional connectivity maps, but a number of analyses, such as Figures 2, 4 and 5 do require supportive inferential statistics.

5) "IHFaS is more prominent in first order nuclei (compared to higher-order nuclei)" is not really quantified. The authors should specify in Figure S2, which nuclei are first order nuclei and which are non-first order nuclei. Perhaps the labels on the x-axis could be colored differently for first order and non-first order nuclei.

Reviewer #1:

The manuscript reports the results of a study examining the linear correlation between white matter tracts and AD- related pathology in the grey matter regions connected by the white matter tracts. The integrity of the tracts were measured using FA, MD, AD, RD (corrected for free water) and free water index (FW) and apparent fiber density (AFD). The white matter tracts examined were the cingulum (main and posterior branch), uncinate fasciculus, and fornix. The population studies were older healthy subjects at risk (based on family history) for developing AD. The AD related pathology were tau and amyloid measured using PET. The study was very well done and it addresses key questions in regards to the p-clinical phase of AD.

Questions:

a) It would be very helpful to the reader to understand the distribution of the global ABeta SUVR and temporal tau SUVR - given that studies dichotomise study participants based on high & low deposition, it would help readers better understand the context of the results. The mean and range given in table 1 is not enough.

b) Related to previous question, I would suggest that the same graphs be made for the ROIs at then end of the tracts - again it would help a reader understand the context of the study.

c) I am surprised that APOE e4 allele was not included as a covariate in the statistical model. Why not? Given that APOE increases risk of developing AD, it would seem to be a relevant parameter. Amyloid positivity has been shown to be associated with age, sex and APOE e4 status.

d) The negative results of the posterior cingulate and yet statistically significant results for the uncinate fasciculus are an interesting contrast. Both tracts connect regions with presumably high Beta and high tau deposition. Have there been studies that have compared the amyloid deposition in posterior cingulate cortex and anterior cingulate/anterior frontal regions? It might be supportive of the idea that posterior cingulate is further along the disease progression compared to the anterior frontal regions. Having the data plots as described in (a) and (b) could help in supporting the points made in the discussion.

Reviewer #1:

The manuscript by Carreño-Muñoz seeks to tackle an important problem in behavioral neuroscience, that is classifying behavior at fine resolution during free exploration in rodents. Though the goals of this study are lofty, this platform, in my opinion, isn't a substantive step forward in relation to other tools currently available.

Major concerns:

1) What is presented in this work is a piezoelectric based sensor to detect rodent movements. My main criticism with this work is that the behaviors were coded by hand. If the authors had developed a way to automatically measure spontaneous behaviors of interest, or even train a machine to detect behavioral signatures after some human input, this system would have broader appeal. As is, the experimenter uses standard whole animal tracking with ethovision, then observes what the animal is doing by hand, then quantitation is added to certain movements. This I believe, is not a major advance, as current weight bearing devices already have this capacity.

2) For the breathing and heartbeat studies in figure 2, I am not convinced that this approach is more beneficial than the standard EEG approaches.

3) Figure 3 is poorly developed and the biology is very questionable. "Shaking" after surgery as a read-out of pain is not a measurement currently used or seen in the pain field. Although the authors report that this measurement is reduced with BPN, there are other trivial or pure coincidental explanations for this unusual finding. This reviewer tends to believe that the anesthesia or some other surgical by-product, not with pain as a driver, is contributing to this phenotype. I don't believe the authors have discovered a new post-op pain behavior. If so, substantial data needs to be added to be convincing.

Reviewer #1:

The authors develop a Bayesian approach to modeling macroscopic signals arising from ensembles of individual units described by a Markov process, such as a collection of ion channels. Their approach utilizes a Kalman filter to account for temporal correlations in the bulk signal. For simulated data from a simple ion channel model where ligand binding drives pore opening, they show that their approach enhances parameter identifiability over an existing approach based on fitting average current responses. Furthermore, the approach can include simultaneous measurement of multiple signals (e.g. current and fluorescence) which further increases parameter identifiability. They also show how appropriate choice of priors can help model and parameter identification.

The application of Bayesian approaches to kinetic modeling has recently become popular in the ion channel community. The need for approaches that inform on parameter distributions and their identifiability, as well as allow model selection, is unquestioned. Also, it is ideal to use as much information in the experimental data as possible, including temporal correlations. As such, the authors’ addition is a valuable contribution.

Comments:

I note that my comments are restricted largely to the results rather than the mathematical derivation of the author's approach.

1) I understand that this is somewhat secondary to the paper's intellectual contribution. However, one thing that would be enormously useful is accompanying software usable by others. The supplied code is not well commented, and it is unclear whether it is applicable beyond the specific models examined in the paper. It was supplied as .txt files, but looks like C code. I did not spend the time to get it working, so an accompanying GitHub page or some such with detailed instructions for how to apply this approach for one's own model of interest would make this contribution infinitely better. Even better if there was a GUI, although easily adaptable code is of primary importance.

2) What are the temporal resolutions of the current and fluorescence simulations shown in Fig 1? I assume that they are the same. However, most current recordings are much higher temporal resolution than fluorescence recordings. If you were to reduce the sample rate of the binding fluorescence relative to current simulations to something experimentally reasonable, how would the resulting time averaging of the binding signal impact its enhancement of parameter identifiability?

3) For comparison, it would also be nice to see how addition of the binding signal in the data helps the RE approach. i.e. Is addition of the binding signal more important than choice of RE vs KF, or is optimization method still an important factor in terms of correctly identifying the model's rate constants or in selecting the true model?

4) Fig 7: For PC data, why is RE model BC appear to be better than KF model BC if the KF model does a better job at estimating the parameters and setting non true rates to zero? Doesn't this suggest that RE with cross validation is better than the proposed KF approach? In terms of parameter estimates (i.e. as shown in Fig. 3), how does RE + BC stack up?

Reviewer #1:

In this study Robert et al. describes the properties of long-range projections from the SuM to the CA2 area of the hippocampus. The authors identified direct excitatory and indirect inhibitory drive from SuM inputs on CA2 pyramidal neurons and showed that direct excitatory drive impinges on PV-positive basket cells. The overall effect of the input on CA2 activity was an increased precision of APs. The study also suggests that the input from the CA2 drives inhibition in the CA1 area. The study provides very interesting and new information about the cellular properties of SuM input in the CA2 area. This is an important question given the increasing importance of SuM inputs in social memory encoding. The study is timely, currently we have very limited data about the features and exact cellular profile of this input. The study is using elegant technical approaches to answer the central question of the study. While the study is addressing an important question and provides novel data, the author's central claim about the role of feed-forward inhibition would need to be strengthened by the addition of experiments addressing how E-I balance changes in trains in individual neurons and how this can be linked to changes in the temporal precision of synaptically evoked APs.

Action potentials are evoked with a current step. Since the study is focused on the network effects of feed-forward inhibition, it would be useful to see how the properties of synaptically evoked action potentials change. In the cortex and in the CA1 feed forward inhibition was shown to limit the temporal summation of excitatory inputs which lead to decrease in AP jitter (Gabernet et al., 2005, Pouille and Scanziani 2001). In order to map these dynamics APs should be evoked via synaptic stimulation and not through current injection.

The authors show recordings of monosynaptic EPSCs in pyramidal cells and interneurons. It would be important to know how inhibitory and excitatory PSCs change in a train. Recordings from single cells held at E-GLUT and E-GABA would allow the authors to monitor excitatory and inhibitory events in a train and map how their balance changes. Can the change in E-I balance explain the change in AP jitter?

What are the characteristics of the SuM-driven inhibitory currents? Does the latency and jitter of monosynaptic EPSCs and disynaptic IPSCs differ? If one is monosynaptic and the other is disynaptic one would expect significant differences in both of these parameters.

How do the authors exclude the contribution of feed-back inhibition? Feed-forward and feed-back inhibition both could have an impact on the temporal precision of APs.

Reviewer #1:

Expansins are mysterious cell wall proteins because they lack known hydrolytic activity but are somehow correlated with acid-induced cell wall loosening/extension and cell expansion. Here the authors catalog the tissue expression of several native promoter driven expansin-FP fusions (EXPA1, 10, 14, 15) and find partially overlapping expression patterns and evidence that some expansins are restricted to particular cell wall regions (e.g. tricellular junctions (Figs 1-4). Using Brillouin light scattering (BLS) microscopy they find that, contrary to several previous reports for EXPA1, EXPA1 overexpression induces tissue stiffening that is relatively independent of extracellular pH (Fig 5, 7). They corroborate these data using AFM of different cell walls in a similar tissue (Fig 8). Thus, EXPA1 overexpression results in shorter roots (Fig 9). While BLS seems like an interesting technique for studying cell walls, essential controls are missing making it difficult to interpret these results.

Major Comments:

1) Expansins have traditionally been identified with promoting cell wall extension by loosening the cell wall under acidic conditions. Recent reports have corroborated this: Ramakrishna et al., 2019 showed decreased lateral root initiation in mutants, implying EXPA1 plays a role in loosening, while Pacifici et al 2018 showed decreased cell elongation in expa1 mutants and increased cell elongation in EXPA overexpression lines, but only when grown on low pH (pH 4) media. All of these results are consistent with EXPAs playing a role in cell wall loosening. By contrast, the authors here find that EXPA1 overexpression causes cell wall stiffening and reduced root growth, that low pH (pH 4) media decreases this stiffening (Fig 5). Their discussion of these discrepancies is insufficient. For example, how do their levels of EXPA1 overexpression compare to Pacifici et al., 2018? How can they reconcile the results in these previous papers with their study?

2) Since the authors only really see changes in BLS of their EXPA1 line with over 10,000x overexpression (their inducible EXPA1-mCherry line with "only" >100x expression relative to wild type does not cause significant changes to cell wall "stiffness"), it is unclear how sensitive this technique is to cell wall changes. Controls are required to interpret these BLS experiments. For example, a known mutant or overexpression line with increased cell wall stiffness and another with decreased cell wall stiffness.

3) It will also be important to document whether the authors can replicate the lack of changes to cell wall stiffness in the expa1 mutant using AFM.

4) It would be helpful to see a detailed correlation analysis between the new technique (BLS) and an established cell wall analysis technique (AFM) across multiple data points (i.e. positive and negative controls for cell wall stiffness changes).

5) These AFM values are also presented on a scale that is almost 7x higher than previous data from the authors (e.g. Peaucelle 2014 JoVE). Please discuss.

6) The authors are comparing BLS data from the inner longitudinal cell wall versus AFM data from the outer longitudinal cell wall, which have very different properties. Please discuss.

7) EXPA1 gene overexpression is determined 7 days after Dex induction, but BLS experiments are conducted on plants that have been induced for a much shorter time (e.g. 3h). What is the expression of the EXPA1 gene over this timeframe of induction? Ideally, the authors would also use an EXPA1 antibody to monitor protein levels, since this is what is actually relevant.

8) It is difficult to see from the BLS shift maps provided (e.g. Fig 5A) where in the root the authors are imaging. Given that this is a relatively new technique to the cell wall field, it would be helpful to provide additional images to provide context to readers.

9) "Data not shown" (e.g. trans-zeatin treatments, line 149; EXPA1 protein levels, line 360) must be included as supplemental figures or the claims removed from the manuscript.

Reviewer #1:

In this manuscript, the authors report on two separate experiments designed to understand the relationship between lip-movement induced theta phase and auditory processing. In the first experiment, subjects detected tones embedded in noise while viewing silent videos. The results demonstrate that tone detection performance improved when tones are presented later relative to earlier in a trial. It was also demonstrated that correct detection, for tones that occurred later in the trial, was systematically linked with the phase of the theta oscillatory activity conveyed by the lip movements. In the second experiment EEG was recorded while participants viewed the silent videos and performed an emotion judgement task. Theta phase coupling was demonstrated between auditory and visual areas such that oscillations in the visual cortex preceded those in the auditory cortex.

The authors conclude that these results demonstrate that lip movements directly affect the excitability of the auditory cortex. However, due to the indirect nature of the reported effects, I do not believe this conclusion is justified. I elaborate on this concern below:

1) In experiment 1, the main finding that performance is better later in the trial could arise from many factors including non-specific attentional effects.

2) The analysis reported in the bottom of page 5 (comparing vector lengths for hits vs misses) is critical to the argument but the results are inconclusive (significant interaction, but subsequent comparisons not quite significant. Likely because the experiment is underpowered?).

3) In Experiment 2: the task performed by the listeners might have biased them towards speech imagery leading to the pattern of effects observed. Indeed, the observed involvement of the left hemisphere may be consistent with the involvement of speech imagery. This would render the observed link between visual and auditory cortices as somewhat trivial and not new (such links have been reported in many previous studies as acknowledged by the authors).

4) Most importantly, the authors do not provide any direct evidence that the auditory effects observed in Experiment 2 are related to those observed in experiment 1.

Other comments:

1) For the analyses in Figure 2A, were the number of trials over which the analysis is conducted adjusted for "first tone" vs "second tone"? Since the hit rate is higher for the second tone, there may be a concern that including more trials in the analysis would result in better SNR and hence a more robust effect.

2) In Experiment 2 the analysis is focused on phase effects. Can you report whether there are any power differences in the delta band in the "early" vs "later" time windows?

3) Line 176, the authors write "these results established that entrainment of theta lip activity increased in time". It is not clear to me which aspect of the results supports this statement.

4) Line 405: "any lag between visual and auditory stimuli onsets was later compensated...". I could not find mention of this elsewhere (i.e. how lags were compensated, how large they were). This is critical for interpreting the results and therefore should be described in detail.

5) Line 430-437 why did you choose to quantify the envelope in this way rather than just taking the wide band envelope?

6) Figure S3 is important and should be in the main text.

7) Line 473 "auditory pure tones"

8) The description in lines 478-481 doesn't make sense. It is unclear how loudness reported in line 480 (91dB SPL; incidentally this is very loud) relates to the later reported value of 72dB SPL.

9) Line 485 "embedded"

10) Please clarify whether in your loudness adjustment procedure you were adjusting the loudness of the tone, the noise or the SNR (and thus keeping the overall loudness of the stimulus fixed)

11) Line 537 "preceding"

Reviewer #1:

The manuscript 'Integron activity accelerates the evolution of antibiotic resistance' by Souque et al. investigates the genetic variations created by a class 1 integron during antibiotic exposure. In the study, the authors examine the evolution of an integron encoded on a R388 plasmid; they introduce three antibiotic gene cassettes into the integron and follow its evolution in the presence of one corresponding antibiotic - here gentamicin. They find that antibiotic exposure leads to a rapid re-shuffling of the integron cassette. The re-shuffling favors the aadB gene in the first position downstream of the integron promoter while mainly keeping the (original) last position in the integron. The study represents an interesting example of rapid adaptation to increasing concentrations of an antibiotic that is facilitated by mobile elements. While the experiments are overall interesting and very well designed, the study lacks a certain depth. In the sense that their results might be as well explained by random mutations (genetic diversity). In addition, the two parts of the experiments (integron analysis & chromosomal evolution) need to be connected as it is so far unclear what role the chromosomal mutations have in the integron-facilitated evolution.

Major Comments:

1) The authors don't mention whether they detected re-arrangements in the negative control that was evolved without antibiotics. Furthermore, re-arrangements might appear but at a very low frequency. What is the sequence coverage used in the study? How can the authors ensure they don't miss a low frequency of re-arrangements? It might be possible that random re-arrangements appear at a very low frequency that are only fixed under changing conditions (similar to mutations). The authors should clarify this point.

2) Did the authors measure the Integrase expression levels? This could ensure that there is no expression without stress to the cell.

3) Regarding the mutational analysis: Is there any sign of a cost to the integrase activity? The authors conduct an intensive analysis on chromosomal and plasmid mutations. Nonetheless, it is unclear how these mutations are generally connected to the integrase activity (and not only to the AB treatment).

4) The authors call the integrase activity 'adaptation on demand'. It would be interesting to know how fast a potential reversal would appear in the integron in the populations. Is there any evidence for a deletion of the duplication of the aadB gene after removal of the antibiotic? In the same line of thought, do the authors expect the other AB resistance genes to follow the same path when incubated in the corresponding antibiotic? It would be interesting to know how antibiotic 'type' dependent the experimental result might be.

Reviewer #1:

The importance of host associated microbiomes for health and disease of their hosts cannot be overstated. Fungi tend to feature more prominently in microbiome studies of soil or plants, but microbiome work in animals has mostly focused on bacteria, with fungi having received comparatively less attention. The current study addresses the question whether there is evidence for co-evolution or consistent ecological filtering of fungal communities in the animal gut, similar to what has been reported for bacteria. Such patterns have been termed "phylosymbiosis", even though the ecological interactions that underlie such patterns are largely unknown.

The strength of the study is the wide range of animals investigated, 49 species from eight different classes of vertebrates and invertebrates. However, this wide sampling also is a weakness, as few groups are well sampled. Members of the same species are found to have relatively similar bacterial and fungal microbiota, and fungal microbiota are found to be somewhat correlated with phylogenetic distance. There is also correlation between bacterial and fungal communities, but whether this is driven by independent effects of the host on both groups, or primarily by interactions between the two microbial groups remains unknown. Some of the other observations, such as the tendency of bacterial diversity to be higher than fungal diversity, are more difficult to parse, since it is not clear what the proper yardstick for diversity comparisons is (i.e., whether functional differences between fungal ASVs are comparable to functional differences between bacterial ASVs). This study provides interesting insight regarding the general characteristics of the fungal microbiome and its relationship to the bacterial communities and the host. It does not directly reveal how these communities might affect the host. As the authors themselves state, "The drivers of phylosymbiosis remain unclear".

Reviewer #1:

Major Comments:

The experimental design is inconsistent in at least three ways:

1) The genomes of 14 resistant clones were analyzed by whole exome sequencing (WES), whereas the genomes of the remaining 21 clones were analyzed using whole genome sequencing (WGS).

2) And the sequencing approach even differed among the six lines evolved in three separate drugs: doxorubicin, paclitaxel, and gemcitabine.

3) We feel the authors did not adequately explain how the different sequencing methodologies could affect their results and the inferences drawn from them. For example, one is likely to miss information with respect to copy number variants by only sequencing exomes. The authors highlight this fact in their discussion, but they do not explain by how much they could be off in their assessment.

In some cases the same parental clone was used to find replicate lines subjected to the same selective pressure, and in other cases, the same parental clone was used to find replicate lines subjected to different selective pressures.

Lines were evolved anywhere from seven to thirty weeks, and the length of the evolution experiments does not correlate with the selecting drug (e.g., three replicate lines were evolved in doxorubicin for 9 weeks and three other lines were evolved to this same drug for 12 weeks). Did the authors normalize by generations? Again, the authors do not address this issue in their manuscript.

Reviewer #1:

The manuscript by Kilroy and colleagues centers on demonstrating that inactivity is deleterious for DMD zebrafish and that electrical stimulation is highly beneficial in the model. The authors identify a subpopulation of inactive DMD (sapje) zebrafish that progress faster in dystrophic disease muscle breakdown. They use tricaine to restrict movement and show a faster myofiber breakdown in the severe DMD fish cohorts. The authors then use neuromuscular electrical stimulation (NMES) to improve muscle pathologies and overall DMD zebrafish outcomes. The authors go into extensive details in characterizing the consequences of NMES on normal and DMD zebrafish muscle growth, health, and overall function. Transcriptomic analysis reveals fibrotic and regenerative genes are modulated by NMES.

Overall, this is a strong manuscript on the effects of NMES/electrical stimulation on DMD muscle growth. It does lay several parameters for evaluation of NMES in the zebrafish model. The manuscript is fairly well-written and most of the experiments are presented in a straight-forward manner with clear interpretations. I do have some issues with one or two points that the authors try to extrapolate from their studies. I have significant issues with the description and use of tricaine as an inactivity paradigm in these studies as there are multiple interpretations of these findings. I have a few points about the NMES stimulation protocol and NMJ contribution that should be addressed. This is a good manuscript and can be an important addition to the field if these points are addressed.

1) The inactivity paradigm (e.g. figure 2) using tricaine as a means of inducing inactivity has pluses and minuses. There are issues with comparing it to rodent and human inactivity experiments (which usually involve hindlimb/limb immobilization), as the authors here are using chemical inhibition. Tricaine has systemic effects on multiple tissue types and organ systems including neurological and respiratory systems. I would be careful to call this model an inactivity model as a more appropriate model would be to physically restrain the zebrafish larvae to prevent movement. While technically challenging this experiment can be done and would likely be more reflective of the consequences of physical inactivity in the DMD fish than tricaine anesthesia. Mdx mice have respiratory consequences due to pulmonary muscle weakness, independent of an inactivity (Burns et al., J.Physiol., 2017).

The authors need to rule out if the consequences of tricaine administration is due to inactivity or pulmonary/secondary dystrophic pathology issues (e.g. swim bladder or respiration).

2) The NMES protocol is more extensively established by the authors and has a clearer interpretation. That being said, the main benefit of NMES is to stimulate muscle force/function in the absence of proper innervation by the NMJ, which is also disrupted in DMD. The authors do an excellent job in demonstrating that the NMJ does not change in morphology via immunofluorescence and anatomical observations. Can/have the authors evaluated the functional output of the NMJ in the NMES-treated DMD zebrafish? Were any electrophysiological measurements performed on the NMES treated DMD fish, independent of any therapeutic experimental protocol?

3) Hmox1 overexpression has been pursued as a strategy for DMD in mice by the Zoltan Arany and Joseph Dulak's groups, so the findings in figure 10 are supported. Have the authors evaluated whether or not the entire Hmox1 pathway was affected in the NMES-treated DMD fish?

Reviewer #1:

Obstructive sleep apnea is an important medical problem, with elevated cardiovascular risk as a common association. Intermittent hypoxic episodes are a good predictor of such risk so a connection is indeed plausible. Thus the manuscript starts with a good premise, but what limits my enthusiasm is the large number of loose ends in the story that make it likely that what we are seeing is a small amount of signal, with a large amount of noise, limiting potential mechanistic insights that are translatable.

Major comments:

1) OSA and intermittent hypoxia are clearly different things. Further the hypoxia of OSA is much less in the lung compared to the systemic organs. To illustrate this point, an upper estimate for alveolar CO2 is the venous CO2, or more commonly 10-15 mm Hg elevation over normal i.e. 55 mm Hg. At even 60 mm Hg CO2, local oxygen tension in lungs would be above 80 mm Hg. Systemic desaturation is because of widening A-a gaps and physiological/pathophysiological shunts. While severe OSA with prolonged apnea could indeed be worse, the clinical associations are seen even with milder disease. Thus a-priori it is very unlikely that the model reflects the disease accurately.

2) Given the limitations of the model, it is imperative that at least the pathways elicited by intermittent hypoxia be clearly defines so that even if we do not gain fully understanding of OSA, we may understand the consequence of intermittent hypoxia that may be relevant in another context. Here too the manuscript is lacking. The genomic analysis is interesting and indeed data rich. However, more attention could have been paid by exploring a hypothesis, ensuring multiple markers for target cell populations, and building a mechanistic model. In current form, the work is hypothesis generating, based on limited markers and analysis, and is extrapolated widely to other pulmonary disease without a solid rationale.

Reviewer #1:

In the manuscript entitled “ASIC1a is required for neuronal activation via low-intensity ultrasound stimulation in mouse brain", Lim et al. investigate the mechanism underlying the activation of brain neurons by transcranial low-intensity ultrasound stimulation. The authors propose that ultrasound stimuli-induced movements of the extracellular matrix and the cytoskeleton cause mechanical activation of ASIC1a in cortical neurons, which leads to Ca2+ influx and subsequent expression of pERK, which the authors used as a surrogate marker for neuronal activation.

While I agree that the finding that ultrasound activates neurons via activation of a mechanosensitive ion channel is per se very interesting, I have to say that in my opinion most of the conclusions and claims are not supported by the actual data.

1) The entire study is purely correlative. Thus, the authors made two independent experiments; on the one hand they show that in-vivo transcranial ultrasound stimulation induces pERK in various brain regions and on the other hand they shown that ultrasound-evoked Ca2+ influx in cultures of cortical neurons is probably mediated by ASIC1a. From this data they conclude that pERK activation is also mediated by ASIC1a activation. This is, however, pure speculation. The authors must provide additional evidence to support their claim. In my opinion the sole use of PcTx1 is not sufficient to prove that the Ca2+ signals are mediated by ASIC1a. Hence, firstly the authors should demonstrate that ASIC1a is indeed activated by ultrasound. This is a very simple experiment. All they would have to do is express ASIC1a in a cell line (e.g. HEK293, CHO, etc) and show that this expression renders the cells sensitive to ultrasound. Second, I would appreciate it if the authors would show that cortical neurons, especially those that show pERK activation, express ASIC1a in the first place. This would also be quite simple - just co-stain the brain sections with an anti-ASIC1a antibody. Third, if the authors want to keep up their claim (see title) that ASIC1a is required for ultrasound activation of brain neurons they should examine ultrasound-induced pERK activation in ASIC1a-knockout mice.

2) It is difficult to evaluate the Ca2+ imaging experiments, because the method - especially the ultrasound stimulation - is not very well described. Hence it is unclear to me how close to the cell the ultrasound stimulator was placed. Moreover, the N-numbers of the Ca2+ imaging experiments are rather small (by the way, it would make reading much easier if the N-numbers were indicated in the figure). Most importantly, it is unclear if the inhibitors (Gadolinium, GsMTx4 etc - Figure 2B-H) were applied to the control cells from the same panel or to different cells. In this context it would be important to know how many control cells actually responded to the ultrasound stimulation. Considering the low N-number, I was wondering if the authors may have had a hard time finding cells that responded and that this is the reason why the N-numbers are so small? I suggest examining many more control neurons and provide information about the proportion of cells that respond. If only for the controls as well as for the cells treated the various channel inhibitors.