Reviewer #2 (Public Review):

Defining the subunits' role in forming the eukaryotic signal peptidase complex is essential to understand the process of protein maturation and selection. This fundamental knowledge can have direct implications in the context of biotechnological protein production. Here, Yim et al. focus on the yeast signal peptidase protein subunit Spc1. Identifying the role of this membrane protein is a difficult task because this small subunit is non-essential. Using yeast strain lacking Spc1, the authors report that particular model signal-anchored proteins become more susceptible to cleavage by signal peptidases, whereas overproduction of Spc1 tends to reduce their maturation.

The work is carefully presented and executed. For example, the authors employ radioactive pulse-chase experiments for precise tracking of the dynamic protein maturation process and series of signal sequence variants in the N-terminal and hydrophobic region to test the contribution limit of Spc1 in the reaction specificity. They also introduce mass spectrometry controls, to show for instance that deletion of Spc1 does not affect the stability of the other subunits in the signal peptidase complex.

A limitation of the work as presented concerns the mechanism of action of Spc1. It is shown that Spc1 does not affect signal peptidase recognition efficiency, yet it is unclear how Spc1 would regulate the activity of the signal peptidase. Some co-immunoprecipitation experiments suggest that membrane proteins carrying un-cleaved signal-anchor can be isolated with Spc1, suggesting some direct interaction with Spc1. Yet, the extent of this analysis (presented in the last Fig. 6) is insufficient as yet to firmly support the conclusion that Spc1 functions to shield transmembrane segments from signal peptidase action.

Reviewer #1 (Public Review):

The Signal Peptidase Complex (SPC) processes signal peptides in a wide variety of secretory and membrane proteins that are inserted into the ER membrane in eukaryotic cells. How SPC discriminates transmembrane segments of membrane proteins from signal peptides remains elusive. The work of Kim et al shows that the Spc1 subunit of the SPC enzyme is involved in the quality control function of SPC facilitating accurate cleavage of signal peptides and preventing it from cleaving after transmembrane segments. The work is well executed and the experiments are done in triplicate.

The approach that Kim et al use is to investigate signal peptide cleavage of carboxy-peptidase Y (CPY) variants with extended amino-terminal regions of various lengths and with low, intermediate or high hydrophobicity of the H region of the signal sequence. The results show that signal peptide processing by Signal Peptidase Complex (SPC) depends on the n-region and the h-region. Signal peptide cleavage of the internal signal sequences is greater in the Spc1 knock out strain.

In addition, Kim and coworkers study SPC-mediated cleavage after a TM segment of single spanning and a double spanning membrane protein, based on the model membrane protein leader peptidase. Their results (Fig. 4 and Fig. 5) show Spc1 acts as a negative regulator of cleavage of the model single and double spanning membrane proteins when the stretch is quite hydrophobic. Notably, the presence of Spc1 does not completely eliminate SPC cleavage of the artificial membrane proteins. It is not an all or nothing effect, suggesting that the artificial constructs are not faithfully mimicking a membrane protein, which you do not want to be cleaved.

Given this, it remains unclear what would be observed for physiological membrane proteins that are not normally SP processed but do have a potential signal peptide cleavage site immediately at the C-terminal region. The authors model predicts that such proteins should be cleaved by SPC in the absence of Spc1, and, if this were observed, it would conclusively prove that Spc1 plays a fidelity function and contributes to the accuracy of signal peptide cleavage by SPC.

Reviewer #3 (Public Review):

Sorrentino et al. utilise Magnetoencephalography (MEG) and diffusion MRI tractography to investigate the mapping between the structure and function of the human brain and any constrains imposed from this coupling. Their work builds upon a growing number of studies that use functional Magnetic Resonance Imaging (fMRI) to provide evidence of structure shaping neural functioning. In this case, the authors utilise the fine temporal resolution of MEG to explore the propagation of the neural signal and investigate how this can be linked to a structural connectome derived from deterministic diffusion MRI tractography. Following critical dynamics analysis pipelines, they identified neuronal avalanches in the MEG data and showed that their spread is more likely between pairs of grey matter regions with increased structural connectivity strengths, quantified by the streamline count among them. This result provides new evidence on how the structural architecture of the human brain can influence intrinsic neural dynamics and suggests a potential mechanism, based on scale invariant properties in space and time, for similar previous findings based on the slower temporal scales of fMRI.

The analyses presented are clear and concise. They highlight an efficient and clever way to combine MEG and diffusion data, maximising the benefits of both modalities, to explore structure-function associations. The authors have tested a number of different configurations, using multiple connectome mapping pipelines, atlases, as well as a replication sample from the Human Connectome Project and the results were robust both at the individual and the group level, which is reassuring and impressive.

Given the short report format of the manuscript, it is understandable that some additional information and results were described very briefly or omitted altogether. However, there are a few points that, I think, if discussed (even succinctly) could improve the strength of the presented evidence and increase the manuscript's impact to the field. For example:

Given that the foundations for all subsequent functional analyses are the time bin length and the branching parameter, it would be useful to have a couple of graphs showing their relationship. i.e. a graph showing the association between bin size and σ, for a wider range of bins (in addition to 1, 3, and 5 that are reported). Is bin size 3 the only bin size that σ = 1 and if not, how does this affect the rest of the results (especially the transition matrix). A second interesting graph dealing with avalanche dynamics would be to show the avalanche size distributions for a single subject and the group, for different bin lengths, highlighting whether they are following a power law, indicator of critical dynamics, and briefly discussing their power law exponents, α.

The correlation between the structural connectivity and randomised transition matrices still seems relatively high. It'd be of interest for the authors to provide a brief interpretation of this, along with a justification for keeping the spatial structure unchanged during their randomisation routine.

As the different size of parcels in the atlases can have an effect for both structural and functional analyses, it would be of interest to know if the authors controlled for that and how.

Given the varying SNR that the AAL parcels will have due to their location, it could be of interest to present some information about the avalanches' spatial distribution (i.e. but not limited to a whole-brain map, where each parcel's intensity could correspond to the number of times it goes supra-threshold on average). This could highlight any issues where avalanches involve some parcels more (or less) than others due to challenges in recording and localising their activity.

In addition to the above challenges with MEG, deterministic tractography analyses also present limitations on how accurately they can describe the underlying structural connectome. i.e. issues with crossing fibres (of varying degree among parcels due to their location), spurious tracts, and invalid, non-biologically plausible connections. A brief mention of these challenges both for MEG and DWI and how they might affect and impose limitations on the manuscript's results would be beneficial.

Finally, values in the scatter plots in Figure 2 are probably mean centered? For visualisation purposes it might be better if they were not, as it seems a bit odd to have negative values or numbers higher than 1 for structural connectivity and transition probabilities. Also, there seems to be lots of ROI pairs with 0 structural connectivity but high transition probabilities, which might justify a brief mention in the manuscript and an interpretation.

Reviewer #2 (Public Review):

Is this submission Sorrentino and co. are investigating the relationship between the structural and electrophysiological functional connectome. In particular they are asking whether the white matter structure is a large contributor to the patterns of function we see, and (importantly) whether this is or not a source-reconstruction artefact. The relationship between structure and the emergence of these functional networks is of interest to many, it has been previously shown in fMRI and I believe a lot of modelling work to match empirical observations of the electrophysiology has been previously done.

The paper is clear in its motivations, and I believe fairly clearly reported. The simplicity of this is definitely one of the strengths of the report. Conceptually I believe this is a plausible hypothesis and of interest and (assuming the technical methods are correct) I'd say this is an elegant approach to supporting this.

Reviewer #1 (Public Review):

Sorrentino et al explore the possible link between 'neuronal avalanches' in resting MEG signal and structural connectivity in the human brain. They estimate neuronal avalanches by applying a threshold to identify large perturbations in the source reconstructed MEG data before binarising the time-series to define 'active' and 'passive' windows in each voxel. Sequences of 'active' voxels are identified starting with any region becoming active and ending when all voxels become passive. The probability of an avalanche transitioning between any two voxels in the MEG data is compared to network structure identified from diffusion imaging in the same individuals. The authors show that brain regions with a high function transition probability are also likely to be structurally connected. Whilst the core finding is interesting, the results are undermined by a lack of controls for confounds.

Strengths

This paper utilises a straightforward and intuitive analysis approach to tackle a complex question - how does functional activity spread throughout the brain? The simple thresholding in the neuronal avalanches approach avoids a number of complex steps typically associated with electrophysiology connectivity estimation such as strong filtering and complex frequency transforms. Sorrentino et al are able to show that this simple time-domain measure is able to provide an interesting overview of functional network structure. Moreover, this method naturally works to explore networks structure in transient, aperiodic signals which are often overlooked in favour of an oscillatory perspective.

The authors consider a range of analysis pipelines to show that the core results are robust to key analysis decisions. Two different parcellations and methods for computing transition probabilities are considered and the results are shown to hold when using diffusion MR data from the HCP project.

Weaknesses

The authors claim that these results are unlikely to be caused or affected by linear mixing or volume conduction - however this is not clear to me based on the presented information. Specifically, if a perturbation arises in one region and is mixed by volume conduction into a second region, part of its shape will be preserved but this will be at a lower overall amplitude. Therefore, as the whole perturbation shape will be scaled down in the second mixed region, it is likely that its rising edge will reach the z-score threshold at a later time than in the original signal. In this way linear mixing by volume conduction has the potential to create spurious time-lagged in this analysis. Previous literature on neuronal avalanches in MEG have included extensive control analyses and discussions on linear signal mixing for this reason (10.1523/JNEUROSCI.4286-12.2013). This point is not tackled in the analysis and not clearly discussed in the paper.

The correlation in Figure 2 B and C is interesting but is not supported by control analyses to account for confounds. For example, ROI size could potentially lead to more apparent structural connectivity and stronger MEG signal driving an apparent correlation between the modalities. This authors conclusions would be better supported if such effects were ruled out.

The main results are not well developed from the available data. The group level correlations are visualised and the subject-specific correlations are brieflly shown but not described in detail. It is unclear which regions and connections show the highest correlations. Similarly, there is wide between subject variability in the structure<->function correlation which ranges betwee 0.1 and 0.35 but the analysis does not explore whether this is reproducible, neuronal variability or driven by differences in SNR.

Reviewer #3 (Public Review):

In this study, the authors use a pooled GWAS approach combined with a case-control sampling of pairs of apparently drought-damaged and apparently healthy European beech trees in standing populations, to identify locations across the beech genome that may be associated with drought resistance.

Major strengths:

• The paired case-control design allows the authors to draw conclusions from standing trees in forests, where the observed phenotypes, though more difficult to interpret, are more relevant than phenotypes observed for tree seedlings under controlled conditions.

• The paired design avoids many possible complicating factors (conflating influences on the genotype-phenotype relationship). The authors also for example include an important control analysis showing that the genome similarity is comparable among the different groupings, indicating that there are no large and systematic demographic differences among the different groupings. This is not surprising because the trees were taken entirely from a small area in Hessen, Germany.

• Although the limited geographic coverage itself is not a strength, it does show that genetic differences in stress tolerance or resilience can be detected locally. It is these local differences among trees that may cross-breed that represent the adaptive potential in populations.

• The genome-wide analysis is conducted to high standards, combining a pool-seq approach pooling individuals from climatic regions and observable stress (e.g. South-stress, South-healthy, North-stress, North-healthy) with high coverage resequencing (20x) of 100 individuals and an analysis of likely linkage among SNPs which significantly varied by stress status.

• The dataset and the authors' analysis could be a very valuable resource to support ongoing work in forest genetics and climate change, including added value from future monitoring of the same tree populations. The authors point out some of these uses at the end of their Discussion.

Major weaknesses:

• The opportunistic use of trees in natural environments (not trees planted for the experiment) is nevertheless a challenge for clear interpretation of the phenotype. The authors aim to identify phenotypes specific to drought stress, which is a complex stress that causes many nonspecific stress responses. They address this by measuring other traits of chosen trees and showing that dried leaves and leaf loss differ, but measures of tree size, canopy closer, and competition do not differ between their drought-stressed and non-stressed classes. Of course there are other causes of dried leaves and leaf loss that cannot be fully excluded from these observations, such as differential damage from insects or pathogens. The authors state that they only chose trees "free from obvious mechanical damage, fungal infestations or other signs of illness", but did not score these phenotypes, measure any correlates, or otherwise record any information regarding these possible confounding factors. It would be more convincing if they had longer-term monitoring data for these trees showing that such differences became apparent after major recent drought events (2018) and were not apparent before these events, if they had other direct measures of water availability and status in the selected trees at relevant timepoints, or if they had measured other more specific indicators of drought stress such as abscisic acid levels at a relevant time point. The authors present long-term climate data from the study area, but no corresponding tree phenotype data to go along with it. This should be addressed.

• Related to this: the gene candidates (Table S2) include many that are implicated in stress responses, several of these being drought stress responses. In almost all cases these are genes involved in hormonal signaling, protein regulation, growth and development, which are certainly involved in drought responses but are hardly specific to such responses; and there is one involved in spermine synthesis. This plausible involvement, but lack of specificity, is consistent with the gentle gradient of allele frequency changes, and contributions to phenotype prediction, shown in Figs 4 and 5.

• Also related: the sampling is not representative of the range of Fagus sylvatica. This is most problematic because the genomic locations found to be significant in this association study might not be significant indicators of plant damage in a more diverse dataset. In the worst case, it could be that these trees are so similar in their adaptation to stress, that only small differences in growth and development and environmental signaling can be identified, which each may have a very minor influence; in contrast, if a comparative study were done including more marginal populations which face more frequent drought (while accounting for demographic differences), genes which are generally more important for a robust drought stress response might be identified. This should be discussed with reference to existing literature.

• The authors do not report the proximity of their pairs (unless I missed this), rather that these were "mutually the closest neighbors with contrasting damage status". Although they do have GPS coordinates for each tree, they do not report how the exact locations were determined and under what conditions, and thus we do not know how precise or accurate these coordinates are. GPS coordinates taken under a leaf-on canopy with a handheld device are likely to have an uncertainty of 10 m and an unknown offset which will differ for each tree due to multipath reflection, so these coordinates cannot be used to judge the relative position of trees in the same population. This can be ameliorated with aerial coordinates, or with a well-anchored reference point in the open combined with a surveying approach to map the trees in reference to this coordinate, but it is not clear if the authors did this. This should be clarified and if necessary, remedied as it is also crucial to the value of the authors' dataset, e.g. for future monitoring of these trees to test predictions.

• The analysis is rigorous and relevant and suggests important hypotheses regarding the drought resistance or resilience of European beech. However, these hypotheses are neither very precisely articulated, nor tested in this study.

Did the authors achieve their aims?

The authors are able to predict membership of an individual in the healthy versus damaged group based on its genotype at significant loci with near 100% accuracy. This does not indicate whether the groupings themselves (healthy versus damaged) are good indicators of tolerance or resilience under drought stress, which may be better supported by other data as well as future monitoring as discussed above, but does support that the GWAS is rigorous.

Reviewer #2 (Public Review):

This study uses a genome-wide association approach with pool-seq data, which provides a cost-effective alternative to whole genome sequencing of individuals, to dissect the genetic basis of drought resistance in European beech. European beech is an ecologically important forest tree species and drought resistance is a trait that is likely to be becoming increasingly relevant to the survival of these trees as climate change leads to more frequent and prolonged periods of drought. Knowledge of the genetic basis of such traits can help with management of tree populations in the face of increasing threats, especially when such information is used to develop tools for predicting individuals that are likely to have the highest chances of survival, such as was done in this study.

However, despite the potential importance of this study's findings for those concerned with protecting and managing forests, it is not currently clear that the data analysed meet some of the underlying assumptions of the methods used and essential details of some of the analyses are missing. Whilst this may simply be a case of more information being needed to confirm that the data are suitable for the approaches used, and that all relevant controls and quality checks have been conducted, at the moment the reader cannot be entirely sure that the analyses have been performed in an appropriate way or that the results are robust. Moreover, there are inconsistencies throughout the paper in relation to the number of individuals included in the various analyses, which creates confusion and casts doubt on the rigour of the investigation.

Reviewer #1 (Public Review):

Summary:

The authors present a very interesting and appealing approach to relate physiological damages observed after the extreme drought in 2018 to drought resistance genes in the European tree species Fagus sylvatica. Climate change and observed drought damages are a pressing issue for the forestry sector. The species is widespread through Europe and an important timber tree. Sampling took place in Hessen, Germany, in a 90x150km area. The authors used poolGWAS and a recently established reference genome to infer associated SNP loci by contrasting allele frequencies with replicated pools of drought susceptible and resistant trees. The authors also test the detected loci by a linear discriminant analysis based on an additional set of trees from the same region (SNP assay). The authors found systematic and quantitative genomic differences for drought susceptibility in the sampled population based on 7 significant loci located within genes and a few more loci (12) to be located close to genes associated with drought susceptibility in previous studies. The authors conclude that the significant loci found help to accelerate and monitor adaptation of beech to climate change. And they conclude from their results that there is enough genetic variation in beech to adapt to increasing drought and future climate change.

Strengths:

• The authors used a so-called XP-GWAS or poolGWAS approach, a relatively new (Yang et al. 2015, Zou et al. 2016), time and cost efficient whole genome sequencing method. By using a strictly pair-wise sampling design (XP-GWAS), pitfalls of traditional GWAS studies are avoided. In addition, the authors make use of the recently established reference genome of Fagus sylvatica (however, the used version not yet published). This genetic approach was successfully used in a similar way for crop plants and another broad-leaved tree species (Fraxinus excelsior, Stocks et al. 2020). Sampling effort and sequencing resolution seem to be adequate according to Yang et al. 2015 and yield 106 significantly phenotype associated SNP loci.

• Environmental variables used in this study contain very detailed climate data from 1950 onwards and mean monthly evaporation potential from 1991 onwards for all sites used for sampling. There are other drought indicators that better correlate with vitality traits such as the ratio of actual versus potential evapotranspiration or minimal site water balance (based on local field capacity) as has been shown for other central European beech forest sites (Braun et al. 2020, Schweiz Z Forestwes 171). But such climate data are difficult to obtain for dense and large-scale samplings without a monitoring background.

Weaknesses:

• Small-scale differences in soil water availability and other possible abiotic or biotic factors at the sampling scale of tree pairs are not considered in this study. In the first hypothesis statement, local environmental variation is ruled out by the fact, that the selected trees stand next to each other and, thus, are neighboring trees in the forest. But such small-scale variation should be at least be considered and discussed Literature recommendations: Kätzel 2008, Bolte et al. 2008 (see Sutmöller et al. 2008), Carrière et al. 2019. Furthermore, the (maximum) distance between the pairs of trees is not stated.

• The selection criteria used for damages versus resistant trees are unclear and the stated selection criteria are not specific to drought stress but rather more general stress indicators (see Wohlgemuth et al. 2020, Schweiz Z Forestwes 171). Traits used seem not to be consistent with protocols from internationally recognized monitoring networks (e.g. ICP forest manual, www.icp-forests.net). Attention should be paid also to the fact that observed stress symptoms have a multivariate background. Modelling analysis of long-term data show that other environmental factors such as N deposition are correlated with the changes in health status observed in central European beech forests and show interactions with drought indicators (Braun et al. 2020, Schweiz Z Forstwes 171, see also Pflug et al. 2018).

• The authors found only few drought-associated loci (7) to be located within genes and a few more (12) to be located close to genes associated with drought susceptibility in previous studies. Although most of the genes found in this study had putative homologs in other plant species, none were involved in a transcriptomic study on drought response in beech saplings (Müller et al. 2017, see discussion paragraph). It is questionable whether these systematic and quantitative genetic differences are large enough to infer that there is a genomic basis for drought resistance in beech and that genetic variation is large enough in this species to cope with future climate change, also with respect to its distribution across Europe.

The authors study the genomic basis of drought susceptibility and found systematic and quantitative genomic differences. However, the results seem not to be very strong in supporting the conclusions drawn. It is not clear whether the power of the GWAS study is affected by the precision of phenotyping, pool size, selection intensity, marker density or the depth of sequencing. Moreover, geographic limitations of the study and how that limits conclusions with respect to the species range have not been considered. This could, for example, be based in estimates of genetic variation across the species range such as in Magri (2006) New Phytologist and other newer references.

This study presents an important issue in forestry and forest ecology and implemented a recently developed, time and cost efficient, genetic approach that has only rarely been applied to woody long-lived species.

Reviewer #3 (Public Review):

Van der Plas et al established a mass-spectrometry based work flow for the analysis of peptidome in wound fluids. They found that wound fluids contained a higher degree of peptides as compared to plasma which is expected because of proteolytic events in wound fluids. Authors identified unique peptide patterns in healing and non-healing (infected) wounds and nicely discuss many of the identified peptides/peptide patterns and their likely roles in innate immunity, healing etc. The established methodology seems to be robust and yields interesting insights into proteolytically generated peptides in wound fluids. Authors speculate that assessing the peptidome of wounds would result in the identification of potential biomarkers for wound healing and infection.

The manuscript is the first that determines the peptidome in wound fluids using an unbiased technology. However, the results gained are largely confirmative or "as expected" because others have previously reported an increase in peptides number in wound fluids due to proteolytic activity. Also the same group recently published a related paper without discussing it. The main novelty of the manuscript is thus more of technological interest, as long as the translational perspective (diagnostic approach) has not been demonstrated.

Reviewer #2 (Public Review):

The authors used mass-spectrometry to analyze the peptides that are present in wounds as a result of proteolysis. The authors thoroughly investigated multiple aspects of the methods for peptidomics. The best sample preparation was determined and robustness was shown by comparing multiple injections or multiple sample preparations. Subsequently, different types of samples were tested, i.e. normal plasma, sterile acute wound fluid and infected wound fluid, in order to be able to distinguish e.g. common proteins. Wound fluids were shown to contain more and smaller peptides than plasma. Further analysis showed clear differences in peptide profiles between wound fluids and plasma. In high inflammatory samples, which contain high levels of cytokines, the protein degradation correlated with enzymatic activity (zymograms). Many proteins were identified that were found exclusively in the low or in the high inflammation group. This will help elucidating the pathways during wound healing and/or infection but also for diagnosis or biomarker discovery.

The conclusions of this paper are well supported by data. Although interesting differences were found between low and high inflammation, only a limited number of patient samples have been analyzed.

Reviewer #1 (Public Review):

This paper focuses on using liquid chromatography and mass-spectrometry (LC-MS) to compare peptidome of human wound fluid. In this study, uninfected healing wound fluid and infected would fluid were evaluated for potential differences that can predict wound status and infection risk. The authors concluded differences between plasma and wound fluid as well as differences between non-inflamed/non-infected wounds fluid in term of signature of LG-MS peptidome and peptide alignment maps.

Through their analysis they found many traditional biomarkers associated with wounds such as the cytokines IL-1β, 403 IL-6, IL-8 and TNF-α; the major novel findings come from the vast number of new peptide sequences they described, that could be used as wound biomarkers or drug targets in the future. The major counterargument for their otherwise novel findings is the same group's recent publication on wound biomarkers recently published in Frontiers in Immunology, "Bioinformatic Analysis of the Wound Peptidome Reveals Potential Biomarkers and Antimicrobial Peptides".

Reviewer #2 (Public Review):

Certain biological structures have evolved to attain certain forms that may enhance their function. The authors suggest that the shape of a cilium can enhance its sensory function in both quiescent fluid and shear flow, and compared the extent of this enhancement in a number of representative settings. This simple yet compelling possibility has not been explored in detail previously, and is deserving of further attention from both theoretical and experimental perspectives.

The present work is clearly a step in the right direction, proposing a quantitative framework and systematic approach to address this problem. The authors first extended the classical study by Berg and Purcell for spherical absorbers to prolate spheroids with slender aspect ratio, and compared this with a circular patch, showing the effectiveness of a cilium as a receptor. They then incorporated shear flow, showing that the cilium again outperforms a patch. Finally, they considered the case of an actively beating cilium or a motile bundle - a case which may be important for symmetry breaking in the vertebrate node.

However, a weakness of the current set-up is that it is highly idealised. To improve the overall impact and biological relevance of this work more careful analysis and simulations would be needed.

Reviewer #1 (Public Review):

The authors consider the effects of the cilium geometry and motility on its performance in detecting chemicals in the surrounding fluid. They begin by presenting a classic solution of the diffusion equation in an infinite fluid domain at rest, bounded internally by a single cilium. The cilium is modeled as a cylinder of finite length and perfectly absorbing boundary. They compare the capture rate of ambient chemicals at the cilium boundary to that of an absorbing circular patch on a reflecting wall of similar surface area. The latter is another classic solution of the diffusion equation. They find that the capture rate by the cilium exceeds the capture rate by the circular patch. Then, they solve the advection-diffusion equation around the cilium numerically, assuming perfectly absorbing boundary conditions along the cilium and reflecting boundary conditions on the wall. They apply this numerical framework to cases (i) where cilium is at rest in an external shear flow, (ii) where the cilium is actively beating, and (iii) where a bundle of hydrodynamically-interacting cilia are either at rest or actively beating. They observe an increase in capture rate when shear flows and motility are accounted for.

Reviewer #2 (Public Review):

The location and longevity of Toxoplasma infection in neurons accompanied by continuous immune infiltration to the brain provides many specific questions about the long term implications of this common infection as well as a broadly applicable model for neurotropic infections. Here Mendez and colleagues continue the use of a reporter system to reveal neurons that have been injected with parasite proteins (TINs) to determine the anatomical and cellular localization of parasite-neuron interactions and the electrophysiological properties of these neurons. This is a technically impressive piece of work first using the Allen Brain Atlas to map the location of TINs that may be a new gold standard for this type of work. Secondly, for the first time there is a record of functional data from parasite manipulated neurons suggesting that these cells ultimately die due to infection. The full consequences of this data do not seem to be fully addressed and certain limitations of the data are worthy of discussion.

1) Although acknowledged on the first page of the introduction, there is a distinction between TINs and infected neurons. This important distinction is not continued later in the paper. Indeed, one conclusion is a change in neurotropic dogma regarding the long-lived nature of neurons being an attractive location for infections. The combination of methods used in this study allows major conclusions to be made regarding Toxoplasma injected neurons and as a result is an exciting body of work however it cannot distinguish what is happening in infected/cyst containing neurons.

2) The electrophysiology study is well controlled by data from bystander neurons in the same tissue. These bystander neurons show a significant (p<0.001) increase in resting membrane potential. This striking significance seems underplayed for the rest of the study somewhat overshadowed by the extreme read outs on TINs. It would be interesting to hear what this mild depolarization functionally means for these bystander neurons. The data may suggest there is greater variation of membrane potential between neurons from uninfected mice and bystander neurons. The significance is lost later in the paper and the conclusions are summarized with bystander neurons being 'akin' to neurons from uninfected mice which seems not accurate.

3) Two pieces of data support the concept that neurons that have been infected with parasite proteins die - firstly that readings from TINs are highly depolarized and secondly there is a loss of neurons by 8 weeks post infection. This is a significant piece of new information that is not stated in the abstract. Some hesitation in making this conclusion may be from the difficulty in obtaining electrophysiology data from these cells. Another way to support this conclusion would be helpful for this shift in our thinking of the effects of Toxoplasma infection on the brain.

4) The impressive data investigating the anatomical location and the cellular specificity of TINs is further strengthened by the use of two types of parasites a Type II and Type III. The properties of these different 'strains' that may lead to alterations in neurons is not fully explored and conclusions about similarities or differences are unmade.

Reviewer #1 (Public Review):

The primary strength of this paper is the attempt to characterize the neurons injected by Toxoplasma and the electrophysiological changes that ensue. Three major problems are however noted.

1) Figure 1 attempts to identify regions of the brain more profoundly impacted by Toxoplasma and does so by normalizing the numbers of injected neurons to the size of the region. But since the reporter system used requires the parasite injected protein to interact with a neuron's nucleus, The authors claims can only be valid after normalizing not to size but to density of nuclei in a region. This is especially important in the cortex where different layers have distinct architectures.

2) The authors claim that inhibitory neurons are significantly less injected than excitatory ones. But how do they know that the inhibitory ones just don't die more quickly.

3) All of the electrophysiological changes that are reported to happen in the injected neurons can be most easily explained by the fact that they are unhealthy due to the injection. This does not mean that the data are insignificant since increased neuronal damage/death in injected neurons is a critical finding.

Reviewer #2 (Public Review):

In this manuscript, McLeod and Gandon present a thorough mathematical modeling framework to describe the evolution of multi-drug resistance (MDR) in microbial populations. By expressing the model in terms of linkage disequilibrium, the equations take on a form that make it easier to identify the key drivers of MDR evolution and propagation. This work helps to unify and generalize previous studies and constitutes an important advance in our understanding of microbial population dynamics.

Reviewer #1 (Public Review):

In this manuscript, McLeod and Gandon propose a framework for understanding multidrug resistance (MDR) evolution in a structured population in terms of linkage disequilibrium (LD) dynamics, and apply this framework to three concrete examples of MDR evolution. I was asked to evaluate this manuscript, as well as the authors' response to comments from previous reviewers. My expertise is in epidemiological modelling of antibiotic resistance; I am not hugely familiar with population genetics.

Overall, I think the authors address an important and interesting question, and I think the approach has the potential to generate valuable insights. I also think the authors addressed the previous reviewers' comments well. However, I have substantial concerns about the modelling framework and the interpretation of the results. In particular: i) there are some problems with the interpretation that LD arises from variation in susceptible density; ii) presenting these results as a re-interpretation and generalisation of Lehtinen et al. 2019 is incorrect; and iii) the modelling of additive transmission costs needs further thought/explanation.

1) Interpretation of results and re-interpretation of Lehtinen et al. 2019.

The authors present their results as a generalisation of the effect observed in Lehtinen et al. 2019. Both models show that variation in the strength of selection for resistance between populations can give rise to LD in a model of multiple resistances. In Lehtinen et al., this variation in selection is attributed to variation in clearance rate. The authors re-interpreting the effect as arising from variation in susceptible density instead. This re-interpretation is incorrect: the change in how costs of resistance are modelled (additive here, multiplicative in Lehtinen et al.) changes the evolutionary dynamics, so the two models capture different evolutionary effects. (See points 2 and 3 for further discussion of additive vs multiplicative costs).

One way to see this is to consider a simple model of single resistance as presented in Lehtinen et al. eqn 1, in which resistance is selected for when: B_r/a_r > B_s/(a_s + tau), where "B" is the transmission rate, "a" the clearance rate and tau the treatment rate. Re-arranging for tau shows how the threshold of selection for resistance depends on the strain's properties (B and a) under different assumptions about cost. With an additive cost in transmission (i.e. B_r = B_s - c), this threshold depends on both transmission rate and clearance rate, predicting LD if populations vary in either transmissibility or duration of carriage. With an additive cost in clearance, this threshold is independent of the strain's properties, predicting no LD. These are precisely the results the authors describe lines 268-277 and Figure 3.

However, if the costs are multiplicative, this threshold depends on clearance rate only, whether costs are modelled as part of clearance or transmission rate. This is why the model in Lehtinen et al. 2019 predicts LD when populations vary in duration of carriage, even when there is no transmission cost. The author's re-interpretation of the effect in Lehtinen et al. as arising from variation in the density of susceptibles, contingent on an explicit transmission cost, is therefore not correct. More generally, representing one model as a generalisation of the other is misleading.

I am also not sure about the authors' interpretation that the effects in the model with additive costs arise from variation in susceptible density. Variation in the density of susceptibles can also be generated by variation in the overall population density, so if I understand correctly, this interpretation would predict that LD would arise if the population density was different between populations? And that the selective pressure on single resistance would also depend on overall population density (argument stating line 261)? I am not able to reproduce this dependence of population density in a simple model. I would instead interpret the effect the authors observe as arising because the same additive transmission cost is much more significant if the baseline transmission rate is low (e.g. with c = 1, a strain with B_s 1 would never evolve resistance because B_r would be 0, which would not be the case for a strain with baseline transmission rate B_s = 3).

The problem with the interpretation in terms of susceptible density is clear in the section on serotype dynamics. The main text refers to serotype-specific susceptibles (S^x) (line 303) and explains observed effects in terms of variation in S^x. In the supporting information however, the authors present a model of serotype dynamics which does not have serotype-specific susceptible classes and the pool of susceptibles is the same for all serotypes (eqn 43). While I absolutely agree this is a better model to study transient effects than introducing a serotype-specific susceptible class, I don't understand what the authors mean by serotype-specific susceptible density in the main text.

2) The use of an additive transmission cost

The use of an additive transmission cost requires further consideration/discussion. An additive transmission cost is difficult to interpret epidemiologically and can lead to implausible consequences. For example, if costs are high enough compared to baseline transmission rate, additive costs with no epistasis would lead to a negative transmission rate for the dually resistant strains, which does not make sense (say B_ab = 2 and B_Ab = B_aB = 0.5, then B_AB = -1).

3) Why is epistasis defined in terms of an additive rather than multiplicative expectation?

I also have quite a basic question about the overall framework (eqn. 2). In the modelling framework, epistasis is the difference between the actual per capita growth rate of the dually-resistant infections and the expected growth rate, defined as the sum of the difference between the growth rates of the singly-resistant infections and the baseline rate. It was not obvious to me whether the expectation needs to be additive, or whether this is a question of definition (could the expectation be defined, for example, as a multiplicative rather than additive effect?). In particular, I was wondering about this in the context of the authors' suggestion that multiplicative costs are problematic because they give rise to epistasis - this seemed a little tautological to me because epistasis has been specifically defined as deviation from an additive expectation. I think a discussion about why epistasis is defined in terms of additive effects, and the implications for the derivation of the dynamics of D, would be very interesting and also helpful in making the paper more accessible.

Joint Public Review:

Chen et al. identify LIN37, a member of the DREAM transcriptional repressor complex as a new regulatory factor in DNA double-strand break (DSB) end resection. The study commences with a CRISPR-Cas9 screen for chromatin associated RPA in quiescent pre-B cells that lack DNA ligase 4. In addition to the established anti-resection components 53BP1-Shieldin, findings that validates the screen, the authors identify the transcriptional repression complex LIN37-DREAM. This well executed study makes a number of compelling observations. Namely, that LIN37 limits end-resection only in quiescent cells, that it is not epistatic with 53BP1-Shieldin, and loss of Lin37 allows expression of hundreds of genes including genes involved in DSB end resection and homologous recombination. yielding Rad51 filament formation and HR. All phenotypes were recapitulated by a LIN37 mutant that that does not interact with DREAM. Moreover, generality is shown across human and mouse cell types using either Cas9 breaks or IR.

The results presented in this manuscript are fascinating and should open a new avenue to study cell cycle dependent regulation of DSB repair. The combination of cell biological and end-seq approaches make a very convincing argument for this unanticipated finding. The conclusions drawn from this work are for the most part well-supported by the data, which are of high-quality, and the experiments are rigorously performed. What is unclear is whether these effects are direct or indirect, including whether other DREAM factors also participate in this end-resection suppression. It would also be nice to know the cellular consequences of dysregulated HR that occurs in quiescent LIN37-deficient cells.

Reviewer #2 (Public Review):

Sphingolipids are key components of cellular membranes and act as signaling molecules in multiple physiologically relevant processes. In their manuscript, Fan et al. discover a role for SIRT1 in regulation of sphingolipid metabolism in embryonic stem cells and investigate its implication in neural differentiation. Based on metabolomics analysis and subsequent confirmatory experiments, the authors observed elevated levels of sphingomyelin in samples from SIRT1-deficient mouse ES cells in comparison to wildtype cells, suggesting a regulatory function for this enzyme in sphingolipid metabolic processes. Mechanistically, the authors describe that SIRT1 controls c-myc-dependent expression of SMPDL3B, a sphingomyelin-degrading phosphodiesterase, which regulates sphingomyelin content in ES cells. In vitro and in vivo models of neural development further corroborated a lipid metabolism-related role of SIRT1 and SMPDL3B in ES cell biology.

This work describes an intriguing connection between sphingolipid homeostasis and ES cell function/differentiation that provides insight on how lipid metabolism is intertwined with cellular physiology. Following their initial metabolomics-based finding of altered lipid composition in SIRT1 KO ES cells, the authors employ an impressive array of complementary experimental approaches to pinpoint the molecular mechanism and consequences behind this observation. In general, these experiments appeared technically well conducted and conclusive, but still leave some important questions open.

Reviewer #1 (Public Review):

This paper shows that the sphingomyelin-degrading enzyme SMPDL3B is under transcriptional control of SIRT1 and c-Myc in ESCs, and that loss of SIRT1 lowers sphingomyelin content of ESCs. Sphingomyelin accumulation in SIRT1-deficient ESCs is associated with changes to membrane fluidity and the abundance of several differentiation markers. The paper also includes interesting data showing that maternal HFD feeding increases the SM content of SIRT1 KO embryos. The studies presented are thorough and the data are interesting.

Reviewer #3 (Public Review):

Meier et al. used electroencephalography (EEG) to test the mechanism underlying a well-known phenomenon where stress induces subjects to behave in a more habitual way during decision-making, as opposed to using a more deliberative goal-directed strategy. The authors tested two groups of human subjects who were randomly assigned to a stress manipulation or a similar control manipulation. These participants then carried out a reinforcement learning task where they had to choose between two alternative responses to a stimulus. On some blocks the value of one response would be 'devalued' such that the alternative action would be more appropriate. Participants who went through the stress manipulation were more likely to persist with an action that previously yielded a high reward outcome even when this response had been devalued - indicative of a failure in goal-directed decision-making. Critically, the authors associated responses and outcomes with stimuli that were decodable from EEG signals, making it possible to evaluate whether participants were prospectively considering the correct response or outcome prior to committing a response or receiving feedback. Meier et al. find that, over time, the stressed participants came to prospectively represent the coming response more and the outcome less, while the control group showed reduced prospective representation of the response. The degree of this change toward greater representation of responses versus outcomes across participants was also correlated with a more habit-based decision strategy in devaluation trials.

Overall, this is a well-designed and sophisticated study that makes an important contribution to our understanding of the mechanism by which stress promotes more habit-like behavior, with broad implications for our understanding of how maladaptive behaviors might be formed in many clinical conditions. The conclusions are well supported by the data and confidence in the results is bolstered by several additional control measurements. However, I would have appreciated more effort to link this work to other related literature, as well as some more detail in some parts of the methods and additional control analyses to rule out alternative explanations for some of the main results of interest.

Reviewer #2 (Public Review):

A number of psychological states and traits have been demonstrated to render behavior under goal-directed or habitual control, stress being one of them. In this paper, using electroencephalography, the authors investigated the neural representations of stimulus, responses and outcomes in a task whose aim was to distinguish between the two types of behavioral control. By training a classifier to distinguish between neural signals related to the representations of instrumental responses and the outcomes produced by those responses, the authors found that during the last block of the experiment (after more extended training in the task), signals for outcome representations were weaker and response representations stronger in a stress-induced group compared to a control group. This is consistent with the idea that habits are performed when there is a stronger link between stimuli and responses that does not require a representation of the outcomes that follow from behavior. Although the methods of this paper are sound and the idea interesting and relevant for the current state of the art in habit research, it is not clear if the underlying theoretical contribution it should motivate is supported by the data produced by the experimental design employed by the authors.

Reviewer #1 (Public Review):

The authors used EEG-based multivariate pattern analysis and acute stress induction to assess the neural representations mediating a previously demonstrated influence of stress on the balance between goal-directed and habitual responding. They found that stress reduced neural outcome representations and enhanced response representations - results that are consistent with associative structures thought to mediate goal-directed and habitual response strategies, respectively. The study addresses an important and open question, and the combination of clinical, neural and behavioral assays is appealing. However, the interpretability, and thus impact, is threatened by an apparent lack of temporal synchrony between relevant measures, and by the potential effects of social feedback.

Specifically, it is hard to understand how neural and behavioral devaluation differences between groups can be stress related given that they emerge at a point when differences in stress measures (e.g., cortisol) are no longer present. It seems more likely that, at the time when devaluation insensitivity became more pronounced in the stress group, this group was being released from stress, perhaps experiencing corollary fatigue or buoyancy.

Another concern is that it is unclear whether the "Error" feedback screen was being employed during devaluation blocks. This is important, because most human psychology experiments use accuracy as the only incentive, and it appears to be a pretty effective motivator. Given that participants in the stress group had just been subjected to an aversive social stressor, they might have found the socially relevant error feedback more painful than the relatively minor response cost.

Reviewer #3 (Public Review):

The paper has some novel insights implicating DR4 expression in oxaliplatin-resistant CRC as potentially a therapeutic vulnerability. The paper includes ex vivo treatment of oxaliplatin-resistant CRC CTCs with TRAIL liposomes and implication of DR4 localization of lipid rafts. The paper suggests that TRAIL pathway therapeutics may be helpful for oxaliplatin-resistance CRC. Such therapeutics have been tested in the past to treat CRC (TRAIL/Dulanermin in combination with FOLFIRI) but TRAIL development was discontinued due to lack of adequate patient responses. Currently TRAIL agonist antibodies are in various clinical trials including in combination with chemotherapy for CRC. While the findings are interesting, there are several major concerns with regard to data interpretation and experimental rigor. Based on limited data in the manuscript, the upregulation of DR4 does not appear to be a general mechanism associated with oxaliplatin resistance in CRC. The authors have not rigorously tested the role of DR4 in the sensitization to TRAIL. Similarly, the effect of resveratrol has not been rigorously attributed to DR4. Some data implicates DR5 in the sensitization (Supplementary Figure 6) but in the end the authors emphasize DR4. There could be other mechanisms involved.

Reviewer #2 (Public Review):

Greenlee et al. describe a potentially new therapeutic approach for oxaliplatin-resistant (OxR) colorectal cancer (CRC). This group shows that OxR CRC cells have increased sensitivity to TRAIL-mediated apoptosis. Mechanistically, this work suggests enhanced DR4 palmitoylation and translocation into lipid rafts, and that perturbation of these LR impacts sensitivity of TRAIL. In support of this premise, treatment of blood samples with TRAIL liposomes caused a reduction circulating tumor cells (CTCs) from CRC patients. Collectively, these findings highlight a potentially translational avenue whereby CRC patients that acquire resistance to Oxaliplatin may benefit from treatments that target TRAIL-mediated cell death, including TRAIL-loaded liposomes. Overall, the work represents an interesting study with potential for translational relevance in CRC. However, some of the claims are not sufficiently rigorously backed by the data presented. For example, the claim that TRAIL-sensitized OxR cell lines have enhanced co-localization of DR4 to lipid rafts is supported by IF in Figure 4A, yet the Western blot data in Figure 4D is extremely modest and not reflective of this claim. Lastly, while there are data on lipid rafts in human CRC patients, the effects are on circulating tumor cells (CTCs), and there are no corroborating data on human metastatic lesions, or pre-clinical in vivo models of metastasis. In sum, while the findings are potentially interesting, more data would strengthen the claims and significance of the manuscript.

Reviewer #1 (Public Review):

The novelty is that the sensitivity to TRAIL and co-localisation in lipid rafts is maintained in oxaliplatin-resistant cells post-treatment (previously shown in cells treated simultaneously with Oxaliplatin and TRAIL), and that this has the potential to target colorectal cancer cells through lipid-based TRAIL delivery. While the implications of the findings are compelling, the study would benefit from clarifying several open questions from the findings and demonstrating robust methodology.

Specifically, the findings could be strengthened by improvements of the image and statistical analysis. The authors compare the area with stain positivity per cell, which assumes no morphological differences between OxR cells and parental cells and also relies on a threshold. To avoid this, robust methods would need to be used to compare the intensity distributions and pixel intensity spatial correlation. Furthermore, there is a lot of variability in the effect of reduced cell viability of circulating tumour cells across patients and draws. Inter- and intra-patient variation should be considered when statistically comparison the cell viability of circulating tumour cells to TRAIL-conjugated liposomes.

Reviewer #3 (Public Review):

This paper describes the oscillatory activity of the habenula using local field potentials, both within the region and, through the use of MEG, in connection to the prefrontal cortex. The characteristics of this activity were found to vary with the emotional valence but not with arousal. Sheding light on this is relevant, because the habenula is a promising target for deep brain stimulation.

In general, because I am not much on top of the literature on the habenula, I find difficult to judge about the novelty and the impact of this study. What I can say is that I do find the paper is well-written and very clear; and the methods, although quite basic (which is not bad), are sound and rigourous.

On the less positive side, even though I am aware that in this type of studies it is difficult to have high N, the very low N in this case makes me worry about the robustness and replicability of the results. I'm sure I have missed it and it's specified somewhere, but why is N different for the different figures? Is it because only 8 people had MEG? The number of trials seems also a somewhat low. Therefore, I feel the authors perhaps need to make an effort to make up for the short number of subjects in order to add confidence to the results. I would strongly recommend to bootstrap the statistical analysis and extract non-parametric confidence intervals instead of showing parametric standard errors whenever is appropriate. When doing that, it must be taken into account that each two of the habenula belong to the same person; i.e. one bootstraps the subjects not the habenula.

Related to this point, the results in Figure 6 seem quite noisy, because interactions (i.e. coherence) are harder to estimate and N is low. For example, I have to make an effort of optimism to believe that Fig 6A is not just noise, and the result in Fig 6C is also a bit weak and perhaps driven by the blue point at the bottom. My read is that the authors didn't do permutation testing here, and just a parametric linear-mixed effect testing. I believe the authors should embed this into permutation testing to make sure that the extremes are not driving the current p-value.

Reviewer #2 (Public Review):

In this study, Huang and colleagues recorded local field potentials from the lateral habenula in patients with psychiatric disorders who recently underwent surgery for deep brain stimulation (DBS). The authors combined these invasive measurements with non-invasive whole-head MEG recordings to study functional connectivity between the habenula and cortical areas. Since the lateral habenula is believed to be involved in the processing of emotions, and negative emotions in particular, the authors investigated whether brain activity in this region is related to emotional valence. They presented pictures inducing negative and positive emotions to the patients and found that theta and alpha activity in the habenula and frontal cortex increases when patients experience negative emotions. Functional connectivity between the habenula and the cortex was likewise increased in this band. The authors conclude that theta/alpha oscillations in the habenula-cortex network are involved in the processing of negative emotions in humans.

Because DBS of the habenula is a new treatment tested in this cohort in the framework of a clinical trial, these are the first data of its kind. Accordingly, they are of high interest to the field. Although the study mostly confirms findings from animal studies rather than bringing up completely new aspects of emotion processing, it certainly closes a knowledge gap.

In terms of community impact, I see the strengths of this paper in basic science rather than the clinical field. The authors demonstrate the involvement of theta oscillations in the habenula-prefrontal cortex network in emotion processing in the human brain. The potential of theta oscillations to serve as a marker in closed-loop DBS, as put forward by the authors, appears less relevant to me at this stage, given that the clinical effects and side-effects of habenula DBS are not known yet.

Detailed comments:

The group-average MEG power spectrum (Fig. 4B) suggests that negative emotions lead to a sustained theta power increase and a similar effect, though possibly masked by a visual ERP, can be seen in the habenula (Fig. 3C). Yet the statistics identify brief elevations of habenula theta power at around 3s (which is very late), a brief elevation of prefrontal power a time 0 or even before (Fig. 4C) and a brief elevation of Habenula-MEG theta coherence around 1 s. It seems possible that this lack of consistency arises from a low signal-to-noise ratio. The data contain only 27 trails per condition on average and are contaminated by artifacts caused by the extension wires.

I doubt that the correlation between habenula power and habenula-MEG coherence (Fig. 6C) is informative of emotion processing. First, power and coherence in close-by time windows are likely to to be correlated irrespective of the task/stimuli. Second, if meaningful, one would expect the strongest correlation for the negative condition, as this is the only condition with an increase of theta coherence and a subsequent increase of theta power in the habenula. This, however, does not appear to be the case.

The authors included the factors valence and arousal in their linear model and found that only valence correlated with electrophysiological effects. I suspect that arousal and valence scores are highly correlated. When fed with informative yet highly correlated variables, the significance of individual input variables becomes difficult to assess in many statistical models. Hence, I am not convinced that valence matters but arousal not.

Page 8: "The time-varying coherence was calculated for each trial". This is confusing because coherence quantifies the stability of a phase difference over time, i.e. it is a temporal average, not defined for individual trials. It has also been used to describe the phase difference stability over trials rather than time, and I assume this is the method applied here. Typically, the greatest coherence values coincide with event-related power increases, which is why I am surprised to see maximum coherence at 1s rather than immediately post-stimulus.

Reviewer #1 (Public Review):

The study by Huang et al. report on direct recordings (using DBS electrodes) from the human habenula in conjunction with MEG recordings in 9 patients. Participants were shown emotional pictures. The key finding was a transient increase in theta/alpha activity with negative compared to positive stimuli. Furthermore, there was a later increase in oscillatory coupling in the same band. These are important data, as there are few reports of direct recordings from the habenula together with the MEG in humans performing cognitive tasks. The findings do provide novel insight into the network dynamics associated with the processing of emotional stimuli and particular the role of the habenula.

Recommendations:

How can we be sure that the recordings from the habenula are not contaminated by volume conduction; i.e. signals from neighbouring regions? I do understand that bipolar signals were considered for the DBS electrode leads. However, high-frequency power (gamma band and up) is often associated with spiking/MUA and considered less prone to volume conduction. I propose to also investigate that high-frequency gamma band activity recorded from the bipolar DBS electrodes and relate to the emotional faces. This will provide more certainty that the measured activity indeed stems from the habenula.

Figure 3: the alpha/theta band activity is very transient and not band-limited. Why refer to this as oscillatory? Can you exclude that the TFRs of power reflect the spectral power of ERPs rather than modulations of oscillations? I propose to also calculate the ERPs and perform the TFR of power on those. This might result in a re-interpretation of the early effects in theta/alpha band.

Figure 4D: can you exclude that the frontal activity is not due to saccade artifacts? Only eye blink artifacts were reduced by the ICA approach. Trials with saccades should be identified in the MEG traces and rejected prior to further analysis.

The coherence modulations in Fig 5 occur quite late in time compared to the power modulations in Fig 3 and 4. When discussing the results (in e.g. the abstract) it reads as if these findings are reflecting the same process. How can the two effect reflect the same process if the timing is so different?

Be explicit on the degrees of freedom in the statistical tests given that one subject was excluded from some of the tests.

Reviewer #3 (Public Review):

In this study, Hao et al. developed an automatized operant box to perform decision-making tasks and optogenetic perturbations without requiring the experimenter's manipulation. For this aim, mice learn to head-fix and to perform a task by themselves. The optogenetic experiment using red-shifted opsins allows manipulation of circuits without the need of an implanted optical fiber. The automation of behavioral tasks in home cages (isolated rodents or in groups) is an intense area of research in neuroscience. The possibility of coupling home cage behavioral analysis with optogenetic manipulation and with complex tasks that require precise positioning of the animal for controlled stimulations (vibrating stimulation, visual .....) is thus of great interest and I commend the authors for their comprehensive dissection of the automated behavioral training setup. Some clarification, reporting of additional behavioral measures and refinement of analyses could improve the impact of this work.

1) The first part of the paper nicely describes the experimental procedure to automate such a complex task. The procedure is very well described, the important points (e.g. the possibility for the animal to disengage...) are properly highlighted, and the online site allows to download the plans and 3D descriptions of the tools and the procedures. The authors compare task learning in automated versus manual training and show that there are overall very few differences. Whisker trimming reduces performance, indicating that animal used information to make the choice. This part of the work is already impressive. Apart from that, the authors do not consider in their description what could be an essential aspect of experiments in a home-cage, i.e the control of the motivation to perform the task. Mice perform the task (here, engage in the head fixation to obtained reward) when they wish and thus, compared with the manual training, there is no explicit control of the animal motivation. This could have consequence on i) the inter-fixation intervals that become an element of the decision and ii) questioned whether the commitment to the task is always motivated by drinking, or whether there is also a commitment to explore, or to check... This could impact the success in the task (e.g. if the animal is not motivated by water, it can explore...). Adding data analyses (information about the daily water consumption, are the inter-fixation intervals correlated with the success or failure in the last trial ...) and even short discussion or introduction of these aspects (see for example Timberlake et al, JEAB 1987 or Rowland et al 2008, Physiol behavior for distinction between close and open economies paradigm) could strengthened the behavioral description.

2) In the second part of the work, the authors focus on the description of choice behavior. To characterize it, the authors used a logistic model to predict choices. They suggest that at the beginning of the task the animals biased their current choice by their last choice (parameter A1) and that once the task is learned they alternate according to the current stimulation (parameters S0). The model was a logistic function of the weighted sum of several behavioral and task variables and has 19 parameters (the ß parameters). If the animal only used these two informations, can a model that only takes into account A1 and S0 reproduce the data? If not, this certainly indicates that other informations (even distributed) are necessary; and also indicates individual strategies. Finally, analyses are made by considering trials as a discrete chain (trial n, n+1...). However, the self-head-fixed methodology causes the trials to be organized with more or less time between successive trials depending on motivation (see above). Again, do the authors note differences in performance according to the timing between trials? Could it be a variable in the model?

3) The third part described optogenetic manipulations. It is clear that group sizes are small. Nevertheless, if the objective was to show that the method works, the results are convincing. Some experimental details and in particular the choice of the statistical procedure need clarification.

Reviewer #2 (Public Review):

Hao and colleagues developed an automatic system for high-throughput behavioral and optogenetic experiments for mice in home cage settings. The system includes a voluntary head-fixation apparatus and integrated fiber-free optogenetic capabilities. The authors describe in detail the design of the system and the stages for successful automatic training. They perform proof-of-concept experiments to validate their system. The experiments are technically solid and I am convinced that their system will be of interest to some laboratories that perform similar experiments. Despite the large variety of similar automated systems out there, this one may prove to become a popular design.

The weak side of the work is that it is not particularly novel scientifically. The system is complex but there it is not an innovative technology. The body of the study has too many technical details as if it is a Methodological section of a regular manuscript. There are bits of interesting information scattered around the paper (like the insights about the strategy mice use, which stem from the regression analysis), but these are not developed into any coherent direction that answers outstanding questions. The potential advantages of this system compared to other systems is marginal. In my eyes, the fact that manual training is so similar to the automatic one is not only a positive point. Rather, it signifies that the differences are mainly quantitative (e.g. # of mice a lab can train per day, etc). Thus, even as a methods paper, the lack of qualitative difference between this and other methods weakens it as a potential substrate for novel findings.

Reviewer #1 (Public Review):

The authors demonstrate in this study that it is possible to train mice to perform a challenging tactile discrimination task, in a highly controlled manner, in a fully automated setup in which the animals learn to head-fix voluntarily. A number of well described tricks are used to prolong the self-fixation time and thereby obtain enough training time to reach good performance when the decision perceptual decision is difficult. In addition the study establish that this experimental design allows targeted silencing of relatively deep brain areas through a clear skull preparation.

It has already been demonstrated that mice can perform voluntary head-fixation and can do behavioral tasks in this context. However, this is the first time this methodology is applied to first to a tactile task and second to a task that mice learn is thousands of trials. Another advantage of the present technique is that it is fully automated and allows training without virtually any human intervention.

The demonstration that optogenetic silencing can be performed in this context is nice but not very surprising as already done in other contexts. Nevertheless it is an interesting application of self head-fixation. The authors should make sure that a maximum of information is available relative to the efficiency of the silencing (fraction of cells silenced) and about its impact on the behavior (does it result or not in a complete impairment?).

Reviewer #3 (Public Review):

In this paper, the authors develop a method and device to use electrophysiology recording arrays in freely moving rodents. Such methods will empower more researchers to use these recording devices in their labs and ultimately save significant time and resources in developing such methods on their own.

The strengths of this paper are its very clear explanation of the protocol (in both writing, images, and a video) and the device that they developed. With the detailed instructions provided here, other researchers will readily be able to adapt this protocol in their own labs. The device is reproducible using common 3D printing services and can be easily modified thanks to its CAD format. There are a few areas in which the written protocol could be improved, but these will be easy to address. Overall, the way that the authors have presented their protocols with such clarity and detail should serve as a precedent for future papers like this.

However, this paper is lacking transparency around several aspects: how often the use of this device is successful, with what kinds of recording devices it can be used, and how animals behave with the device implanted. The manuscript would be significantly strengthened by a clear explanation of how many neurons can be recorded with which probes, and the stability of these recordings over time. It would be beneficial for experimenters planning on using these devices to know which recording probes can be used as well as the chances of successfully recovering the probe. Lastly, there is limited description of how animals manage the weight of the entire device after implantation. Describing this with more detail would be very useful to researchers who wish to use these devices for freely moving behaviors that require quick, unimpeded movement.

Ultimately, this paper will provide an easily adaptable method for other researchers to use in their labs. This will save other researchers significant time and will enable more efficient and reproducible freely moving electrophysiology experiments.

Reviewer #2 (Public Review):

This manuscript provides an updated guide on the procedures for performing chronic recordings with silicon probes in mice and rats in the lab of the senior author, who is one of the leaders in the use of this experimental method. The new set of procedures relies on metal and plastic 3D printed parts, and represents a major improvement over the older methodology (i.e. Vandecasteele et al. 2012).

The manuscript is clearly written and the technical instructions (in the Methods section) seem rather detailed. The main concerns I had are as follows.

1) The present design is an improvement over Chung et al. (the most similar previously published explantable microdrive design, as far as I am aware) in terms of the footprint and travel distance. However, a main disadvantage of the system in its present form is that (apparently) it does not support Neuropixels probes. While such probes might not be suitable for some uses (e.g. to record from large populations in dorsal hippocampus), Neuropixels probes are of considerable interest to many labs.

2) The total weight of the mouse implant seems quite high (together with the headstage, I estimate it is >= 4gr). Could the authors provide the exact value, and describe whether this has any impact on the way the animal moves? Also, the authors should describe how the animals are housed (e.g. do they carry the headstage even when not being recorded). The authors say that a mouse can be implanted with more than one microdrive. The authors should clarify whether they actually have an experience with such implants, or is this just a suggestion based on their educated estimate?

3) There is no information in the results section on the number of implants performed, the duration the animals were implanted, the quality of the recordings obtained, number of successes or failures failures. The figures merely provide examples of one successful recording in a mouse and in a rat. All these details should be provided, along with details of how many probes were reused and how many times (a brief mention of one case, lines 252-253 and 359-360, is not sufficient).

4) In fig. 2, spike waveforms are classified as pyramidal, wide or narrow interneurons. I did not find any description of how this classification was performed.

5) Also in fig. 2, refractory period violations are reported in percent (permille in fact). First, it is not clear how refractory period was defined. Second, such quantification is incorrect in principle: we use refractory period violations to infer the rate of false positives. Yet the relationship between fraction of ISI violations and false positive rate depends on the firing rate of the neuron. For example, 0.1% of ISI violations is quite good for a unit spiking at 10 spikes/s, is so so for a unit spiking at 1 spike/s, and is very bad if the firing rate is 0.1 spike/s (see Hill et al. JNeurosci. 2011 for derivation). Alternatively, the authors can follow an approach described in an old paper by the same lab (Harris et al., JNeuropsysiol. 2000), quantifying the violations in spike autocorrelogram relative to its asymptotic height.

6) Line 477: the authors write that the probes were mounted on a plastic microdrive. This seems to contradict the key claim of the manuscript (namely that the microdrives were from stainless steel).

7) I believe that the work of Luo & Bondy et al. (eLife 2020) and should be references and compared to.

Reviewer #1 (Public Review):

In this ms, Voroslakos et al., describe a customizable and versatile microdrive and head cap system for silicon probe recordings in freely moving rodents (mice and rats). While there are similar designs elsewhere, the added value here is: a) a carefully designed solution to facilitate probe recovery, thus reducing experimental costs and favoring reproducibility; b) flexibility to accommodate several microdrives and additional instrumentation; c) open access design and documentation to favor customization and dissemination. Authors provide detailed description to faccilitate building the system.

Personally, I found this resource very useful to democratize multi-site recordings, not only for standard silicon probes, but also more novel integrated optoelectrodes and neuropixels. While there are other solutions, this design is quite simple and versatile. A potential caveat is whether it could be perceived as just an upgrade, given some similitudes with previous designs (e.g. Chung et al., Sci Rep 2017 doi: 10.1038/s41598-017-03340-5) and concepts (Headley et al., JNP doi: 10.1152/jn.00955.2014). However, the system presented in this paper provides added value and knowledge-based solutions to make silicon probe recordings more accessible.

Reviewer #3 (Public Review):

In the manuscript, "Integrative transcriptomic analysis of tissue-specific metabolic crosstalk after myrocardial infarction" by Arif et al., the authors describe analyses of transcriptomes of +/- myocardial infarction (MI) mice. The study is useful and reports interesting results. These results could be of interest to further develop cellular insight in effects and treatments for MI. However, I do not find any methodological advances here. The manuscript appears to be a repository of transcriptomics analyses. All the techniques used have been tried and applied to other scientific problems. The authors have presented differential expression analysis, followed by GSEA, and then they perform different network analyses - co-expression networks, reporter analyses, multi-tissue model, etc.

My main issues are that the authors do too many different analyses but neither of them get sufficient light in the paper. Also no other independent quantitative evidence is shown in support of results of their analyses. Further, validation was done the same way the pipeline was built. This makes their results comes across as circular. For e.g. when validating metabolic models of cells built using transcriptomic data, CRISPR-Cas9 essentiality screens are used. Here, they basically repeated the same analyses on the same transcriptome from a different experiment it appears.

Reviewer #2 (Public Review):

The authors collected post-myocardial infarction (MI) transcriptome data from a mouse model as well as sham-operated control mice to identify systemic molecular changes in multiple tissues at pathway level. The data were collected at two time points (6 hours and 24 hours post-MI), and several computational systems biology tools were applied to the dataset to identify altered molecular processes. The applied tools vary from very standard tools (eg. enrichment analysis) to advanced methods based on mapping data on biological networks. A specific focus was put on the altered signaling pathways as well as metabolic pathways and metabolites. Identified up-/down-regulated pathways were in agreement with the literature.

Strengths:

• One unique aspect of the work is the fact that the transcriptomic data were collected from not only heart, the source tissue for MI, but also from three more tissues (liver, skeletal mouse, adipose). Therefore, molecular alterations in the related tissues were also able to be monitored and discussed comparatively. The introduced transcriptomic dataset has a high re-use potential by other researchers in the field since coverage of responses by four tissues at two different time points makes it unique.

• Correlation-based coexpression networks were created for all four tissues, and some of the clusters in these networks were shown to be tissue-specific clusters, which nicely validates both the experimental and computational approach in the paper.

• The results were validated by using independent transcriptomic datasets available in the literature. The authors showed that there is a high overlap between their dataset and the literature datasets in terms of identified differentially expressed genes and enriched pathways. This additional validation strengthens the results reported in the manuscript.

• Use of a variety of computational approaches and showing that they point to similar or complementary molecular mechanisms increase the impact of the paper. The employed computational tools include not only information-extraction methods such as enrichment, coexpression networks, reporter metabolites, but also predictive methods based on modelling. The authors construct a multi-tissue genome-scale metabolic network covering all four tissues of interest in the study, and they show that this model can correctly predict some major post-MI changes in the metabolism. It is interesting to see that two completely different computational approaches (constraint-based metabolic modeling versus information-extraction based approaches) point to same/similar molecular mechanisms.

Weaknesses:

• Regarding predictions made by multi-tissue metabolic network modeling, the control case fluxes were predicted by maximizing the rate of lipid droplet accumulation in the adipose tissue. Although there is an agreement between the model predictions and the results obtained by other bioinformatics tools used in the study as well as literature information, it looks rather oversimplification to assume that all other three tissues are programmed to serve for maximum fat production in adipose tissue. This should be further elaborated by the authors.

Reviewer #1 (Public Review):

The authors note how previous studies on myocardial infarction have usually studied individual tissues and not examined the cross talk between tissues and their dysregulation. To address this challenge they have therefore performed, in a mouse model of MI, an integrated analysis of heart, liver, skeletal muscle and adipose tissue responses at 6 and 24 hours. They have then validated their findings at 24 hours in two independent mouse model data sets.

A major strength is their comprehensive approach. They have used high throughput RNA seq and applied integrative network analysis. They show for multiple genes whether they are up regulated or down regulated in these four tissues at the 6 and 24 hour time points and whether the regulation directions are concordant or opposite and note in particular that for the liver both concordant and opposite effects occur. They identify key tissue specific clusters in each tissue and identify the key genes in each cluster. Finally they use whole body modelling to identify cross talk between tissues.

A further strength of this paper is the integration of transcriptomic data (differential expression, functional analysis and reporter metabolite analysis). The final strength is the very clear presentation of the findings and their implications such that the reader gets a very clear message and at the same time can go in to more detail if this is their area of research interest.

There are no major weaknesses. The authors have achieved their aims and the data supports their conclusions.

This work represents a major advance in both methodology and understanding of a multi tissues approach to the study of the metabolic impact of MI and the underlying up and down regulation of relevant genes.

The relevance of these findings in human MI will need to be tested and may ultimately have therapeutic implications.

Reviewer #2 (Public Review):

Paluh and colleagues have investigated the presence and absence of teeth in 523 species of amphibians spanning 515 genera. They have specifically utilized microCT scanning to identify the dentulous bones as well as those that may be missing teeth in all three modern orders of amphibians, with a particular focus on Anura (frogs and toads). It is known that many anurans are entirely edentulous, while frogs only have teeth in the bones of the upper jaw (with the exception of one species). Through Bayesian analysis, reconstruction of ancestral states, they've found that teeth have been completely lost at least 22 times in frogs. Remarkably, they also uncovered six reversals, back to a toothed state, although only in the upper jaw. They then attempt to find a correlation between tooth loss and the diet, jaw morphology, and body size of the edentulous species. Through Bayesian analysis of an impressive, detailed dataset of dietary fact notes from the available scientific literature, the authors find a strong correlation between edentulous species and microphagy (eating ants, termites, other small arthropods). A subsequent phylogenetic logistic regression analysis reveals a significant relationship between edentulism and shortened lower jaws, while failing to find a similar correlation between edentulism and body size.

The major strengths of this paper are the sheer number of species analyzed (including diet data as well as jaw and SVL measurements), combined with up to date analytical methods. Additional strengths of the paper are the robust statistical confirmation obtained from the aforementioned methods.

While there are not many weaknesses, the one major weakness is the correlative nature of the results (e.g. edentulation and smaller lower jaws). While the authors are careful not to directly attribute smaller jaw to tooth loss, it is insinuated from the statistical significance of the analyses, when in reality, these two morphological features could both be independent results of adaptation to microphagy. This possibility should brought to the forefront of the discussion by the authors.

That said, the authors did achieve their goal of identifying how many times teeth were lost in frogs and how many times regained. This is the main aim of the manuscript and is a fascinating glimpse into the phenomenon of edentulation in one of the most speciose group of vertebrates on the planet. This information will allow both the herpetological community as well as the tooth evolution community to reassess some key aspect of their respective fields.

Reviewer #1 (Public Review):

This study examines the evolution of tooth loss (endentulism) across amphibians, finding many cases of endentulism and the majority of losses in frogs and toads (Anura). The study is the first to characterize amphibian endentualism at this scale and the authors have collected an impressive dataset. The study shows correlations among endentulism, diet, and jaw length and discusses potential proximal and ultimate explanations for endentulism in anurans. There are important data provided in this manuscript (e.g. region-specific tooth loss - dentary, maxilla, premaxilla, palate), which could further illuminate developmental pathways responsible for endentulism as well as evolutionary correlates of region-specific tooth loss. The authors collated an impressive breadth of diet data but clearer documentation and examination of that data would allow readers to better evaluate the support for the relationship between diet and endentulism. Overall, this study reveals an interesting evolutionary pattern of endentulism, with the number of independent evolutionary cases of endentulism in amphibians (and anurans, in particular) dwarfing those found in other tetrapod clades.

Reviewer #3 (Public Review):

The authors tested HIV-1 DNA and RNA levels in two large cohorts of ART-treated HIV-1 patient to evaluate possible differences in HIV-1 reservoir cell markers between NNRTI- and PI-based ART regimens, this question is relevant since millions of people living with HIV are currently receiving HIV treatment with these agents. Their major finding is that NNRTI-based treatment is associated with reduced cell-associated HIV-1 RNA and DNA levels; this finding is not entirely novel and well in line with a number of previous observations. The strengths of the study are the large clinical cohorts for which detailed clinical and demographical data are available. The analysis of HIV-1 DNA and RNA is informative, but the assays used do not distinguish between replication-competent and defective proviral species; this is appropriately identified as a limitation of this work. The authors do not address possible immunological consequences of higher HIV DNA levels in PI-treated patients - is this associated with higher levels of inflammatory markers? In addition, it is possible that higher levels of cell-associated HIV-1 RNA may stimulate cell-intrinsic innate (type I IFN-mediated) immunity in PI-treated patients - an aspect that the authors do not address. In the absence of such additional immunological data, it is difficult to assess the true significance and importance of the described observations.

Reviewer #2 (Public Review):

This is a well-written study that will be of interest to many investigators working in the field of HIV persistence during ART. The strengths of the study include the analysis of samples from two relatively large cohorts of individuals (n= 100 and 124) and the use of multivariable models to adjust for numerous parameters. One weakness is the fact that the authors do not consider alternative models that may explain their results. The data is important but should not be overinterpreted, because it does not demonstrate that NNRTI have a better ability to suppress HIV replication. It shows that NNRTI usage is associated with lower levels of HIV persistence markers but does not provide a mechanistic explanation for that (and should not attempt to do so, at least not in the abstract). Overall, this is a well-conducted and important study, with new findings that have potential clinical implications.

Reviewer #1 (Public Review):

The authors examine measures of viral reservoir to understand how different antiviral treatment regimens impact residual virus in HIV infection. They find that NNRTI-based treatments are associated with lower viral reservoirs than PI-based regimens, suggesting they may have some advantage at reducing HIV levels long term.

Reviewer #2 (Public Review):

As the most common reason for infertility, the underlying mechanism of endometrial fibrosis remains largely unknown. Although some progress has been made about the pathogenesis of endometrial fibrosis, the role of cirRNAs during this process remains elusive. In this investigation, Song et al. propose a novel mechanism that increased epithelial circPTPN12 reduces miR-21-5p, which contributes to upregulation of ΔNp63α to induce the epithelial mesenchymal transition (EMT) of EECs (EEC-EMT). There are several interesting findings in this manuscript including 1) there are hundreds of miRNAs are differentially expressed between control and endometrial fibrosis; 2) miR21-5p is mainly located in epithelial cells in normal endometrium 3) There are also some circRNAs are significantly changed between control and IUA; 4) Moreover, functional studies reveal that circPTPN12 is a critical ceRNA for miR21-5p; 5) Different in vivo evidence from the established animal model also unravels that circPTPN12-miR21-5p participate EMT process. Although the author provides comprehensive evidence to support their hypothesis, there are still some minor concerns raised during reviewing this manuscript.

Reviewer #1 (Public Review):

The study by Song and colleagues explores the role of circRNAs in fibrosis of the endometrium. Endometrial cells for patients with and without fibrosis were subjected to expression profiling analysis, and circPTPN12 and miR-21-5p were strongly separate in fibrosis in endometrial, with circPTPN12 acting as an inhibitory factor for miR-21-5p. Through the use of various molecular approaches, the authors further that miR-21-5p inhibition results in upregulation of ΔNp63α, and transcription factor that induces EMT. The role of circPTPN12 was also confirmed in vivo using a mouse model of mechanically induced endometrial fibrosis. The authors concluded that targeting the path circPTPN12/miR-21-5p/∆Np63α may be a therapeutic strategy for endometrial fibrosis.

The authors clearly and convincingly show the involvement of the circPTPN12/miR-21-5p/∆Np63α in EMT and its potential involvement in endometrial fibrosis. Whether or not this can be a therapeutic target is too preliminary at this point. First because the in vivo experiments confirm the link between circPTPN12/miR-21-5p/∆Np63α at the RNA level only (p63) and it would be more convincing to see protein data as well. The involvement of p63 in the process remains a little elusive in this paper. In addition, if the authors believe this pathway can be a real future target to treat endometrial fibrosis, they could better contextualise such a statement, specifically describe what kinds of therapeutic intervention they think of, like regression or prevention of fibrosis. These should be tested in vitro and in vivo. More evidence of the involvement of circPTPN12/miR-21-5p/∆Np63α and the correlation between the three players using clinical material is also necessary.

Reviewer #2 (Public Review):

Since early 2020, the SARS-CoV-2 pandemic has presented numerous challenges to healthcare facilities around the world. Given the highly transmissible nature of SARS-CoV-2 virus, and the confined nature of most hospital settings, hospital acquired infections with SARS-CoV-2 are a frequent occurrence and pose major challenges for hospital infection prevention teams. The increasing use of genomic epidemiology, facilitated by cheaper/faster genetic sequencing tools and user-friendly algorithms for data analysis, creates new opportunities for using virus sequencing to track virus spread in healthcare facilities. While opportunities are increasing, there remain two important bottlenecks to meaningful and widespread use of genomic epidemiology in well-resourced healthcare settings - 1. the turnaround time from sample collection to delivery of sequenced and analysed result; 2. a lack of training among many infection prevention personnel in interpreting genomic epidemiology output.

The study by Stirrup et al tries to alleviate these issues through the development of an algorithm that synthesises inferences from virus genetic sequences and hospital epidemiological data to provide easy to interpret information about whether or not there is likely to be ongoing virus transmission within a medical facility. In general, these kinds of approaches are highly worthwhile and can have important translational value as they facilitate the use of powerful new technologies without necessarily requiring extensive professional training to interpret the results. Indeed, there is an urgent need for tools that can synthesise multiple data streams to provide real time information to healthcare professionals.

In this study, the authors describe their new algorithm and apply it in two retrospective cases to evaluate its potential value to provide valuable information to infection control teams. While it seems clear that the algorithm reliably detects nosocomial transmission in situations where there are obvious hospital outbreaks, it is much less clear that it performs meaningfully in situations where nosocomial transmission is more questionable. To this end, it is not clear if the algorithm provides useful or meaningful information that would help to reduce the burden of hospital acquired SARS-CoV-2 infections. Towards the end of the discussion section, the authors mention that analyses on the utility of the algorithm in prospective use cases were ongoing from late 2020 to early 2021. These analyses will provide essential information on the value of this tool.

While the development of these sorts of tools is important, it is unclear from this study if the tool has value in prospective use or if it would be useful in settings where virus genetic sequencing is less frequent and/or slower than the retrospective use cases considered here. Additionally, in many infection prevention scenarios the existence of an outbreak is clear but tracing the routes of transmission is the primary object of investigation. Because the algorithm does not include phylogenetic information infection tracing potential transmission routes is not possible.

Reviewer #1 (Public Review):

-In the present paper the authors have attempted to develop a novel statistical method and sequence reporting tool that combines epidemiological and sequence data to provide a rapid assessment of the probability of HCAI among HOCI cases (defined as first positive test >48 hours following admission) and to identify infections that could plausibly constitute outbreak events.

-As healthcare-associated infections in hospitals present a significant health risk to both vulnerable patients and healthcare workers, significant improvements to provide a rapid assessment of the probability of HCAI among HOCI cases is of utmost importance in a pandemic setting.

-The strength of the paper is that they have successfully used a large number of virus sequence data from two UK cities with selected hospitals and developed a statistical method to bring these together with classical epidemiological data, which has resulted in a sequence reporting tool (SRT) that was evaluated in relation to:

-The IPC classification system recommended by PHE,

-The PHE definition of healthcare-associated COVID-19 outbreaks (using a 2 SNP threshold).

-They show the added value of combining the two systems. Obviously, this can only work prospectively in a setting like in the UK, where indeed a system like the COVID-19 Genomics (COG) UK initiative is effectively in place. They conclude that through their retrospective application to clinical datasets, to have demonstrated that the methodology is able to provide confirmatory evidence for most PHE-defined definite and probable HCAIs and provide further information regarding indeterminate HCAIs. Therefor, the SRT may allow IPC teams to optimise their use of resources on areas with likely nosocomial acquisition events.

-The acquisition of the extensive prospective datasets necessary to use the system requires a non-negligible investment that is possible in a setting in which sequencing routine and phylogenetic analyses can be carried out in real time. The added value of the methodology should eventually justify the investment.

Reviewer #3 (Public Review):

In this study the authors provide a quantitative assessment of the dense microtubule network in dendrites of mammalian neurons. They used 2D and 3D STED as well as Expansion Microscopy to resolve single tubules in the soma and in dendrites. Also, they marked specifically for microtubules specific modifications such as tyrosination and acetylation, which tend to be associated with dynamic and stable tubules respectively.

I believe the authors achieved very interesting findings, which includes that 1-acetylated microtubules accumulate in the core of the dendritic shaft, surrounded by a shell of tyrosinated microtubules 2- a rigorous quantification of the tyrosination and acetylation levels at the single tube level reveal that the two modifications are anti-correlated and define two distinct microtubule subsets 3-in dendrites the absolute number of acetylated and tyrosinated microtubules is 65-75% and ~20-30% of all microtubules.

My overall impression is that the choice of methods suits well the study and the image analysis performed is very robust throughout the paper supporting their major findings.

The fact that they use different methods, both in terms of imaging (2D STED, 3D STED and Expansion) and analysis, and arrive at the same conclusion regarding for example the percentage of dynamic and stable microtubules is very reassuring that they are quantifying relevant numbers in their analysis.

Overall, I also appreciate how openly they provide their analysis scripts and thorough explanations on the analysis they do and how to use their pipelines, which makes it much easier to check the code and ascertain that it performs as described.

Reviewer #2 (Public Review):

In this work the authors take a quantitative approach towards deciphering the spatial distribution of microtubules carrying different tubulin posttranslational modifications (PTMs) in the neuronal cell body and dendrites. They compare two different super-resolution microscopy techniques - STED and expansion microscopy. They also develop a new approach to image the neuronal dendrites perpendicularly in order to further increase the visibility of single microtubules. The team has previously reported that acetylated and tyrosinated microtubules have distinct patterns of organization within the neuron (Tas et al. 2017; Jurriens et al. 2021). The present investigation confirms these findings and takes a step further towards a quantitative segregation of these microtubule subsets. The authors simultaneously provide a thorough pipeline of the process that can be used to address the distribution of other posttranslationally modified microtubules or broadly to quantify absolute microtubule numbers in confined environments such as neurites.

Altogether, this study demonstrates the power of recently developed super-resolution microscopy techniques for the imaging of single microtubules in neurons. This is especially important in the context of visualizing tubulin PTMs, which require immunostaining for detecting and for which electron microscopy is not a good alternative.

Reviewer #1 (Public Review):

In this manuscript, Katrukha et al. use advanced microscopy techniques to quantify the organization of microtubules within neuronal dendrites. This is a challenge due to the tight bundling of microtubules along neuronal processes, and they tackle it using two techniques: STED microscopy and Expansion Microscopy. They thus measure by two independent procedures the density of microtubules along proximal dendrites and the proportion of acetylated and tyrosinated microtubules, showing that dendritic microtubules are either acetylated or tyrosinated, but rarely present both or none of these post-translational modifications.

The manuscript is a significant methodological advance, with a combination of new sample preparation and image analysis procedures that provide images of dendritic microtubules with enhanced quality. These enhancements don't allow the tracing of a significant number of individual microtubules. They are used for refined intensity-based statistics that provide a worthy insight into the organization of dendritic microtubules. Here the approaches are robust, with clear explanation of the quantitation workflow using open-source tools that are made available. One can regret that these measurements don't address the longitudinal dimension of microtubules, which could help revisiting and answering a couple of important questions (existence of modification domains along single microtubules, microtubule orientation...).

Nonetheless, the authors bring forward very good imaging and solid quantification workflows that allow them to convincingly answer the main question they ask: what is the radial organization of dendritic microtubules, including their post-translational modification state?

Reviewer #3 (Public Review):

About 30 million years ago the ancestors of Old World primates lost the ability to produce the glycan a-gal due to the fixation of several loss-of-function mutations in the GGTA1 gene. The evolutionary advantage of such loss remains elusive. The current study builds upon previous work by the authors showing (i) that the presence of a-gal expressing bacteria in ggta1 deficient mice led to production of antibodies capable of clearance of malaria-causing plasmodia carrying a-gal (Yilmaz et al., 2014), and (ii) that ggta1 deficiency is associated with increased resistance to sepsis via the enhancement of IgG effector function (Sigh et al., 2021). Here they expand on these findings to show that ggta1 deletion in mice is associated with altered composition of the gut microbiome due to the action of IgA targeting of a-Gal expressing bacteria. In addition, they show that the absence of a-gal results in a microbiome that is less pathogenic (i.e., less likely to induce sepsis in their experimental model). Although some aspects of the work are not very novel (e.g., the fact that ggta1 is associated with a remodeled microbiome had already been shown in their previous publications) the work does provide additional insights into the pleiotropic role of ggta1 in immune function, susceptibility to sepsis, and eventual fitness advantage. The work is extremely well done and all conclusions are supported by solid data. Indeed, I felt that the authors were reading my mind every step of the way. Each time I questioned one of the conclusions the next paragraph would address that exact concern. There are, however, a few points that I think would deserve additional clarification.

1 - I was a little surprised that they found no difference in the microbiome of F2 mice between a-gal deficient and wild-type mice. Although I understand that this might be due to antibodies received by the mom, the fact that the divergence in only seen in F3 to F5 would also be compatible with drift and not necessarily a genotype-driven phenotype. Are the microbiome differences detected in F3-F5 overlapping to those observed at F0? If the original differences were controlled by host genetics - the hypothesis being tested - we would expect to see some convergent (at least at the level of specific taxa)

2 - I was really surprised that ggta1 deficient mice lacking a functional adaptive immune system (Figure S8) were equally resistant to systemic infection with the cecal inoculum isolated from ggta1 deficient mice. In the previous work they show that the increases resistance to sepsis comes from increases effector function of IgG. If that is the case, how come mice not having an adaptive system (hence no IgG) are equally protected? Is the pathogenicity of the microbiome of ggta1 deficient mice that reduced? It seems unlikely. More generally, I would like to have seen a better discussion about how these new findings connect to their past work. In the context of increased resistance to sepsis what seems to be more important - the remodeling of the microbiome by IgA or the increased effector function of IgG?

Reviewer #2 (Public Review):

The authors aimed to examine the impact of GGTA1 deletion on host-microbial interactions using a mouse model of a primate-specific mutation. This is a very informative model system that provided interesting insights into the consequences of aGal elimination from host glycoproteins, with subsequent 'release' of immune tolerance breaks and generation of antibody responses agains bacterial aGal epitopes.

The study is well executed and and the conclusions are well supported by the provided evidence. The findings are interesting for a broad audience of biologists.

The identity of IgA targeted bacteria in GGTA1 vs WT mice would be interesting to investigate in the future studies.

Reviewer #1 (Public Review):

This work is a powerful example of thinking across silos. It combines much knowledge of innate and adaptive immunity, with primate evolution of certain antigens lost only in certain primate lineages and tests an important idea about host-mediated, antibody dependent shaping of gut microbiota using laboratory mice with different engineered genetic alterations. Gut microbiota are all the rage these days, but is often forgotten that these microbial communities represent formidable danger that is really too close (one epithelial layer away) for comfort. The authors demonstrate in laboratory mice, how antibodies against non-self sugar molecules present on bacteria can shape the microbiome. Claims and conclusions seem justified by the data presented.

Reviewer #3 (Public Review):

Orofacial actions show exquisite coordination among many muscles, yet the pools of motor neurons exciting each of these muscles is specific to that muscle. The coordination of activity across muscles therefore relies on circuits of premotor neurons that excite the motor neurons. Work by the authors and others has produced major progress in delineating these complex premotor circuits. Recent work using transsynaptic viral tracing has overcome limitations associated with traditional retrograde tracing methods, such as a lack of adequate specificity. However, these transsynaptic viral methods have been unsuccessful in animals older than approximately postnatal day 8 (P8). This is a problem because circuits continue to develop far beyond P8 in mice. Here, the authors overcome this limitation by introducing a novel viral transsynaptic tracing method that can be applied in adult mice.

The authors apply their method to trace premotor circuits for whisking, licking, and jaw movements. They align their anatomical data to the Allen Mouse Brain Common Coordinate Framework and make it available with the manuscript, greatly facilitating its quantitative use by other laboratories. The authors find premotor circuits in adult mice that are almost entirely consistent with results from younger mice, with some important exceptions that they highlight and discuss. The authors quantify overlap of premotor circuits for whisking, licking and jaw movements and discuss the implications of interactions among these circuits.

The experiments and analysis are carefully performed, and the results put into proper context. Overall, this is a straightforward and valuable contribution to our knowledge of the premotor circuits that coordinate orofacial behaviors. It will be of wide interest to neuroscientists.

Suggestions:

-The methods applied in neonatal mice (Takatoh et al. 2013; Stanek et al. 2014), while obviously different, are similar enough that it may be worth including discussion of any possible ways that differences between the neonatal and adult results could be due to methods, rather than age. I defer to the authors about whether such discussion is worthwhile, but readers may benefit from knowing what was considered.

-Spatial correlation in Figure 5C. To interpret this properly it's important to know the degree of smoothing. I could not find this in the relevant methods section describing the kernel density estimation or elsewhere.

Reviewer #2 (Public Review):

The authors describe a variant of retrograde monosynaptic rabies tracing from skeletal muscle. They make use of AAV2-retro-Cre to infect brainstem motoneurons projecting to muscles involved in regulation of orofacial movements (whisking, genioglossus, masseter motoneurons). The strategy that worked most efficiently and with specificity was to inject AAV2-retro-Cre intramuscularly at P17, followed 3 weeks thereafter by central injection of Cre-dependent AAVs expressing TVA and oG, and 2 weeks thereafter followed by central injection of EnvA(M21)-ΔG-RV-GFP. Five days after this final injection, experiments were terminated to analyse the distribution of premotor neurons. This allowed the authors to reconstruct and compare the distribution of premotor neurons to the whisking (lateral 7N), tongue protruding genioglossus (12N), and jaw-closing masseter (5N) motoneurons. To do so, they used the Allen Brain Atlas as a reference for 3D reconstruction, into which they integrated all data. Notably, the authors found that for all three injection types, the highest density of neurons was found in the IRt and PCRt, but the precise peak of highest density was consistently distinct for the three different injection types. The peak for whisker premotor neurons was most caudal-ventral, for masseter premotor neurons most rostro-dorsal, and jaw-closing genioglossal premotor neurons in between these. The authors also make use of the strong expression of fluorescent proteins through rabies virus to analyse collateralization to other motor nuclei. Interestingly, they found cross-talk to other motor nuclei in selective patterns, supporting a model whereby some premotor neurons to one brainstem motor pool also interact with other output circuits, perhaps to coordinate orofacial behaviors. Using a split-Cre retrograde approach from motor nuclei, dual-projecting premotor neurons were identified to be located in dorsal IRt and SupV.

This is a high-quality study making use of several methods not previously brought together in one study. Particularly interesting is the 3-way virus strategy in wild-type mice allowing visualization of premotor neurons in the adult. Second, alignment in a common reference brain is also very useful. And finally, the beginning of understanding dynamics of premotor circuit distribution between development and adult is also a value of this paper. Overall, the study is very interesting for the field.

Reviewer #1 (Public Review):

Facial muscles control the execution of essential tasks like eating, drinking, breathing and (in most mammals) tactile exploration. The activity of motor neurons targeting different muscles are coordinated by premotor regions distributed throughout brainstem. The precise identity of these cells and regions in adults is presently unclear, largely due to technical challenges. In the current work, Takaoh and colleagues develop an elegant strategy to label premotor neurons that target select muscles and register these cells on a common digital atlas. Their work confirms and also extends previous studies in neonates and provides a useful resource for the field.

Reviewer #3 (Public Review):

Diboun et al used a case-control study design to identify DNA methylation sites and regions that differ between individuals with Paget's Disease of Bone (PDB) and controls. Cases were identified from an ongoing PDB clinical trial. Spouses of cases were used as controls. Candidate methylation sites were identified in a discovery set and then tested in a validation set to confirm association with PDB. Meta-analysis was used to combine effects from the discovery and validation sets. A machine learning approach was then used to prioritize candidates and build a prediction model capable of differentiating PDB cases from controls. The model was associated with high level of accuracy (AUC >0.90) in the discovery and validation sets.

A major strength of the study is the collection of a large population of individuals with a rare bone disease. Epigenetic features are appealing for building prediction models as they may represent interplay between genetics and environment. Using this approach, the authors built a prediction model with a high level of accuracy. The results advance our understanding of the etiology of PDB.

Overall, the primary conclusions are generally well supported. However, there are several aspects of the paper that will require additional clarification.

I commend the authors for using a split sample cross validation approach to maximize experimental rigor. However, this approach is distinct from a true external replication. Given that the 'training' and the 'test' sets come from the same overall population, we expect the 'replication' results to be optimistic relative to results from a true, external replication population. Given the absence of a suitable external replication population due the unique nature of the disease, this limitation is acceptable. However, I expect the authors to discuss the potential limitations of this approach in their discussion section and I encourage the authors to refer to the 'replication' set as a 'cross-validation' set to more appropriately convey their experimental approach to the broader scientific community.

The authors look for functional validation using the BIOS qTL database. This reference provides valuable information about functional role of methylation in gene expression in whole blood (eQTM). We know that eQTMs are tissue specific. Do the authors have any evidence whether the methylation plays a similar role in bone tissue?

The authors report the markers from their 'best set' for prediction have potential functional relevance. The potential clinical relevance, however, requires additional context. The data were obtained after onset of PDB. The potential for reverse causation cannot be overlooked. Do the authors have any evidence that the methylation markers precede clinical diagnosis? Appropriate temporality is an essential requisite for an effective clinical prediction model.

Reviewer #2 (Public Review):

This unique study has shown that epigenetic (therefore, potentially environment-driven) factors contribute to the pathogenesis of Paget's Disease of Bone (PDB). Although PDB is not very rare condition, its early diagnosis is problematic. The bone tissue is not easily accessible, thus many cases are not diagnosed till later in life. Thus, having diagnostic markers measured in blood, normalized to cell type count, might be of use for possible diagnostic applications.

The PRISM trial's sample, comprising 232 cases and 260 controls from UK, was divided in two - discovery and replication sets - based on power calculations for EWAS. Meta-analysis of data from the discovery and replication sets revealed significant differences in DNA methylation. Among gene-body regions/loci, many associated with functions related to osteoclast differentiation, mechanical loading, immune function, etc. two loci were suggested as functional through expression quantitative trait methylation (eQTM) analysis. Further, there was some value in assessing the risk of developing PDB. The AUC of 82.5%, based on the 95 discriminatory sites from the "best subset" analysis, is promising for clinical applicability. If confirmed in independent samples and further studies, chromosomal loci found in this study may offer diagnostic markers for prediction of the disease.

Reviewer #1 (Public Review):

The study by Diboun et al. aims to investigate methylation profiles in Paget's disease of bone patients. Many of the genes identified near areas of differentially methylated sites were known to be involved in osteoclast differentiation, viral infection and mechanical loading. These gene pathways are known to play a role in the pathogenesis of PDB. The strength of this study is that it is the first study to look at changes in methylation profiles in Paget's disease of bone patients. Additionally, the genes identified as having differentially methylated sites suggest that environmental factors such as host immune responses may be altered and play a role in the pathogenesis of PBD. The main weakness of this study is that the cells that were analyzed for changes in methylation sites were not osteoclasts the cells of interest in PBD. While many of the genes identified have been shown to play a role in regulation of the skeletal system, results should be interpreted with caution until they are validated in bone tissue.

Reviewer #3 (Public Review):

The authors compared how semantic information is encoded as a function of time between a recurrent neural network trained to link visual and verbal representations of objects and in the ventral anterior temporal lobe of humans (ECOG recordings). The strategy is to decode between 'living' and 'nonliving' objects and test/train at different timepoints to examine how dynamic the underlying code is. The observation is that coding is dynamic in both the neural network as well as the neural data as shown by decoders not generalizing to all other timepoints and by some units contributing with different sign to decoders trained at different timepoints. These findings are well in line with extensive evidence for a dynamic neural code as seen in numerous experiments (Stokes et al. 2013, King&Dehaene 2014).

Strengths of this paper include a direct model to data comparison with the same analysis strategy, a model capable of generating a dynamic code, and the usage of rare intracranial recordings from humans. Weaknesses: While the model driven examination of recordings is a major strength, the data analysis does only provide limited support for the major claim of a 'distributed and dynamic semantic code' - it isn't clear that the code is semantic and the claims of dynamics and anatomical distribution are not quantitative.

Major issues:

1) Claims re a 'semantic code'. The ECOG analysis shows that decoding 'living from 'nonliving' during viewing of images exhibits a dynamic code, with some electrodes coding to early decodability and some to later, and with some contributing with different signs. It is a far stretch to conclude from this that this shows evidence for a 'dynamic semantic code'. No work is done to show that this representation is semantic- in fact this kind of single categorical distinction could probably be done also based on purely visual signals (such as in higher levels of a network such as VGG or higher visual cortex recordings). In contrast the model has rich structure across numerous semantic distinctions.

2) Missing quantification of model-data comparison. These conclusions aren't supported by quantitative analysis. This includes importantly statements regarding anatomical location (Fig 4E), ressemblenes in dynamic coding patterns ('overlapping waves' Fig 4C-D), and presence of electrodes that 'switch sign'. These key conclusions seem to be derived purely by graphical inspection, which is not appropriate.

3) ECOG recordings analysis. Raw LFP voltage was used as the feature (if I interpreted the methods correctly, see below). This does not seem like an appropriate way to decode from ECOG signals given the claims that are made due to sensitivity to large deflections (evoked potentials). Analysis of different frequency bands, power, phase etc would be necessary to substantiate these claims. As it stands, a simpler interpretation of the findings is that the early onset evoked activity (ERPs) gives rise to clusters 1-4, and more sustained deflections to the other clusters. This could also give rise to sign changes as ERPs change sign.

Reviewer #2 (Public Review):

The current work makes the case that local neural measurements of selectivity to stimulus features and categories can, under certain circumstances, be misleading. The authors illustrate this point first through simulations within an artificial, deep, neural network model that is trained to map high-level visual representations of animals, plants, and objects to verbal labels, as well as to map the verbal labels back to their corresponding visual representations. As activity cycles forward and backward through the model, activity in the intermediate hidden layer (referred to as the "Hub") behaves in an interesting and non-linear fashion, with some units appearing first to respond more to animals than objects (or vice-versa) and then reversing category preference later in processing. This occurs despite the network progressively settling to a stable state (often referred to as a "point attractor"). Nevertheless, when the units are viewed at the population level, they are able to distinguish animals and objects (using logistic regression classifiers with L1-norm regularization) across the time points when the individual unit preferences appear to change. During the evolution of the network's states, classifiers trained at one time point do not apply well to data from earlier or later periods of time, with a gradual expansion of generalization to later time points as the network states become more stable. The authors then ask whether these same data properties (constant decodability, local temporal generalization, widening generalization window, change in code direction) are also present in electrophysiological recordings (ECoG) of anterior ventral temporal cortex during picture naming in 8 human epilepsy patients. Indeed, they find support for all four data properties, with more stable animal/object classification direction in posterior aspects of the fusiform gyrus and more dynamic changes in classification in the anterior fusiform gyrus (calculated in the average classifier weights across all patients).

Strengths:

Rogers et al. clearly expose the potential drawbacks to massive univariate analyses of stimulus feature and task selectivity in neuroimaging and physiological methods of all types -- which is a really important point given that this is the predominant approach to such analyses in cognitive neuroscience. fMRI, while having high spatial resolution, will almost certainly average over the kinds of temporal changes seen in this study. Even methods with high temporal and moderate spatial resolution (e.g. MEG, EEG) will often fail to find selectivity that is detectable only though multivariate methods. While some readers may be skeptical about the relevance of artificial neural networks to real human brain function, I found the simulations to be extremely useful. For me, what the simulations show is that a relatively typical multi-layer, recurrent backpropagation network (similar to ones used in numerous previous papers) does not require anything unusual to produce these kinds of counterintuitive effects. They simply need to exhibit strong attractor dynamics, which are naturally present in deep networks with multiple hidden layers, especially if the recurrent network interactions aid the model during training. This kind of recurrent processing should not be thought of as a stretch for the real brain. If anything, it should be the default expectation given our current knowledge of neuroanatomy. The authors also do a good job relating properties detected in their simulations to the ECoG data measured in human patients.

Weaknesses:

While the ECoG data generally show the properties articulated by the authors, I found myself wanting to know more about the individual patients. Averaging across patients with different electrode locations -- and potentially different latencies of classification on different electrodes -- might be misleading. For example, how do we know that the shifts from negative to positive classification weights seen in the anterior temporal electrode sites are not really reflecting different dynamics of classification in separate patients? The authors partially examine this issue in the Supplementary Information (SI-3 and Figure SI-4) by analyzing classification shifts on individual patient electrodes. However, we don't know the locations of these electrodes (anterior versus posterior fusiform gyrus locations). The use of raw-ish LFPs averaged across the four repetitions of each stimulus (making an ERP) was also not an obvious choice, particularly if one desires to maximize the spatial precision of ECoG measures (compare unfiltered LFPs, which contain prominent low frequency fluctuations that can be shared across a larger spatial extent, to high frequency broadband power, 80-200 Hz).

The authors are well-known for arguing that conceptual processing is critically mediated by a single hub region located in the anterior temporal lobe, through which all sensory and motor modalities interact. I think that it's worth pointing out that the current data, while compatible with this theory, are also compatible with a conceptual system with multiple hubs. Deep recurrent dynamics from high-level visual processing, for which visual properties may be separated for animals and objects in the posterior aspects of the fusiform gyrus, through to phonological processing of object names may operate exactly as the authors suggest. However, other aspects of conceptual processing relating to object function (such as tool use) may not pass through the anterior fusiform gyrus, but instead through more posterior ventral stream (and dorsal stream) regions for which the high-level visual features are more segregated for animals versus tools. Social processing may similarly have its own distinct networks that tie in to visual<->verbal networks at a distinct point. So while the authors are persuasive with regard to the need for deep, recurrent interactions, the status of one versus multiple conceptual hubs, and the exact locations of those hubs, remains open for debate.

The concepts that the authors introduce are important, and they should lead researchers to examine the potential utility of multivariate classification methods for their own work. To the extent that fMRI is blind to the dynamics highlighted here, supplementing fMRI with other approaches with high temporal resolution will be required (e.g. MEG and simultaneous fMRI-EEG). For those interested in applying deep neural networks to neuroscientific data, the current demonstration should also be a cautionary tale for the use of feed-forward-only networks. Finally, the authors make an important contribution to our thinking about conceptual processing, providing novel arguments and evidence in support of point-attactor models.

Reviewer #1 (Public Review):

In this technically difficult study of a crucial and understudied area of the human anterior temporal lobe (ATL), the authors set out to investigate the possibility that representations in this area are dynamic, in keeping with its putative role as a semantic hub. In short, they report evidence for stable representations in posterior areas and dynamic representations in anterior areas.

The major strength of this paper is in the nature of the physiological data (ECOG) and the complexity of the associated modeling and computational work. In particular, the consideration of and attempt to model dynamic representations is a real strength.

The major weakness is a slight lack of direct statistical tests to back up certain claims. For example, there is a discussed difference between the posterior and anterior electrode, but not much of direct statistical comparison of those areas. The model which has the best performance clearly changes over time, but there are not direct statistical comparisons of the models performance over that period.

Overall, there is some evidence for a dynamic representation in this area, and the analyses here do point at the need for a more thorough (i.e., considering the possibility of dynamic change) and generally applied approach to studying representations.

Reviewer #2 (Public Review):

The authors sought to understand the mechanisms determining whether the kinase RIPK3 induces apoptosis or necroptosis and the physiological significance of this dual function. They identified a new phosphorylation event on RIPK3 (S164/T165) that appears to inhibit its capacity to induce necroptosis and make it a potent inducer or apoptosis. Low levels of the chaperone HSP90/CDC37 seem to favor S164/T165 RIPK3 phosphorylation, which is suggested to be important for luteal regression by inducing apoptosis in luteal granulosa cells in the ovaries of female mice.

The results presented expand on previous studies showing that whereas RIPK3 induces necroptosis by phosphorylating MLKL, inhibition of RIPK3 kinase activity by small molecules or by D160N mutation caused apoptosis and embryonic lethality. The authors provide experimental evidence supporting that phosphorylation on S164/T165 promotes apoptosis in vitro and in vivo, however the mechanisms regulating this transition remain poorly understood. The data on HSP90/CDC37 is supportive but largely correlative. The authors speculate that association with this chaperone is necessary for proper folding of RIPK3 into a configuration that can only be activated by upstream necroptosis inducers, while at low HSP90/CDC37 levels RIPK3 is not correctly folded and likely auto-phosphorylates on S164/T165, however this remains to be demonstrated. The authors propose that this process is particularly important in luteal granulosa cells and provide some evidence suggesting that RIPK3 phosphorylation on S164/T165 occurs in the ovaries of older mice. This seems counterintuitive given that corpus luteum involution occurs as part of the ovulation cycle and should therefore be especially relevant in young, sexually mature mice. Most importantly, there is no evidence that RIPK3 phosphorylation at these sites is important for female reproductive function, questioning its physiological significance. It would be important to know whether RIPK3 deficient or S165a/T166A mutant mice show any reproductive defects that would be expected by the lack of the proposed RIPK3-mediated apoptosis program in luteal granulosa cells.

The in vivo data in the knock-in mouse models clearly show that phosphomimetic mutations (RIPK3S165D/T166E) on RIPK3 cause severe pathology in multiple organs associated with increased numbers of dying cells. However, rescue experiments, for example by crossing to caspase-8 knockout mice, to prove that the pathology is indeed induced by apoptosis are lacking. It is also interesting that heterozygous expression of the phosphomimetic mutants does not cause any pathology in vivo. The authors speculate that a threshold of expression is required for activation of this mutant, however an alternative explanation could be that the presence of the wild type protein prevents its activation, e.g. by trans-autophosphorylation on S227. Introducing a RIPK3 null allele to generate heterozygous RIPK3S165D/T166E mice that do not express wild type RIPK3 could help resolve this question, as in that case the phosphomimetic mutant will be expressed at the same level but in the absence of the wild type protein.

Finally, most of the in vitro mechanistic studies rely on overexpression of the different mutants in cell lines. Using cells from the knock-in mice expressing the mutated proteins at endogenous levels would be a more appropriate experimental system to explore the mechanistic underpinnings such as the interaction with HSP90/CDC37.

Reviewer #1 (Public Review):

The protein kinase RIPK3 was widely known to promote a form of lytic cell death termed necroptosis. However, RIPK3 could also promote apoptotic cell death under certain conditions. However, the mechanism by which RIPK3 promotes apoptosis and the physiological relevance of this apoptotic activity were not understood. In this study, the authors provided answers to these two questions.

Strengths:

The authors found that a specific phosphorylation on RIPK3 plays a critical role in the switch of RIPK3 into an apoptosis-inducing protein. The authors provided strong evidence to support their conclusion using mouse genetics and demonstrated a role for this RIPK3 activity in reproductive physiology.

Weaknesses:

Although the authors succeeded in finding the protein phosphorylation that controls the form of cell death mediated by RIPK3, key questions remained as to how this modification prevents RIPK3 from promoting necroptosis. Also, the authors implied that the kinase activity of RIPK3 is critical in this switch to apoptosis. However, the phenotypes of mice that lack RIPK3 kinase activity do not match that of the mice that harbor mutations that mimic this phosphorylation.

Overall, this work should provide useful information for future studies to further examine the mechanism by which RIPK3 controls different types of cell death in normal and pathophysiology.

This manuscript is in revision at eLife

The decision letter after peer review, sent to the authors on August 20 2020, follows.

Summary:

In this manuscript, Mughrabi et al reported a technical advance of long term vagus nerve stimulation (VNS) in mice. VNS has been used in clinics for treating certain patients with epilepsy and depression and pioneered in clinical trials for a number of disorders including inflammation. Yet, VNS has not been widely used in mice for mechanistic studies largely due to technical challenges dealing with the small size. Here, the authors developed a method for chronic implantation of VNS stimulator in mice, and tested the effectiveness of the method using measurements of heart rate changes and effects on inflammation. This method is potentially useful to investigate the therapeutic potential of long-term VNS in chronic disease models in mice. While reviewers were positive about the work performed in this study including that it was carried out by multiple labs, there are major concerns about certain points and additional essential experiments are needed. These include the need for robust data related to the LPS inflammation studies and histological analysis. There were also missing details of methodologies that decrease the enthusiasm for this study.

Essential Revisions:

1) At least two papers (PMID: 28628030, 32521521) have reported implants usable for the same application (long-term VNS in mice) although more extensive validation and characterization were performed in this manuscript. A comparison between those implants and the one in this manuscript needs to be discussed. As the authors stated, one technical challenge is that the vague nerve in mice is very small and fragile. However, it is unclear how the approach presented here is different from previous designs, and in particular, how mechanical damage is reduced using the reported apparatus.

2) If the paper is going to be a resource, the authors should provide detailed descriptions of the materials and construction of the electrode. Currently the details are sparse and the photos of poor resolution. It is unclear how the custom cuff was built (no details provided in the method section), what materials were used, and whether these materials are bio-compatible. Also, it is not clear whether and how the cuff electrode is appropriately insulated to prevent stimulation of surrounding muscles/nerves. In addition, the touching point between the nerve and the cuff is very easy to be damaged. With the description of the implantation procedure, it should also be made clearer as to when the cuff electrode is place on the nerve. A clear description could prevent torsion or other injury to the nerve.

3) LPS experiments: All reviewers thought the LPS experiment needed improvement. This study is under-powered and lacks a control group (saline + Sham stim). The LPS study is inconclusive due to a small number of animals. Increasing N to get conclusive data is important because this implant will be very useful to investigate the anti-inflammatory effect of long-term VNS in chronic disease models in mice. Related to this point, out of the 4 animals with bradycardia, 2 animals did not show a decrease in serum TNF. This raises a concern that using heart rate threshold may not be appropriate to deliver a consistent stimulation dose within/across animals if the goal is to get a consistent anti-inflammatory effect. It is likely that vagus efferent fibers responsible for HR decrease (innervating the sinoatrial and atrioventricular nodes) and those responsible for an anti-inflammatory effect are different populations. Those two populations might be differently affected by the implantation surgery and repetitive stimulation. In addition, performing VNS in awake animals is closer to the human situation.

4) Please confirm that 0.1mg/kg is the correct dose, this seems low to induce this amount of TNFa.

5) The histology of the vagus nerve raised questions and needs to be addressed. Here were relevant comments by reviewers.

• In fig 4b, the vagus nerve in the cuff is quite clear, as is the carotid artery. But there are other nerve fragments and/or auto-fluorescent tissue immediately adjacent. What are these? Leads one to wonder if they only stimulated the vagus? The cervical sympathetic travels with the cervical vagus and care is needed to separate them from the carotid sheath. On the right side of fig 4b, the "control" side, they highlight a nerve nowhere near the carotid artery. This is intact tissue, so the vagus has to be next to the carotid artery. There is a big nerve next to the right carotid that I would bet is the vagus. I think they've got it wrong. It is not clear at what level these photos are taken, is it the cervical vagus? The authors should indicate the left and right carotid in these figures.

• Figure 4. I do not see how fibrosis is determined. Is this actually collagen? Can the sections in B be stained with mason's trichrome. In "B" I am not sure that I see that the indicated regions are in fact the vagus nerve. It is hard to tell what other nerves would be present as there are few indications of the anatomical area these sections are from other than neck. Thus it Is hard to discern if this really is the vagus or not. I would have thought that the carotid artery should be visible in close proximity to the nerve bundle, this seems not to be the case and leads to uncertainty that this is the correct nerve.

• Was there any difference in histology between mice with functioning and non-functioning cuffs? As stated in Discussion, left VN without surgery in different animals would be a better control than right VN in the same animals.

6) In the data presented in fig 2 or any of the studies where the kent scientific pulse/ox was used, Did O2 saturation decrease with the change in breathing?

7) Why didn't animals receiving awake VNS show visible changes in BR, which is in contrast to remarkable changes in BR in anesthetized animals?

8) In video 1, it is unclear when the stimulation starts or stops. As a result, it is uncertain if the mouse scratching is due to stimulation. Is this a pain/nociceptive response?

9) Fig 3 is presented in a confusing manner. In "A", I'm not sure why two mice are presented for different days post implantation and what this is showing. There is a clear effect of VNS on the heart rate and breathing (rate, and air flow), is this the minimum current for each day that was found to induce the heart rate threshold change. While I appreciate that the longer pulse widths are less susceptible to the effect of bio-encapsulation of the electrode over time, I'm not sure how one compares 100 uA at 100 us to 400 uA at 600 us. In B how is the HRT achieved without damaging the electrode as the ICIC is exceeded, or are we not understanding this graph correctly? In C there are days that seem to be missing given the legend. The supplementary figure also appears to have data points missing or obscured?

10) Success rate tops out at 75% with a skilled surgeon, and ranges between 40-60% for your average player. I'd say this is not too good.

11) It would be nice to show that the implant does not cause chronic inflammation as this would impact its usefulness as a method. The authors should measure tnfa 14 days Post implanted in cuff implanted and sham implanted mice.

12) What behavioral experiments were done, and what were the results? These are mentioned in several places (line 172, line 279 etc) but not reported.

13) The vagus nerve is critically involved in many essential body functions. Chronic implantation of the VNS stimulator may cause severe inflammation, nerve damage, and neuronal dysfunction. Therefore, it is critical to demonstrate that the chronic implantation does not alter nerve function. The chronic effect of the VNS stimulator implantation needs to be carefully monitored. For example, whether there is any change in body weight, food intake, as well as the sensitivity of diverse physiological reflexes such as the baroreflex, the Hering-Breuer reflex, and the stomach accommodation reflex.

Reviewer #3 (Public Review):

In PD, pathological neuronal activity along the cortico-basal ganglia network notably consists in the emergence of abnormal synchronized oscillatory activity. Nevertheless, synchronous oscillatory activity is not necessarily pathological and also serve crucial cognitive functions in the brain. Moreover, the effect of dopaminergic medication on oscillatory network connectivity occurring in PD are still poorly understood. To clarify these issues, Sharma and colleagues simultaneously-recorded MEG-STN LFP signals in PD patients and characterized the effect of dopamine (ON and OFF dopaminergic medication) on oscillatory whole-brain networks (including the STN) in a time-resolved manner. Here, they identified three physiologically interpretable spectral connectivity patterns and found that cortico-cortical, cortico-STN, and STN-STN networks were differentially modulated by dopaminergic medication.

Strengths:

1) Both the methodological and experimental approaches used are thoughtful and rigorous.

a) The use of an innovative data-driven machine learning approach (by employing a hidden Markov model), rather than hand-crafted analyses, to identify physiologically interpretable spectral connectivity patterns (i.e., distinct networks/states) is undeniably an added value. In doing so, the results are not biased by the human expertise and subjectivity, which make them even more solid.

b) So far, the recurrent oscillatory patterns of transient network connectivity within and between the cortex and the STN reported in PD was evaluated/assessed to specific cortico-STN spectral connectivity. Conversely, whole-brain MEG studies in PD patients did not account for cortico-STN and STN-STN connectivity. Here, the authors studied, for the first time, the whole-brain connectivity including the STN (whole brain-STN approach) and therefore provide new evidence of the brain connectivity reported in PD, as well as new information regarding the effect of dopaminergic medication on the recurrent oscillatory patterns of transient network connectivity within and between the cortex and the STN reported in PD.

2) Studying the temporal properties of the recurrent oscillatory patterns of transient network connectivity both ON and OFF medication is extremely important and provide interesting and crucial information in order to delineated pathological versus physiologically-relevant spectral brain connectivity in PD.

Weaknesses:

1) In this study, the authors implied that the ON dopaminergic medication state correspond to a physiological state. However, as correctly mentioned in the limitations of the study, they did not have (for obvious reasons) a control/healthy group. Moreover, no one can exclude the emergence of compensatory and/or plasticity mechanisms in the brain of the PD patients related to the duration of the disease and/or the history of the chronic dopamine-replacement therapy (DRT). Duration of the disease and DRT history should be therefore considered when characterizing the recurrent oscillatory patterns of transient network connectivity within and between the cortex and the STN reported in PD, as well as when examining the effect of the dopaminergic medication on the functioning of these specific networks.

2) Here, the authors recorded LFPs in the STN activity. LFP represents sub-threshold (e.g., synaptic input) activity at best (Buzsaki et al., 2012; Logothetis, 2003). Recent studies demonstrated that mono-polar, but also bi-polar, BG LFPs are largely contaminated by volume conductance of cortical electroencephalogram (EEG) activity even when re-referenced (Lalla et al., 2017; Marmor et al., 2017). Therefore, it is likely that STN LFPs do not accurately reflect local cellular activity. In this study, the authors examined and measured coherence between cortical areas and STN. However, they cannot guarantee that STN signals were not contaminated by volume conducted signals from the cortex.

3) The methods and data processing are rigorous but also very sophisticated which make the perception of the results in terms of oscillatory activity and neural synchronization difficult.

4) Previous studies have shown that abnormal oscillations within the STN of PD patients are limited to its dorsolateral/motor region, thus dividing the STN into a dorsolateral oscillatory/motor region and ventromedial non-oscillatory/non-motor region (Kuhn et al. 2005; Moran et al. 2008; Zaidel et al. 2009, 2010; Seifreid et al. 2012; Lourens et al. 2013, Deffains et al., 2014). However, the authors do not provide clear information about the location of the LFP recordings within the STN.

Overall, the methods and analysis strategy are innovative and rigorously conducted. However, there are still defects/flaws in the methodological approach which should be corrected in order to guarantee that the results reported in this study support authors' claims and conclusions. The paper will be of particular interest for neuroscientists and clinical community interesting in the PD pathophysiology and the development of new therapeutic approaches aiming at restoring normal cortico-basal ganglia activity.

Reviewer #2 (Public Review):

Sharma et al. investigated the effect of dopaminergic medication on brain networks in patients with Parkinson's disease combining local field potential recordings from the subthalamic nucleus and magnetencephalography during rest. They aim to characterize both physiological and pathological spectral connectivity.

They identified three networks, or brain states, that are differentially affected by medication. Under medication, the first state (termed hyperdopaminergic state) is characterized by increased connectivity of frontal areas, supposedly responsible for deteriorated frontal executive function as a side effect of medical treatment. In the second state (communication state), dopaminergic treatment largely disrupts cortico-STN connectivity, leaving only selected pathways communicating. This is in line with current models that propose that alleviation of motor symptoms relates to the disruption of pathological pathways. The local state, characterized by STN-STN oscillatory activities, is less affected by dopaminergic treatment.

The authors utilize sophisticated methods with the potential to uncover the dynamics of activities within different brain network, which opens the avenue to investigate how the brain switches between different states, and how these states are characterized in terms of spectral, local, and temporal properties. The conclusions of this paper are mostly well supported by data, but some aspects, mainly about the presentation of the results, remain:

1) The presentation of the results is suboptimal and needs improvement to increase readers' comprehension. At some points this section seems rather unstructured, some results are presented multiple times, and some passages already include points rather suitable for the discussion, which adds too much information for the results section.

2) It is intriguing that the hyperdopaminergic state is not only identified under medication but also in the off-state. This is intriguing, especially with the results on the temporal properties of states showing that the time of the hyperdopaminergic state is unaffected by medication. When such a state can be identified even in the absence of levodopa, is it really optimal to call it "hyperdopaminergic"? Do the results not rather suggest that the identified network is active both off and on medication, while during the latter state its' activities are modulated in a way that could relate to side effects?

3) Some conclusions need to be improved/more elaborated. For example, the coherence of bilateral STN-STN did not change between medication off and on the state. Yet it is argued that a) "Since synchrony limits information transfer (Cruz et al. 2009; Cagnan, Duff, and Brown 2015; Holt et al. 2019) , local oscillations are a potential mechanism to prevent excessive communication with the cortex" (line 436) and b) "Another possibility is that a loss of cortical afferents causes local basal ganglia oscillations to become more pronounced" (line 438). Can these conclusions really be drawn if the local oscillations did not change in the first place?

Reviewer #1 (Public Review):

The largest concern with the manuscript is its use of resting-state recordings in Parkinson's Disease patients on and off levodopa, which the authors interpret as indicative of changes in dopamine levels in the brain but not indicative of altered movement and other neural functions. For example, when patients are off medication, their UPDRS scores are elevated, indicating they likely have spontaneous movements or motor abnormalities that will likely produce changed activations in MEG and LFP during "rest". Authors must address whether it is possible to study a true "resting state" in unmedicated patients with severe PD. At minimum this concern must be discussed in the manuscript.

This reviewer was unclear on why increased "communication" in the medial OFC in delta and theta was interpreted as a pathological state indicating deteriorated frontal executive function. Given that the authors provide no evidence of poor executive function in the patients studied, the authors must at least provide evidence from other studies linking this feature with impaired executive function.

In this article, authors repeatedly state their method allows them to delineate between pathological and physiological connectivity, but they don't explain how dynamical systems and discrete-state stochasticity support that goal.

Reviewer #3 (Public Review):

Jenny I. Aguilar et. al. present a manuscript that methodically investigates the behavioral, structural, functional, and physiological consequences of a Cys substitution at R445 in the human dopamine transporter. Parkinson's disease is a common progressive neurodegenerative disorder that affects millions of people worldwide. In most patients, the underlying cause their disease is unknown, but some genetic forms of Parkinsonism have been identified. In this manuscript, the authors investigate the effect of a mutation in the gene that encodes the dopamine transporter that was identified in a patient with infantile Parkinsonism-Dystonia. Using a Drosophila model and an abundance of tools, the data show that the mutation produces: 1) a reduction in spontaneous motor activity, movement vigor, compromised flight initiation, and impaired coordinated movements; 2) a decrease in dopamine content and the number of tyrosine hydroxylase containing neurons in fly brain; 3) a decrease in amphetamine-induced dopamine efflux and dopamine uptake; 4) altered dopamine transporter structure leading to increased probability of open conformations on both sides of the transporter; 5) a reduction in dopamine transporter surface expression and transport capacity. Chloroquine, used as means to limit dopamine transporter lysosomal degradation, increased the ratio of mature to immature dopamine transporter and improved flight initiation. So why does a decrease in dopamine reuptake promote a dopamine-deficient Parkinson phenotype? The Authors conclude that an overall reduction in dopamine transporter would deplete dopamine stores by promoting excessive extracellular dopamine. The decrease in vesicular release would be further exacerbated by DA stimulation of presynaptic dopamine-D2 receptors on dopamine axons. This rather novel counterintuitive hypothesis appears to be supported by the outcome of this investigation. Overall, the study may highlight the mechanism underlying a rare type of Parkinsonism that can affect children as well as adults.

Reviewer #2 (Public Review):

I have reviewed Psychomotor Impairments and Therapeutic Implications Revealed by a Mutation Associated with Infantile Parkinsonism-Dystonia by Aguilar et al. The authors first express hDAT in the dDAT loss of function background to explore in vivo effects. The comparison of hDAT rescue flies to wild type flies and the DAT mutant provide a nice control for the functionality of the hDAT transgene. A better control might have been rescue using dDAT with the same driver but this is a very minor concern since the wild type flies and the hDAT rescue look so similar. They then show that the R445C mutant decreases "movement vigor" and flight initiation. They use HPLC and immunolabeling to convincingly show deficits in both total tissue DA and a decrease in the number of detectable DA cells and use amperometry in the fly brain to quantify defects in efflux. Amperometry in the fly brain is technically impressive since few other labs have accomplished this without fouling the carbon electrode. In the second section of the paper, the authors perform a structural analysis, using LeuT to model DAT. The combination of Rosetta modeling, X-ray crystallography and EPR spectroscopy further adds to the technical strength of the paper. They show that substitution at the position in LeuT R375 analogous to DAT R445 disrupts a previously identified salt bridge and the IC vestibule. They then generate X-ray crystal structures of LeuT WT, LeuT R375A and LeuT R375D at resolutions of 2.1-2.6 Å. Their analysis confirms that substitution at LeuT-R375 disrupt salt bridge formation consistent with Rosetta modeling. They further conform the disruption of the interaction between R375 and its partner using a variant of EPR and show that substitutions at this site bias toward open conformations. In the final figure of the paper they heterologously express the DAT mutants in cell culture and show that cell surface expression, transport and efflux are compromised, similar to previously published findings from another lab. Finally, they show that chloroquine can rescue some of the behavioral deficits in the fly.

The authors present a remarkably comprehensive and technically sophisticated analysis of the structure, function and behavioral sequelae of a mutation in the DAT (hDAT R445C). The analysis is translationally relevant since the mutation was identified in a patient suffering from a rare movement disorder relevant to Parkinson's disease. The combination of behavioral and biochemical analysis in a transgenic animal with X-ray crystallography and modeling is extremely unusual and from a technical standpoint the paper is unusually strong. The insight gained from comparing the structural and functional halves of the paper is also useful. The partial pharmacologic rescue of the behavioral deficits further elevates this work.

Concerns It might be argued that the insights obtained from comparing the various data on modeling, structural analysis, biochemical assays and the behavior of the R445 mutant may not always be consistent with one another, making it difficult to determine the physiological relevance of each effect. This concern is balanced by the idea that we cannot know which aspects of any given mutant will or will not conform to expectations without the comprehensive analysis used here. As such, the paper provides an important example of examining a risk allele in a variety of different ways to determine which molecular deficits may be relevant to the observed phenotype and to the function of the transporter. That said, the authors should add text to acknowledge that some of the molecular defects they observe may be overshadowed by others and/or may not be as relevant to the in vivo defects in activity. For example, the idea that efflux may play a role in the R445 phenotype similar to other mutants and neuropsychiatric illness in general is provocative, but seems difficult to reconcile with the observation that relatively low levels of protein are present at the cell surface.

The behavioral analysis is elegant and takes advantage of high-speed video recording to determine subtle defects in movement. The specificity of the defect is also interesting since grooming is not affected. However, it is difficult to determine whether the data represent a true deficit in movement versus wakefulness or overall activity of the animal. Dopamine is well known to be required for sleep in the fly and it is unclear whether the "deficits in movement vigor" are caused by the flies being "sleepy". Alternatively, higher order decision making processes rather than movement per se might be compromised. These explanations for the observed deficits would not take away from the importance of the findings. Indeed, as the authors acknowledge, the non-motor symptoms of PD are just as important as the motor symptoms. However, it seemed at times that authors felt compelled to fit their data into a motor paradigm rather than taking a more general view on the relationship of the observed defects to other problems that accompany PD. The authors should address these issues with additional text. Additional experiments to address this issue are likely beyond the scope of the current manuscript which is already quite lengthy.

Minor points:

The authors discuss a model in which loss or DAT reuptake and an increase in extracellular DA could down regulate TH. Since they use TH labeling to count DA cells they should acknowledge the possibility the cells are not absent in the mutant (even if they are functionally compromised) but are simply not detectable.

It is unclear why (Brand and 147 Perrimon, 1993); are cited on line 146.

Typo in "Initiate" on Y axis of Fig 3B.

State somewhere in the text or in the Fig 3 legend that HPLC was used to measure tissue concentrations of DA to make it more obvious that amperometry was not used

Reviewer #1 (Public Review):

The authors generated new transgenic fly lines with the human dopamine transporter (hDAT-WT) and the hDAT with the R445C mutation (hDAT-R445C). Studies in the hDAT-R445C flies show a decrease of tissue DA content and a loss of TH+ PPL1 neurons indicating an effect of the DAT mutation on dopamine neuron phenotype or cell survival rather than general DA levels per se. The motor phenotypes observed in the fly include a decrease in the time to initiate flight and in the velocity of locomotion (vigor) but not in the velocity of locomotion initiation or grooming behavior. These behaviors are consistent with the bradykinesia observed in patients. This model system could potentially be used to assay for specific modulators of the mutant to restore surface expression, TH expression and motor behavior.

In the recombinant cell culture system (HEK Cells), the major consequence of the mutation is a decrease in cell surface expression (there is a decrease in conversion to the mature form). A change in the Km is difficult to ascertain with such a dramatic change in the cell surface expression level but looks to be dramatically decreased (higher affinity). These data differ somewhat from those reported in the study by Ng et al, 2014 where the Bmax for CFT was slightly reduced and the affinity was significantly decreased (Km was ~8 fold higher) as was the Ki for DA inhibition of CFT. It should be noted that the decrease in cell surface expression of R445C reported by Ng et al was also not as dramatic as what the same group demonstrated for the other mutation, R87L, that was compound heterozygous in this family. Differences in the transport properties between the two studies should be discussed.

X-ray crystallography and molecular modeling provide novel insights into how the mutation (and other substitutions at this site) affects structure-function relationships of the transporter with respect to gating, uptake and efflux. This information could be used to design modulators of the transporter mutants to rescue cell surface expression or function.

The behavioral effect of CQ on the mutant flies was on the time to flight initiation, which decreased. Locomotion was not tested.

The value of the study is the creation of the flies for screening and the crystallography and molecular modeling studies which examined the impact of this residue on function in detail. The weakness of the study was the limited characterization of the transport properties and cell surface expression in the flies. Being able to tie together the different studies into a cohesive understanding of what happens in patients and thus what needs to be corrected in patients is an important goal of the study. Some of the key questions needed to achieve this understanding were not fully addressed.

Reviewer #3 (Public Review):

In the manuscript entitled "Probe the effect of clustering on EphA2 receptor signaling efficiency by subcellular control of ligand-receptor mobility" by Chen and colleagues, the authors develop an innovative method to directly evaluate the effect of membrane receptor clustering on signaling. Using a fabrication system in which they are able to produce neighboring mobile and immobile substrates, the authors studied the effects of EphA2 receptor mobility in Grb2:SOS and Nck:N-WASP signaling pathways. The authors found that EphA2 clustering enhances signal transduction and results in increased dwell-time of signaling molecules on membranes, analogous to what has been observed in vitro with LAT and nephrin signaling clusters.

This manuscript is well-constructed and provides the reader with an innovative tool to directly evaluate clustered vs. non-clustered receptor in a cellular context. The images present are well-analyzed and provide clear data that support many of the authors conclusions. Importantly, the data presented here directly shows the importance of Eph2A receptor clustering in a cellular context. However, this work and the conclusions regarding distinct physiochemical properties of clusters would be strengthened by direct comparisons of substrate:receptor densities and signaling molecules. This work offers new insight into Eph2A signaling mechanisms as well as new techniques that can be used to study numerous receptor tyrosine kinase signaling pathways. As such, this study will be of interest to a wide variety of readers who study membrane-associated signaling and phase separation.

Reviewer #2 (Public Review):

The authors describe a system using lithographically patterned substrates that contain small patches or corrals, consisting of either supported lipid bilayer allowing free diffusion of a specific ligand ("mobile"), or PEG-derived regions in which the ligand is fixed ("immobile"). Areas around the patches are functionalized with RGD peptides to facilitate cell adhesion via integrins. Cultured cells are incubated on the patterned substrate, allowing direct comparison within the same cells of signaling responses (receptor phosphorylation, adaptor and effector engagement) to patches in which receptors cluster, vs. those in which clustering is not possible. An important feature is that the same amount of ligand (ephrin-1 in this case) is found in the mobile and immobile patches, allowing direct quantitative comparison between the two.

The strength of this manuscript is in the experimental approach, which brings methodologies and precision more associated with in vitro reconstitutions, to studies of living cells. However the advantage of assaying clustered vs. unclustered patches in the same cell are also to some extent a disadvantage, in that it is not really possible to assess the impact on downstream signaling. In this sense it is somewhat disappointing that the investigators did not compare downstream effects in cells plated on patches where only immobile ligands are available vs. those where only mobile ligands are available (for example, Erk nuclear localization could serve as a downstream readout of Grb2/Sos engagement). If the relatively modest differences in receptor phosphorylation and in adaptor/effector recruitment seen in clustered vs. unclustered patches are really biologically meaningful, then we would expect to see significantly more nuclear Erk (on average) in cells where ephrins are mobile and allow clustering.

Related to the former point, the authors suggest that there is so much cell-to-cell variability that they only were able to see an effect of clustering where mobile and immobile patches are clustered in the same cell. However, there appears also to be a great deal of variation in the behavior of patches within the same cell as well. It would be informative to see a quantitative analysis of the variation from patch to patch in the same cell vs. the variation in overall signals for different cells. If, as appears from the figures, there is indeed great variation from patch to patch in individual cells, that would be quite interesting and lead to future experiments to find the source of intracellular variability and its impact on downstream outputs.

A second major question regarding these studies is the effect of time on both clustering and on signal output. Typically, tyrosine phosphorylation reaches a maximum very quickly (within a minute or two) upon stimulation of RTKs. Most of the data in these studies are recorded after much longer times, e.g. 30-60 minutes. I understand some of this is likely due to the time needed for cells to adhere to the patterned substrate, but downstream outputs such as Erk activation also tend to be relatively rapid, so inability to monitor differences between mobile/clustered and immobile ligands means the investigators may be missing the most important and relevant window for downstream signaling (and may actually be measuring cells at a time when feedback inhibition and receptor internalization dominate).

It also would be quite useful in the spt-PALM experiments to see whether the apparent diffusion rate of tracked particles is different between the mobile and immobile patches. It has been suggested that SH2-containing proteins repeatedly rebind to membtanes with high local concentrations of phosphorylated receptors, so counterintuitively one might expect lower apparent diffusion for SH2 domains when receptors are mobile and clustered (where local concentrations of phosphorylated receptor are high) vs. when receptors are fixed, and rebinding is relatively inefficient. This data should already be available to the investigators, since all particles are tracked for the duration of observation.

In conclusion, the strength of this manuscript is the nanofabrication approach allowing direct comparison in cells of signal outputs from clustered vs. unclustered receptors. The rigor of this approach provides new capabilities for understanding the role of clustering in signal processing. The weaknesses are in my view a lack of attention to downstream signal outputs (to assess how small differences in dwell times to clustered vs. unclustered receptors actually impact outputs), and a failure to take into account the dynamics of the response to receptor binding. These factors diminish the overall impact of these studies on our understanding of the precise role of clustering in information processing by RTKs.

Reviewer #1 (Public Review):

One proposed function of biomolecular condensates is in controlling the membrane dwell time to modulate activity of signaling factors. This has been well quantified in vitro but is challenging to quantitatively demonstrate in cells because of substantial cell-to-cell variability in behavior. This is important because cell-to-cell variations can obscure quantitatively measuring the functional impacts of clustering.

In this manuscript, Chen et al. describe the application and adaptation of a micro-patterned substrate that addresses this intrinsic heterogeneity problem by analyzing the activity from clustered (condensed) and non-clustered (diffuse) in the same cell. This was achieved by creation of discrete zones of mobile (membrane-bound) and immobile (polymer-bound) ligands. Using this substrate, the authors examine receptor clustering upon binding to mobile ligands. This clustering drives receptor phosphorylation, increases molecular dwell time of key downstream signaling effectors, and drives local actin polymerization. Importantly, this patterning of substrate allows the authors to de-couple the effects of receptor binding from receptor clustering within individual cells. While there is not a major new conceptual insight in the study, the technical platform allows for a critical quantitative analysis of kinetic proof-reading in signaling pathways that appear phase-separated in cells.

The results are convincing and conclusions are well supported by the data. The impact of this work include providing quantitative, in vivo evidence for functions of membrane-associated condensates and may extends to other questions related to signaling where it is critical to assess diffusive versus non-diffusive ligands within the same cell.

Reviewer #3 (Public Review):

In this manuscript, Hutcherson and Tusche investigate the role of the DLPFC in normative behavior. Challenging some standard accounts, they propose that the DLPFC response track a value-based evidence accumulation process. This claim is supported by qualitative computational simulations - of a an attribute-based neural drift diffusion model aka anDDM), and a model-based reanalysis of three fMRI studies.

Overall, I find the theoretical proposal quite convincing: the model makes sense, and seem to account pretty well for the behavioral data (choices and reaction times) in several experiments and decision contexts. Yet, the computational model (anDDM) seems close to the one previously used in (Tusche and Hutcherson, 2018). I am really sympathetic to the authors' approach (testing a well formulated computational theory on several datasets), and to the proposition that DLPFC's role in decision making might be actually much more "downstream" (i.e. response selection stage) than usually assumed. In that respect, this paper could have a nice impact in the field of neuroeconomics/decision neuroscience. I am, however, less convinced by the second step of the demonstration - i.e. the translation of the model in terms of brain activity and the neuroimaging analyses.

My main concern is that, although I am quite convinced that the anDDM accounts well for behavior, I find very unclear what the predicted activity (the sum of neural activation across the two pools over the decision time) accounts for - or could be confounded with. In short, the predicted activity seems to closely correspond to - and correlate with - a linear transformation of %choice and/or RT (see Figure 2 and Figure S1) . This raises several important questions/concerns.

Reviewer #2 (Public Review):

I am sympathetic to the views presented in the paper and I believe there is definitely merit to what the authors are claiming. I also appreciate the combination of computational modeling and fMRI. I do somewhat question the novelty of the findings and think that there are other, related, interpretations of the results that the authors could discuss. No individual study provides sufficient evidence for the authors' conclusions. However, that is the benefit of this mini meta-analysis. There are potentially other explanations for the authors' results, such as the DLPFC becoming active when subjects disobey experimenters' instructions, though perhaps the correlation of the DLPFC with accumulated evidence assuages this concern. Overall I think this is an interesting, compelling study, but it could benefit from more evidence on the correspondence between behavior and brain activity.

Reviewer #1 (Public Review):

Hutcherson and Tusche address an important question: what is the role of dorsolateral prefrontal cortex in normative decision-making? Some have argued that dlPFC plays a role akin to cognitive control - overriding non-normative choices or holding in mind and enhancing the weight of normative goals. Others have argued that dlPFC reflects the accumulation of evidence, akin to a drift diffusion process, during decision-making, and that any apparent special role in normative choices follows from this. Hutcherson and Tusche provide evidence in favor of the second view from three neuroimaging studies across two domains where norms are relevant (altruistic choice and healthy food choice). The key prediction that distinguishes the evidence accumulation hypothesis is that, when normative considerations are weighted more strongly (for example, due to regulatory focus instructions), non-normative choices should be associated with stronger activation than normative ones. The authors observe moderate support for this prediction.

Reviewer #2 (Public Review):

The present study addresses the hypothesis that a decline in the cholinergic tone in ageing or neurodegenerative diseases such as Alzheimer's disease may lead to an increased microglial reactivity. This hypothesis is supported by several in vitro evidence, indicating that the alpha 7 nicotinic receptor is responsible for the anti-inflammatory activities of ACh on microglia and macrophages; however, the hypothesis has not been fully explored for example by conducting in "in vivo" studies. In particular, the Authors use the mu-p75-saporin immunotoxin injected into the lateral ventricles, to obtain a partial lesion of the basal forebrain cholinergic nucleus, from where cholinergic neurons project to specific regions of the hippocampus. The microglial phenotype is then studied in isolated microglia from the hippocampus and in homogenates from the same brain area. Intraperitoneal LPS injection is used as secondary inflammatory insult. Changes in hippocampal ACh levels are also measured in freely-moving mice through a microelectrochemical biosensor.

The study is technically sound, well performed and presented. The results are solid and confirm the hypothesis, by showing that the loss of cholinergic tone is associated to a higher microglial reactivity and leaves microglial cells more vulnerable to secondary inflammatory insults, causing an exaggerate response to as compared to control mice.

Reviewer #1 (Public Review):

This manuscript explores the effect of loss of cholinergic innervation on microglia homeostatic phenotype. The authors show that loss of cholinergic tone "primes" microglial towards an activated phenotype, which makes them more prone to an exacerbated response to an inflammatory stimulus administered when microglial cells have already reverted to a "basal state". They further show that this overresponse is due to impaired nicotinic signalling mediated by the α7-nAChR expressed in microglial cells. The authors conclude that cholinergic signalling normally contributes to maintain microglia cells in a resting state. In its absence or reduction, microglial cells are primed and enter an acute proinflammatory state upon a second stimulus.

This is an interesting and well conducted study that provides a molecular mechanism underlying the feed-forward contribution of microglial cells to conditions such as brain aging or neurodegenerative pathologies such as Alzheimer's disease. A few aspects of the manuscript could however be improved:

• Figure 2D. In all graphs there are two samples that do not show any increase in cytokine content with respect to saline treated animals? Are these the same animals? Do the authors have an explanation for their lack of response?

• Can the authors provide double immunostaining analysis to show that Iba1 positive cells express α7-nAChR? This will nicely support FACS results given that FACS represent after all a stress for the cells.

• Figure 5. The data are somewhat confusing and in part they seem to repeat those reported in Fig 2 and 3. They are not easy to follow for non-specialists in the field and will benefit from re-writing. Furthermore, why looking at circulating levels of the cytokines? It would be more appropriate to determine cytokine content in the brain

Reviewer #3 (Public Review):

Meyer, Benjamin et al. identified the enzyme involved in the transfer of the second GlcNAC residue on the nascent oligosaccharide in protein N-glycosylation of the thermophilic Crenarchaeon Sulfolobus acidocaldarius. Although N-glycosylation is well-known in Euryarchaeota, the enzymes involved in this process, their substrates, and the mechanisms followed to produce the mature glycan are still elusive in Crenarchaeota, a phylum belonging to TACK archaeal superphylum, which contains also Thaumarchaeota, Aigarchaeota, and Korarchaeota. The authors, by screening the data banks with the sequences of the bacterial MurG and yeast ALg13/Alg14, which catalyze the transfer of GlcNAC in N-glycosylation, identified a gene, named saci1262 and alg24 showing very low identity. The authors characterized in deep the product of this gene with a very complete approach. Firstly, the authors could demonstrate by molecular modelling that Alg24 enzyme shows a 3D structure similar to those of MurG and Alg13/Alg14, and catalytic residues similar to the latter enzyme. The functional characterization was very complete, showing that alg24 is essential in vivo, and that the recombinant Alg24 specifically uses UDP-GlcNAC and lipid-GlcNAc as donor and acceptors substrates, respectively. In addition, the enzyme was thermophilic, did not require metals for catalysis, and followed an 'inverting' reaction mechanism in which the anomeric configuration of the product is the opposite to that of the substrate. Experiments of site-directed mutagenesis demonstrated that His14 is essential for catalysis as predicted by sequence multialignments and inspection of the 3D models, while the role of Glu114, also invariant, remained obscure. Then, the phylogenetic analysis of Alg13/Alg14 on TACK archaeal superphylum, showed that Alg24 are widespread among Archaea, suggesting that N-glycosylation in Eukaryotes was inherited by an archaeal ancient ancestor. This observation fostered the hypothesis that the first eukaryotic cell originated from Asgard superphylum.

Strengths:

The main question of the work, which is the enzyme involved in the first crucial step of protein glycosylation in Archaea? is of general interest in glycobiology. Although this process, in the past believed a peculiarity of Eukaryotes, has been well studied in Euryarchaeota, it is almost unknown in TACK superphylum that, being considered the closest to the Last Eukaryotic Common Ancestor, is a very interesting matter of study. The work shows several strengths:

1) The authors unequivocally demonstrated that Alg24 is the enzyme catalyzing the transfer of the second GlcNAc unit on the nascent oligosaccharide, thereby completing the puzzle of the first step of N-glycosylation, for which only AlgH enzyme was known so far.

2) The approach used to identify Alg24, the choice of the model system, the characterization of the enzyme are absolutely excellent and set a new standard to study N-glycosylation in Archaea.

3) The identification and characterization of a novel Glycosyl Transferase is of great importance in glycobiology. GTs are elusive enzyme, difficult to purify, due to their instability and association to membranes, and to characterize because of their extreme specificity for donor and acceptor substrates. In addition, GT enzymatic assays use very expensive substrates and very laborious procedures. For this reason, characterized GT are by far less common than, for instance, glycoside hydrolases. This study is a milestone for glycobiology. GTs from thermophilic microorganisms could be interesting subject studies in general. Thermostable GT could be more easy to purify and characterize if compared to their mesophilic counterparts.

4) The knock-out in vivo of alg24 gene, was possible because S. acidocaldarius model system is one of the few Crenarchaea for which reliable molecular genetics tools can be used. These experiments, confirmed that N-glycosylation is essential in Crenarchaeota as previously shown for AlgH and AlgB.

Weaknesses:

There are not many weaknesses in this work.

1) How the characterization of Alg24 is directly connected to the evidence that N-glycosylation in Eukaryotes was inherited from an ancestral archaeal cell should be better explained.

2) The novelty of the presence of Alg13/14 and Alg24 homologues in TACK superphylum shown in this paper should be commented in comparison with the available literature.

3) The Cover Art should be revised. The 'take home message' is not clear and the phylogenetic interdependencies of the different superphyla are a bit confusing.

Reviewer #2 (Public Review):

The paper by Meyer and collaborators first describes the in silico identification of a putative beta 1-4-N-acetylglucosaminyltransferase in the model Crenarchaeon Sulfolobus acidocaldarius. Beta 1-4-N-acetylglucosaminyltransferases are involved in the N-glycosylation pathway for the synthesis of glycoproteins. To detect this enzyme, the authors have used as baits the bacterial enzyme MurG and the eukaryotic enzymes Alg13 and Alg14. These enzymes have no detectable similarities and it was not possible to detect their Sulfolobus homologs by simple BLAST search. However, they detected several putative candidates with very low sequence identity (10-17%) using Delta-BLAST. They selected one of them which was retrieved using the Alg14 protein of Saccharomyces cerevisiae as bait. They report that the overall topology of this candidate protein is identical to those of MurG and that the N and C terminal part of the protein could correspond to the eukaryotic proteins Alg14 and Alg13, respectively. They give the name Agl24 to this protein (why 24?) and then describe the enzyme as if its identification was already demonstrated. Closely related homologues of this protein are present in most Crenarchaeota, except in Thermoproteales, but absent in other archaea (Fig. S7).

At this stage of the manuscript (lane 153), the identification of Agl24 is only based on the detection of several patches of conserved amino-acids between the different enzymes (Fig.1, B and D) and on a structural model that fits the structure of the homologous enzymes. I suggest that the authors slightly change this part of the manuscript by first describing how they modelled the structure of their candidate enzyme (this is not indicated in the M&M) and how they identified these conserved amino-acids. They can conclude that the structural and sequence similarities (although low) suggest that they have selected the right candidate and name it (then follow up with their detailed comparison of the different enzymes). An interesting result is the identification of a motif (GGxGGH) conserved between Alg24, MurgG and Alg14. If it's really the first time that this motif is detected, thanks to the identification of Alg24, this is worth to be much more emphasized, especially since the authors demonstrate later on that the histidine is essential.

Lane 37 of the abstract, this sentence is ambiguous, there is no strong similarity between Alg24 and eukaryotic Alg13/14. There is possibly strong structural similarity but it is not obvious that it is higher than with MurG from Figure 2A. Is it possible to quantify these similarities? It could be also wise to compare with the structure of a bacterial EpsF, since, from the phylogenetic analysis of the authors (see below) Alg24 could also exhibit more sequence similarities with EpsF than with MurG.

In my opinion, the next section should not be "Agl24 is essential" but the biochemical characterization of the enzyme which confirms the in silico prediction. The authors have produced and purified a recombinant Alg24 enzyme. They show that the enzyme is membrane bond and has an inverting beta-1,4-N-acetylglucosamine-transferase activity. The section "Alg24 is essential..." could be possibly removed and the result mentioned in the discussion since they are not conclusive concerning the biological role of Alg24 but confirm the previous observation of Zhang and colleagues made by transposon mutagenesis on the Alg24 homologue in Sulfolobus islandicus.

In comparison with the first part of this paper, the last part, dealing with evolutionary aspects raises many problems. The authors do not seem familiar with evolutionary concepts as indicated by the use of the term "lower eukaryotes" lanes 163, 175 that is not used by evolutionists since it is highly biased (a bit racist!), animals, including ourselves, being " higher" eukaryotes. This weakness is also apparent from the use of the expression "conserved from yeast to human" lane 57. Yeast and Human belong to the same eukaryotic subdivision (Opistokonts) so this conservation does not testify for the presence of an enzyme in the Last Eukaryotic Common Ancestor (LECA). Similarly, the authors often limit their description of "lower eukaryotes" to Leishmania/Trypanosoma or Dictyostelium/Entamoeba. A more extensive survey of the Alg13/14 topology in all eukaryotic major groups would be necessary to conclude for instance that the protein was a monomer or a dimer in LECA.

The authors have performed two single gene phylogenetic analyses of several groups of Agl24 homologues, including either the eukaryotic Alg13 or Alg14, which correspond to the N and C-terminal domains of Alg24. These phylogenies are valid to identify different subgroups of enzymes, but they are not reliable to provide real information about the evolutionary relationships between these different groups and within these groups (for instance, they did not recover the strong clade formed by Thaumarchaeota, Bathyarchaeota and Aigarchaeota which is present in all robust archaeal phylogenies, see for instance Adam et al., PMID: 28777382). This is not surprising considering the small size of the genes and the very low similarities between the different subgroups. Since the two phylogenies are rather congruent, in particular for the identification of the different subgroups, it could be interesting to perform a concatenation (removing Methanopyrus kandleri which is a fast evolving species and disturb the phylogeny with Alg14).

I personally identify 4 subgroups in the two phylogenies that I will discuss in some detail below.

Group 1: A first group includes a wide variety of archaea belonging to different phyla. Importantly, Euryarchaeota and other archaea (including one sequence of Odinarchaeota) are well separated. This group possibly correspond to descendants of an ancestral enzyme that was present in the Last Archaeal Common Ancestor (LACA). If correct, this indicates the position of LACA in the two trees. Some Crenarchaeota are present in this part. Did they correspond to Thermoproteales or did some Crenarchaeota have both Alg24 and this form?

Group 2: A second group corresponds to orthologues of Alg24 include Crenarchaeota (Sulfolobales, Desulfurococcales) and Bathyarchaeota,. Questions: How many Bathyarchaeota? Are they widespread in Bathyarchaeota? Since Bathyarchaeota are also present in group 1 (same questions), these group 2 Bathyarchaeota could correspond to MAG contamination with Crenarchaeota? Or LGT between Crenarchaeota and Bathyarchaeota?

The enzymes of group 2 were probably not present in LACA, except of they have evolved more rapidly than the other bona fide archaeal enzymes of group 1 (drastic modification of their function?). More likely, they have been introduced at the base of the Sulfo/Desulfo clade from an unknown source (extinct lineage?)

Group 3: A third group corresponds to EpsF in Archaea and Bacteria Question: How widespread in Bacteria? Were they present in the Last Bacterial Common ancestor? They are sister group to the eukaryotic enzymes. Are they their orthologues? Could it be that the eukaryotic enzymes originated from bacterial EpsF via mitochondria?

Group 4: A fourth group includes all Eukaryotes (monophyletic) and very few sequences of archaeal MAGs belonging to different phylums, a few Thorarchaeota and one Odinarchaeota, but also several Verstraetearchaeota, one Geothemarchaeota, and one Micrarchaeota (DPANN). The lane 39 in the abstract is thus misleading since the phylogenetic analysis revealed similar sequences not only in two phylums of Asgard but also in Verstraetearchaeota, Geothemarchaeota, and Micrarchaeota! Moreover, these similarities remain very low. This does not fit with the classical situation observed in universal tree of life in which archaeal and eukaryotic proteins always exhibit a high level of similarity.

The authors suggest a split (red arrow) at the origin of the Group 3 and 4. However, since the tree is unrooted, one cannot exclude a fusion at the origin of groups 1 and 2?

More importantly, it is profoundly misleading to conclude from this analysis that eukaryotes emerge from Asgardarchaeota!!!! The position of the lonely archaeal sequences in group 4 suggests either problems of MAG reconstruction (contamination, recombination, mis annotation) or, more interestingly, independent LGT of proto Alg13/14 from proto-eukaryotes to these archaeal lineages. Moreover, the few sequences of Asgard present in group 4 only correspond to two Asgard phylums, while the number of Asgard phylums has skyrocketed in recent years. I did a rapid BLAST search and homologues of the Thorarchaeal sequences of group 4 are absent not only in Heimdall and Loki but also in Hela and Gerda. The authors could contact two Chinese groups who published recently preprint describing several additional Asgard phyla (Liu, Y et al. BioRxiv 2020, Xie R et al., BioRxiv 2021).

Obviously, the authors have chosen to fit their paper into the mold of the now popular two domains (2D) scenario in which Eukaryotes emerged from Asgardarchaeota. There is presently a debate between proponents of the 2D and 3D (classical Woese) universal tree of life. The authors are obviously strong proponents of the 2D since they don't mention any of the papers that have recently supported the 3D scenario (Da Cunha et al., 2017, 2018). From lane 457 to the end of the paper, all the discussion turned around the 2D model and the Asgard origin of eukaryotes! They possibly consider that the debate has been closed by the paper of Williams and colleagues (Nat Ecol Evol, 2020) who criticized the work of Da Cunha and colleagues. They should notice that Williams et al still obtained a 3D tree with RNA polymerase (supplementary figure 1) except when they use amino-acid recoding a method that reduce the phylogenetic signal (Hernandez and Ryan, BioRxiv, 2020). A 3D tree was again obtained with the RNA polymerase (including those of giant viruses and the three eukaryotic RNA polymerases) by Guglielmini et al., PNAS, 2019. The debate should thus be considered as still open.

In any case, the phylogenies presented by the authors are not universal tree of life and cannot be used in the 2D versus 3D debate. A proponent of the 3D scenario would said that the Odin sequence present in group 1 corresponds to the real position of Asgardarchaeota, in agreement with the results of Da Cunha et al (2017) who found that Asgardarchaeota are not sister group to eukaryotes but branch deep within archaea.

Since the enzymes studied here are apparently absent in most Asgards, it is profoundly misleading to label Asgard the group close to eukaryote in the Cover art and to have a highlight claiming that eukaryotic Alg13/14 are closely related to the Asgard homologs, suggesting their acquisition during eukaryogenesis, since the number of these Asgard homologues are very limited

It is also profoundly misleading to conclude in the title that their result "strengthens the hypothesis of an archaeal origin of the eukaryal N-glycosylation". One can only said that archaeal and eukaryotic N-glycosylation pathways are evolutionarily related.

However, in the case of Alg13/Alg14, it seems that these eukaryotic proteins are more closely related to bacterial enzymes (EpsF) than to their archaeal homologues (group 1 and 2)! We would like to know more about the phylogeny and distribution of EpsF in Bacteria and Archaea. According to the authors, they are only present in Euryarchaeota but widespread in Bacteria, suggesting a LGT from Bacteria to Archaea. Was this enzyme present in the Last bacterial common ancestor? In summary, the authors conclusions and formulations on the evolutionary part of their paper, especially in the title, the summary, the discussion and the Cover Art are misleading and should be corrected.

Reviewer #1 (Public Review):

N-glycosylation, the covalent attachment of glycans to selected asparagine residues of target proteins, is a post-translational protein modification that occurs across evolution. However, in contrast to the relatively well described eukaryotic and bacterial N-glycosylation processes, the parallel event in Archaea is less well defined. In this manuscript, Meyer and colleagues report on Agl24, a new component of the N-glycosylation pathway of Sulfolobus acidocaldarius, an archaeal species that lives in hot and acidic conditions. Reminiscent of what is seen in eukaryotic N-linked glycans, the N-linked glycan of S. acidocaldarius also relies on a chitobiose (two N-acetylglucosamines) core. Agl24 was shown to add the second such sugar. This activity is similar to that catalyzed by eukaryotic Alg13/14 and bacterial MurG. Accordingly, similarities among these enzymes were first used to predict Agl24 function. Such activity was subsequently confirmed using various experimental approaches. Finally, the evolutionary implications of Agl24 contributing to the assembly of a chitobiose moiety much like Alg13/14 in eukaryotes were considered. Phylogenetic analyses lend support to the idea that eukaryotic N-glycosylation originated from a sub-set of Archaea.

Reviewer #2 (Public Review):

This paper constructs one of the most comprehensive phylogenetic analyses of Eulipotyphla to date, using 23 genes from Meredith et al. (2011), especially concentrating on Talpidae (moles). The authors use this phylogenetic hypothesis to reconstruct lifestyle among eulipothphylans with the aim of understanding transitions to a semi-aquatic lifestyle. The authors also model myoglobin structure and calculate electrophoretic mobility, demonstrating that semiaquatic eulipotyphlans have a higher net surface charge than fossorial, semifossorial, and terrestrial relatives. They reconstruct the evolution of myoglobin using the time-calibrated tree of Eulipotyphla and infer 5 convergent increases in myoglobin net surface charge that correlate with semiaquatic lineages. The authors discuss the implications of this, including the use of myoglobin reconstructions to infer lifestyle at selected nodes.

There are really very few things bad to say about this paper and highly recommend this paper for publication with only very minor changes. Overall it is a very well-written paper, and terrific contribution to studies of mammalian molecular evolution, myoglobin evolution, and eulipotyphlan phylogenetics.

Reviewer #1 (Public Review):

The authors of this study investigated the evolutionary process of the mammalian group of species including moles, shrews, hedgehogs, and solenodons with molecular approaches, with a reference to their diverse lifestyles. They first unveiled the among-species relationships and the chronological pattern of diversification by comparing molecular sequences of commonly shared genes. The highlight of the study is the inference of net surface charge and three-dimensional structure of the oxygen-storing muscle protein myoglobin, which reflected the varied lifestyles, with the Russian desman, the smallest endothermic diver, exhibiting a prominently altered disposition of myoglobin, possibly resulting from the adaptation to a semi-aquatic lifestyle.

Reviewer #2 (Public Review):

This is a broad and ambitious study that is fairly unique in scope - the questions it seek to answer are difficult to answer scientifically, and yet the depth of the questions it seeks to answer and the framework in which it is founded seem out of place in a clinical journal.

And yet, as a scientist and clinician, I found myself objecting to the claims of the authors, only have them to address my objection in the very next section. The results are surprising, but compelling - the authors have done an excellent job of untangling a very complicated question, and they have tested (for our field) a large number of subjects.

The main two results of the paper, from my perspective, are as follows:

1) Persons with an amputation can form better models of new environments, such as manipulandums, than can those with congenital deficiencies. This result is interesting because a) the task did not depend on significant use of the device (they were able to use their intact musculature for the reaching-based task), and b) the results were not influenced by the devices used by the subjects (cosmetic, body-powered, or myoelectric).

2) Persons with congenital deficiency fit earlier in life had less error than those fit later in life.

Taken together, these results suggest that during early childhood the brain is better able to develop the foundation necessary to develop internal models and that if this is deprived early in childhood, it cannot be regained later in life - even if subjects have MORE experience. (E.g., those with congenital deficiencies had more experience using their prosthetic arm than those with amputation, and yet scored worse).

The questions analyzed by the researchers are excellent and the statistical methods are generally appropriate. My only minor concern is that the authors occasionally infer that two groups are the same when a large p-value is reported, whereas large p-values do not convey that the groups are the same; only that they cannot be proven to be different. The authors would need to use a technique such as ICC or analysis of similarities to prove the groups are the same.

Reviewer #1 (Public Review):

Maimon-Mor et al. examined the control of reaching movement of one-handers, who were born with a partial arm, and amputees, who lost their arm in adulthood. The authors hypothesized that since one-handers started using their artificial arm earlier in life then amputees, they are expected to exhibit better motor control, as measured by point-to-point reaching accuracy. Surprisingly, they found the opposite, that the reaching accuracy of one-handers is worse than that of amputees (and control with their non-dominant hand). This deficit in motor control was reflected in an increase in motor noise rather than consistent motor biases.

Strengths:

• I found the paper in general very well and clearly written.

• The authors provide detailed analyses to examine various possible factors underlying deficits in reaching movements in one-handers and amputees, including age at which participants first used an artificial arm, current usage of the arm, performance in hand localization tasks, and statistical methods that control for potential confounding factors.

• The results that one handers, who start using the artificial arm at early age, show worse motor control than amputees, who typically start using the arm during adulthood, are surprising and interesting. Also intriguing are the results that reaching accuracy is negatively correlated with the time of limbless experience in both groups. These results suggest that there is a plasticity window that is not anchored to a certain age, but rather to some interference (perhaps) from the time without the use of artificial arm. In one-handers these two time intervals are confounded by one another, but the amputees allow to separate them. I think that the results have implications for understanding plasticity aspects of acquiring skills for using artificial limbs.

Weaknesses:

• While I found that one of the main conclusion from the paper is that the main factor that is related to increased motor noise is the time spent without the artificial arm, it felt that this was not emphasized as such. These results are not mentioned in the abstract and the correlation for amputees is not shown in a figure.

• The suggested mechanism of a deficit in visuomotor integration is not clear, and whether the results indeed point to this hypothesis. The results of the reaching task show that the one-handers exhibit higher motor noise and initial error direction than amputees. The results of the 2D localization task (the same as the standard reaching task but without visual feedback) show no difference in errors between the groups. First, it is not clear how the findings of the 2D localization task are in line with the results that one-handers show larger initial directional errors. Second, I think that these results suggest that the deficiency in one-handers is with feedback responses rather than feedforward. This may also be supported by the correlation with age: early age is correlated with less end-point motor noise, rather than initial directional error. Analyses of feedback correction might help shedding more light on the mechanism. The authors mention that the participants were asked to avoid doing corrective movement and imposed a limit of 1 sec per reach to encourage that. But it is not clear whether participants actually followed these instructions. 1 sec could be enough time to allow feedback responses, especially for small amplitude movements (e.g., <10 cm).

Reviewer #3 (Public Review):

Evolution is a historical phenomenon that plays out over time through the complex interaction of the stochastic processes of mutation and genetic drift and the deterministic process of natural selection. Biology has seen a vibrant debate over the last few decades over what this means for the repeatability of evolution, and to what degree evolutionary outcomes are shaped by the combination of necessity, chance, and historical contingency. This debate has led to intense empirical study of these factors in evolution. Reconstruction and examination of functional protein evolution has been one of the cleverest and most interesting systems used in this study. Here, the authors seek to examine the roles of chance, contingency, and necessity in the evolution of protein-protein interactions (PPI) between BCL-2 family proteins and their coregulators. They specifically look at the evolution of specific interaction between BCL-2 and BID and the more generalized interaction between MCL-1 and coregulators BID and NOXA. They authors reconstructed the last common ancestor protein of BCL-2 and MCL-1 and a series of intermediates along their respective lines of descent. They then used a very clever Phage Assisted Continuous Evolution (PACE) system to subject replicates from each time point to selection for different PPIs and examined variation in sequence variation. By looking at evolution in replicates from different time points, they were able to disentangle the effects of chance, contingency, and necessity. They found that necessity played little role in protein evolution, with little predictability between replicates of single time points and among those from multiple time points, indicating that there was no single pathway through sequence space to the selected function. They did, however, find strong and synergistic effects of chance and contingency. They did tests to demonstrate that the effects of contingency were due to epistatic interactions that affect the viability of particular historical paths. Chance, meanwhile, had effects because multiple mutations could lead down paths to the selected function. The authors conclude that history and chance must be considered when attempting to understand protein function evolution, and that the sequences of proteins with given functions reflect do not reflect necessary pathways or constrained endpoints, but particular and idiosyncratic histories. Moreover, they suggest that contingency may need to be considered as a fundamental aspect of the evolutionary process, along with mutation, drift, and selection.

Altogether, this is a wonderful and interesting manuscript that makes a substantial and material contribution to our understanding of how history and chance affect evolution. It even speaks to the nature of more fine-grained protein sequence evolution relative to neutral and adaptationist theories. The amount of work and thought that went into the research is nothing less than astonishing. Every time I found myself wondering, "but did they check this...", I found that they, in fact, did in the next section. The work is solid, and the results are robust. I do not see anything that concerns me in the nitty gritty of the actual scientific work. I do, however, think that the authors should engage the work that philosophers of science have done in the last decade or so to better develop our conceptual understanding of contingency and reconsider the meaning of their findings in light of that work.

Reviewer #2 (Public Review):

The extensive description of mutational paths using high-throughput phenotyping combined with sequencing provides a rich and useful data set. However, the experimental setup has some serious limitations.

First, the authors want to address the evolution of protein-protein interactions, but they actually do so comparing the interaction of actual and ancestral proteins with actual human BID and NOXA proteins. The analysis would have been stronger with reconstruction of ancestral sequences also for the BID and NOXA proteins, to test interaction of two proteins at the same evolutionary node. Actually, characterization of protein-protein interactions between proteins from Trichoplax, for example, suggest that the results may be different (Popgeorgiev et al., Science Advances 2021).

Second, the specificity of the binding of NOXA to MCL-1 and not to BCL-2 seems to be an artifact due to the use of peptides instead of full-length protein during interaction assays. This is explicitly indicated in one of the reviews the authors cite in their introduction (Kale et al., 2018, p67). This review mentions a JBC paper clearly demonstrating that BCL-2/NOXA interaction do occur even in human cells: Smith AJ, Dai H, Correia C, Takahashi R, Lee SH, Schmitz I et al. Noxa/Bcl-2 protein interactions contribute to bortezomib resistance in human lymphoid cells. J Biol Chem 2011; 286: 17682-17692.

Third, the same review also stresses that these proteins are partially membrane-bound in vivo. So testing their interactions in soluble protein bioassays is far from physiological relevance. Actually, such a warning appears already in one of the bullet points from the Kale review:

"The majority of studies examining the interactions between BCL-2 family proteins use truncated proteins or peptides of the BH3 region at physiologically irrelevant concentrations or in the absence of membranes leading to confusion in defining the core mechanisms of the BCL-2 family proteins."

Reviewer #1 (Public Review):

This manuscript reports a novel and original approach to examine the possible mutational paths underlying directed protein evolution.

The authors conclude from their experiments that "Necessity was almost entirely absent" (line 209). Indeed, the vast majority of states evolved in just one replicate from one starting point. But this is the problem of a half-full glass: is it half full or half empty? If I understand Fig.4F correctly, one can still detect amino acid changes that recapitulate historical substitutions, and others that revert to the historical state, so it does not seem that necessity is "almost entirely absent". Furthermore, several of the amino acid changes that were detected may not have any effect on NOXA or BID biding, maybe they occurred because of mutational bias, drift or hitchhiking. If this is the case, then one cannot compare all acquired states in each trajectory and conclude about the importance of chance, as in this sentence for example: "Pairs of trajectories launched from the same starting point differed, on average, at 78% of their acquired states, indicating a strong role for chance" (line 219). There are causal mutations that arose repeatedly during PACE replicates from each starting genotype and these mutations do indeed confer the selected-for specificity in their "native" background (as is nicely shown in Figure 6A-B). So this, to me, is evidence for necessity.

Loosing a binding property can probably occur via multiple ways, which are likely to be more numerous than gaining binding for a given protein. It would be nice to discuss this point in more detail.

The experiments presented are limited to one protein family and to the binding properties to two different proteins. In living organisms, each protein is likely to exhibit particular properties such that it can bind or not bind to hundreds of different proteins, and not just two as tested here. So the constraints present in living organisms may be much larger than the ones present within this experimental evolution set up. Furthermore, the tested proteins probably encounter other constraints in their native environment besides affinity for other proteins, and it is yet unclear whether the variant forms obtained here via experimental evolution would be fine to replace the endogenous proteins in living organisms. It is therefore difficult to generalize from the obtained results to all types of evolutionary changes. In general, the conclusions should be toned down and focused on this particular example.

Reviewer #4 (Public Review):

The authors analysed flavinylation across different species. They analysed impressive number of 31.910 prokaryotic genomes. They mined flavinylation associated gene clusters using a bioinformatic approach. They define five different protein classes responsible for transmembrane electron transfer. Moreover, they predicted and validated flavinylation of two domains with unknown functions (by ApbE). Unfortunately, the vast majority of predictions made in this study were not experimentally validated. It is therefore very difficult to judge the reliability of predictions, proposals and claims made in the manuscript.

Reviewer #3 (Public Review):

Summary

The authors have applied a comprehensive bioinformatics analysis to 31,910 prokaryotic genomes and found evidence for extracytosolic flavin transferases ("ApbE") in approximately 50% of the genomes. Moreover, they have analyzed associated gene clusters resulting in the hypothesis that five protein classes are involved in transmembrane electron transfer. Furthermore, the authors postulate that these protein classes are subject to flavinylation by ApbEs. Although the exact biochemical role of these five classes of protein remains unknown, the authors hypothesize that they might be involved in iron assimilation and respiration, at least in some cases. In this context, the authors also identified multi-flavinylated proteins and propose that these might exert a similar role as multi-heme cytochromes, for example under iron depletion; in other words, multi-flavinylated systems might replace multi-heme cytochromes if iron is limiting.

Strength & weaknesses

As is evident from the summary, the basis of the article is the bioinformatic analysis of prokaryotic genomes leading to a number of interesting hypotheses with regard to transmembrane electron transport of hitherto uncharacterized protein complexes. Thus, the proposed functions of the potentially flavinylated membrane complexes will stimulate biochemical studies to characterize the suggested involvement of flavinylated protein complexes in prokaryotes. I would consider this as the main strength of the paper that it has generated multiple challenging hypotheses to follow up experimentally.

As mentioned by the authors, about 50% of the prokaryotic genomes analyzed harbor targets for flavinylation/and the FMN transferase. However, no discussion and not even a hint is provided what these 50% of prokaryotes have in common and what distinguishes this group from the other (50%) prokaryotes. Is it lifestyle (environment), energy production, ...?

On the other hand, the presented study leaves many issues unmentioned creating the (false) impression that all it takes to transport electrons across the membrane is a series of hemes and/or flavins along the way. For example, in the discussion of the very interesting hypothesis that flavinylation might replace multi-heme cytochromes under iron deficiency, discussed on page 20 (last para), the authors mention that "flavins possess two-electron transferring properties (ref. 46)" in contrast to the heme system. If this were true than the switch from heme to flavin would also imply that the electron transport itself would have to change from one- electron to two-electron transport. It is unclear that this would be compatible with all other components of the electron transport system. On the other hand, flavins can also - under certain circumstances and in certain environments - carry out one-electron transfer processes, e. g. DNA-photolyases, flavodoxins, etc. Thus, it is conceivable that the flavins operating in the suggested systems in prokaryotes also perform one-electron transport, similar to the operating mode of heme cytochromes. It is clear that we currently lack the biochemical/physical information to know what is really going on, but at least it should be discussed more thoroughly. Equally, several other aspects of the (multi-)flavinylation should be addressed:

• What is known about the environment of the flavin(s)? - Is the flavin embedded in a protein matrix or freely accessible, in other words does it "behave" like a "free" flavin?

• How does the binding of the flavin affect the redox potential (this is very important in order to understand the direction of electron transport).

• In contrast to other covalent flavin attachments, the flavinylation addressed in the current work is reversible. Is anything known about the removal of flavins from the protein complexes in question?

• Are there any enzymes that carry out de-flavinylation? If so, how are they regulated?

• Connected to the last bullet point: Is the reversibility of flavinylation used for the overall regulation of electron transport?

I assume that most of the questions cannot be satisfactorily answered yet, but I think these issues should at least be addressed in the discussion in order to stress the need for further in depths biochemical studies that target the obvious complexity of these systems.

Reviewer #2 (Public Review):

Interesting bioinformatics. The strength of this article lies in the extensive search for flavinylated domains in prokaryotic genomes. This has resulted in several new ideas about the functions of these domains in transmembrane electron transport. The comparison with (multi-heme) cytochromes and thioredoxins is interesting, and needs experimental validation in future work.

Some weaknesses: In the introduction, I miss a clear explanation about the mode of flavinylation of the FMN-binding proteins and how this relates to other covalent flavinylation systems (where an increase in redox potential of the flavin is a prominent effect of covalent binding). It is also not clearly explained whether the predicted flavinylation of the phosphate moiety of FMN is reversible.

Results and Discussion: The electron transfer properties of flavoproteins are not well explained. Quite some flavoproteins (e.g. flavodoxins) mediate one-electron transfer processes, and this is most likely the preferred way in the discussed transmembrane electron transport systems.

I was wondering if there is any protein structural information about this mode of flavinylation, for instance is the flavin hidden in the protein or accessible? Can the authors tell us more whether the amino acid sequence results explain in more general terms the site(s) of flavinylation?

I would also like to know how sure the authors are that the conserved motif always represents covalent flavinylation.

Along similar lines, regarding the reversibility of the covalent flavinylation, I am curious how sure the authors are that the flavin is always covalently bound and what would be the consequence if this is not the case. For example, might there be next to iron limitation, also flavin limitation?

Finally, I am wondering whether more could be said about the comparison with thioredoxins and cytochromes when we look at the 50% of bacteria that do not contain the flavinylation domains.

Reviewer #1 (Public Review):

The manuscript by MeHeust reports identification of flavinylation proteins that can potentially function as cellular redox mediators related to electron transfer systems in prokaryotes.

The work is useful and informative. The authors used bioinformatic approach to illustrate wide distribution of these proteins in a variety of prokaryotes. Although exact functions of these proteins are not known, this work should inspire further investigation by researchers in the fields of redox enzymology and bioenergetics.

Reviewer #2 (Public Review):

This is paper constitutes an experimental tour de force in understanding bacteriophage T4 replication. The T4 replication system has served as a model for elucidating universal DNA replication mechanisms. Specifically, in this study a new platform for deep mutagenesis was developed, validated and successfully applied to yield a complete profile of mutationally sensitive sites in the DNA polymerase clamp loader gp62 and the DNA sliding clamp gp45. The platform supports high-throughput testing of mutations in replication genes for functional fitness and could be adapted to enable future in-vitro evolution studies of the replication proteins. The mutational profile, along with sequence conservation analysis, demonstrates that clamp loader residues in the AAA+ modules exhibit high tolerance to mutation. Mutationally sensitive residues appear to be directly involved in either ATP hydrolysis or DNA binding. The residue Gln118 was the one notable exception, being distal from both the active site and the DNA. Subsequent detailed molecular modeling and structural analyses establish a structural basis for the observed Gln118 sensitivity. Notably, Gln 118 participates in a critically important hydrogen bond network linking the ATP active sites around the circumference of the clamp loader and likely plays a role in allosteric communication during the clamp loading cycle. Mutation of Gln118 disrupts this network and affect the structural rigidity of an element of the clamp loader termed the central coupler. Function restoration by a second-site suppressor mutation clearly establishes the functional importance of this previously unanticipated mechanism.

Overall, the manuscript makes progress on a topic clearly important to the DNA replication field. The findings are novel and well supported by the data. In particular, the molecular dynamics simulations and analysis appear to have been done using appropriate simulation protocols. Both the experimental and computational methods are described in sufficient detail. Approaching T4 replication from multiple angles, using multiple experimental and computational techniques is a notable strength of this manuscript.

One potential weakness is that among all the questions posed at the beginning of the study not all received a definitive answer. In particular, the question "To what extent does the mutational sensitivity of the system in a particular organism, carrying out the essential function of DNA replication, reflect the sequence diversity seen across the spread of life?" is only partially addressed. The second question, "The clamp loader subunits respond cooperatively to the clamp, ATP and DNA. How do the mechanisms underlying this cooperativity impose constraints on the sequence?", has not been answered and goes beyond the scope of this study.

On the technical side, more rigorous analysis of the molecular simulations performed as part of the study would be welcome. In particular, quantifying the effects Gln118 on dynamics and on the rigidity of the central coupler could have used additional analysis.

Reviewer #1 (Public Review):

The authors sought to understand the relationship between sequence conservation and biological function for a protein complex that undergoes conformational changes during its functional cycle. This included understanding the extent to which phylogenetic comparisons can guide identification of functionally important residues for a specific family member. The particular focus was on the bacteriophage clamp-clamp loader complex, with the long-term goal of understanding structure-function principles that might facilitate the design of novel AAA+ ATPase proteins.

A systematic mutagenesis screen was used to determine the relative fitness for single site mutations throughout the bacteriophage T4 clamp-loader and clamp proteins. A feature of the screen is selection for fitness in an (almost) authentic biological context. The vast majority of residues are highly permissive to substitution, which is notable because the considerable conformational changes required during the loader-clamp reaction cycle might have been expected to place more constraints throughout the structure. It is demonstrated that tolerance to mutation within the T4 proteins does not correlated well with conservation across 1,000 other bacteriophage sequences, thereby illustrating the importance of specific context and limits of inference from phylogenetic comparisons. The only critical residue distant from a catalytic active site or binding surface is a glutamine, whose importance was not previously noted, but imparts rigidity to the structure by coupling functionally-important clusters through a hydrogen bonded network. Inspection of distantly related AAA ATPases indicates that this residue is important for many, although not all members of this large and diverse family of molecular machines.

One major concern is that the levels of protein expression and folding are not verified. This is concerning for the Gln118 mutation because lack of fitness could result trivially from misfolding or accelerated degradation that might result from increased flexibility and conformational stability. Moreover, the authors' finding that it was not possible to purify Gln118 mutant proteins for biochemical studies is consistent with this sort of trivial explanation for apparent lack of biological function.

Reviewer #2 (Public Review):

Zhao et al. investigated how a single trial of aversive conditioning could produce a "merged" long-term memory (mLTM). This mLTM is composed of two negatively associated memories: CS+ (odor paired with electric shock) and CS- (odor unpaired), which can be experimentally achieved by the presentation of a third novel odor at the time of testing (memory retrieval). Through a series of behavioral experiments, they determined that both CS+ and CS- LTM depends on protein synthesis. This was supported by cycloheximide feeding prior to aversive conditioning or flies experiencing a cold shock anesthesia after training. Next, they found that mLTM is derived from the same memory component. The re-presentation of either the CS+ or CS- odor at some point before retrieval extinguished mLTM. The authors also show that mLTM forms regardless of odor exposure sequence, but rather does not occur when the temporal interval between CS+ and CS- during training is extended to 20 minutes. They next determined the neural circuit supporting mLTM. Blocking synaptic output from all PPL1 dopamine neurons (DAN) during training impaired expression of mLTM. Downstream of PPL1 DAN, inhibiting synaptic release from the mushroom body neurons (MBN) similarly blunted mLTM which was further mapped to the axons of α2sc MBN. The plasticity was then ascribed to the α2sc mushroom body output neurons (MBON); blocking α2sc MBON behaviorally impaired mLTM. Lastly, the authors showed that the odor-evoked responses to the CS+ and CS- odors in α2sc MBON were significantly depressed when compared to the novel odor. Overall, they propose that the PPL1 DAN: α2sc MBN: α2sc MBON circuit is responsible for generating mLTM.

Strengths:

The key conclusions stem from a series of behavioural experiments that display a consistent and reproducible phenotype. The data presentation and manuscript text are simple, direct and easy to follow. The interesting observations may potentially garner interest to address how animals incorporate different types of strategies to adapt to their environments when they encounter a threat once or multiple times.

Weaknesses:

Although the manuscript describes several intriguing observations, they are outweighed by a number of weaknesses that substantially limit the impact of the manuscript. The data supporting the main conclusions are thin, experimental approaches are not rigorous, and some writing sections are incomplete.

1) The observation that presentation of a novel third odor leads to mLTM after only a single session of aversive conditioning is intriguing. Authors describe in their methods using three odors for their experiments (as CS+, CS- or novel), but did not alternate/rotate the different combination pairings used as the "novel" one. A panel of odors as "novel", not listed in the manuscript, should be tested which will strengthen the larger conceptual framework and impact. In addition, the authors should perform at least a subset of the experiment using air during testing rather than a 3rd odor.

2) The authors show that the contiguity of CS+ and CS- is critical, and that a 20 min interval leads to no mLTM. What is the maximum temporal interval that supports the formation of mLTM?

3) The authors claim that PPL1 DAN during training are key for mLTM (Figure 3A). The GAL4 line used was TH-GAL4 whose expression pattern (Figure S1A) extends beyond the PPL1 cluster. The more specific TH-D'-GAL4 is suitable and needed to rule out other dopamine clusters labeled by the much broader TH-GAL4 line. Additional split-GAL4 lines can be used to fine tune the PPL1 subpopulations that are important for their proposed circuit. Related to this issue, Figure S1 is useless to the reader for deciphering the expression pattern of the Gal4 lines used given the poor resolution. A better general option would be to simply reference papers/websites that have high resolution images of the expression patterns for those lines used widely and provide high resolution images in manuscripts for only those lines that have not been exhaustively described before.

4) The authors mapped the importance of α2sc MBN for the retrieval of mLTM (Figure 3B). This observation could be strengthened by incorporating additional GAL4 lines that drive expression in α2sc MBN (R28H05-GAL4 or NP3061-GAL4). Inhibiting α2sc MBN via optogenetics (UAS-eNPHR3: inhibitory halorhodopsin) may further support the behavioral phenotype observed, which can also be applied to the notion above using TH-D'-GAL4.

5) The authors claim that α2sc MBON as the last part of their circuit. This is a massive jump of a conclusion directly from the α2sc MBN side of the proposed pathway. There are six MBON (α3, α2sc, α2pα3p, α1, α2α'2a, α1>α) that are downstream from the α2sc MBN. The authors need to rule out the other neurons before directly claiming α2sc MBON only as the main player. Moreover, the R71D08-GAL4 line (supported by the expression pattern in Figure S1F, and cartoons in Figure 4) drives expression in other MBON, and again the authors should use more specific lines that are available.

6) More experimentation and discussion regarding the differences between single trial conditioning to form mLTM and spaced conditioning to form complementary LTM, is required. The authors contrast/merge their behavioral results with those published by Jacob et al (2020). The authors should reproduce the essence of those found by Jacob et al and publish them in this paper. Replication of experimental results across labs is very important, especially for behavioral outcomes and when models are constructed using results obtained by other investigators. The authors allude to the two pairs of DAN that project to α2sc MBN for this plasticity, but did not specifically mention those DAN (lines 220-221) nor elaborate on this speculation.

Reviewer #1 (Public Review):

Using a third odor for memory test, the authors found that single-trial conditioning produces a protein synthesis-dependent LTM leading the avoidance to both CS+ and CS- for more than 7 days. By acutely blocking neurotransmissions from target neurons, they showed that the merged LTM requires outputs from TH-positive dopaminergic neurons during training, and from αβ Kenyon cells and α2sc mushroom body output neurons during testing. Several lines of evidence support their claim that the long-term avoidances of CS+ and CS- after single-trial conditioning are based on the same memory component: 1, Five independent disruptions of the system produced the same level changes of avoidance from CS+ and CS-; 2, Re-exposure to either CS+ or CS- alone abolished both CS+ avoidance and CS- avoidance; 3, The same PPL1 DANs, αβ KCs, and α2sc MBONs involved in both avoidances; and 4, Similar responses of functional depression to CS+ and CS- occurred in the same α2sc MBONs. Based on these results, the authors suggest that animals can develop distinct memory strategies for occasional and repeated threatening experiences.

While the entire manuscript is well-written and the data are well presented, the conclusion has several weaknesses. First, memories for CS+ and CS- are clearly distinct from each other at least during the first 24 hours after training (Figure 1B). During this period, CS+ memory shows a gradual decline similar to conventional LTM. Oppositely, CS- memory shows a gradual increase. When these flies were tested immediately after training, they seemed to approach CS-, a result similar to the previous study using a similar third-odor test showed that multiple-trial conditioning induces "two independent LTMs of opposite valence for avoiding CS+ and approaching CS-". How does "the approaching CS- memory" turn into "the avoiding CS- memory"? Or, "the avoiding CS- memory" is derived independently, similar to the anesthesia-resistant memory?

If initial behavior responses to the CS+ and CS- memories are opposite and thus separated, where does each of them occur? Where and how are they merged after one day? The authors claimed that both CS+ and CS- memories require the same PPL1 DANs, αβ KCs, and α2sc MBONs. But, they manipulate PPL1 DANs with TH-Gal4 that expressed in many other dopaminergic neurons. Similarly, the αβ KCs include at least three major cell populations, each has hundreds of neuron with distinct functions. Thus, while both CS+ and CS- memories require the same family of neurons, it remains uncertain whether these events actually occur in the same neurons.

In general, the identified merged LTM is original and brings a new concept to the field of learning and memory. The finding suggests that two forms of protein-synthesis-dependent LTM induced respectively after occasional or repetitive experiences are encoded in the brain by different neuronal mechanisms. It appears that merged LTM after single-trial training remembers the event with limited details and repetitive spaced trials of training then adds more details to the classical LTM.

Reviewer #3 (Public Review):

Mutations in DHCR7, a key enzyme in cholestrol biosysnthesis have been shown to result in Smith-Lemili-Opitz syndrome. However, the mechanism by which loss of this enzyme alters brain development has not been resolved.

In this study, the authors demonstrate that DHCR7 depletion results in depletion of cholestrol in the brain and also the accumulation of the substrate 7 dehydrocholestrol. These observations are conserved in both the brain of DHCR7 knockout mice as well as patient derived iPSC differentiated in vitro.

The authors present evidence that the developmental defects in the brain are a consequence of accelerated differentiation of NSC into neural cells. These defects could be recapitulated by the addition of 7DHC metabolites on wild type cells.

Throughout the manuscript, the authors demonstrate that their findings are conserved between DHC7 k/o mice and patient derived iPSC for SLO syndrome.

To explain the mechanism underlying the cellular phenotypes described, authors propose that the accumulated 7DHC metabolites bind to and activate the glucocorticoid receptor leading to transcriptional activity.

Overall this paper attempts to provide a comprehensive mechanistic explanation for the neurodevelopmental phenotype arising from the loss of a lipid metabolizing enzyme.

Reviewer #2 (Public Review):

The authors study in this report enzymes and sterols implicated in SLOS. They have performed in-vitro and in-vivo experiments. They show that a major metabilte, DHCEO, mediates the effects in neurogenesis and neuronal localisation. They have studied the mechanism of action of this effect. Pharmacological intervention can rescue the negative effects.

The Introduction is clearly written and provides nice background information on the disorder, the implicated enzymes and sterols.

The authors analyse extensively cell survival, neurogenesis, proliferation, several progenitor markers in both cell culture and in the Dhcr7-KO mice. In vivo they study several developmental stages.

They have generated SLOS hiPSCs and studied those too.

The analysis of sterol and oxysterol levels in WT vs Dhcr7-KO is very interesting and informative.

The Dhcr7 shRNA experiments show clear effects on neurogenesis and cycling precursor cell population number.

The RNAseq experiments also give interesting gene expression results and possible signaling pathways involved.

Reviewer #1 (Public Review):

The study is focused on neural deficits in Smith-Lemli-Opitz syndrome (SLOS) that is caused by loss of function of 3b-hydroxysterol-D7 -reductase (DHCR7) and results in lower cholesterol. Individuals with SLOS have cognitive impairment and the authors use mouse models and human iPSCs to investigate the effects of the SLOS mutation on neural progenitor proliferation and neurogenesis. Data show that the loss of DHCR7 leads to premature differentiation of cortical progenitors and altered cortical development. However, the work offers little mechanistic insight.

Reviewer #3 (Public Review):

The regulation of the calcium pump SERCA by phospholamban has been studied extensively over many years as this system has become a focus of many biophysical approaches to study the interplay between protein dynamics, the biological function of calcium transport, and its regulation via protein-protein interactions, all of which are occurring within the environment of the sarcoplasmic membrane of heart muscle.

The authors themselves have a long track record with working on this system and the specific focus here is on the detailed mechanism of how phosphorylation of phospholamban leads to a release of its inhibitory function when bound to SERCA. Much effort has been spent on this question in the past, and the field has progressed over the years by deriving increasingly detailed structural models for SERCA-phospholamban interactions. There is now a structure from crystallography showing the interaction of the phospholamban TM domain with the SERCA TM helices and there is additional data from various biophysical methods that partially describe the conformational ensemble of the extramembrane N-terminal region of phospholamban and its interaction with SERCA. Some of that insight has distinguished between phosphorylated and unphosphorylated phospholamban, but despite much data and many simulation efforts, the exact mechanism for how phosphorylation of phospholamban alters its interaction with SERCA and thereby modulates its inhibitory functions has so far not been clearly described. This is the main goal of the present work.

There is new experimental data presented here from oriented-sample solid-state NMR experiments with the main finding of orientational shifts of the phospholamban TM helix upon binding to SERCA and upon phosphorylation. Taking advantage of this data, the main part of the study is concerned with results from computer simulations that were restrained by experimental data to develop conformational ensembles of the SERCA-phospholamban complex with and without phosphorylated phospholamban. From that, new mechanistic hypotheses are developed. While the direction of the work proposed here is promising, there are concerns about the overall approach and - as a consequence - the significance of the reported findings:

1) A main concern is the treatment of the extramembrane portion of phospholamban, which includes the serine that is being phosphorylated to relieve the inhibitory effect. Previous studies have described a helical conformation for the N-terminal segment that may be in equilibrium with a less-ordered/less-helical structure upon binding to SERCA. It is largely still not clear, however, how exactly that part of phospholamban would interact with SERCA. The idea put forth here is that a largely disordered conformation would interact with SERCA. That may be so, but it is unclear how much of that is a direct result of experimental constraints and how much could simply be a consequence of inadequate sampling. It seems that helical conformations for the N-terminal segment of phospholamban were not considered, while there is not enough discussion of why such conformations would be ruled out based on the experimental data.

2) The simulations are probably too short to fully explore the full conformational landscape of a (partially) disordered N-terminal phospholamban and it is unclear how much the experimental constraints are really limiting the conformational space in that region.

3) It is not completely clear how the present work relates to the crystal structure of the SERCA-phospholamban complex. Why were the starting structures for the SERCA-phospholamban complex initially taken from the available crystal structure (at least with respect to the TM domain of phospholamban) but then subsequently refined using much lower-resolution cross-linking data before initiating the simualtions? Is the crystal structure in significant disagreement with other experimental data considered here? More discussion and explanation is needed.

4) The main focus of the analysis of the simulation results is on the impact of phosphorylated phospholamban on the conformational sampling of SERCA. That is the key step for developing new mechanistic hypotheses. However, given that the SERCA-phospholamban complex is very large and flexible and based on the results presented, it appears that the length of the simulations may not be sufficient to fully characterize the shift in the conformational ensemble of SERCA as a function of phospholamban phosphorylation. At the minimum, some time of convergence analysis is needed to establish confidence that the difference in conformational ensembles shown most prominently in Figure 2 are indeed significant. Moreover, related to Figure 2, it is unclear whether the projection of the conformational sampling onto just two principal coordinates is sufficient for a full characterization of the conformational dynamics. It is also unclear whether the principal coordinates are the same when projecting the sampling for PLN and pPLN, if not, the comparison between the two would be further complicated.

Reviewer #2 (Public Review):

In this paper, the authors present an extensive ssNMR study on the mini-membrane protein phospholamban (PLN), which regulates the Ca2+ ATPase SERCA. PLN stabilizes the low-affinity Ca2+ state of SERCA, which can be reversed by phosphorylation or increase in [Ca2+]. Despite extensive, studies this mechanism is still unknown: Although interaction sites within the membrane have been identified, not structural changes within PLN have been detected. In the paper, the authors address this question by oriented ssNMR, an approach which is highly suited to map topological changes of membrane embedded peptides and proteins. While oriented ssNMR is conceptionally very appealing, it has been hampered by sample preparation restrictions preventing its widespread use on more complex samples. A breakthrough has been magnetic alignment of membrane proteins embedded in bicelles as demonstrated here. The presented spectra represent in principle a projection of labelled transmembrane helices onto a spectroscopic plane by which re-orientations of these helices can be elegantly visualized. Based on high quality data, the authors are able to convincingly demonstrate that PLN is in a topological equilibrium, which shifts upon phosphorylation at Ser60. In complex with SERCA, phosphorylation or Ca2+ binding triggers a topological change of the whole PLN transmembrane domain, which then act as a 'switch' on SERCA.

All presented data are of high quality and data interpretation is convincing. The paper addresses a complex and relevant biomolecular question by very advanced methodology.

The authors have identified a topological allostery for PLN connecting a posttranslational modification at the cytoplasmic site with signal transduction across the membrane. They argue that the underlying mechanism might be of general relevance for the regulatory role fulfilled by miniproteins.

Reviewer #1 (Public Review):

The regulation of highly dynamic interactions is for many biological processes of great importance. The authors study the regulatory interaction of the single transmembrane helix protein Phospholamban with the P-type ATPase SERCA which is responsible for removing calcium ions from the sarcoplasm and restoring its concentrations in the sarcoplasmic reticulum. The inhibitory interaction between both proteins is relieved by phosphorylation of a single residue in the cytoplasmic domain of Phospholamban. The authors show by a combination of solid state NMR as well as MD simulations that phosphorylation results in a order to disorder transition in the cytoplasmic part which leads to an re-arrangement of electrostatic networks which is propagated into weakened hydrophobic interactions between the transmembrane parts, thus activating SERCA. Phospholamban has been studied extensively by solid state NMR, liquid state NMR or hybrid methods. For example the phosphorylated form was studied previously, showing that it interacts differently with lipids (doi: 10.1021/bi0614028) and that Ser-16 phosphorylation alters the structural properties of the cytoplasmic domain with respect to the lipid bilayers (doi:10.1016/j.bbamem.2009.12.020). There have also been EPR and other studies, in principle showing the same effect. The current paper adds to this a new solid state method that shows additional details that could not be investigated previously. The work confirms less well determined previous models. The major new aspect is an MD simulation that provides a more detailed view than what was previously possible.

Reviewer #3 (Public Review):

Using a combination of powerful approaches authors demonstrate large variability in the number of release sites at hippocampal excitatory synapses onto fast spiking interneurons in slices. High resolution studies of individual synapses showed highly variable amounts of Munc13-1within the AZs that have the same number of release sites. The authors further revealed a synapse size-independent variability in the number of Munc13-1 clusters per AZ and in the Munc13-1 content of individual clusters. There results support the presence of multiple independent release sites and provide insight into molecular heterogeneity of release sites.

This is a high quality study using most advanced techniques available to study molecular determinants of AZ organization. In addition to some technical issues, my main concern is conceptual: this work, although of very good quality overall, is rather incremental because it largely confirms several previous studies showing a large variability in the number of release sites per AZ in small central synapses, the association between Munc-13 and release site properties, and variability in Munc-13 content. Surprisingly only one of the three of these previous studies have been cited or discussed. My second concern is that the paper could be written more clearly - there are multiple terms used to refer to the same concepts making it difficult to follow and there is some conceptual logical fallacy in the way the results are discussed.

Reviewer #2 (Public Review):

Karlocai et al addresses a prevailing concept of synapse diversity, asking whether diversity of release probability is caused by varying number of release sites and/or the properties of individual release sites. In other words, are there functionally uniform release sites (RS) that scale in numbers with the size of the AZ and thus regulate release probability (Pv), or are, in addition, RS may be heterogeneous in composition and function. Performing quantal analysis 2.0 by combining ephys from pyramidal-to-parv interneurons in hippocampus with quantitative anatomy of a presynaptic key transducer, Munc13, they define N, Pv and Q and compare it to the numbers of munc13 clusters and densities. As expected from previous studies, RS numbers covary with the size of the AZ, but the amounts of Munc13-1 are highly variable at individual RSs, providing a possible additional source of Pv variability.

Overall the quality of data is just superb, and the conclusion are well supported by the data as sufficient electrophysiological experiments were performed, and importantly also correlated with multiple, highly quantitative microscopy techniques. Only very few labs can do this at this level.

The findings carry enough impact as they negate the hypothesis that RS are made out of predefined release sites. Also, the finding that the post synapse as defined by PSD95 labeling was much less variable, indicates that pre- and postsynaptic makes do not necessarily correlate, arguing somewhat against the transsynaptic nano column concept as a main organizing principles. Thus, pre- and post-synapses are only loosely linked in their composition and function.

Reviewer #1 (Public Review):

The authors address the broad question of what is responsible for the large diversity of presynaptic function at synapses arising from a single type of neuron. They use a variety of sophisticated and complementary approaches to address the functional and molecular heterogeneity of hippocampal pyramidal cell to fast-spiking interneuron synapses. The rigorous functional and molecular analysis is clearly described and compelling. The conclusions are consistent with the current view that each presynaptic active zone contains a variable number of release sites, and this variability makes a substantial contribution to the heterogeneity in postsynaptic response amplitude at unitary synaptic connection. Using state-of-the-art imaging approaches, the authors report variability in the content of Munc13-1, a core component of release sites, between release sites. Although these results and conclusions are well-supported, the functional significance of Munc13-1 variability at release sites is unclear.

Reviewer #2 (Public Review):

Wnt signaling plays critical roles in cell fate determination in essentially every tissue in all animals, regulates tissue homeostasis in many adult tissues, and is inappropriately activated in many human cancers. It has been the focus of research for decades, and we have an outline of signal transduction. However, remarkably, key questions remain controversial. Central among these are questions about the nature of the negative regulatory destruction complex, its mechanism of action and how it is turned down by Wnt signaling. Here Saskia and colleagues take a novel and very exciting approach to these questions, combining innovative quantitative live-cell imaging and computational modelling.

What I can say unequivocally is that there is data in this manuscript that will force a re-evaluation of our current models of Wnt signaling, and also serve as the foundation for future research. Particular notable are: 1) precise measurements of the concentrations of beta-catenin in the cytoplasm and nucleus before and after Wnt signaling and after inhibition of GSK3. 2) Definition of a high MW complex, likely the destruction complex, whose assembly state appears to be regulated by Wnt signaling, and 3) Intriguing evidence that at steady state this complex appears not to contain multiple copies of beta-catenin. These data are exceptionally interesting and timely, as controversy continues about the size/assembly state of the destruction complex.

Reviewer #1 (Public Review):

The authors have done a great job in carefully labeling the β-catenin with fluorescent protein SGFP2 and quantitatively measuring the β-catenin behavior during Wnt pathway activation with advanced biophysical methods. This is an excellent effort on quantitative biological studies. The knock-in constructs, the cell lines the authors made are great resources for the Wnt field. And the quantification like the β-catenin concentration, β-catenin diffusion coefficient are great knowledge for future studies. The finding that S45F mutation lead to higher fraction of the slow-moving complexes is interesting. Other areas could borrow the research ideas and methods used in this manuscript. My primary concern is the difficulty of interpreting some of the quantitative results in the biological context.

The authors have concluded that β-catenin has two major populations: free population and slow-diffusing complexed population. The authors have concluded with FCS that the diffusion coefficient of free β-catenin to be 14.9 um2/s (line 259) and the complexed β-catenin to be 0.17 um2/s (line 327). Similar to the authors' argument in the manuscript, this difference means about a 100-fold change of the complex length scale. If the complex is linear, this means a 100-fold change in molecule size, but if the complex is spherical, this means a one-million-fold increase of the molecule size. Furthermore, in the next section, with the N&B method, the authors have suggested that "few, if any, of these complexes contain multiple SGFP2-CTNNB1 molecules" (line 366). When combining the two parts of information, it is hard to imagine a complex that contains one thousand to one million molecules only have one or a few β-catenin subunits. From the biology point of view, APC is the backbone of the destruction complex, which has several β-catenin binding sites by itself. Additionally, APC also contains several Axin1 binding sites where each Axin1 can also recruit one β-catenin. It is unlikely that one APC complex contains only one β-catenin, not mentioning the potential oligomerization of APC. The conclusion that most of the β-catenin containing complexes has only one β-catenin could either be real or due to the misinterpretation of experimental data.

Reviewer #3 (Public Review):

This is an interesting paper combining several impressive techniques to argue that synaptically released glutamate is allowed to diffuse to and activate receptors at much greater distance than previously thought. iGluSnFR recordings show that glutamate released from single vesicles activates the indicator with a spatial spread (length constant) of 1.2 um, substantially farther than previous estimates based on the time course of glutamate clearance by glial transporters (PMC6725141). Similar parameters are observed with spontaneous and evoked events, large or small, or when glutamate is released via 2P uncaging. Further uncaging experiments show that both AMPARs and especially NMDARs are activated a substantial distance. AMPARs, previously thought to be recruited only within active synapses, are activated with a spatial length constant that compares quite closely with the average distance between synapses in the hippocampus. More heroic experiments and some geometric calculations show that this behavior enables neighboring synapses to interact supralinearly. The results suggest that "crosstalk" between neighboring synapses may be substantially more common than previously thought.

The experiments in this paper appear carefully performed and are analyzed thoroughly. Despite all of the quantitative rigor and careful thought, however, the authors fail to reconcile convincingly their results with what we know about neuropil structure and the laws of diffusion. There are very good data in the literature regarding the extracellular volume fraction and geometric tortuosity of the neuropil, the diffusion characteristics of glutamate and the time course of glutamate uptake. These data more or less demand that synaptically released glutamate is diluted over a much smaller spatial range than that suggested here. In the Discussion, the authors suggest that this discrepancy might reflect a simplified view of the neuropil as an isotropic diffusion medium (PMC6763864, PMC6792642, PMC6725141), whereas a more realistic network of sheets and tunnels (PMC3540825) might prolong the extracellular lifetime of neurotransmitter. I like this idea in principle, but there is no quantitative support in the paper for the claim - in fact, it seems at odds with the authors' very nice demonstration that diffusion appears to be similar in all directions (Figure 3B). I don't necessarily think a solution is within the scope of this single paper, but I would suggest that the authors acknowledge the present lack of a compelling explanation.

Reviewer #1 (Public Review):

This MS combines two-photon glutamate sensing (using the iGluSnFR fluorescent probe), two-photon glutamate uncaging, two-photon calcium imaging and electrophysiology to investigate whether synaptically released glutamate activates receptors outside the synapse of release, and at neighboring synapses. The data themselves are very impressive. The authors arrive at the revolutionary conclusion that synaptically released glutamate is able to activate both NMDA and even AMPA receptors at neighboring synapses, remarkably strongly. I say revolutionary, because previous modelling has yielded diametrically opposite conclusions. The reflex would be to prefer experiment over theory, yet the modelling was based upon quite strongly constrained physical parameters that would be quite incompatible with the interpretations reported here. However, I believe the authors have failed to take into account significant technical limitations inherent in the technologies they apply. These include spatial averaging of fluorescence, possible saturation of iGluSnFR and diffusive exchange of (caged) glutamate during uncaging. As a result, the conclusion is wholly unproven. Indeed, I believe it highly probable that all of the data in favor of distal activation will prove to be consistent with synapse specificity and the presence of technical artifacts related to spatial averaging of fluorescence signals and diffusive exchange of (caged) glutamate during uncaging.

Reviewer #2 (Public Review):

This interesting study from Kurashina et al. examines novel postmitotic roles for transcription factors traditionally considered to specify neuronal cell fate. The paper examines a form of synaptic tiling in C. elegans motor neurons to provide evidence that the unc-4 and unc-37 transcription factors, previously implicated in determining cholinergic motor neuron identity, have additional roles in the regulation of synaptic wiring that are at least partially separable from cell fate specification.

The authors develop new tools for defining the temporal actions of unc-4 and unc-37 and the clean dissection of the spatiotemporal requirements for unc-4/unc-37 transcriptional regulation is a major advance offered by the study. In particular, the authors demonstrate that unc-4 acts at a later development stage to control synaptic wiring compared with its role in cell fate regulation. Overall, the paper is clearly written and offers new insight into how transcription factors that act to define neuronal identity may have additional roles in specifying aspects of synapse organization. The study falls a little short in clearly defining mechanism of action downstream of unc-4/unc-37 and in describing the relationship of these newly described roles for unc-4 and unc-37 to those previously described.

The authors use a clever strategy to assess tiling of individual cholinergic motor neurons using DA8 and DA9 as a model, but in some cases observe variable degradation of the RAB-3::GFPnovo, presumably due to weak expression of ZIF1 in some of the mutants. This makes it a little difficult to assess the tiling defects in some of the figures. The residual GFPnovo signal seems to be defined based on colocalization with the more broadly expressed mCherry::RAB-3 marker, but no data is shown for the extent of colocalization in the absence of ZIF1. This analysis would benefit from more explanation.

The analysis of temporal requirements using ts alleles in combination with the AID system is very convincing and quite informative. The authors clearly show a later requirement for proper tiling, at stages when cell fate determination is expected to be complete. However, it is less clear how these newly defined aspects of unc-4 and unc-37 functions relate to their previously defined roles.

The authors examine PLX-1::GFP subcellular localization in DA neurons (using cell specific itr-1 promoter) of unc-4 mutants but do not directly examine plx-1 expression levels in DA neurons. This analysis would further solidify links between plx-1 and unc-4 transcriptional regulation.

Did the authors examine whether degradation of unc-4 and/or unc-37 at much later developmental time points also lead to tiling defects? Is there an ongoing requirement to maintain tiling?

Did the authors examine whether the unc-4::AID and unc-37::AID animals became uncoordinated subsequent to treatment with auxin analog? Do the tiling defects potentially contribute to locomotor changes?

Reviewer #1 (Public Review):

This manuscript by Kurashina et al. describes a novel post-mitotic role in synaptic patterning for a cell fate determining gene, unc-4, and its co-repressor, unc-37. The DA neurons in C. elegans are cholinergic motor-neurons that exhibit unique synaptic tiling of their dorsal axonal segments. Mizumoto et al has previously shown that Semaphorin-Plexin signaling is required to establish the tiling between DA8 and DA9, by functioning in cis in the DA9 neuron. Using temperature sensitive mutant of unc-4, as well as a combination of CRISPR/Cas9 genome editing with the AID system for specific temporal degradation, the authors nicely examine the spatiotemporal requirement of unc-4, and show that unc-4 is required only post-mitotically for synapse tiling, but not during the development of the DA neurons. Interestingly, activity of the corepressor unc-37 is required both during development and postmitotically for correct tiling. unc-4 and unc-37 are suggested to function by inhibiting the canonical wnt signaling. Overall this is an interesting study which sheds light on our understanding of the post-developmental role of cell fate genes in synapse patterning. I only have one major issue that requires some clarification. The authors present in their introduction the results from Kerk et al, regarding the role of unc-4 as a cell fate determining gene for the VAs and DAs. Kerk et al have shown that UNC-4 is specifically required for the expression of DA genes, without affecting ACh pathway genes. Table 1, however, doesn't fully recapitulate the same results and actually shows that unc-4 and unc-37 mutants do not exhibit significant cell fate defects. The authors use these results to argue in the discussion that the synaptic patterning defects can occur independent of the cell fate transformation. The issue of unc-4 as a cell fate determining gene of A type motor neurons needs to be more clearly addressed. The authors should test whether acr-5 expression is elevated in DAs in unc-4 and unc-37 mutants (Winnier 1999, Kerk 2017). In addition, they should also analyze these DB markers in the temp shift experiments (do the VB-DB markers show up in the post embryonic knockout? And if so, will silencing them specifically in DA neurons rescue the tiling defects?). The discussion, accordingly, should also address these issues.

Reviewer #4 (Public Review):

Higashi et al. provide a new "Brownian ratchet" model for DNA loop extrusion mechanism by cohesin, a member of SMC protein family complexes. Based on previous works on crystal structures, cryo-EM structures, and DNA-protein crosslinking experiments, they shed light on two HEAT-repeat DNA binding modules on cohesin - Scc2-head and Scc3-hinge - and their relationships. They hypothesized that the association between Scc2-head and Scc3-hinge modules were dissociated and Scc2-head released DNA upon ATP hydrolysis, driving DNA slipping. By performing FRET experiments, they found that Scc2 and hinge modules indeed come close only in ATP-bound "Gripping" state, while hinge and Scc3 are always close to each other. Therefore, they suggest that, for DNA loop extrusion model, 1) upon ATP binding to the head domains, both Scc2-head and Scc3-hinge modules grip DNA, 2) when ATPs are hydrolyzed, Scc2-head module releases DNA so that DNA-associating Scc3-hinge module pulls DNA depending on stochastic Brownian motion of Scc3-hinge module, then 3) both Scc2-head and Scc3-hinge modules release DNA and go back to the state 1). This "Brownian ratchet" model also provides an explanation of how cohesin entraps DNA by opening the gate between Smc3 and Scc1, which also nicely explains the known facts regarding Scc1 cleavage-dependent DNA release and in vitro behaviors of single cohesin molecules that topologically bound to DNA. In addition, by performing computational modeling, they showed that the Brownian ratchet model well fits all previously reported in vitro loop extrusion assays by cohesin and condensin, making their model rigid and reliable.

Their model is mostly well supported by data, but several detailed points need to be explained or clarified.

1) In Figure 2C FRET experiments, proximity of Scc3-C and Scc2-N does not seem to be drastically increased in Gripping state compared to the case of hinge and Scc2-N. This could be because the FRET pairs (Scc3-C and Scc2-N) are still far. If the authors could label internal part in Scc3, this could solve the problem. In addition, if Scc3-C and Scc2-N are always close to each other irrespective of Gripping state, the authors should consider this fact in their modeling.

2) Major differences between topological loading and loop extrusion is kleisin-gate opening and head gate passage. Even if kleisin-gate wouldn't be opened, DNA should be released after head opening like in the topological loading. In case it happens, DNA and Scc1 would be tangled and it seems to be difficult to come back to next gripping state again. It would be helpful to add the explanation of why such tangling DNAs do not have to be considered in the model.

3) In the manuscript line 338, the authors mention "After DNA dissociation from the Scc3-hinge module, there is a time without tight contact between the cohesin ring and the DNA loop." However, both in Figure 3B and 4F, it seems that head-Scc2 always associates with DNA. This could be discrepancy. The authors should clarify the point if certain free time without any contact to DNA is assumed in the modeling.

4) Generally, initial DNA bending is the most challenging part in loop extrusion models. Especially in Figure 3B-a, such a bent DNA seems to be impossible if we consider the persistence length of DNA is 50 nm. The authors should discuss how DNA loop extrusion could be initiated.

Reviewer #3 (Public Review):

Bifurcation between topological loading and loop extrusion is determined by DNA passing through the N-gate. For loop extrusion to occur processively, this decision needs to be made only once at the beginning. However, the authors also argue that Scc2 dissociation between rounds of ATPase cycles is required for symmetric loop extrusion. In combination, the model requires that N-gate opening is allowed only at the very beginning and cannot occur during loop extrusion, even when the cohesion loader is released. The authors should state whether this interpretation is correct and feasible given the structural data.

Loop extrusion has never been observed using yeast cohesin. It will be important to learn how the authors reconcile their model and the lack of experimental demonstration of loop extrusion in a reconstituted system.

The discrepancy in speed and the measured ATPase rate is not discussed. In vitro, loop extrusion rates are about 1000 bp per second and in vivo measurements of gamma-H2AX spreading from a double strand break, ~150kbp per min according to PMID: 32527834, which was proposed to be caused by loop extrusion (PMID: 33597753), also matches that in vitro rate. But the authors model accounts for only about 100 bp extrusion per ATPase cycle whereas the average ATPase rate is 1 per second. They do mention that the model requires 9 ATP hydrolyzed per second but do not make an attempt to explain the discrepancy.

Reviewer #2 (Public Review):

How the genome chromatin fiber is folded into loops and topologically associating domains (TADs) remains unclear. A recent attractive model is that these genomic structures are formed by a loop extrusion process mediated by cohesin. While the Uhlmann group has proposed an alternative mechanism, the diffusion capture model, to make loops (Cheng et al., 2015; Gerguri et al., 2021), in this paper, Higashi et al. proposed a structure-based model providing mechanistic insight into the reported loop extrusion activity of cohesin. For its topological DNA binding, cohesin inserts DNA into the cohesin ring by sequential passage through a kleisin N-gate and an ATPase head gate. Hisgashi et al. suggested that the gripping state in which DNA has not passed the kleisin N-gate might facilitate the loop extrusion activity reported. This paper is very intriguing, and informative to the chromatin/chromosome field. My specific comments are the following:

1) Since this paper is primarily based on the detailed structural information on cohesin loading onto DNA, which the Uhlmann group published in Mol Cell (2020), it might be hard for general readers to follow the whole story in this paper. For better understanding, the authors should provide readers with Supplemental Fig. corresponding to the Graphical abstract and Figs. 6E/7G in the Mol Cell paper, and adequately explain it first. Structural models such as Fig. 1 are accurate but might be difficult to capture cohesin's dynamic behavior with DNA.

2) Although this paper is very intriguing, it looks like a review paper, and the authors' message is not so clear. Given that the Uhlmann group has proposed an alternative mechanism to make loops, I wonder whether the main message might be that the loop extrusion, like reported in vitro, is unlikely to occur in vivo. If so, the authors should clearly state the point and shorten the Discussion part to enhance the paper's impact.

3) Page 24. The critical issue of the loop extrusion mechanism proposed is "not opening" of kleisin N-gate. The authors discussed that the low salt condition in vitro could be a reason: " For instance, electrostatic interactions contribute to keeping the kleisin N-gate closed and these are augmented in a low salt buffer." However, I assume that the condition also helps the topological loading, and this explanation is not so convincing.

4) While I agree with the authors' loop extrusion mechanism, there are other models to explain cohesin loading onto DNA (e.g., Shi et al., 2020; Collier et al.). They might want to discuss its compatibility with them.

Reviewer #1 (Public Review):

Higashi et al. present a molecular mechanism of how the cohesin complex, a supramolecular assembly of several proteins, can topologically embrace DNA and actively extrude DNA into loops. The loop-extruding activity of cohesin and of related condensin complexes have been proposed to represent a cornerstone of genome organization. While recent in vitro studies demonstrate that cohesin and condensin complexes are capable of extruding loops, the molecular mechanisms driving loop extrusion, i.e. how ATP energy is utilized, and what underlies the processivity of the loop extrusion, remains enigmatic. The cohesin complex consist of two long flexible protein "arms" connected at the 'hinge' ends. The other, 'head' ends are linked by the kleisin protein and also can dimerize to form an ATP-binding chamber. Defining how transitions in the cohesin complex structure and its ATPase activity underlies known cohesin functions has been the object of numerous studies for over two decades. Here, the authors build upon these studies.

The authors start by analyzing available structural data for cohesin domains and associated loading factors. First, by combining the structure of the cohesin-head-domain complex engaged with DNA in ATP-bound state and the corresponding free crystal structures, they show that the 'head module' in the ATP-bound state can tightly wrap around DNA, and upon ATP hydrolysis the DNA-embracing cavity will dilate. In other words, the complex transitions from a 'DNA-gripping state' into a 'DNA-slipping' state after ATP is hydrolyzed. Next, they show that the other DNA-binding module, the 'hinge module', does not change its interaction with the DNA after ATP hydrolysis. The authors also conclude that ATP hydrolysis weakens the interaction between the head and hinge modules, suggesting that the cohesin ring alternates between folded (with head and hinge closed) and unfolded ('free' hinge) states. The authors next carried out FRET experiments to provide experimental evidence for the predicted change in spatial arrangement between the head and hinge modules. Based on this structural analysis, they propose that whether DNA is passed (or not) through the 'kleisin gate' before binding to the head module (into the gripping state) determines if the DNA will be released inside the cohesin ring (i.e. 'topological entry') or if the DNA will remain loosely associated with the head module (i.e. 'loop extrusion') upon ATP hydrolysis. In the latter case, repetitive simultaneous binding of DNA to the head and hinge modules in a folded state followed by relaxation of the cohesin ring while DNA remains bound to the hinge module, may result in a overall 'inward' directed motion of the DNA thread relative to the head domain, i.e. loop extrusion. Stochastic simulations of a coarse-grain model further support that such a model can give rise to loop extrusion.

The real strength of the paper is in its combination of several pieces of structural and biophysical data that results in a compelling mechanism for cohesin function. The outcome is a united model for cohesin's two characteristic activities - topological engagement of the DNA and DNA loop extrusion. Importantly, the authors explore the role of ATP hydrolysis in driving conformational changes, and, thus, the translocation of DNA, as well as the role of the DNA binding kinetics. The authors go on to relate these findings to the consequences for cohesin function inside cells, where it must content with chromatized substrates. For example, they suggest that while a single nucleosome probably can be bypassed by the cohesin complex, an array of the nucleosome may present a significant hindrance.

Given its interdisciplinary nature and important conclusions, I believe that this paper will be of broad interest to scientists across disciplines and will influence and stimulate future consideration of how cohesin contributions to the spatiotemporal organization of chromatin.

Reviewer #2 (Public Review):

The paper is well written, and the data are well analyzed and presented. My concerns centre on terminology and alternative explanations of some of the data, which the authors might deal with in the introduction or discussion.

1) I am slightly confused about some of the data shown in Figure 1. If B cells are defined as GFAP expressing cells, then why do only 25% of the B cells in the plot in Figure 1C express GFAP? I may be missing something here, as other readers may as well. Similarly in the same panel, only 25% of astrocytes seem to be expressing GFAP or GFP driven by a GFAP promotor.

2) The authors term the germinal zone of the adult mouse brain - the ventricular-subventricular zone. They should discuss the evidence that the adult germinal zone is made up of cells from both the ventricular zone and the sub ventricular zone in the late embryo, where those zones are described clearly on the basis of morphology. Many of the early embryonic neural stem cells are present in the ventricular zone before the sub ventricular zone has developed and continue to be present into the adult. If there is not clear mouse evidence that the progeny of embryonic sub ventricular cells are present in the adult germinal zone independent of embryonic ventricular zone progeny, then the authors might consider calling the zone - the adult ventricular zone, or alternatively terming the neurogenic area around the lateral ventricle the adult germinal zone or by a more straightforward descriptive term - the adult subependymal zone or the adult periventricular zone. Also, I think the first word in line 6 on page 3 should be neural rather than neuronal.

3) The authors refer to their molecularly described B cells as stem cells. Certainly, their lab and others have shown that adult olfactory bulb neurons are the progeny of those B cells, however the classic definition of stem cells (in the blood or intestine for example) require demonstration that single stem cells can make all of the differentiated cells in that tissue. Is their evidence that a single adult B1 cell can make astrocytes, neurons and oligodendrocytes? Indeed, what percentage of the single adult B cells characterized here on the bases of RNA expression can be shown to be multipoint for both macroglial and neuron lineages in vivo or in vitro? Perhaps progenitor or precursor cells might be a better term for a B cells that appears to give rise to neurons primarily.

4) This may be more than a semantic issue, as the rare clonal neurophere forming neural stem cells that do make all three neural cell types in vitro, and also maintain their AP and DV positional identity through clonal passaging in vitro (Hitoshi et al, 2002). However, Emx1 expressing cortical neural stem cells can be lineage traced as they migrate from the embryonic cortical germinal zone to the striata germinal zone in the perinatal period (Willaime-Morawek et al, 2006). Surprisingly, in their new striatal home the Emx1 lineage cortical neural stem cells will turn down Emx1 expression and turn up Dlx2 striatal germinal zone expression. The switch in positional identities of clonal neural stem cells can be seen also in vitro when the stem cells are co-cultured with an excess of cells from a different region and then regrown as clonal neural stem cells. This may suggested that Emx1 expressing neural stem cells (the clonal neurosphere forming cells), may switch their positional identities in vivo as they migrate into the striatal germinal zone, but the downstream neuron producing precursor B cells studied in this paper may maintain their Emx1 expression into the adult germinal zone. This raises an interesting issue concerning which cells in the neural stem cell lineage can be regionally re-specified.

5) The authors nicely show dorsal versus ventral germinal zone lineages are marked by some of the same positional genes from B cells to A cells, suggesting complete dorsal versus ventral neurogenic lineages giving rise to different types of olfactory bulb neurons. Indeed, they nicely test this idea with dissection of the dorsal versus ventral germinal zones, followed by nuclear RNA sequencing. However, they don't discuss the broader issues concerning the embryological origins of the dorsal versus ventral germinal zones. Emx1 is one of the genes the authors use to mark dorsal lineages. The authors reference papers (Young et al, 2007; Willaime-Morawek et al, 2006;2008) that use Emx1 lineage tracing to show that certain classes of olfactory bulb neurons originate from embryonic cortical neural stem cells that migrate perinatally from the cortical germinal zone into the dorsal subcortical germinal zone. Could cortical versus subcortical embryonic origins of the dorsal versus ventral adult germinal zone explain the origin of different sets of adult olfactory bulb neurons? Further, the authors report that one of the GO terms for their dorsal lineages in cortical regionalization.

6) The percentages of dividing cells based on gene expression is given for some clusters of cells but not others. It might be useful to have a chart showing the percentages of cells in cycle (ki67 expression) for each cluster. This might be especially useful in characterizing some fo the differences between various subclusters of B, A and C cells. On page 9 it is suggested that the heterogeneity amongst C cell clusters was driven by cell cycle genes. However, it is possible to remove the cell cycle genes from the data analysis to see if this then produces clearer dorsal versus ventral positional identities. This may be an important issue as the dorsal versus ventral positional identity genes appear to be expressed more in less dividing A and B cells, than in the more dividing C cells. This leads to a potentially alternative conclusion - that dorsal/ventral regional identity genes are primarily expressed in the non-dividing post mitotic cells in their resident dorsal or ventral region, and not in precursor cells in the lineage.This could be easiy tested by removing the cell cycle genes from the analysis of highly dividing clusters to see if they then break down into doral versus ventral clusters.

Reviewer #1 (Public Review):

Redmond et al. use single-cell and single-nucleus RNA-sequencing to reveal the molecular heterogeneity that underlies regional differences in neural stem cells in the adult mouse V-SVZ. The authors generated two datasets: one which was whole cell RNA-seq of whole V-SVZ and one which consisted of nuclear RNA-seq of V-SVZ microdissected into anterior-posterior and dorsal-ventral quadrants. The authors first identified distinct subtypes of B cells and showed that these B cell subtypes correspond to dorsal and ventral identities. Then, they identified distinct subtypes of A cells and classified them into dorsal and ventral identities. Finally, the authors identified a handful of genes that they conclude constitute a conserved molecular signature for dorsal or ventral lineages. The text of the manuscript is well written and clear, and the figures are organized and polished. The datasets generated in this manuscript will be a great resource for the field of adult neurogenesis. However, the arguments and supporting data used to assign dorsal/ventral identities to B cells and A cells could be strengthened, and more rigorous data analysis could result in new biological insights into stem and progenitor cell heterogeneity in the V-SVZ.

Reviewer #2 (Public Review):

In this manuscript, the authors characterise a GluA4-knockout mouse with respect to changes of cerebellar cortical circuit properties and behaviours.

They demonstrate a clear reduction in the component of mossy fibre--granule cell synaptic transmission mediated by AMPA receptors, as expected. They also show two parallel changes in granule cells that could be considered partially compensatory: tonic inhibition of granule cells is reduced and the NMDAR-mediated component of the mossy fibre input is upregulated. The overall effect of the mutation is nevertheless to reduce the efficacy of the mossy fibre input; spike emission is therefore reduced in frequency, delayed, and has less precise timing.

Two other key synapses in the mossy fibre pathway are shown to be apparently unaffected in the knockout mouse, namely mossy fibre to Golgi cell transmission and also granule cell to Purkinje cell transmission.

The authors then model representation in the granule cell layer and downstream learning by the Purkinje cell, focusing on a reduction of the effective coding space available in the expansion performed by the granule cell layer and the downstream reduction of learning speed in the Purkinje cell.

In a final, behavioural, section, the authors show that locomotion is little affected but that eyelid conditioning is essentially abolished, with two different conditioned stimuli.

Overall, the experiments, analysis and presentation are of excellent quality.

However, the conceptual framework and broader interpretation of the work is quite ambitious and I believe that it requires more nuanced presentation.

A first and reasonably straightforward issue is the fact that the authors are, as they are well aware, working with a systemic knockout. Logically, therefore, the behavioural effects on eyeblink conditioning could reflect interference with any part of the input-output loop. Within the cerebellar circuit, the authors address this reasonably comprehensively, by confirming that mossy fibre to Golgi cell and granule cell to Purkinje cell transmission are unaffected. Nevertheless, one quickly wonders whether the activity of interneurones, climbing fibres or cerebellar nuclei might somehow be altered. The authors address possible extracellular effects of the knockout by showing that eyeblink conditioning is essentially abolished with two different modalities of conditioned stimulus. Again, it remains logically possible that both inputs or the common output could be altered.

Experimentally verifiying all possible stages of the behavioural input-output loop is not feasible, while the ideal experiment of a granule-cell-specific knockout would amount to redoing the whole project, which is obviously out of scope. Nevertheless, I believe the issue does require slightly more open and detailed discussion; maybe the developmental down-regulation of GluA4 in relevant tissues could be substantiated better with reference, for instance, to expression atlases of the Allen Brain Institute. Ultimately, if the locus of action is not completely certain, that should be reflected in the conclusions.

Finally, I'm a little uncomfortable with the ambitious conclusion that learning and behaviour have been constrained by the reduced coding expansion by the granule cell layer. Although the changes observed are indeed almost certain to reduce coding expansion as defined, I feel that the failure of learning could also be understood in more prosaic terms. In particular, the inputs to the Purkinje cell may simply be too weak, too delayed or too unreliable to be an effective plasticity substrate for rapidly developing a conditioned response before the air puff. To a large extent the lower-level modifications will correlate with the higher-level coding expansion, so the concepts are more or less synonymous. Yet, it feels different to conclude that patterns can't be separated because they produced no granule cell activity (to consider a logical extreme) and to conclude that their separation is too difficult because of output similarity and saturation of learning.

Furthermore, there are ways to view coding expansion that wouldn't necessarily align with the authors' conclusion. Specifically, the combinatorial pattern separation analysed in the original Marr paper would, I believe, increase as the ratio of mossy fibre input strength to granule cell threshold decreases. In other words, for given overlapping mossy fibre inputs, the overlap between granule cells outputs could decrease as the input/threshold ratio decreases.

Addressing these issues experimentally is certainly unfeasible. However, it might be possible to explore correlations/overlaps between input and output patterns in the modelling. The discussion could be made a little less assertive on these issues, and the question of input delay should be addressed.

Reviewer #1 (Public Review):

This study focuses on the consequences of deleting the GluA4 subunit of AMPA receptors for cerebellar synaptic transmission and cerebellar-dependent behaviors. The manuscript is well organized and the information is clearly presented. The first aim of the study is to investigate the effect of the deletion at the level of synaptic function. This is well achieved by a combination of patch-clamp recordings from cerebellar slices and modeling. It is found that deletion of the GluA4 subunit results in a strong decrease in synaptic currents from mossy fibers (MF) to granule cells (GC) as well as in two «compensatory» changes pertaining to NMDA Rs and tonic inhibition. As a consequence, MF-GC transfer is strongly reduced at high frequencies but less affected al low frequencies. The second part of the work investigates the effect of the GluA4 deletion on cerebellar-dependent behaviors. GluA4 knock-out mice are found to have no deficits in locomotion but exhibit a total absence of associative learning in an eye-blink conditioning paradigm. Both, at the slice level and at the behavioral level the strength of this work resides on the quality of the data and the rigorous analysis. A shortcoming of the work stems from the «compensatory» changes which complicate interpretation. However modeling strategies are implemented incorporating those changes and they are able to well predict the observed alterations in GC firing pattern, thus limiting the negative impact.

Joint Public Review:

The way homologous chromosomes identify one another and become paired is an intriguing phenomenon that has a long history of study, yet the molecular mechanism remains unclear. Recent studies have led to a phenomenological button model for homolog pairing, which hypothesizes that pairing is initiated at discrete sites along the length of each chromosome. The authors aimed to rigorously investigate this idea using biophysical modeling and live imaging. They first constructed a simple polymer model with buttons distributed along the chain that possess locus-specific interactions, and thoroughly investigated its property via stochastic simulation in 3D. Their study confirms that homolog-specific interactions are necessary for homolog pairing. They also tested the effect of time, interaction strength, initial inter-homolog distance, and button density. The authors went on to perform live imaging of pairing dynamics at two selected loci, using the fluorescent signal from nascent mRNA at the corresponding locus. They fitted the model to the experimentally quantified pairing probability of the selected loci over a 6-hour developmental window, and used the constrained model to predict the individual pairing dynamics. The predicted inter-homolog distance post pairing agrees very well with experimental observation.

Their study supports a button mechanism for homolog pairing, where stable pairing is initiated by reversible random encounters that are propagated chromosome-wide. This work suggests that active processes are not necessary to explain pairing and paves the way for further investigating the molecular mechanism of such a pairing phenomenon.

Reviewer #3 (Public Review):

The manuscript entitled "Biosynthesis system of Synechan, a sulfated exopolysaccharide, in the model cyanobacterium Synechocystis sp. PCC 6803" is a scientifically sound manuscript and is of interest for a broad scientific audience. It provides interesting and valuable new insights and many experiments were performed. However, there are some points which must be addressed to make the manuscript more consistent and easier to grasp.

• Title: I would suggest to change the title, since Biosynthesis system is not a common term.
• Abstract: Cyanbobacteria are not unique in having sulphated polysaccharides. What is about Carrageenan's and also exopolysaccharides from Porphyridium strains (see current publications on that). If it means that amongst bacteria the cyanobacteria are the only ones, this should be clearly stated.
• Would avoid to use may utilize the polysaccharides... Please be more specific or delete this.
• Lane 32: Can really every bacterium produce several EPS? This should be carefully evaluated.
• Lane 34: The applications named are very broad and not specific, what are the real applications there?
• Lane 49: again uniquely?
• Lane 56: the sulphated polysaccharides are used for colony and biofilm... This sentence must be rephrased and corrected.
• Lane 84: bubbling culture etc. I can´t find any detailed explanation on the cultivation systems, what is essential for the methods part. Please add volume, light source and principle of illumination (inside outside etc.). Please rephrase the sentence that the light was generated by fluorescent lamps.... They were used for illumination.
• Lane 97: GTs can not be screened by disruption, it is their function what is screened.
• Figure: would suggest to use A) instead of A,
• Table S1: What does Importance mean in the table, would suggest to change that towards a more specific value/information
• Lane 232: to see the transcriptome... this should be rephrased
• The description of the different EPS is a bit confusing, since it is only described that the WT contains several sugars, which are then given in table 1. The deletion strain shows a different composition. This should be explained a bit straighter. Why is ribose given in table 1, if there is no ribose observed? In general, the whole manuscript needs correction of the English language to make it clearer in some aspects. Also, the structure of the manuscript might be reworked a bit, since it is a bit confusing in some parts. Especially the effect of the different deletions should be given clearly and straight. Also, the complexity of the manuscript will be easier to grasp by some rearrangements of the results. The current complexity might come from having all supplement figures already in the manuscript, but it also comes from sometimes complex sentences, as well as jumping a bit in between the topics. But finally, this is a really valuable and interesting study.

Reviewer #2 (Public Review):

The paper does a very thorough job of identifying genes important for the production and export of a sulfated exopolysaccharide in Synechocystis, leading to a clear and well-justified model for EPS production and its regulation. The authors also make a convincing case for the importance of EPS production for the formation of floating multicellular aggregrates or "blooms". However, the relationship between EPS production and bloom formation is not quantitative (some mutants show markedly reduced EPS production without any discernible effect on bloom formation) which indicates that bloom formation must involve additional factors which are not currently discussed.

Reviewer #1 (Public Review):

The authors have identified an entire set of genes for the synthesis of sulfated exopolysaccharides (EPS) in the cyanobacterial model Synechocystis 6803. They show convincingly that the respective gene products are involved in the production of these compounds and they have extensively characterized the regulation of these genes. Among the regulators they found a STAND protein. STAND proteins include animal and plant regulators of programmed cell death but were rarely characterized in bacteria. Last but not least they come up with an entirely new model for the buoyancy regulation of cyanobacteria (as light-dependent aquatic organisms it is important for cyanobacteria where they are in the water column). The authors suggest a mechanism in which EPS-entrapped cells together with extracellular gas bubbles derived from photosynthesis form multicellular complexes that float at certain depths. This would be a very important function and explain the extensive regulatory and signaling apparatus in controlling the synthesis of these sulfated EPS.

Reviewer #2 (Public Review):

The authors aimed to address the lack of therapeutic treatments for the Rett Syndrome by (a) identifying novel functional partners of MECP2 (mutations in which underlie Rett Syndrome), and (b) demonstrating the druggability of the partners using in-use drugs. The authors accomplish this by performing phylogenetic profiling across more than thousand species to identify genes that coevolved with MECP2. Using drugs that target three of their top hit genes in RTT models, they demonstrate the potential efficacy of these drugs against RTT and validate their new molecular targets.

Strengths:

Overall, the manuscript is very well written and easy to follow even for people outside the fields, and provides insights into an important biological process and identifying much needed therapeutic targets. The authors reproduced various RTT phenotypes in human neural cells with reduces MECP2 expression and demonstrated the ability of the three drugs to rescue the phenotypic profiles. In doing so, the authors were able to shed light on some of the potential mechanisms of action through which these drugs operate. Given that all three drugs have approved safety profiles, with further pre-clinical investigation, these drugs could serve as potential therapeutic agents for Rett Syndrome.

Weakness:

The biggest weakness of the paper is the lack of a strong link between comparative phylogenetic profiling and the identification of potential therapeutic agents. The paper is currently framed as a 'comparative genomic pipeline' to identify novel drug targets, yet the authors didn't demonstrate the robustness of the pipeline using appropriate positive and negative controls. Basic network analyses weren't performed to demonstrate a wide usability of the methodology beyond RTT.

While the authors do a good job of demonstrating the RTT phenotype-rescuing abilities of the three drugs, they don't exhaustively demonstrate how their comparative evolutionary pipeline was essential for identifying the three drugs. MECP2 forms a complex with HDACs and all three of the drugs selected here have known direct/indirect effects on HDAC activity. It is therefore plausible that the drugs are mediating their effects through HDACs, in which case the comparative genomic pipeline was not required to select these drugs.

Reviewer #1 (Public Review):

Major Comments/Concerns

On line 101 - The use of only the longest transcript for each gene could miss important functional sections of the genome. This could create bias against genes with many isoforms and miss exons that do not happen to lie in the longest transcript. How different would the resulting profiles of conservations be if all coding regions or exons of every gene were used?

On line 106 - Does this approach create good specificity to our gene of interest rather than just broad functional similarity? For example, with this approach, are there any major neuronal function genes that have NPP very different from MeCP2? Could authors provide a more objective evaluation to baseline/null?

Minor Comments/Concerns

On line 132 - It seems fair to examine this set of genes first, but I am not sure this approach to filtering in particular moves us further towards finding a therapeutic for Rett. These genes could be all good potential targets, and your subset of focus are just the best ones for current validation.

Figure 2C could be made with all 390 co-evolved genes to strengthen the argument that chr19p13.2 is an important region for MeCP2s role.

Figure 3, 4, 5, 6 - Dynamite plots. While the stats tests are great for understanding the impact of different treatments, box plots or jittered dots would be even more clear.

Reviewer #3 (Public Review):

In this work, the authors used in vitro binding and liposome fusion assays to study how Sec17 and Sec18 regulate SNARE-driven fusion. In previous studies, it was found that deletion of the C-terminal layers of the Qc SNARE involved in yeast vacuole fusion blocks fusion but the inhibition can be partially bypassed by addition of Sec17 and Sec18. This work extended the finding and showed that Sec17 and Sec18 can even restore fusion when two Q SNAREs are C-terminally truncated and the third chain bears point mutations. The authors conclude that HOPS and membrane anchored Rabs first promote the tethering of vacuole membranes. Subsequently, HOPS promotes membrane docking - the initial assembly of the SNAREs, likely through the SM protein in the HOPS complex. Then Sec17 and Sec18 kick in to activate the zippering of membrane-proximal regions of SNAREs. This function seems to require interactions of Sec17 with HOPS. The findings are unexpected and raise important questions.

Reviewer #2 (Public Review):

This manuscript shows that the Sec17/18 machine can do more than we might have expected, and places new constraints on models for how this works. As the field expects from the Wickner lab, the work is creative and beautifully executed. I do still have some reservations, however, about whether the manuscript ultimately forwards our mechanistic understanding enough to merit publication in eLife. Some of the outstanding mechanistic questions articulated by the authors include:

1) Why is HOPS required for Sec17/18/ATPγS activity? The authors suggest that HOPS and Sec17 bind to one another, but the assay (Figure 4) is rather non-physiological and the result does not really answer the question.

2) What is the mechanistic role of Sec18? An intricate inhibitor experiment (Figure 9) suggests that Sec18 acts later than Vps33. This is consistent with current thinking on the early role of SM proteins, but does not further delineate the mechanistic role of Sec18.

3) Does "entropic confinement" explain the role of Sec17? This very interesting question was not, so far as I could tell, directly addressed. My understanding is that the concept of entropic confinement comes from studies of chaperonins such as GroEL/ES, which entirely enclose their substrates in what Paul Sigler memorably described as "a temple for protein folding". Here, it's much less clear that Sec17 could sufficiently constrain the presumably-unfolded juxtamembrane regions of the truncated and/or mutant SNAREs to drive membrane fusion. Indeed, Schwartz et al. (2017) noted "open portals" between adjacent Sec17 molecules that would "allow SNARE residues spanning the partially-zipped helical bundle and the transmembrane anchors to pass cleanly between pairs of adjacent Sec17 subunits".

4) What is the mechanistic role of the "hydrophobic loop" at the N-terminus of Sec17? Previous work from the Wickner lab (Song et al., 2017) concluded that its main function under normal circumstances was to promote Sec17 membrane association, but when zippering was incomplete it might act as a wedge to perturb the bilayers. These experiments made use of artificially membrane-anchored Sec17, either wild-type or the "FSMS" hydrophobic loop mutant. This approach was extended here (Figure 8) but did not, so far as I could tell, greatly advance our mechanistic understanding.

Reviewer #1 (Public Review):

The energy released upon zippering of SNARE complexes from the N-terminus to the membrane-proximal C-terminus is widely believed to provide the driving force for membrane fusion, and the cis-SNARE complexes resulting after fusion are disassembled by formation of a 20S complex with Sec17 and Sec18, followed by ATP-hydrolysis by Sec18. This paper now shows that membrane fusion still occurs when the hydrophobic interactions that drive C-terminal zippering of the yeast vacuolar SNARE complex is completely prevented by C-terminal truncation of two of the SNAREs and replacement of the hydrophobic residues at the C-terminus of the SNARE domain of a third SNARE with polar residues, and that such fusion requires Sec17, Sec18 and non-hydrolyzable ATP homologues, in addition to the HOPS tethering complex, which mediates SNARE complex assembly. The results also show that Sec17 plays a key role in fusion through hydrophobic residues in an N-terminal loop that are known to interact with membranes. These results suggest that the core membrane fusion machinery is formed by the SNAREs, Sec17 and Sec18 rather than by the SNAREs alone, and that fusion is driven by a combination of SNARE C-terminal zippering and perturbation of the lipid bilayers by the hydrophobic loops of Sec17. These conclusions are strongly supported by a variety of membrane fusion experiments. FRET assays to SNARE complex assembly also support the conclusions but are less convincing.

Reviewer #3:

The prevalent treatment options for LSCC are limited in efficacy. Through genetic inactivation of Usp28 in a novel lung cancer mouse model, and chemical inhibition of Usp28 in induced LSCC in mice and human LSCC xenograft tumors, the authors demonstrated the specific dependency of LSCC (but not LADC) on the protein deubiquitinase Usp28. The authors also showed that loss of Usp28 by either means leads to depletion of the oncoproteins c-Myc, p63 and c-Jun in LSCC. Finally, the authors described a novel small molecule that is specific for Usp25/28 among a group of assessed deubiquitinases. Based on these results, the authors suggested chemically targeting USP28 as a potential therapeutic option for human LSCC patients.

Strengths: The presentation of the work is clear, concise and easily readable. The data presented largely supports the authors' conclusions on the role of USP28 in LSCC tumorigenesis and that inhibition of USP28 is a viable therapeutic option for LSCC treatment. The generation of the KFCU mice model that can give rise to both LADC and LSCC concurrently is interesting and presents a valuable tool for the wider cancer community.

Weakness: The manuscript can benefit from a deeper analysis of the relationship between FBW7 and USP28 in patient cohorts. A comparison of the activity/efficacy of FT206 to existing USP28 inhibitors will also be helpful.

Reviewer #2:

In this work Ruiz et al, use a couple of elegant mouse genetic models - KFCU (Fbxw7 deletion and mutant Ras over-expression) and KPCU (p53 deletion and mutant Ras over-expression) - to generate both LADC and LSCC tumors. Using this system, the authors show that deletion of USP28 resulted in less LSCC but not LADC tumor formation. However, both tumor types showed an overall decrease in tumor size (in KFCU; data are not shown in KPCU). These results are the genetic proof of concept that USP28 inhibition will be particularly detrimental in the context of LSCC tumors. They further test a compound (FT206) that was previously found to target USP28 and show that indeed this compound is specific for USP28 binding among USPs and can reduce the tumor numbers and size only in LSCC tumors and not LADC in the KF model and in three separate LSCC cell line xenograft models. Altogether, they make the argument that targeting LSCC tumors with chemical inhibitors of USP28 is a promising clinical strategy for LSCC cancers. Overall this paper is interesting and the results provided in vivo are strong and nicely demonstrate an on-target effect of FT206 and its specificity in LSCC tumors. The work is very similar to a recent publication of (Prieto-Garcia EMBO Mol Med 2020) describing very similar results for USP28 dependency in LSCC tumors and previous findings regarding the chemical matter used in this paper (FT206).

The major strengths of this paper is that the authors use several very elegant mouse models to establish that Usp28 is a good candidate target for potential therapeutic development designated for LSCC patients. They also show the proof of concept using a compound that is described as a Usp28 inhibitor (FT206). It should be noted that much of the genetic data, showing the importance of Usp28 in LSCC was previously described (Prieto-Garcia EMBO Mol Med 2020) including the potential benefit of chemical inhibition of USP28 . A potential weakness is that there is no rigorous characterizing of Usp28 substrate ubiquitination and degradation following FT206 treatment. This work will likely motivate the development of the USP28 inhibitor(s) for further preclinical assessment in Usp28 dependent tumors such as LSCC.

Reviewer #1:

The authors investigate a role for a candidate new inhibitor of USP28 in destabilizing c-MYC to reduce the growth of lung squamous carcinomas. They demonstrate that c-MYC levels are higher in lung squamous cell carcinomas (LSCC) versus lung adenocarcinomas (LADC), and depletion of c-MYC reduces LSCC cell growth. The deubiquitinase USP28 is known to stabilize c-MYC; the authors show that depletion of USP28 also decreases c-MYC protein levels. USP28 action opposes that of a ubiquitin complex targeted by the FBXW7 tumor suppressor. The authors create a new mouse model in which FLP recombinase initially causes deletion of FBXW7 and activation of KRAS to cause tumorigenesis with LSCC and LADC, followed by tamoxifen-dependent CRE recombinase deletion of USP28. Loss of USP28 in this model reduced numbers of LSCC but not LADC, and led to decreased expression of c-MYC and other short-lived proteins such as c-JUN and deltap63. A limitation of the data shown is that tumor number calculations are shown for a relatively small number of mice. Deletion of USP28 also did not restrict LADC growth in a second mouse model, with tumors forming based on activation of KRAS and loss of TP53. The authors then describe a compound, FT206, which they show is a specific inhibitor of USP28 among other ubiquitinases. They demonstrate that this compound reduces expression of c-MYC, c-JUN, and deltap63, but do not demonstrate this effect is directly mediated through USP28. They also show FT206 reduces growth of LSCC but not LADC in the KRAS/FBXW7 tumor model, and in human LSCC xenografts. These latter data suggest the compound FT206 may be useful as a lead compound. However, the current data are not sufficient to demonstrate FT206 binding and biological effect is specific for USP28, as the compound may also bind and regulate other non-deubiquitinase proteins.

Reviewer #2 (Public Review):

In this manuscript Ma et al., sought to investigate the breadth of genetic mechanisms available across various lineages of clinical isolates of Klebsiella pneumoniae, with a specific focus on carbapenem resistance evolution. The authors systematically evaluated how different carbapenems and genetic backgrounds affect the rate of evolution by measuring mutation frequencies. The authors found three major observations: First, that a higher mutational frequency is dependent on genetic background and high-level transposon activity affecting porins associated to carbapenem resistance. Importantly transposon activity was not only higher than SNP acquisition rates in distinct backgrounds, but was also reversible, thus emphasizing that resistance evolution via this mechanism might impart less of a cost than by the accumulation of mutations in other genetic backgrounds. Second, that CRISPR-cas systems have the potential to restrict the horizontal acquisition of resistance elements. Importantly, determining the presence or absence of such systems alone is not enough to determine wether a strain is "resistant" to certain foreign elements, but specific sequences within the different spacers can be more informative of the exact range of plasmids or genetic elements to which the system is restrictive. Third, pre-selection with ertapenem increases the likelihood of resistance evolution against other carbapenems both via de novo mutation and HGT.

Altogether, these results emphasize the importance of additional factors, other than MIC values, such as genetic background, plasmid/transposon activity, and drug identity and choice in determining the rate at which resistance can evolve in K. pneumoniae. I consider that the data generally supports the authors conclusions and provides relevant observations to the field. I do not have any major concern and think the authors have done a very complete and systematic evaluation of the data necessary to answer their questions.

My only minor concern is regarding the authors emphasis in their introduction and discussion on how these kind of data is relevant for clinical decision making. It remains unclear to me exactly how. While I completely agree that genomic information and drug choice play a major role in the evolution of antibiotic resistance, it is unclear to me how to efficiently and promptly translate all of this information at the bedside. Genome sequencing, however economical it has become in the recent years, is still not affordable to be implemented at the scales needed for diagnosis at the clinic. Perhaps the authors could expand on how they envision this could be implemented?

Reviewer #1 (Public Review):

In this manuscript, Ma, Hung and colleagues rewind the tape to explore the genetic landscape that precedes carbapenem resistance of Klebsiella pneumoniae strains. The importance of this work is underscored by the paucity of new drugs to treat CPO (carbapenemase producing organisms). 'Given the need for 35 greater antibiotic stewardship, these findings argue that in addition to considering the current 36 efficacy of an antibiotic for a clinical isolate in antibiotic selection, considerations of future 37 efficacy are also important.' And so I would say the major weakness of the paper is the aspirational nature of how this work could be used by clinicians in antibiotic selection or treatment of the patient.

The strains selected for these experiments and the evolutionary in vitro models are both well considered. One idea that has stuck with me from the figures of a review article by Kishony (https://pubmed.ncbi.nlm.nih.gov/23419278/, figure 4) is the concept of constraining the evolutionary pathways or fitness landscape for antibiotic resistance. Are there any peaks that a microbial strain reaches that optimize resistance to one AbX but basically leave it inherently unable to evolve resistance to another AbX? This could have application for dual drug therapy or pulsed therapy. When you sequence the isolates that have increased their MIC do you find 'unrelated' mutations in genes that would control protein synthesis or other functions that might be compensatory mutations. Developing a clearer understanding of the rewiring of the bacterium's basic processes might also elucidate both integrated functions and potential weaknesses. You mention mutations in wzc, ompA, resA, bamD.

Point of discussion. Classic ST258 carries blaKPC on pKpQIL plasmid. Your ST258 strain (UCI38) carries blaSHV-12 on pESBL. Am I to assume that pESBL is in lieu of pKpQIL? Transformation of CPO have many variables and in vitro data does not always mirror what is observed in vivo. So the findings of Fig 2f might need to be considered under different laboratory conditions (substrate, temperature) [https://pubmed.ncbi.nlm.nih.gov/27270289/].

Reviewer #2 (Public Review):

In this manuscript, the authors investigate the role of Relish in the Drosophila lymph gland (LG). They establish that relish is expressed in PSC cells and that reducing its expression in these cells (by expressing relish RNAi with a PSC-gal4 driver) leads to an enlarged PSC, increased plasmatocyte differentiation, no effect on crystal cell numbers, and fewer progenitors in the medullary zone (MZ). In the PSC, Relish controls Wingless levels that in turn control PSC cell proliferation and thus PSC size. This study also establishes that the knock down of relish in the PSC leads to increased levels of several actin binding proteins, reduced filopodia formation in PSC cells and a decrease in Hh (HhExt) release from the PSC. In addition, relish knock-down in the PSC leads to the activation of the JNK pathway in the PSC. Epistasis experiments establish that JNK acts downstream of Relish to control filopodia formation and HhExt. Under normal conditions, Relish levels in the PSC are under the control of ecdysone. Finally, in response to an E.coli infection, a decrease in Relish levels in the PSC is observed together with increased plasmatocyte differentiation.

This is an important study describing a yet unknown regulation of Drosophila LG hematopoiesis.

Reviewer #1 (Public Review):

Mandal and colleagues identified novel functions of Relish in the hematopoietic niche development and its coordinative role in innate immunity. The authors found that Relish is expressed in the PSC, which is essential for various developmental functions, including the maintenance of hematopoietic progenitors, the number of PSC cells, expression of Wg, and the PSC actin cytoskeletal structure. Furthermore, Relish acts as an inhibitor of JNK signaling and functions downstream of the ecdysone pathway in the PSC. The authors moved on to find the developmental and physiological relevance of this phenomenon and discovered that Relish is downregulated upon bacterial infections to accommodate immune responses. These findings show that Relish plays a critical role in hematopoiesis as a downstream of hormonal control and in switching between the developmental and physiological mode of the PSC.

Conclusions are well-supported by data, and experiments were carefully performed and analyzed. Given that most of the studies on Relish describe its function in innate immunity and that it is the first study showing critical roles of Relish in blood development, this study will draw broad attention and contribute to understanding insect hematopoiesis and immunity.

Reviewer #3 (Public Review):

This paper demonstrates the additional utility that can be extracted from short-read genome resources such as the genomes from the 1000 Genomes Project by leveraging variant discovery in long-read platforms. These genotyped variants can be used for eQTL studies, or to identify potential signatures of selection. Thus, low-coverage population-scale sequencing datasets such as the 1000 Genomes data can still be of use when coupled with other datasets.

One of the challenges I have with this manuscript however is clearly understanding the novel aspects of the reported results in the context of previous work in this field. Initially, it is unclear how many of the genotyped variants are already in the 1000 Gnomes dataset, this should be clearly reported. Comparisons of LD to nearby SNPs does not take into account that the SV discovery in the 1000-genomes project was done separately from the SNP calling. Thus, while it is suggested as presented that most of these variants were previously intractable, this is insufficiently explored. Additionally, discussion of low LD with SVs is well documented in 1KG and elsewhere. Subsequently, the eQTL analyses are "broadly consistent" with previously reported eQTL analyses from both the 1000 genomes project and GTEx, but no direct comparison is performed. If the overall goal is to point out that using additional datasets can identify new variants that can be genotyped, it is important to perform comparisons to other population-scale datasets such as HGDP and SGDP (Almarri et al Cell, Hseih et al Science, etc). In these cases, higher coverage sequencing allowed discovery of variants which could then be genotyped, similar to this paper's assertion that long-read sequencing provided a new discovery set for subsequent genotyping. Indeed, the two highly stratified variants selected for follow up are reported in gnomAD. The paper mostly focusses on the identification of highly stratified loci. Again, comparison to previously reported highly stratified loci (1KG, Sudmant et al 2015, and Almarri 2020, Hseih et al) is necessary here.

Furthermore, while the analyses of the IGH hapotype are clearly presented and interesting, as noted in the manuscript, these have already been identified. The authors mention that this locus was already identified but suggest it was "not further examined," due to "stringent filtering" however this locus was reported as one of 11 "high frequency introgressed regions" thus this description seems to mischaracterize Browning et al's recognition of the importance of this locus. The strongest part of the manuscript is the ABC modelling of the IGH haplotype elucidating the putatively extremely strong selective signatures at this locus. More focus on these results and the importance of following up and fully understanding such loci would benefit the manuscript. Broadly, this paper is well written and clearly presented however would be very much strengthened by placing it more broadly in the context of previous work and focusing more on the novel modelling analyses of specific loci that are performed.

Reviewer #2 (Public Review):

The technical challenges of identifying and quantifying the frequency of structural variants (SV) on a population scale has been a major limitation to the study of recent human adaptation. This manuscript applies a recent graph-based genotyping method that leverages a library of SVs identified by long-read sequencing to identify SVs in large short-read based cohorts. This is a sensible and powerful approach that highlights several examples of likely adaptive SV evolution in different human populations. The key findings and examples are well supported by the data and methods used. However, the manuscript would benefit from: 1) testing more hypotheses rather than listing examples and 2) more framing of how the results and methods expand on several recent studies of SVs across populations. In addition to providing novel examples of adaptive SV evolution, I anticipate this analysis can serve as a template for future analyses that merge long-read and short-read datasets.

Reviewer #1 (Public Review):

Yan et al. take a comprehensive look at structural variants in the 1000 Genomes Project high-coverage dataset, using recent developments that can link short- and long-read data. Combined with genomic simulations, they identify and characterize the timing and origin of a likely selected region in Southeast Asian populations. The combination of multiple data types adds depth to the interpretation.

The study is timely, combing recently released data and methods, and had interesting biological implications. Tree main areas would help interpretation and robustness of the paper:

1) Further context and interpretation of the original SV set found is needed, for example comparisons to previous work to identify clearer "positive controls" or sanity checks on the method, and to understand what the contribution of the method/dataset/paper is.

2) The above is particularly important across ancestries/populations which differ in their LD levels. How does population-specific LD patterns impact the ability to detect these SV patterns? and therefore to make cross-population comparisons or infer differences in frequency that are central to the selection scan and the 220 highly differentiated SVs of interest. Perhaps this is in the original methods paper, but is central to this paper so should at least be explained or analyzed.

3) The genomic simulations to infer the strength selection was a nice addition, a step beyond common empirically-driven work. It would help to know how to interpret the ABC model in the context of the later finding that the region was introgressed from Neanderthals--the model seems to not include this aspect.

Reviewer #3 (Public Review):

The article by Sprenger et al. uses the power of yeast genetics to generate mutants of the ESCRT-III subunits, and study their impact on the formation of a functional ESCRT-III complex. By using functional FP tags of subunits Snf7, Vps2 and Vps24, and of the CPS cargo, they essentially follow recruitment of subunits to the vacuolar membrane, formation of Class E compartments and sorting of CPS as readouts of the endosomal ESCRT-III function. They found that recruitment of Vps2, Vps24 and Snf7 is unaffected by deletions of other subunits (Did2, Ist1, Vps60), supporting the view that Vps2-Vps24 and Snf7 form an initial subcomplex.

To decipher molecular interactions between Vps2-Vps24 and Snf7 subunits, they use point mutants to replace well-chosen hydrophobic residues in two subunits by cysteines, and cross-link them to probe the interactions of those residues in the functional case. They also change hydrophobic residue pairs into charged residue pairs to replace the hydrophobic interaction by an electrostatic interaction, and restore functionality (only when both mutants were used).

Overall, it is an elegant study, with very clear and well executed experiments, and which give strong support to a so far hypothetical architecture of the Vps2-Vps24-Snf7 as a double-strand filament, one of which is Snf7 only, and the other is an alternative repeat of Vps2 and Vps24.

Reviewer #2 (Public Review):

ESCRT-III is a filament-forming machinery that is necessary for a variety of physiological and pathophysiological membrane remodelling events. These events are linked to an ability of an ESCRT-III filament to assemble and remodel cellular membranes. In recent years, it has become clear that whilst the ESCRT-III component Snf7 is likely the major component of ESCRT-III, individual filaments can form lateral interactions with alternate filaments, that remodelling the composition of ESCRT-III subunits within a filament likely allows its geometric changes and that it is unclear what role the Vps2/Vps24 subunits of ESCRT-III have alongside the major Snf7 filament. Building upon a previous publication in eLIFE, in which the authors used advanced microscopical approaches to quantitatively document the assembly kinetics of ESCRT-III upon endosomes (demonstrating transient co-assembly of Snf7, Vps2 and Vps24), Sprenger et al have now used biochemical and microscopical approaches to understand individual interactions within the ESCRT-III holo-filament.

Protein-protein interactions are typically driven by two different modes that rely upon the physicochemical properties of the amino acids involved (namely electrostatics, or the shielding of hydrophobic residues by mutual interaction). Using published and modelled structural data, Sprenger et al., identify hydrophobic interactions governing longitudinal interaction of ESCRT-III monomers and electrostatic interactions that govern lateral interactions. They make elegant use of targeted mutagenesis to switch the interaction mode between individual monomers, and employ pairwise mutagenesis to rescue the disrupted interactions. They also employ chemical crosslinking to stabilise these transient interactions, and integrate this with an analysis of cargo sorting to the vacuole lumen, which is the archetypal function of ESCRT-III in yeast. In contrast to models proposing the Vps2/Vps24 unit as a 'cap' for a Snf7 filament, the authors propose that these subunits instead form a parallel filament that has important implications for our understanding of how Vps4 can access subunits within the ESCRT-III holo-filament.

The strengths of this manuscript are the integration of molecular and biochemical data with clear functional readouts of vacuolar sorting and the use of knock-in techniques bearing functionally tagged versions of the ESCRT-III proteins to analyse phenotypes. I think some improvement could be made to the description of the author's selection of residues for mutagenesis and to the degree of quantification of the data throughout the manuscript. I also wonder if there are different interpretations of the cross-linking experiments that could be integrated into their discussion.

Reviewer #1 (Public Review):

In this work the authors address inter-subunit interactions leading to ESCRT-III function during MVB sorting in a yeast model system. ESCRTs mediate function in multiple biological processes, however the fundamental question of how ESCRT-III mediates membrane remodeling is not understood. As such this represents a topic of considerable interest despite significant technical limitations surrounding the issue. Random and rational mutagenic strategies, including compensatory mutations, are combined with protein-protein interaction studies and in vivo functional assays to identify residues within Vps24 and Vps2 mediating associations with each other and Snf7. Based on these analyses the authors put forth a series of "rules" governing ESCRT-III assembly and function. While beneficial to our conceptual understanding of ESCRT-III these rules fall short in explicitly defining the structural basis of assembly and function including explaining requisite heterodimerization of Vps24-Vps2. This work represents a significant step forward in addressing this challenging question, the experimental design and implementation are convincing, however the limitations of this work could be conveyed more clearly.

Reviewer #3 (Public Review):

In this manuscript, Takaine et. al. leveraged their QUEEN ATP biosensor to ask an interesting and important question: how and why cells maintain high and stable ATP concentrations in Saccharomyces cerevisiae.

The strength of their approach is to obtain single-cell quantification of ATP concentration over time. They use the technology to demonstrate the importance of the AMP kinase, and two other proteins involved in ATP synthesis/homeostasis (the adenylate kinase, ADK1, and the transcription factor, BAS1) in the maintenance of stable and high levels of ATP.

The main novelty of their findings with respect to ATP homeostasis is the detection of sudden, transient decreases in ATP concentration in mutants. The main claim in the title and abstract of the paper is that "High and stable ATP levels prevent aberrant intracellular protein aggregation". In our opinion, the data do not yet support this claim.

Essential issues:

1) The most important missing experiment, which would be required to support the title, is to image both ATP levels and protein aggregation events in the same cell. The current dataset shows that the mutants under study have both decreased ATP levels and suggest that these levels are less stable, and finally that complete ATP depletion leads to protein aggregation, but it is not possible to extrapolate these observations to the current conclusions.

2) The second most important issue is a lack of statistics with respect to spontaneous drops in ATP concentration. A couple of examples are shown, but it should be possible to obtain data for hundreds of cells. Do the examples in figure 2 represent 90% of cells? 1% of cells? 1/1000? We need to be given a more complete sense of the penetrance of these effects.

Reviewer #2 (Public Review):

Takaine et al., use a fluorescent reporter to quantify ATP levels within single yeast cells with high temporal resolution. With this approach, they aim to understand the molecular components required to maintain cytoplasmic ATP levels at a constant 4 mM concentration. They identify two enzymes (ADK, AMPK) and one transcription factor (Bas1) that cooperate in buffering cellular ATP levels. Without these proteins, yeast cells experience transient depletions of ATP, which the authors term "ATP catastrophes". These stochastic events are sometimes reversed, but sometimes not, leading to death of the cell. Such ATP catastrophes also make the cell prone to aggregation of neuropathic peptides, which could explain why protein aggregates occur in aging neurons (which experience declines in ATP levels). Their experiments provide strong in vivo evidence that cells maintain high levels of ATP to keep proteins soluble in a crowded cytoplasm.

Strengths:

1) This work moves the field forward by providing a single-cell approach. Previous studies of ATP levels analyzed extracts taken from cell populations, which could hide cell-to-cell variability. Indeed, using their ATP reporter, Takaine et al. demonstrate how ATP levels are dynamic, different between cells, and can even undergo dramatic stochastic changes.

2) The authors use a variety of orthogonal approaches to test their hypotheses. They use the ATP probe QUEEN as their primary approach, but back it up with biochemical analysis of ATP levels in cell populations. Furthermore, they use genetic knockouts, acute insults (chemicals to deplete ATP), and rescue experiments to corroborate their results.

3) The paper is well written and the logic is easy to follow.

Weaknesses/Criticisms:

1) Possible indirect effects due to knock outs of AMPK, ADK, and Bas1. These proteins are involved in many biochemical pathways, including lipid homeostasis, mitophagy, and ATP regulation. How do we know that snf1 KO (AMPK knock out) directly effects ATP levels? Also, it is possible that these yeast have acquired suppressor mutations that let them survive at reduced ATP levels, which could confound interpretation of the results.

2) Lack of wild-type controls in Figure 2. The authors do quote their previous paper, but I want to see the controls done the exact same way. I need to know that transient changes in ATP levels are due to the mutations and not to user error or a different microscope setup. This is really important, since observation of the "ATP catastrophe" is a major finding of this paper.

3) Insufficient quantification of the ATP catastrophe phenotype. Figure 2 shows only two cells, so I'm not sure how representative these data are. This is an important discovery, so it deserves better quantification and characterization. It would be important to quantify: a) how many cells in a population experience ATP catastrophe, b) the average time interval of depressed ATP levels before restoration, c) frequency of ATP catastrophes in a single cell, and d) how long can ATP levels be suppressed before the cell dies.

Reviewer #1 (Public Review):

For bacteria, yeast and mammalian cells, energy depletion has been linked to a vitrification of cytosol and protein aggregation. Previous studies have postulated this is in part due to acidification and the shift in pH to match a large set of proteins pIs resulting in large-scale protein aggregation as well as changes in crowding of the cytosol. Additionally, a more direct role for ATP in protein aggregation has been proposed through its chemical properties as a hydrotrope. The appeal of this hypothesis is that the steady-state levels of ATP far exceed the Kd of most enzymes pointing to a potential non-enzymatic role for the high levels.

In this study, the authors take advantage of a FRET-reporter for ATP that they developed previously called "Queen". They then manipulate ATP levels using mutants in AMP kinase(Snf1) or Adenyl kinase (Adk1) and find null mutants indeed have lower concentrations of ATP and experience sudden drops in ATP levels which the authors term ATP catastrophe. These mutants also show genetic interactions with protein folding/glycosylation pathways and are sensitive to conditions that generate proteotoxic stress. Hsp104 forms foci in the genetically induced lowered ATP levels as well as exogenous ectopic aggregation prone proteins such as alpha-synuclein. The authors attempt to show that the cause of aggregation is due to limiting ATP directly by adding excess adenosine to the media and showing this diminished the formation of foci, potentially due to the ability of increased exogenous to raise ATP levels according to previous reports.

The issue of whether ATP levels play a direct or indirect role in preventing protein aggregation is extraordinarily challenging to address. While ATP can act as a hydrotrope, the formation of aggregates could be due to limitations of the activity of chaperones and helicases which would not be surprising role for ATP in the cell. While the experiments are carefully performed, well analyzed and fairly interpreted; questions still remain about the impact of these experiments on understanding how ATP impacts cytosol.

Reviewer #3 (Public Review):

Cellular quiescence, the reversible exit from the cell cycle, is essential for long-term cell survival. One feature of quiescent cells is transcription inactivation and this paper examines gene reactivation during quiescence exit and the accompanying changes to chromatin structure. Using a variety of genome-wide analyses, including 4tU-seq, ChIP-seq, and MNase-seq, the authors show that transcription occurs within minutes of quiescence exit, and for most genes, the initial rate of transcription exceeds that of normal cycling cells. Moreover, this work shows that gene repression during quiescence, and activation upon quiescence exit, are associated with distinct chromatin organization, particularly over promoters. Finally, the authors uncover a role for the RSC chromatin-remodeling complex in establishing a chromatin organization that facilitates normal gene expression during quiescence exit. To support the above findings, the authors generated an impressive amount of sequencing datasets that robustly support their findings and will undoubtedly be of great use to many yeast transcription researchers. Although more transparent and consistent bioinformatic analyses of these data would better communicate the findings, this work enhances our understanding of gene expression changes during the transition between key cell states and thus will be of interest to a broad spectrum of readers ranging from molecular to developmental biologists.

Reviewer #2 (Public Review):

In this manuscript, Cucinotta et al investigate the role of the conserved RSC chromatin remodeler in preparing cells for hypertranscription during exit from quiescence using cellular perturbations and a range of genomic techniques. They find that upon exit from quiescence there is a large and rapid increase in transcription (within 5 minutes) and this hypertranscription cannot be explained solely by alterations to histone acetylation. Therefore, the authors investigated what is driving this process and identified that RSC, a well describe chromatin remodeler with activities in altering chromatin structure to promote transcription, has altered binding profiles within quiescent cells relative to log cells, and loss of RSC results in altered nucleosome positioning within gene bodies and increased histone occupancy within nucleosome depleted regions (NDRs). They find that RSCs biochemical activity is important for promoting transcription and is required for appropriate RNAPII occupancy during exit. Finally, they find that RSC is required for appropriate transcription as depletion of RSC results in increase aberrant transcription, leading to the model that RSC is important for regulating chromatin structure for appropriate binding of RNAPII throughout the genome during exit from quiescence. The conclusions of this paper are well supported by data, but some aspects of data analysis need to be extended.

Strengths:

To my knowledge, this is the first mechanistic description of quiescent exit, adding to the many roles of the important RSC chromatin remodeling complex. The data are extensive to support the claims made by the authors. Data are also clearly described within the text and put into great context within the field.

Weaknesses:

Correlations are not directly drawn across the datasets, and aspects of data presentation could be clarified. For example, there is little comparison between the expression data (4tU-seq) and the localization (ChIP-seq) or nucleosome positioning (MNase-seq) datasets. Direct comparisons of where locations have altered factor occupancy and/or nucleosome changes with the expression changes or aberrant transcription increases would help facilitate a mechanistic description.

Reviewer #1 (Public Review):

Cucinotta et al. examine the widespread, transient transcription of genes that occurs within minutes of refeeding quiescent Saccharomyces cerevisiae cells, focusing on the role of the RSC remodeler complex in this process. A range of appropriate genomic approaches are used to characterize the initial burst of transcription, changes in localization of RSC and RNA Pol II, and changes in the occupancy and positioning of nucleosomes during the first minutes after nutrient repletion. Several new insights are reported including the role of RSC in maintaining promoters in state that is ready to respond rapidly to nutrient repletion, the relocalization of RSC into genes following initiation of transcription, a role for TFIIS in exiting quiescence that was not apparent in log phase, the timing of histone acetylation in response to transcription, changes in chromatin architecture during the exit from quiescence, and the effects of chromatin changes on transcription start site selection and repression of antisense transcription from downstream nucleosome depleted regions. Given how little is known about the emergences of cells from quiescence and how common and important this transition is in long-term viability, development, and carcinogenesis, these insights are certain to have broad impact. The data are of high quality and the manuscript is very clearly written, with good correlation between the level of support provided by the data and the strength of the conclusions drawn. Only minor issues remain to be addressed.

Reviewer #3 (Public Review):

In this manuscript, authors seek to resolve conflicting models for corepressor function using the elegant synthetic auxin response system. Auxin signaling is governed by a de-repression paradigm and is ideally suited to interrogate co-repressor function - in this case, the TOPLESS (TPL) co-repressor. Several contradicting models have been put forward for the mechanism of TPL-mediated gene repression, ranging from a requirement for protein oligomerization for activity, interaction with distinct partners, and even which regions of the protein are required for repressive activity. Leydon et al use the yeast-based synthetic auxin response system to interrogate these models using a single reporter locus, allowing for straight-forward examination of TPL function.

Reviewer #2 (Public Review):

In this manuscript, the authors studied the specific domains of the plant A. thaliana TPL corepressor using a synthetic auxin response circuit (ARC) in the yeast S. cerevisiae that allows to monitor the repression and response to auxin of the reporter expression. Two domains of TPL corepressor that independently contribute to repression in this system were identified. Moreover, one of these domains interacts with Med21 and Med10 Mediator subunits. The authors show that this interaction is required for TPL-mediated auxin-responsive repression in plants. On the contrary to some repression models, they propose that multimerization of TPL is not required for repression mechanisms. Taken together, the work provides important information on auxin-responsive repression mechanisms involving TLP corepressor and the Mediator complex.

A lot of work was done to analyze the TPL domains and critical residues involved in repression using ARC system, TPL interaction with Mediator using yeast cytoSUS and two-hybrid assays, completed by CoIP experiments with yeast and plant extracts. Point mutations, small deletions or Anchor Away-mediated depleted strains were used to analyze their consequences on TPL-Mediator interactions and auxin-responsive repression in artificial system in yeast and directly in plants.

The mechanism of how TPL-Mediator interaction is involved in auxin-responsive repression remains to be determine. No results were provided in the manuscript on the composition of Mediator upon auxin induction and a discussion sentence that "as supported by our synthetic system, auxin-induced removal of TPL is sufficient to induce changes in the composition of the Mediator complex" is not supported by the results. In general, the transition between transcriptionally repressed and active states was not analyzed. The authors have made considerable efforts to answer the reviewers' criticism and to include a number of new experiments and approaches. However, several points and conclusions need to be further developed and specified. In particular, CoIP experiments in plant extracts lack a negative IP control to conclude on the specificity of CoIP signal. Moreover, the relevance of ChIP experiments on yeast plasmid remains questionable and appropriate control regions (chromosomal ACT1 gene body is completely inappropriate as a background for Pol II ChIP), regulatory, core promoter and transcribed regions, as well as experiments with untagged control strains should be added. The ChIP occupancy was analyzed only in transcriptionally repressed state and essentially on a plasmid and no results are provided for transition to the active state.

Many problems with inappropriate citations for Figures or Figure panels did not facilitate the reading of the manuscript.

Reviewer #1 (Public Review):

In this study, Leydon et al., use an elegant multi-component genetic system to address the mechanisms of repression by the Arabadopsis TOPLESS (Tpl) protein. Taking advantage of the genetic tools and knowledge of the structure of the Tpl protein the authors determine two short alpha helical regions that act as independent repression domains. They provide evidence that the target of one of these domains is the N-terminal region of the Med21 subunit of the mediator complex. Chromatin immunoprecipitation experiments, anchor-away loss of function and co-immunoprecipitation assays indicate that Tpl mediated repression involves formation of a promoter complex comprising the mediator complex along with several general transcription factors, but lacking RNA polymerase II. The authors also show that Tpl-Med21 interactions are involved in Tpl mediated repression in plants.

Reviewer #3 (Public Review):

In this manuscript, the authors aim to elucidate the evolutionary history of the paired NLRs Pik-1/Pik-2 in rice. They ask two primary questions:

(1) When (in evolutionary history) did the paired Pik-1/Pik-2 locus arise and when was the integrated domain integrated into the locus?

(2) Has the binding affinity of the integrated domain changed over evolutionary time?

The authors convincingly demonstrate that the integrated domain is undergoing positive selection, that its integration is ancient (~15MYA) and that inferred ancient alleles bind modern AVR-PikD with poor affinity. The subsequent biochemistry experiments and structural analyses identify which residues are important for interactions with AVR-PikD and which allelic combinations induce autoimmunity.

The biochemical work, while interesting in and of itself for identifying the interacting residues and interactions between domains, was less informative about the evolution of the NLR-effector interaction, and most of the work did not advance our understanding of the questions listed above. The most emphasized biochemistry finding was that of reduced binding affinity of ancestral Pik-1 integrated domain. Specifically, the authors demonstrate that modern AVR-PikD has poor affinity with the ancient Pik-1 integrated domain. From this result the authors infer that ancestral Pik-1 likely bound a different effector. But it was not clear how the authors ruled out binding to an ancient AVR-PikD? I was confused as to why the authors excluded this possibility. Perhaps the authors contend that the absence of the Avr-PikD in other modern blast lineages indicates Avr-PikD is unique to modern rice-infecting M. oryzae. But this modern absence does not preclude Avr-PikD in the ancestral population. Furthermore, changes in binding over time would be the effective null hypothesis in the scenario of coevolving NLR and effector. Their finding seems consistent with expectations of coevolution, a phenomenon that has been widely reported in interactions between NLRs and effectors. The novelty in this manuscript stems from the synthesis of molecular evolution analysis with ancestral state reconstruction and testing.

Overall this manuscript is exemplary in its integration of biochemical and evolutionary analyses to study plant-pathogen coevolution. While the findings are unsurprising, future emulation of this type of data integration will likely lead to significant insight into the coevolution of plants and their pathogens.

Reviewer #2 (Public Review):

In this study, Bialas et al. aimed at understanding the evolution of the diversity of Pik-1 immune receptors. First, using phylogenetic and selection analyses they determined that the Pik family of immune receptors is present in multiple grass species, with both Pik-1 and Pik-2 evolving before the radiation of the PACMAD and BOP clades. The author dated the insertion of an HMA domain in a Pik-1 subclade before the radiation of the Oryzinae and detected signs of positive selection on this domain. Using a combination of ancestral sequence reconstructions and biochemistry they determined that two of the extant Pik-1 haplotypes (Pikp-1 and Pikm-1) evolved independently the ability to associate at high affinity with the AVR-PikD effector following two different evolutionary paths. The authors determined that the increased binding correlates at least in one case with the improved ability to induce cell death when co-expressed in tobacco leaves with Pik-2 and AVR-PikD.

Main strengths:

The study combines a large diversity of methods to comprehensively address an important question. Despite the large amount of presented data (including a large number of variant names) it was a pleasure to read this very well structured manuscript. The work conducted here by the authors on the ancestral sequence reconstruction, the chimera and the biochemical assays (on two haplotypes!) is impressive and supports a very exciting conclusion. The presentation of all the experimental replicates as supplementary figure is a model of transparency and strengthen the conclusions.

Weaknesses:

The conclusions reached by the authors are mostly supported by the presented data, although there are a few points that need to be clarified. The Pik-1 phylogeny (Fig 1A): From the phylogenetic tree presented in Figure 1A it seems that Pik-1 experienced a duplication before the radiation of the BOP and PACMAD clades, with varying patterns of gene retention/loss (for instance loss of both copies in Brachypodium, loss in one clade for maize) and expansion (massive in wheat for instance in the clade where the fusion with the HMA domain did not occur, not in the other). I did not find this point discussed in the manuscript, although this could have an important impact. This would support the hypothesis that the HMA integration occurred before the radiation of the PACMAD clade. A better resolved phylogeny is needed to further test this possibility. In that context, the nomenclature should restrict the Pik-1 name to the actual orthologs, changing the number of Pik-1 per species (in panel 1D for instance).

In Figure 4C and S13 the Pikp-1 variant I-N11 seems to associate more significantly with AVR-PikD than all the other variants, including I-N2 that was selected for the swap experiments. The reason why I-N2 was selected over other options (including I-N11) should be better explained.

The correlation between evolution of high-affinity binding to AVR-PikD and the ability to induce immune response should be tested in reconstructed ancestral Pikm-1 variants. The presented data demonstrate nicely the gain of high-affinity binding in Pikm-1, but the impact this may have on the actual immunity function was not tested. It would be important to know whether additional mutations were required or not to turn the ancestral Pik1 into a functional Pikm-1 given that it is the basis for the model proposed in Figure 9. Alternatively, as the result of this experiment would not contradict the model even in absence of immune abilities (it would just add one extra step from high-affinity binding to immune function) the authors could propose this second evolutionary scenario as a supplementary figure.

The nomenclature used for the Pik variants is not consistent throughout the manuscript, please homogenize as it is not always easy to follow.

I am not familiar with the besthr R library used for the statistical analyses of the cell death assays, and I am not an expert in biochemistry (SPR, cristal structure) and cannot properly evaluate these aspects of the work.

Reviewer #1 (Public Review):

This paper was a pleasure to read. It is a tour-de-force study that is well-written, clear, and transparent. The study recounts how the HMA domain became integrated into the Pik NLRs and how it evolved higher affinity binding to a pathogen effector. Strikingly the authors demonstrate adaptability of distinct regions of the HMA:effector interface on two Pik NLRs, driving the convergent evolution of high-affinity binding to the effector. The study furthermore provides a framework for understanding protein evolution in the context of host-microbe interactions. The breadth and depth of the experiments that support the authors conclusions is extraordinary in my view.

Reviewer #3 (Public Review):

In this study, the authors present a high-resolution single-cell transcriptomic atlas of the pancreatic ductal tree. Using a DBA+ lectin sorting strategy murine pancreatic duct, intrapancreatic bile duct, and pancreatobiliary cells were isolated and subjected to scRNA-seq. Computational analysis of the datasets unveiled important heterogeneity within the pancreatic ductal tree and identified unique cellular states. Furthermore, the authors compared these clusters to previously reported mouse and human pancreatic duct populations and focused on the functional properties of selected duct genes, including Spp1, Anxa3 and Geminin. Overall, the results presented here suggest distinct functional roles for subpopulations of duct cells in maintenance of duct cell identity and implication in chronic pancreatic inflammation. Finally, such detailed analysis of the pancreatic duct tree is relevant also in the context of cancer biology and might help elucidating the transition from pancreatitis to pancreatic cancer and/or different predisposition to cancer.

The study is very well done, with careful controls and well-designed experiments.

Reviewer #2 (Public Review):

In this study the authors address the heterogeneity of the mouse ductal cell at the single cell level and conduct functional studies for selected marker genes. They isolated duct cells using the DBA lectin as a molecular surface marker. This is an noteworthy approach as it does not rely on the specificity and expression levels of reporter lines. Isolated cells contained a majority of non-duct cells that were identified by their transcriptomic profile and excluded from further analysis. The transcriptomic profiles of bona fide duct cells were then subjected to standard analyses for differentially expressed genes, activated pathways and lineage relationships. Of particular interest is the comparison of these data with human data from a recently published study that used a different sorting strategy for duct cells. As more studies at the single cell level are conducted, these types of comparisons need to become part of them in order to derive commonalities and identify deficits due to methodological or technological limitations. The study was by necessity descriptive up to this point and the authors addressed this with functional studies on SPP1 and GMNN which suggested that SPP1 is necessary for the maintenance of the ductal differentiated phenotype whereas GMNN protects cells against DNA damage during increased proliferation triggered by chronic pancreatitis.

It is an interesting study, but there are caveats, particularly concerning the functional studies. The functional analysis of SPP1 needs to be strengthened and some findings on the the analysis of GMNN clarified. There is also an over reliance on the outcome of pathways analyses and upstream regulators which are often treated as actual findings rather than possibilities to be explored in this or future studies. The single cell RNA Seq analysis would benefit from reducing speculation and restrict descriptions to the essential features of each cluster. Main figures for this analysis could also be simplified along the same lines.

Reviewer #1 (Public Review):

The study by Hendley et al takes advantage of duct-specific DBA-lectin expression to purify pancreatic ductal populations that were then subjected to scRNA-seq analysis. The ability to enrich for this relatively low abundant pancreatic cell population resulted in a more robust dataset that had been generated previously from whole pancreas analyses. The manuscript catalogs several different gene clusters that delineate heterogeneous subpopulations of three different pancreatic ductal subpopulations in mice: mouse pancreatic ductal cells, pancreatobiliary cells, and intra pancreatic bile duct cells. Additional comparisons of the resulting data sets with published embryonic and adult datasets is a strength of the study and allows the authors to subclassify the different ductal cell populations and facilitates the identification of potentially novel subpopulations. Pseudotime analysis also identified gene programs that led the authors to speculate the existence of an EMT axis in pancreatic ducts. Overall, the data analyses is strong, but the authors tend to draw conclusions that are not fully supported by the presented data.

The second half of this study focuses on three candidate proteins that were identified in the transcriptome analysis - Anxa3, SPP1 and Geminin. Crispr-Cas9 was used to delete each gene in an immortalized human duct cell line (HPDE). Deletion of each gene resulted in increased proliferation; SPP1 mutant cells also displayed abnormal morphology. Additional functional studies of the cell lines or in mouse models suggested a role for SPP1 in maintaining the ductal phenotype and Geminin in protecting ductal cells from DNA damage, respectively. Although the provided phenotypic analysis suggest important functional roles for these proteins, follow up studies will be required to fully understand the role of these genes in homeostatic or cancer conditions.

Strengths:

1) Enrichment of pancreatic ductal populations enhanced the robustness of the scRNA-Seq dataset

2) Quality of the sequencing data and extensive computational analysis is extremely good and more comprehensive than previously published datasets

3) Comparative analysis with existing mouse and human data sets

4) Use of human ductal cell lines and mouse models to begin to explore the function of candidate ductal genes.

Weaknesses:

1) There are many suppositions based on gene expression changes that are somewhat overstated.

2) The conclusion that there is an EMT axis in pancreatic ducts is not fully supported by the gene expression and immunofluorescence data

3) A good rationale for choosing Anxa3, SPP1 and Geminin for additional functional analysis is not provided. In addition, it isn't clear why Anxa3 function isn't pursued further.

4) Although extensive models (transplanted cells for SPP1 and mouse conditional KOs for Geminin) were generated, the functional analysis for each gene is preliminary; additional longer term studies will be necessary to fully understand the role of these proteins in pancreatic duct development and cancer.

Reviewer #3 (Public Review):

The new models proposed here provide some potentially useful alternatives to estimating the generation time, serial interval, and the relative infectiousness of pre-symptomatic infections. The framing of the paper seems very focused on improving fits to the transmission pair data, however, and I think it would be more impactful to consider the implications of poor estimation of pre-symptomatic transmission and the generation time. I think this shift in focus could also help strengthen the narrative of the paper, which wavers between focusing on model fitting and the importance of implications for contact tracing.

I was a bit lost in the application of the models to the contact tracing example. The definition of the contact elicitation window (lines 142-144), where identification of contacts would occur up to x days prior to contact symptom onset, makes sense theoretically in this model comparison setting, but it is hard to translate these findings to real-world application. Are there any implications that could be useful for informing contact elicitation strategy (e.g., for how many days after time of infection or symptom onset could contact tracing have a measurable benefit in preventing onward transmissions?)

Lines 147-151: Given that the impact on onward transmission events is so dependent on the contact tracing assumptions, I would recommend stating the assumptions explicitly here, reporting the results in relative terms as compared to a single model, or both.

How different are the variable infectiousness model results from parameter estimates from the original studies that reported the transmission pairs data?

Can the authors comment on the plausibility of the infectiousness distribution in their new proposed models? While better model fitting certainly provides a measurable improvement to leveraging existing data, I'm not aware of studies that support the discontinuous assumptions about infectiousness made here.

Assuming alpha means the same thing across the models, why is the 95% credible interval so large for the Feretti model? In general, the model parameters should be more clearly explained for this model.

Reviewer #2 (Public Review):

In this analysis, the authors consider the impact of the duration of infectiousness of a person infected with COVID-19 prior to the appearance of clinical signs. This is an important problem, as identification of disease status often relies on a self-reporting, i.e. from people experiencing clinical signs, and in the case of COVID-19 in the UK, where they have then gone on to test positive (typically with a PCR test). The greater the proportion of transmission that occurs before clinical signs appear then, the less likely that methods based on self-reporting will be sufficient to contain epidemic spread.

The general problem is well known, with examples of previous analyses including for livestock diseases such as foot-and-mouth disease (see for example, Haydon et al. 1997 https://doi.org/10.1093/imammb/14.1.1 and the very many papers on the 2001 FMD epidemic), and most importantly the seminal paper by Fraser et al. on the SARS-CoV-1 pandemic which laid out the problem in extensive detail https://doi.org/10.1073/pnas.0307506101. In the analyses of the current SARS-CoV-2 dynamics, the authors refer to the paper by Feretti et al. (https://doi.org/10.1126/science.abb6936) which at this point represents the most prominent analysis of this type that is directly relevant to the current pandemic. More broadly, issues with exponential distributions and the impact that their use has on analyses of infection dynamics and epidemic behaviour have been well studies in other systems such as measles (e.g. Lloyd 2001 https://doi.org/10.1006/tpbi.2001.1525, and Conlan et al. 2009 https://doi.org/10.1098/rsif.2009.0284). It would be helpful for the paper to refer to this broader literature in order to contextualise the analysis though this does not of course detract from the relevance to the current COVID-19 pandemic.

In this analysis the authors show that, by choosing a pre-infectious period that is explicitly excludes any probability of infection, they achieve a better fit to the distribution of serial interval for a large number of known transmission pairs (previously analysed in the Ferretti paper). This is an entirely sensible result and a good use of a better mechanistically informed idea of the infection process (in essence, here incorporating explicitly the inevitable delay between virus entering the body, and a person becoming infectious).

By examining the proportion of infections that would be captured by contact tracing when considering a two-day window prior to symptom onset, they show a substantially greater efficacy for contact tracing, compared to a more standard compartmental modelling approach (where the duration of each consecutive period is independently determined).

While the analysis itself is detailed and thoroughly explained I have some questions regarding the utility of the result when making the comparison to other models. As noted earlier, the fundamental problem is already well known, and the application to COVID-19, while useful, is better than poorer models, but only marginally better performing than the Ferretti model. The serial interval estimates are only slightly better (figure 2), there are 84% of contacts when considering tracing two days prior to symptoms, compared to what looks like about 80% for the alternative in figure 4 and by the looks of the violin plots from figure 3, quite a bit of overlap if one considers credible intervals.

As such, while the analysis is a solid, useful addition to the literature, it could use a better exposition on how it advances scientific insight (the fundamental issues regarding exponential distributions having been identified previously), methodologically (given the thorough analysis by Fraser et al in 2004) or in terms of impact (given the limited improvement over the Ferretti model).

Reviewer #1 (Public Review):

The authors develop a mechanistic model for inferring infectiousness profile from data on times of symptom onset in pairs of infector-infectee. The novelty of their approach lies in assuming that infectiousness of an infected individual depends also on the whether or not they have symptoms. The authors fit a data set of time of symptom onset in 191 transmission pairs to a model that assumes that infectiousness varies along the incubation period. They compare the model fit to fits from models and find that their model of differential infectiousness explains the data better than the other models considered.

This is a carefully constructed study, and the conclusions are well supported by the analysis carried out. My only concern is that the data used were obtained during the early stage of the pandemic (January to February 2020). As the pandemic was growing in most countries during this time, we are more likely to have observed shorter serial intervals. Similarly, as isolation of infected individuals would prevent them from transmitting further, longer serial intervals are likely to be under-represented in the data. Indeed, the longest serial interval in the data used was 5 days. It would be interesting to understand whether the conclusions about the proportion of onward transmissions averted by contact tracing and subsequent isolation still hold as the pandemic progresses, and we continue to observe longer serial intervals. If the authors are unable to find more recent data, this caveat should be clearly discussed.

Reviewer #3 (Public Review):

Volatile anesthetics (VA) are thought to cause developmental defects in newborns and the authors previously studied the metabolic consequences of VA on newborn mice. Surprisingly, they found VA exposure rapidly and dramatically dropped circulating levels of the ketone beta-hydoxybutyrate (BHB). Newborn mice use ketones as energetic substrates (compared to glucose in weaned animals) so perturbing ketone metabolism could underpin some of the detrimental side-effects of VA. Therefore, the authors sought to determine why VA cause this drop in ketone availability in newborns.

The authors first found that multiple VAs rapidly (half maximal effect occurs in ~10min) and at subanesthetic doses decrease BHB levels from ~2mM to <1mM in newborn but not in older (older than P19) mice. Extended VA exposure (>60min) also caused a decrease in circulating glucose. BHB levels could be rescued by IP injection prior to anesthesia. Why do VAs cause this effect? Ketones are known to be produced by fatty acid oxidation in the liver. The authors therefore indirectly assessed fatty acid oxidation by measuring levels of acylcarnitines (an intermediate metabolite in fatty acid oxidation) in newborn livers after VA treatment and found lower levels of acylcarnitines consistent with lower levels of fatty acid oxidation in the liver upon VA treatment. Pharmacologically inhibiting fatty acid oxidation could also drop BHB levels in newborn plasma as well. Thus, the authors provide compelling evidence that VA exposure blocks fatty acid oxidation and ketogenesis in the liver of newborns and this underlies the drop in BHB in the circulation.

The authors next asked why VAs decreased fatty acid oxidation. VAs are thought to inhibit the electron transport chain (ETC) which would cause redox imbalances (particularly in the NAD/NADH ratio) that could lead to altered TCA cycle metabolic activity that could potentially impact fatty acid oxidation. The authors therefore indirectly tested this hypothesis by measuring TCA cycle intermediates and did by VA exposure altered newborn liver levels of several TCA cycle metabolites including citrate. Citrate is metabolized by the enzyme ACLY to generate cytosolic Ac-CoA which is used by the enzyme ACC to produce malonyl-CoA, an intermediate in lipid synthesis. Malonyl-CoA is also known to inhibit the production of acylcarnitines and fatty acid oxidation. Therefore, the higher levels of citrate in VA exposed livers prompted the authors to determine if VA exposure specifically in neonates increased malonyl-CoA and if this blocked fatty acid oxidation and ketogenesis. The authors measured malonyl-CoA in newborn livers and observed an increased upon VA exposure. ACC inhibitions have been developed and the authors found that ACC inhibition (which presumably would prevent malonyl-CoA formation) could partially rescue the drop in BHB brought on by VA exposure in newborns. Thus, this study delineates how altered fatty acid oxidation and ketogenesis in the liver underlies the drop in BHB elicited upon VA exposure and opens the door to future studies determining if the drop in BHB contributes to newborn sensitivity to VAs and future studies elucidating exactly how VA exposure alters the TCA cycle and citrate metabolism to block fatty acid oxidation.

Reviewer #2 (Public Review):

Here the author reported that Volatile anesthetics VA induce a rapid depletion of circulating ß-HB and the induction of hypoglycemia by VA in neonates, but not in adults. The phenomenon is very interesting and robust, however it has already been described. Whats new here is that through a metabolomics analysis they demonstrate a role of ACC and CPT1 in this phenomenon. Intermediates of the TCA cycle are reduced as would be expected and this is interesting, but chiefly descriptive, and not mechanistic. The key question what causes these derangements in TCA cycle and for sure it's altered enzymatic activity but again what accounts for these and that questions answered would get at the mechanism, but this study here remains descriptive. Is this a cell autonomous effect? For example could you replicate this in a dish with isolated hepatocyte or myotubes from neonates versus adults?

Reviewer #1 (Public Review):

Stokes, et al. describe the effects of isoflurane on metabolism in post-natal day 7 mice, and older mice. They demonstrate that blood levels of glucose and ß-hydroxybutyrate fall quickly in response to isoflurane, and that the magnitude of the decrease increases with the length of the exposure. Mice 30 days post-natal do not exhibit these changes in response to isoflurane. The authors document the much higher circulating levels of ß-hydroxybutyrate in the post-natal day 7 mice, highlighting the importance of this substrate for supporting the energetics of the developing brain. Important control experiments, administering 100% oxygen without anesthetic to post-natal day 7 mice, as well as administering anesthetics to 30 day old mice on a ketogenic diet, did not result in significant decreases in glucose and ß-hydroxybutyrate blood levels. Remarkably, they observed significant decreases in response to very small, subanesthetic doses of isoflurane, halothane and sevoflurane in post-natal day 7 mice. Administration of bolus glucose corrects the glucose level for these mice under anesthesia, but not the level of ß-hydroxybutyrate, while administration of bolus ß-hydroxybutyrate corrects both levels.

The authors then proceed to a series of measurements in an attempt to determine a direct target of volatile anesthetics on metabolism, focusing on hepatic metabolism. This is something of a Procrustean bed, given that the there is ample evidence that volatile anesthetics affect a large number of different membrane bound processes. Nonetheless, these experiments provided valuable data demonstrating anesthetic induced decreases in fatty acid oxidation. This reviewer finds the arguments regarding impairment of the citric acid cycle a bit unconvincing: 7 and 30 day old mice exhibit the same increase in citrate and isocitrate levels, yet only the 7 day old mice show elevated lactate levels. Rather than exhibiting increased metabolic flexibility, as the authors suggest, this finding seems to argue that 7 day old mice have less metabolic flexibility. The authors demonstrate that several perturbations of fatty acid metabolism can result in depression of ß-hydroxybutyrate, leading them to focus on carnitine palmitoyl transferase-1. They demonstrate that inhibition of this enzyme produces a decrease in ß-hydroxybutyrate; however, they also find that mice with a knockout of this enzyme do not have decreased ß-hydroxybutyrate levels.

The authors are circumspect in their conclusions regarding the targets responsible for the metabolic changes observed in neonatal mice in response to anesthetics. They do correctly highlight the potential importance of these metabolic effects. It will be crucial for future research to determine whether these effects can be directly correlated to measures of cerebral function during anesthesia, e.g., EEG or evoked potentials, and to measures of neuropathological change. Of great interest to clinicians will be demonstration of whether co-administration of glucose or ß-hydroxybutyrate together with anesthetics can abrogate such changes.

Reviewer #3 (Public Review):

Zilova et al. investigate cell differentiation in aggregates made from cells of early medaka and zebrafish embryos upon culture in defined media. Using reporter lines and immunostaining, they find evidence for retinal differentiation and morphogenesis in these aggregates, the extent of which depends on the size of the aggregates. This dependence of patterning and morphogenesis on aggregate size indicates that these processes are at least partially controlled by cell-cell interactions in the population. The authors also perform experiments with cells from genetic mutants that indicate similar genetic control of retinal morphogenesis in aggregates and intact fish embryos.

This work is a nice example of morphogenesis of differentiated cell types upon dissociation and re-aggregation of early embryonic cells. The similar behaviour of aggregates from evolutionarily distant species reported in the manuscript underscores the generality of the findings. Organoid formation from teleost cells recapitulates species-specific timescales and is therefore faster than organoid generation from mammalian cells which constitutes a potential technical advantage of this system. The major advance of this work lies in providing a clear example that organoids consisting of early neural and retinal cells can be formed in non-mammalian species. Such an approach can open up new avenues for describing basic principles of cell differentiation and pattern formation during embryogenesis, and can thereby be useful to the community.

While the reported observations are highly interesting, the level of quantitative analysis currently does not fully support all of the author's interpretations and conclusions.

1) The authors variably interpret their observations as the result of self-assembly or self-organization. At the moment, the data does not allow distinguishing whether the observed phenomena result from cells following largely cell-autonomous differentiation paths and come together through cell sorting, or whether dissociation and aggregation generates a condition that leads to (spatially restricted) retinal differentiation in cells that would not normally adopt this fate. I would say that the first scenario is consistent with self-assembly, while the second one is more self-organized in the sense that the new cell-cell interactions resulting from the aggregation result in emergent cellular behaviours. A first step to distinguish between these possibilities would be to quantitatively demonstrate that aggregation biases cell differentiation towards neural and retinal fates at the expense of other cell types, compared to the intact embryo. The examples shown in Figure 2 and 3d seem to indicate an overrepresentation of neural cells, but it would be good to see a quantitative comparison to the embryo.

2) The authors use the term "primary embryonic stem cells" for the early embryonic cells that they aggregate. I find this problematic as some cells in this population may already have lineage bias and not have true multi-lineage potential. I also understand there is a difference between the cells that are used in this study and the teleost embryonic stem cells referenced in lines 49 and 50, in the sense that the latter were established as true self-renewing cell lines. But correct me if I have missed something here.

3) The authors claim that their system is highly reproducible. Unfortunately, they do not give an indication of the success rate of aggregate formation in figure 1. Figure 4 shows the most complex patterns, but I realize that there is quite a bit of variability in between the aggregates - they are just as likely to have one or two Rx2-expressing areas (panel b). I also could not find information how many aggregates show the patterns in panels e and f, and from how many aggregates the data in panels g - i has been collected.

Reviewer #2 (Public Review):

This study shows that dissociated blastula cells from teleost fishes (medaka and zebrafish) reaggregate to form optic vesicle-like organoids if cultured in the presence of extracellular matrix molecules. Notably, cell number is critical for a reaggregation with movements that resemble those observed in vivo. These organoids acquire dorso-ventral polarity and can differentiate into different retinal cell types.

This is well written manuscript describing a technological advance: the generation of an organoid from teleost cells. Some of the images are impressive as since blastula cells seem to reproduce an organized forebrain with bilateral optic vesicles. Still these vesicles are rudimentary when compared with those obtained from mouse or human cells (see work from Eiraku team).

There are no critiques to the work per se, which is technically impeccable, well illustrated and quantified. However, one wonder what happens to the RPE cells in the differentiation process. In Fig 4, the authors show that the optic vesicle organoids are organized as in vivo with cells expressing RPE markers. These cells are no longer present in Fig 5. What happens to them? There is no mention of this problem in the text.

The discussion is generally informative but somehow fails to provide real advantages of using teleost organoids vs the fish per se or vs for example human organoids. Indeed, obtaining a fish organoid is faster that a human one, but more expensive and time consuming than using fish embryos.

Reviewer #1 (Public Review):

In this manuscript, Zilova et al. show that primary embryonic cells derived from blastula-stage Medaka and Zebrafish embryos can self-organize into retinal organoids. When aggregates of 1000-2000 primary embryonic cell are embedded in Matrigel addition, they form a neuroepithelium under the control of Rx3 which develops into a retinal organoid. The process mirrors some aspects of embryo development. Moreover, another interesting finding is that Rx3 expression is initiated in the absence of Matrigel at day 0, which indicates that the retinal fate occurs by default and is not dependent on extracellular matrix components. The authors compare the ability of cells from Mesaka and zebra fish and show that both are competent to form organoids, though each does it with the time scale of the embryo of origin. The authors show that by reducing the number of Medaka cells to aggregate (500-800 cells), Rx2 and Rx3 are expressed only in restricted regions of the small aggregates, presumably where they organize into discrete circular Rx2 and Rx3 positive neuroepithelial units that develop into structure resembling retinal epitjhelia with some diversity of retinal cell types including amacrine, ganglion, photoreceptor, bipolar and horizontal cells.

This is a novel and original piece of work that reveals the capacity of fish primary embryonic pluripotent cells to behave like mammalian embryonic stem cells and organize optic cup organoids.

Reviewer #3 (Public Review):

In the manuscript, "Dynamic persistence of UPEC intracellular bacterial communities in a human bladder-chip model of urinary tract infection" by Sharma, et al., the authors develop a bladder-on-chip model and provide evidence that this is a useful model for mimicking in vivo infections. The focus is on intracellular infection structures created by uropathogenic Escherichia coli (UPEC) seen in experimental mice infections and "real" human infections; such structures have been most extensively characterized in mouse models for obvious reasons. The authors focus on three key aspects: development of a structure known as an intracellular bacterial community (IBC), the neutrophil response to infecting UPEC, and the bacterial response to antibiotic treatment. There is a minor point about the ability to apply mechanical stretch to the model to mimic bladder filling and voiding.

In my assessment, key strengths of this work are:

1) Integration of both epithelial and vascular endothelial cell types, allowing for multiple fluid spaces and studies of neutrophil migration

2) Ability to apply mechanical stretch to the entire system to mimic changes in bladder volume

3) Extensive microscopic characterization of the model (a key feature enabled by this system) including live microscopy, immunostaining, and electron microscopy

I believe there is one key underlying issue with this paper: as a report on the technical development of a new system / device / technique, the authors have what amounts to a very strong hypothesis, namely that their new system is a good model for the in vivo infection. This leads to a general bias in both the presentation and, in my opinion, the interpretation of the data, to make the system sound "as good as possible". Key manifestations of this bias and overinterpretation include:

1) The immediate interpretation of all intracellular structures as IBCs.

2) The immediate interpretation of all data in Figure 2 as neutrophil swarms and NETs.

3) Some odd behaviors in response to ampicillin, which should not penetrate host cells and has been shown using the same cell types to not affect intracellular UPEC.

4) The claim that a 10% linear change in dimension is "physiologically relevant" and "a significant proportion" of that seen in vivo.

To clarify point 1 (which applies as well to point 2), IBC is an abbreviation for "intracellular bacterial community", and these were first described in mice. There has been very sparing molecular characterization of IBCs, which makes a morphological classification very tricky - I believe the field generally thinks that IBCs refer to a specific structure that is formed (at least) in mice and humans in vivo. Somewhat similar structures have been seen previously in vitro but rightfully are more carefully described with different terms or as "structures resembling IBCs". I think similar care needs to be taken with this model as well.

Overall, the authors have done quite a complete job in characterizing their model and have good data to argue for a morphological similarity to key steps that have been previously described to happen in vivo. I believe they get ahead of themselves both in data interpretation and in the writing of the manuscript, which leads to some oddness where it seems the authors begin to talk as if their model has already been validated. This occurs throughout the manuscript in the use of the IBC abbreviation and also largely in the section on neutrophil responses (in particular swarms and NETs). There are occasional sentences where the appropriate care is taken (i.e. that the data is being collected to argue that the structures seen are indeed NETs), but this is interspersed with writing that is assuming the point is already proven (for example, see lines 286 (appropriate) and 287-289 (not); and 471-476 (appropriate), 477-479 (not), 481-483 (appropriate)).

Regarding the ampicillin data, the odd behaviors are:

1) Apparent elimination of intracellular UPEC (particularly for large collections)

2) Apparent indifference for some intracellular UPEC (they continue to grow)

3) Ampicillin is generally thought to not cross host membranes, and in Blango & Mulvey 2010 it does not affect UPEC harbored within 5637 cells. The authors collect #1 and #2 under "dynamic heterogeneity" and then claim in the discussion that they can "realistically model antibiotic treatment regimens". Given these discrepancies listed above, I do not believe they can yet support this claim.

Finally, the ability to apply mechanical stretch is only used in one pilot experiment at the end, producing a suggestive result (that UPEC burden increases when a duty cycle of stretching and relaxing is used). This is a key advantange of their model that gets a proportionately larger share of attention in the introduction and discussion. It also may provide an explanation for the ability of ampicillin to enter the host cells, or to access intracellular bacteria (through vesicular uptake during contraction, as UPEC themselves are thought to do).

Reviewer #2 (Public Review):

Sharma et al established a bladder on a chip model for studies of E. coli infection using a co-culture HTB9 bladder epithelial cells and primary human bladder microvascular endothelial cells in an organ-on-a-chip device. The two cell types expressed cell-specific markers when cultivated on-a-chip. Linear strain was applied to the sides of the device up to 19% to mimic stretching during bladder filling. The bladder chip was perfused with the diluted human urine during the experiments. The authors also observed formation of neutrophil extracellular traps by neutrophils in the infected bladder chip. They also demonstrate that the planktonic bacteria are eliminated upon application of antibiotics on a chip, with intracellular bacteria retaining the ability to grow after a lag period. The strength of the system is its fine imaging capability. It is necessary to consider if another antibiotic would enable clearance of intracellular bacteria.

Reviewer #1 (Public Review):

The complexity of the infection model developed by the authors is to be praised as it allows the dissection of host-pathogen interactions with multiple players coming together, namely human epithelial cells, endothelial cells and neutrophils, UPEC, urine, antibiotics and mechanical forces at play during bladder filling and micturition. This is truly a tour de force and should provide the authors (and other labs potentially able to recapitulate it) with an unprecedented model to study UTIs and their response to antibiotics. Notably, authors have been able to document the formation of NETs in response to UPEC infection in this model. One small caveat was the choice of antibiotics used to treat the infection in their model. Is Ampicillin really a drug of choice, both because of its inability to reach intracellular niches and it not being a drug of choice in the clinic?

Reviewer #3 (Public Review):

By means of in vitro reconstitution, the authors find that the microtubule associated protein Sjögren's Syndrome Nuclear Autoantigen 1 (SSNA1), know to form fibrils binding longitudinally along microtubules, modulates microtubule instability by reducing dynamicity and inducing rescues, prevents catastrophes in absence of free tubulin or in presence of the tubulin-sequestering protein stathmin, inhibits microtubule severing of spastin and detects spastin-induced damage sites. SSNA1, thus, is revealed as a very potent microtubule stabilizing factor.

The reconstitution of microtubule dynamics is sound and well performed, and the parameters of dynamicity are thoroughly analyzed. The observed intensity of SSNA1 fluorescence demonstrates that the proteins do not bind uniformly along microtubules. Consequently, the rates of microtubule dynamics are not affected globally. Instead the observed rates are affected at different times for individual microtubules and, importantly, directly correlate with locally accumulating SSNA1. The authors thus validly conclude that nucleotide state recognition is not the primary mechanism of SSNA1 localization and activity. Clues towards the mechanism of SSNA1 activity are provided by the observation that SSNA1 detects spastin-induced damage sites, indicating that SSNA1 binds to partial, open microtubule structures and then stabilizes them, which is consistent with cryo-electron-tomograms available in the literature. To me it is not clear, however, if SSNA1 localize-to and act-on distinct sites of microtubule damage exclusively, or if these sites rather serve as positions of initiation or nucleation of cooperative SSNA1 binding, which the kymographs and movies seem to suggest.

The presented observations nicely explain how the microtubule severing enzyme spastin, which directly interacts with SSNA1 and thus recruits it to the very sites of immediate damage, promotes regrowth of microtubules and increases their number and mass in vivo. The manuscript would benefit from further investigation-into and quantifications-of the "progressive accumulation" of SSNA1 on the dynamic microtubules, which, thus far are presented only by way of representational example.

Reviewer #2 (Public Review):

The manuscript provides some long awaited follow-up work to a controversial publication implicating SSNA1/NA14 in microtubule branching (Basnet et al. NCB 2018). The authors have strong expertise in in-vitro microtubule dynamic behaviour. While the experiments are technically strong, the authors use unphysiological amounts of the SSNA1, making interpretations about biological function hard.

The authors take a rigorous approach to analyze details of microtubule dynamic behaviour presented in Figure 1. While I recognize the enormous amount of work that went into Figure 1, in my opinion these experiments shows that SSNA1 has no effect on microtubule dynamics at physiological concentration (sub 100 nM). That finding is i) very publishable and ii) should not take away from SSNA1 as an important molecule, but rather open up alternative ways of thinking about the protein.

I believe similar conclusions should be applied to microtubule slow-down in Figure 2 and the stabilization against tubulin loss by dilution/sequestration in Figure 3. If 5 uM (the only concentration shown) are required to achieve above effects, these observations are likely not relevant to SSNA1's biological function.

Taking into account SSNA1's cellular localization at centrosomes, midbodies, and branch points etc., I am not sure a major effect on microtubule dynamics other than nucleation should be expected.

The authors pursue an alternative and very interesting avenue in Figure 4, by examining the interplay between spastin and SSNA1 with regards to microtubules. Here, (1 uM) SSNA1 has protective effects against severing by spastin.

The discussion could use a direct contrast to differences in findings between the current work and the branched nucleation. It is not stated in the manuscript, though presumably no branching has been observed in several thousands of microtubule growth events? I would find a lot of value in such a potential statement.

Reviewer #1 (Public Review):

In their manuscript, Lawrence et al. investigate the direct effects of the microtubule-associated protein, SSNA1, on microtubule (MT) dynamics and damage using purified proteins and TIRF microscopy. Prior work on this protein showed that SSNA1 self-assembles into higher-order filaments and binds longitudinally along stabilised MTs, inducing MT branching and nucleation. In this study, they find that SSNA1 promotes templated MT nucleation, consistent with prior results, but further define the effect of SSNA1 on MT dynamics. SSNA1 overall dampens MT dynamics by reducing both growth and shrinkage rates, suppressing catastrophe frequency, and increasing rescues. The authors also quantify SSNA1 on GMPCPP over a timecourse both at single-molecule and multi-molecule concentrations. On dynamic MTs, SSNA1 recognizes the growing end and promotes end curvature, but it did not recognize the curves of taxol-stabilised MTs, leading the authors to conclude that it likely induces curvature, rather than recognizes it. Perhaps this is the mechanism by which SSNA1 prevents catastrophe, a role which the authors demonstrate for SSNA1 after both tubulin dilution or stathmin sequestration of tubulin. The most interesting part of this study is found in Figure 4, where the authors show that SSNA1 prevents MT severing by spastin and also localizes to sites of lattice damage. The authors conclude that SSNA1 is a MT stabilizing protein and a sensor of MT damage. The results on MT dynamics do not provide much insight into the mechanism of this protein, which isn't even found to colocalize with MTs in vivo (SSNA1 instead accumulates at branchpoints in neurons). The role of SSNA1 in lattice damage recognition is the highlight of this paper, and also correlates well with its in vivo localization pattern, indicating this could be a true function of this protein. This damage recognition ability could potentially be the first step that leads to SSNA1-induced MT nucleation and branching from an existing MT.

Reviewer #3 (Public Review):

Magnusson et al., do an excellent job of defining how the repeated separator sequence of Wild Type Cas12a CRISPR arrays impacts the relative efficacy of downstream crRNAs in engineered delivery systems. High-GC content, particularly near the 3' end of the separator sequence appears to be critically important for the processing of a downstream crRNA. The authors demonstrated naturally occurring separators from 3 Cas12a species also display reduced GC content. The authors use this important new information to construct a synthetic small separator DNA sequence which can enhance CRISPR/Cas12a-based gene regulation in human cells. The manuscript will be a great resource for the synthetic biology field as it shows an optimization to a tool that will enable improved multi-gene transcriptional regulation.

Strengths:

• The authors do an excellent job in citing appropriate references to support the rationale behind their hypotheses.
• The experiments and results support the authors' conclusions (e.g., showing the relationship between secondary structure and GC content in the spacers).
• The controls used for the experiments were appropriate (e.g., using full-length natural separator vs single G or 1 to 4 A/T nucleotides as synthetic separators).
• The manuscript does a great job assessing several reasons why the synthetic separator might work in the discussion section, cites the relevant literature on what has been done and restates their results to argument in favor or against these reasons.
• This paper will be very useful for research groups in the genome editing and synthetic biology fields. The data presented (specially the data concerning the activation of several genes) can be used as a comparison point for other labs comparing different CRISPR-based transcriptional regulators and the spacers used for targeting.
• This paper also provides optimization to a tool that will be useful for regulating several endogenous genes at once in human cells thus helping researchers studying pathways or other functional relationships between several genes.

Opportunities for Improvement:

• The authors have performed all the experiments using LbCas12a as a model and have conclusively proven that the synSeparator enhances the performance of Cas12a based gene activation. Is this phenomenon will be same for other Cas12a proteins (such as AsCas12a)? The authors should perform some experiments to test the universality of the concept. Ideally, this would be done in HEK293T cells and one other human cell type.

Reviewer #2 (Public Review):

Type V CRISPR-Cas systems are used in a variety of biotechnology applications, which rely on the association of a Cas12a-CRISPR RNA complex association with a complementary target DNA sequence. One advantage of the Cas12a system over other CRISPR-Cas systems is the ability to multiplex by expressing multiple CRISPR RNAs in an array, with the individual RNAs processed from a longer transcript by Cas12a. Magnusson et al. show that the activity of CRISPR RNAs in this system is enhanced by including a short, A/T-rich sequence between each encoded CRISPR RNA. The authors propose that these separator sequences reduce the potential for secondary structure, thereby promoting RNA processing. This is an exciting idea, with obvious applications wherever Cas12a is used. However, while the presented data are consistent with the model, I think the conclusions are too preliminary, and require (i) a more targeted assessment of the importance of RNA secondary structure for RNA processing, (ii) direct measurement of RNA processing, and (iii) a more extensive assessment of the effect of adding spacer sequences to CRISPR arrays in a functional assay.

Reviewer #1 (Public Review):

The authors interrogated an underexplored feature of CRISPR arrays to enhance multiplexed genome engineering with the CRISPR nuclease Cas12a. Multiplexing represents one of the many desirable features of CRISPR technologies, and use of highly compact CRISPR arrays from CRISPR-Cas systems allows targeting of many sites at one time. Recent work has shown though that the composition of the array can have a major impact on the performance of individual guide RNAs encoded within the array, providing ample opportunities for further improvements. In this manuscript, the authors found that the region within the repeat lost through processing, what they term the separator, can have a major impact on targeting performance. The effect was specifically tied to upstream guide sequences with high GC content. Introducing synthetic separator sequences shorter than their natural counterparts but exhibiting similarly low GC content boosted targeted activation of a reporter in human cells. Applying one synthetic separator to a seven-guide array targeting chromosomal genes led to consistent though more modest targeted activation. These findings introduce a distinct design consideration for CRISPR arrays that can further enhance the efficacy of multiplexed applications. The findings also suggest a selective pressure potentially influencing the repeat sequence in natural CRISPR arrays.

Strengths:

The portion of the repeat discarded through processing normally has been included or discarded when generating a CRISPR-Cas12a array. The authors clearly show that something in between-namely using a short version with a similarly low GC content-can enhance targeting over the truncated version. A coinciding surprising result was that the natural separator completely eliminated any measurable activation, necessitating the synthetic separator.

The manuscript provides a clear progression from identifying a feature of the upstream sequences impacting targeting to gaining insights from natural CRISPR-Cas12a systems to applying the insights to enhance array performance.

With further support, the use of synthetic separators could be widely adopted across the many applications of CRISPR-Cas12a arrays.

Weaknesses:

The terminology used to describe the different parts of the CRISPR array could better align with those in the CRISPR biology field. For one, crRNAs (abbreviated from CRISPR RNAs) should reflect the final processed form of the guide RNA, whereas guide RNAs (gRNAs) captures both pre-processed and post-processed forms. Also, "spacers" should reflect the natural spacers acquired by the CRISPR-Cas system, whereas "guides" better capture the final sequence in the gRNA used for DNA target recognition.

A running argument of the work is that the separator specifically evolved to buffer adjacent crRNAs. However, this argument overlooks two key aspects of natural CRISPR arrays. First, the spacer (~30 nts) is normally much longer than the guide used in this work (20 nts), already providing the buffer described by the authors. This spacer also undergoes trimming to form the mature crRNA. Second, the repeat length is normally fixed as a consequence of the mechanisms of spacer acquisition. At most, the beginning of each repeat sequence may have evolved to reduce folding interactions without changing the repeat length, although some of these repeats are predicted to fold into small hairpins.

Prior literature has highlighted the importance of a folded hairpin with an upstream pseudoknot within the repeat (Yamano Cell 2016), where disrupting this structure compromises DNA targeting by Cas12a (Liao Nat Commun 2019, Creutzburg NAR 2020). This structure is likely central to the authors' findings and needs to be incorporated into the analyses.

Many claims could better reflect the cited literature. For instance, Creutzburg et al. showed that adding secondary structures to the guide to promote folding of the repeat hairpin enhanced rather than interfered with targeting. Liu et al. NAR 2019 further showed that the pre-processed repeat actually enhanced rather than reduced performance compared to the processed repeat. Finally, the complete loss of targeting with the unprocessed repeat appears represent an extreme example given multiple studies that showed effective targeting with this repeat (e.g. Liu NAR 2019, Zetsche Nat Biotechnol 2016).

Relating to the above point, the vast majority of the results relied on a single guide sequence targeting GFP. While the seven-guide CRISPR array did involve other sequences, only the same GFP targeting guide yielded strong gene activation. Therefore, the generalizability of the conclusions remains unclear.

Reviewer #4 (Public Review):

This article describes the results of an impressive meta-analysis based on a high number of published effects investigating the relationship between sexual dimorphism in men and their mating and reproductive success.

The article is very well written and covers a vast amount of literature.

Most of my comments are not corrections, but rather subjective ideas on how the text could be restructured. In my opinion, the article is clearly written and the rationale behind research questions and methodology is well explained. I appreciate how the authors present the entire analysis, adding multiple robustness tests and presenting their results in an easy to follow manner (which was not easy, due to the complexity of the methodology implemented).

I cannot criticise any major issues in this manuscript.

The main outcomes of the article not only present a robust test of previously mixed results, but also provide a strong recommendation of how future studies should be conducted (i.e. how to use mating success proxies, and what samples to include).

Reviewer #3 (Public Review):

As the authors lay out, there are a number of theoretical perspectives that expect that male features that are sexually dimorphic and, hence, vary in their levels of "masculinity" (or perhaps less sex-anchored, vary along a male-female dimension) within human males, to have been under sexual selection historically (if not now), which may in part explain their sexual dimorphism. The target article examines associations between a number of such traits that have been examined-bodily strength and muscularity, facial masculinity, vocal pitch, 2nd to 4th digit ratio (2D:4D), height, and testosterone levels-with measures of mating "success" (e.g., sexual partner number) and reproductive outcomes (e.g., reproductive success). With traits keyed such that more positive values reflect greater "maleness," virtually all associations with putative fitness components were found to be positive, though not all associations had confidence intervals that do not cross the zero-point (i.e., not all are "significant").

The strongest associations were with body masculinity. Specific measures included strength, body shape, and muscle or non-fat body mass (though the associations are not broken down by indicator type). In the mating domain, the overall correlation was .13 (.14 in the behavior domain, perhaps most related to mating "success"). In the reproductive domain, the mean correlation was .14, and .16 in high fertility samples (a subset of which may represent natural fertility populations). Especially when strength (e.g., grip strength) was used as the measure of body masculinity, these associations are likely underestimated, due to imperfect validity of the masculinity/muscularity indicator.

Associations with voice pitch were, on average, nearly identical to those involving body masculinity: .13 overall in the mating domain and .14 overall in the reproductive domain. But due to smaller sample size, the confidence interval around the correlation in the reproductive domain included zero.

The next grouping of traits, in terms of strength of association, contains facial masculinity and testosterone levels. There, associations were .09 and .08 in the mating domain and .09 and .04 in the reproductive domain, respectively. Once again, not all confidence intervals were exclusively above zero.

Associations with both 2D:4D and height were weaker: .03 and .06 in the mating domain and .07 and .01 in the reproductive domain, respectively.

I offer a few observations.

First, the meta-analysis, to my mind, offers some interesting data. We need to be aware of its limitations. Many samples are drawn from WEIRD populations (Henrich et al., 2010). It remains unclear to what extent fertility and reproductive success in these samples, even when drawn from high fertility populations, reflect processes that would have operated in ancestral human groups. It makes sense that some of these features may well have been variably associated with fitness components in ancestral populations, but potential key moderator variables (e.g., pathogen prevalence, level of paternal provisioning, level of intergroup violence, degree of female choice [vs. arranged marriages]) may not be available to examination here. To the extent moderation exists, mean levels in this meta-analysis are less meaningful (though not meaningless), as we do not know whether the distribution of moderators in this sample of samples is representative of populations of interest. (E.g., due to advances in modern medicine, these samples may be much healthier than ancestral populations in which these features were subject to selection.) And that is just a partial list of caveats we need to keep in mind. Nonetheless, with those limitations kept in mind, these findings are interesting to reflect upon.

Second, the associations of course do not tell us what processes drive them. They are correlations. Indeed, we do not know whether the traits themselves were directly implicated in the processes leading to their associations with fitness outcomes. (2D:4D surely wasn't-it's a marker of other causal variables-but its associations are among the weakest seen here.) It makes some sense that the stronger the associations, the more likely the trait in question was directly causally implicated in these processes. And again, that may be particularly true of body masculinity, as associations with it may be underestimated due to fallible indicator validity. But even then, we cannot rule out other mediating traits. Perhaps more muscular men exhibit greater confidence and gain leadership roles more readily than less muscular men, giving them an edge in intrasexual competition or intersexual choice due to associated behavior or status. Or maybe they ultimately gain greater control of resources, giving them advantages in competition for mates or provisioning of offspring. This is not to deny that muscularity may well have been (and be) under sexual selection; but it may have been selected along with other traits rather than the direct target of selection itself.

Third, then, we do not know what intrasexual or intersexual selection processes may have been involved historically, even if these traits have directly been under sexual selection. To what extent are these associations due to advantages in intrasexual competition? To what extent might they be due to female preferences and choice? Naturally, as the authors note, these processes are not mutually exclusive. After all, in lekking species, males compete with one another for a symbolic spatial position, which, because it represents the outcome of the competition, leads to mating success via female choice. Still, we might be interested in knowing what processes led to the associations found, and how they speak to sexual selection and mating processes in humans.

Once again, however, the associations reported are interesting to reflect upon. And they could, either directly or indirectly (by stimulating additional research), lead to better answers to issues raised above. One key outcome that relatively little data currently speak to, for instance, is mortality rate of offspring. As the authors note, men who are more successful with respect to mating effort may invest lower amounts of parental investment in offspring. In theory, then, their greater offspring number could be offset to an extent by lower survival rates. In the relatively few data the authors aggregated from the literature, that was not clearly the case. But more data may be needed, especially with respect to the strongest predictors of mating success, and especially in more traditional societies.

Paternal investment in offspring, however, need not pay off just in terms of offspring survival rates; paternal provisioning may permit greater rates of reproduction via shortening of interbirth intervals in traditional societies. The data here show that, at least with respect to body masculinity, more masculine men have greater mating success and greater reproductive success. Yet the data do not necessarily tell us that the female partners of these men have greater reproductive success. More masculine men's rates of offspring production could be spread over more female mates than that of less masculine men. Knowing whether female partners of more masculine men benefit reproductively by mating with masculine men is pertinent to addressing whether the reproductive success of masculine men has been mediated, in part, by female mate choice.

Reviewer #2 (Public Review):

In this manuscript, the authors set out to provide a comprehensive meta-analysis of associations between masculinized phenotypes and fitness-relevant outcomes (mating, reproduction, and offspring viability), so as to assess the current state of evidence for hypotheses of sexual selection on human males across high- and low-fertility populations. I enjoyed reading this manuscript, which is well organized and very clearly written. I also appreciated the depth of the analyses reported by the authors. Overall, I am pleased with this research and think it will make a valuable contribution to the literature on human sexual selection and masculinity more generally.

I do not have any major concerns regarding the methods and results. However, I think the paper would greatly benefit from introducing greater nuance into the theoretical framework and conclusions, which I believe will meaningfully change some of the takeaways presented in the discussion. I have provided references throughout to aid the authors in this effort during revision, though they should certainly not feel compelled to cite each reference provided. I would also appreciate that the authors provide some estimates of (a priori) statistical power when they make claims regarding statistical power in the interpretation of results.

Major comments:

The authors have done a very nice job of efficiently introducing the reader to mainstream hypotheses regarding sexual selection on human male phenotypes, particularly those emphasized within evolutionary psychology. I recognize that the authors' primary contribution is empirical and that they have in large part followed the typical presentation of these hypotheses in previous literature. However, given that this paper may be an important point of reference for future research in this area, I would like to encourage the authors to address some important nuances in greater detail that are frequently overlooked.

(i) The authors argue that "Sexual selection is commonly argued to have acted more strongly on male traits as a consequence of greater variance in males' reproductive output (3) and male-biased operational sex ratio, i.e. a surplus of reproductively available males relative to fertile females (e.g. 4)". This argument then leads to a discussion of why formidability as indexed by strength and other potential indicators of physical dominance are expected to be under selection in males. However, recent work in sexual selection theory has begun to emphasize the importance of the co-evolution of male offspring care and reproductive competition, leading in many cases to opposite predictions compared to classical models of OSR. In particular, more recent models predict that males should often increase rather than decrease offspring care relative to mating effort when men are in relative abundance. These predictions have received support in recent empirical studies in human populations, and help to explain otherwise puzzling patterns such as e.g. the association between male-biased sex ratios and monogamy + low reproductive skew across many taxa. Please see

Kokko, H., & Jennions, M. D. (2008). Parental investment, sexual selection and sex ratios. Journal of evolutionary biology, 21(4), 919-948. Schacht, R., Rauch, K. L., & Mulder, M. B. (2014). Too many men: the violence problem?. Trends in Ecology & Evolution, 29(4), 214-222. Schacht, R., & Borgerhoff Mulder, M. (2015). Sex ratio effects on reproductive strategies in humans. Royal Society open science, 2(1), 140402.

Considering these models, one might expect that a variety of behavioral and psychological phenotypes would be under male-specific sexual selection that are simply not considered in the present study. One might also expect that appropriate proxies of male fitness will also vary across populations, independently of the presence/absence of contraception. The authors argue that they selected mating-based proxies of reproductive behaviors and attitudes under the assumption that "preferences for casual sex, number of sexual partners, and age at first sexual intercourse (earlier sexual activity allows for a greater lifetime number of sexual partners)... correlated with reproductive success in men under ancestral conditions". Yet, in large-scale industrialized societies that have undergone a demographic transition, high status males are often observed to invest more in offspring care and the production of intergenerationally transferable wealth at the expense of greater fertility, which may be an adaptive response to shifting demands in relation to competition for status.

Shenk, M. K., Kaplan, H. S., & Hooper, P. L. (2016). Status competition, inequality, and fertility: implications for the demographic transition. Philosophical Transactions of the Royal Society B: Biological Sciences, 371(1692), 20150150.

In general, long-run fitness may often not map so simply onto promiscuous sexual behavior in such a straightforward way. Measures such as age at first intercourse may also be confounded with environmental heterogeneity among participants, which could instead indicate environmentally induced plasticity within individuals' lifetimes toward a faster pace of life.

(ii) Related to this point, the authors discussion of the relationship between testosterone and male phenotypes is somewhat over-simplified, although again in keeping with much of the previous literature in evolutionary psychology. While it was long emphasized that testosterone is a mechanism of aggression per se, recent work has shown that testosterone is better understood as a mechanism for increasing status-seeking, competitive behavior, which can greatly vary in form across socioecological contexts.

Eisenegger, C., Haushofer, J., & Fehr, E. (2011). The role of testosterone in social interaction. Trends in cognitive sciences, 15(6), 263-271.

Unfortunately, most of the fWHR and 2D:4D literature has ignored these findings and continues to focus solely on aggression even in WEIRD student samples, where we can be certain that aggression is generally not a viable strategy for attaining and maintaining social status. To my knowledge, only a few studies have explicitly tested this more nuanced hypothesis regarding associations between masculinized phenotypes and differing forms of status-seeking behavior, both of which have found support for ecologically contingent effects in regards to fWHR. Martin et al. (2019) predicted and found support in bonobos for higher fWHR predicting higher scores on an affiliative measure of social rank among both males and females, consistent with the importance of relationship strength and social network centrality for competitive advantage among bonobos. Similarly, Hahn et al. (2017) found that fWHR in human males consistently predicts prosocial behavior and leadership in large-scale institutions. This is consistent with the fact that leadership traits, rather than aggression and formidability per se, are often important predictors of status in human societies (and in contexts of relatively higher SES within those societies).

Hahn, T., Winter, N. R., Anderl, C., Notebaert, K., Wuttke, A. M., Clément, C. C., & Windmann, S. (2017). Facial width-to-height ratio differs by social rank across organizations, countries, and value systems. PLoS One, 12(11), e0187957. Martin, J. S., Staes, N., Weiss, A., Stevens, J. M. G., & Jaeggi, A. V. (2019). Facial width-to-height ratio is associated with agonistic and affiliative dominance in bonobos (Pan paniscus). Biology Letters, 15(8), 20190232.

In regard to the male-male competition hypothesis, as noted in the previous comment, we might therefore expect sexual selection to occur on a variety of male traits other than formidability related measures, as well as to be highly population-specific-rather than there being some universal optimum for "masculine" traits-given that what constitutes an adaptive male phenotype likely varies across populations in regard to both male-male competition and female choice. Finally, it should be noted that testosterone is by no means the only sex hormone relevant to considering patterns of human sexual dimorphism. Please see Dunsworth (2020) for a discussion of the centrality of estrogen in proximally explaining sexual dimorphism in body size

Dunsworth, H. M. (2020). Expanding the evolutionary explanations for sex differences in the human skeleton. Evolutionary Anthropology, 29, 108-116.

(iii) The authors should provide more references to (and brief discussion of) mixed results regarding the degree of sexual dimorphism in facial and digit ratio metrics. While they cite a few studies in the introduction, one might leave the text with the impression that there is clear enough evidence for 2D:4D being influenced by (pre-natal) sex hormones and being a sexually dimorphic phenotype. However, these results have been strongly challenged, not only be ref 14 and 20 in the main text, but also various other studies e.g.

Barrett, E., Thurston, S. W., Harrington, D., Bush, N. R., Sathyanarayana, S., Nguyen, R., ... & Swan, S. (2020). Digit ratio, a proposed marker of the prenatal hormone environment, is not associated with prenatal sex steroids, anogenital distance, or gender-typed play behavior in preschool age children. Journal of Developmental Origins of Health and Disease, 1-10. Richards, G. (2017). What is the evidence for a link between digit ratio (2D: 4D) and direct measures of prenatal sex hormones?. Early Human Development. Richards, G., Browne, W. V., Aydin, E., Constantinescu, M., Nave, G., Kim, M. S., & Watson, S. J. (2020). Digit ratio (2D: 4D) and congenital adrenal hyperplasia (CAH): Systematic literature review and meta-analysis. Hormones and Behavior, 126, 104867. Richards, G., Browne, W. V., & Constantinescu, M. (2021). Digit ratio (2D: 4D) and amniotic testosterone and estradiol: An attempted replication of Lutchmaya et al.(2004). Journal of Developmental Origins of Health and Disease.

Similarly, not all metrics of facial masculinity are equally valid given current empirical evidence. In a recent longitudinal study, only cheekbone prominence was found to show consistent evidence of sexual dimorphism across age groups.

Robertson, J. M., Kingsley, B. E., & Ford, G. C. (2017). Sexually dimorphic faciometrics in humans from early adulthood to late middle age: Dynamic, declining, and differentiated. Evolutionary Psychology, 15(3), 1474704917730640.

Overall, I found the authors' discussion of how they selected the specific facial metrics lumped together in their analyses to be underspecified. Please note in the discussion as well that BMI is a well-known confound in studies of facial masculinity and may be a cause of null results in the present study (unless I happened to miss this in the regard to the moderation results - if so, my apologies!).

Geniole, S. N., Denson, T. F., Dixson, B. J., Carré, J. M., & McCormick, C. M. (2015). Evidence from meta-analyses of the facial width-to-height ratio as an evolved cue of threat. PloS one, 10(7), e0132726.

(iv) Finally, please provide reference to and potentially brief discussion of the current state of the literature as regards "good genes" hypotheses of female choice, which is relevant for determining how useful previous studies are for directly addressing this hypothesis. Please see:

Achorn, A. M., & Rosenthal, G. G. (2020). It's not about him: Mismeasuring 'good genes' in sexual selection. Trends in Ecology & Evolution, 35, 206-219.

Reviewer #1 (Public Review):

This manuscript is a meta-analysis of literature, predominantly that from evolutionary psychology. The background seems well-explained, and the discussion and literature review well-written. The authors have done an impressive job of collating and synthesising a truly vast amount of literature that (as they demonstrate) is often pretty ambiguous in its results. The results are well-presented and well-reasoned, without overstating the evidence. The entire manuscript is clear and easy to read and follow. Table 1 makes it particularly easy to follow. I appreciate their emphasis that the various hypotheses about sexual dimorphism are not mutually exclusive, and that this study does not seek to explicitly test either one of them.

There is enough evolutionary anthropology inserted here to see that the authors have a passing familiarity with it, although I would encourage them to dig much more deeply into this literature in framing their work. In short, there is a tension between evolutionary psychology and evolutionary anthropology that can be very fruitfully explored with the results of this analysis, and the authors only scratch the surface of this at the end of the manuscript.

Something that seems crucial here, and in this literature more generally, is the likelihood that men have a number of different effective strategies. The background and discussion do a good job of discussing the various possibilities, and how combinations of possibilities that include both female choice or male-male competition could explain human mating behavior. However, it does not really dig into what the implications might be for how multiple, distinct strategies could impact different aspects of the data. What comes to mind is orangutans, in which the large, masculine males appear to obtain mating opportunities primarily through female choice, while the smaller males that have not developed the large body sizes and facial flanges may obtain additional mating opportunities through sexual coercion. In a large sample or meta-analysis like this, a combination of strategies in human males that are at odds with one another, yet both highly effective, may have results that tend to cancel one another out - is there any evidence of this? Getting more into the primate literature here could be useful.

The authors point out that there could be a strong confounding effect with the way testosterone operates developmentally. Testosterone during adolescence translates well to the development of masculine characteristics, but does not necessarily predict testosterone later in life (hence, the expression of masculine features may not actually relate well to circulating testosterone that could be at least partially drive male-male competition). The authors did an excellent job of discussing these potential confounding effects, but I would have found potential issues like this (and like the one above) to be presented usefully in a table that lays out the different potential confounding issues, and then discusses what the predictions should be in the meta-analysis results for each one.

The meta-analysis seems well-designed, and the methods appropriate. However, it did feel a bit like data mining with so many different variables run against one another. I do not think this is actually the case, and the authors do justify each of their decisions. In fact, one of the main outcomes of this work is that they show how few of these parameters actually relate strongly to one another. However, the authors might want to be aware that this study could be read as data-mining because of the search for significance amongst so many different variables, and offset this with explicit discussion and framing up front that they intend to examine how effective the various study parameters actually are at uncovering the relationships they seek to uncover. This is something the authors discuss very articulately at the end, but I would appreciate seeing this up front as one of the goals of the paper.

Reviewer #3 (Public Review):

This manuscript sheds new light on the regulation and function of a signaling network comprised of the adaptor molecules Cas and BCAR3. The data presented in the manuscript are generated through rigorous experimentation, frequently with the use of multiple approaches to arrive at the stated conclusions.

Minor concerns:

1) Figure 3e. The authors state that "SOCS6 binds BCAR3 and Cas independently" (bottom of page 7). However, while they show that the EE BCAR3 mutant binds to SOCS6 under conditions when it does not bind to Cas, they do not show the reciprocal interaction in this paper. Their previous paper (J Cell Sci 2014) suggests that SOCS6 binding to Cas may be independent of BCAR3 but neither that paper nor the current manuscript explicitly examine that. Unless there is direct evidence that SOCS6 can bind to Cas in the absence of BCAR3, perhaps it would be more accurate for the authors to limit their conclusion by saying that "SOCS6 binds to BCAR3 independently of Cas."

2) Figure 8a and c. Without showing a Western blot to address total pools of phosphorylated Cas, it is not clear whether the depletion in pY165 is targeted to the pool of Cas present in adhesions or to a diminution in phosphorylation of the total pool of Cas in the cell. At a minimum, the authors would need to clarify that phosphorylation at Y165 of Cas in the pool of Cas that is localized to adhesions is reduced in the presence of Y117F, R177K, or the EE mutant of BCAR3.

Reviewer #2 (Public Review):

This is a well-written paper describing the co-recruitment of p117-BCAR3 and Cas to adhesion sites for activation of lamellipodial ruffling and the subsequent ubiquitin-dependent degradation. The completeness of the description of the cycle is a major success of this article and warrants publication. I didn't find major holes in their arguments and they did document that this pathway was not universal but there were possibly analogous signaling processes with other players.

Reviewer #1 (Public Review):