Reviewer #3 (Public Review):

In this manuscript, Yang et al. showed that two nuclear receptor genes, COUP-TFI and -TFII, displayed distinct expression patterns and functions during the development of the dorsal and ventral hippocampus. The phenotypes in the presented single and double conditional knockout mice are striking and intriguing, which expands our knowledge of hippocampus development, especially the ventral part. Nevertheless, the manuscript is a bit descriptive without in-depth molecular mechanisms.

My major concerns as follows:

1. Quantification and statistical analysis to support their conclusions are almost absent throughout the whole manuscript, especially in relation to the numbers of DG, CA1, and CA3 neurons.<br /> 2. Only TFI conditional knockout mice, not TFII knockout mice, were used to test for behavioral abnormalities. It is important to determine whether the abnormal ventral hippocampus in TFII loss leads to any psychiatric illness.<br /> 3. Behavior defects were only tested on TFI conditional knockout mice but not on TFII knockout mice. TFII loss predominantly affects the ventral hippocampus which is involved in psychiatric disorders, and this should be tested.

Reviewer #1 (Public Review):

In this paper, the interocular/binocular combination of temporal luminance modulations is studied. Binocular combination is of broad interest because it provides a remarkable case study of how the brain combines information from different sources. In addition, the mechanisms of binocular combination are of interest to vision scientists because they provide insight into when/where/how information from two eyes is combined.

This study focuses on how luminance flicker is combined across two eyes, extending previous work that focused mainly on spatial modulations. The results appear to show that temporal modulations are combined in different ways, with additional differences between subcortical and cortical pathways.

1. Main concern: subcortical and cortical pathways are assessed in quite different ways. On the one hand, this is a strength of the study (as it relies on unique ways of interrogating each pathway). However, this is also a problem when the results from two approaches are combined - leading to a sort of attribution problem: Are the differences due to actual differences between the cortical and subcortical binocular combinations, or are they perhaps differences due to different methods. For example, the results suggest that the subcortical binocular combination is nonlinear, but it is not clear where this nonlinearity occurs. If this occurs in the final phase that controls pupillary responses, it has quite different implications.

At the very least, this work should clearly discuss the limitations of using different methods to assess subcortical and cortical pathways.

2. Adding to the previous point, the paper needs to be a better job of justifying not only the specific methods but also other details of the study (e.g., why certain parameters were chosen). To illustrate, a semi-positive example: Only page 7 explains why 2Hz modulation was used, while the methods for 2Hz modulation are described in detail on page 3. No justifications are provided for most of the other experimental choices. The paper should be expanded to better explain this area of research to non-experts. A notable strength of this paper is that it should be of interest to those not working in this particular field, but this goal is not achieved if the paper is written for a specialist audience. In particular, the introduction should be expanded to better explain this area of research, the methods should include justifications for important empirical decisions, and the discussion should make the work more accessible again (in addition to addressing the issues raised in point 1 above). The results also need more context. For example, why EEG data have overtones but pupillometry does not?

Reviewer #2 (Public Review):

Previous studies have extensively explored the rules by which patterned inputs from the two eyes are combined in the visual cortex. Here the authors explore these rules for un-patterned inputs (luminance flicker) at both the level of the cortex, using Steady-State Visual Evoked Potentials (SSVEPs) and at the sub-cortical level using pupillary responses. They find that the pattern of binocular combination differs between cortical and sub-cortical levels with the cortex showing less dichoptic masking and somewhat more binocular facilitation.

Importantly, the present results with flicker differ markedly from those with gratings (Hou et al., 2020, J Neurosci, Baker and Wade 2017 cerebral cortex, Norcia et al, 2000 Nuroreport, Brown et al., 1999, IOVS). When SSVEP responses are measured under dichoptic conditions where each eye is driven with a unique temporal frequency, in the case of grating stimuli, the magnitude of the response in the fixed contrast eye decreases as a function of contrast in the variable contrast eye. Here the response increases by varying (small) magnitudes. The authors favor a view that cortex and perception pool binocular flicker inputs approximately linearly using cells that are largely monocular. The lack of a decrease below the monocular level when modulation strength increase is taken to indicate that previously observed normalization mechanism in pattern vision does not play a substantial role in the processing of flicker. The authors present a computational model of binocular combination that captures features of the data when fit separately to each data set. Because the model has no frequency dependence and is based on scalar quantities, it cannot make joint predictions for the multiple experimental conditions which is one of its limitations.

A strength of the current work is the use of frequency-tagging of both pupil and EEG responses to measure responses for flicker stimuli at two anatomical levels of processing. Flicker responses are interesting but have been relatively neglected. The tagging approach allows one to access responses driven by each eye, even when the other eye is stimulated which is a great strength. The tagging approach can be applied at both levels of processing at the same time when stimulus frequencies are low, which is an advantage as they can be directly compared. The authors demonstrate the versatility of frequency tagging in a novel experimental design which may inspire other uses, both within the present context and others. A disadvantage of the tagging approach for studying sub-cortical dynamics via pupil responses is that it is restricted to low temporal frequencies given the temporal bandwidth of the pupil. The inclusion of a behavioral measure and a model is also a strength, but there are some limitations in the modeling (see below).

The authors suggest in the discussion that luminance flicker may preferentially drive cortical mechanisms that are largely monocular and in the results that they are approximately linear in the dichoptic cross condition (no effect of the fixed contrast stimulus in the other eye). By contrast, prior research using dichoptic dual frequency flickering stimuli has found robust intermodulation (IM) components in the VEP response spectrum (Baitch and Levi, 1988, Vision Res; Stevens et al., 1994 J Ped Ophthal Strab; France and Ver Hoeve, 1994, J Ped Ophthal Strab; Suter et al., 1996 Vis Neurosci). The presence of IM is a direct signature of binocular interaction and suggests that at least under some measurement conditions, binocular luminance combination is "essentially" non-linear, where essential implies a point-like non-linearity such as squaring of excitatory inputs. The two views are in striking contrast. It would thus be useful for the authors could show spectra for the dichoptic, two-frequency conditions to see if non-linear binocular IM components are present.

If the IM components are indeed absent, then there is a question of the generality of the conclusions, given that several previous studies have found them with dichoptic flicker. The previous studies differ from the authors' in terms of larger stimuli and in their use of higher temporal frequencies (e.g. 18/20 Hz, 17/21 Hz, 6/8 Hz). Either retinal area stimulated (periphery vs central field) or stimulus frequency (high vs low) could affect the results and thus the conclusions about the nature of dichoptic flicker processing in cortex. It would be interesting to sort this out as it may point the research in new directions.

Whether these components are present or absent is of interest in terms of the authors' computational model of binocular combination. It appears that the present model is based on scalar magnitudes, rather than vectors as in Baker and Wade (2017), so it would be silent on this point. The final summation of the separate eye inputs is linear in the model. In the first stage of the model, each eye's input is divided by a weighted input from the other eye. If we take this input as inhibitory, then IM would not emerge from this stage either.

Related to the model: One of the more striking results is the substantial difference between the dichoptic and dichoptic-cross conditions. They differ in that the latter has two different frequencies in the two eyes while the former has the same frequency in each eye. As it stands, if fit jointly on the two conditions, the model would make the same prediction for the dichoptic and dichoptic-cross conditions. It would also make the same prediction whether the two eyes were in-phase temporally or in anti-phase temporally. There is no frequency/phase-dependence in the model to explain differences in these cases or to potentially explain different patterns at the different VEP response harmonics. The model also fits independently to each data set which weakens its generality. An interpretation outside of the model framework would thus be helpful for the specific case of differences between the dichoptic and dichoptic-cross conditions.

Prior work has defined several regimes of binocular summation in the VEP (Apkarian et al.,1981 EEG Journal). It would be useful for the authors to relate the use of their terms "facilitation" and "suppression" to these regimes and to justify/clarify differences in usage, when present. Experiment 1, Fig. 3 shows cases where the binocular response is more than twice the monocular response. Here the interpretation is clear: the responses are super-additive and would be classed as involving facilitation in the Apkarian et al framework.

In the Apkarian et al framework, a ratio of 2 indicates independence/linearity. Ratios between 1 and 2 indicate sub-additivity and are diagnostic of the presence of binocular interaction but are noted by them to be difficult to interpret mechanistically. This should be discussed. A ratio of <1 indicates frank suppression which is not observed here with flicker.

Can the model explore the full range of binocular/monocular ratios in the Apkarian et al framework? I believe much of the data lies in the "partial summation" regime of Apkarian et al and that the model is mainly exploring this regime and is a way of quantifying varying degrees of partial summation.

Reviewer #3 (Public Review):

This manuscript describes interesting experiments on how information from the two eyes is combined in cortical areas, sub-cortical areas, and perception. The experimental techniques are strong and the results are potentially quite interesting. But the manuscript is poorly written and tries to do too much in too little space. I had a lot of difficulty understanding the various experimental conditions, the complicated results, and the interpretations of those results. I think this is an interesting and useful project so I hope the authors will put in the time to revise the manuscript so that regular readers like myself can better understand what it all means.

Now for my concerns and suggestions:

The experimental conditions are novel and complicated, so readers will not readily grasp what the various conditions are and why they were chosen. For example, in one condition different flicker frequencies were presented to the two eyes (2Hz to one and 1.6Hz to the other) with the flicker amplitude fixed in the eye presented to the lower frequency and the flicker amplitude varied in the eye presented to the higher frequency. This is just one of several conditions that the reader has to understand in order to follow the experimental design. I have a few suggestions to make it easier to follow. First, create a figure showing graphically the various conditions. Second, come up with better names for the various conditions and use those names in clear labels in the data figures and in the appropriate captions. Third, combine the specific methods and results sections for each experiment so that one will have just gone through the relevant methods before moving forward into the results. The authors can keep a general methods section separate, but only for the methods that are general to the whole set of experiments.

I wondered why the authors chose the temporal frequencies they did. Barrionuevo et al (2014) showed that the human pupil response is greatest at 1Hz and is nearly a log unit lower at 2Hz (i.e., the change in diameter is nearly a log unit lower; the change in area is nearly 2 log units lower). So why did the authors choose 2Hz for their primary frequency? And why did the authors choose 1.6Hz which is quite close to 2Hz for their off frequency? The rationale behind these important decisions should be made explicit.

By the way, I wondered if we know what happens when you present the same flicker frequencies to the two eyes but in counter-phase. The average luminance seen binocularly would always be the same, so if the pupil system is linear, there should be no pupil response to this stimulus. An experiment like this has been done by Flitcroft et al (1992) on accommodation where the two eyes are presented stimuli moving oppositely in optical distance and indeed there was no accommodative response, which strongly suggests linearity.

Figures 1 and 2 are important figures because they show the pupil and EEG results, respectively. But it's really hard to get your head around what's being shown in the lower row of each figure. The labeling for the conditions is one problem. You have to remember how "binocular" in panel c differs from "binocular cross" in panel d. And how "monocular" in panel d is different than "monocular 1.6Hz" in panel e. Additionally, the colors of the data symbols are not very distinct so it makes it hard to determine which one is which condition. These results are interesting. But they are difficult to digest.

The authors make a strong claim that they have found substantial differences in binocular interaction between cortical and sub-cortical circuits. But when I look at Figures 1 and 2, which are meant to convey this conclusion, I'm struck by how similar the results are. If the authors want to continue to make their claim, they need to spend more time making the case.

Figure 5 is thankfully easy to understand and shows a very clear result. These perceptual results deviate dramatically from the essentially winner-take-all results for spatial sinewaves shown by Legge & Rubin (1981); whom they should cite by the way. Thus, very interestingly the binocular combination of temporal variation is quite different than the binocular combination of spatial variation. Can the pupil and EEG results also be plotted in the fashion of Figure 5? You'd pick a criterion pupil (or EEG) change and use it to make such plots.

My main suggestion is that the authors need to devote more space to explaining what they've done, what they've found, and how they interpret the data. I suggest therefore that they drop the computational model altogether so that they can concentrate on the experiments. The model could be presented in a future paper.

Joint Public Review:

Barlow et al performed a viral insertion screen in larval zebrafish for sleep mutants. They identify a mutant named dreammist (dmist) that displayed defects in sleep, namely, decreased sleep both day and night, accompanied by increased activity. They find that dmist encodes a previously uncharacterized single-pass transmembrane protein that shows structural similarity to Fxyd1, a Na+K+-ATPase regulator. They go on to show that genetic manipulations of either FXYD1 or the Na/K pump also reduce sleep. They use pharmacology and sleep deprivation experiments to provide further evidence that the NA/K pump regulates intracellular sodium and rebound sleep.

This study provides additional evidence for the important role of membrane excitability in sleep regulation. The conclusions of this paper are mostly well supported by data, with the following strengths and weaknesses as described below.

Strengths:<br /> Elegant use of CRISPR knockout methods to disrupt multiple genes that help establish the importance of regulating Na+K+-ATPase function in sleep.<br /> Data are mostly clearly presented.<br /> Double mutant analysis of dmist and atp1a3a help establish an epistatic relationship between these proteins.

Weaknesses:<br /> The authors emphasize the role of increased cellular sodium. It will be interesting to also see the consequences of perturbating potassium. The potassium channel shaker has been previously identified as a critical sleep regulator in Drosophila.

Reviewer #1 (Public Review):

The authors used a meta-mask based on previous LC structural studies to delineate the LC on functional scans within two large public datasets (3T CamCAN and 7T HCP).

The rostral part of the LC was characterized by connections to the posterior and anterior cingulate cortices, medial temporal lobe, hippocampus, amygdala and striatum, while the caudal part projected to the parietal cortex, occipital cortex, precentral and postcentral regions, and thalamus. Older ages were associated with less rostral-like connectivity and increased asymmetry. The gradient explained variance above the effects of age, sex and education on some emotional and cognitive measures. In particular, the old-like functional gradient (loss of rostral-like connectivity and more clustered functional organization) was associated with worse performance on emotional memory and emotion regulation tasks but not to executive functioning or self-rated sleep quality.

Participants with higher anxiety and depression also showed less rostral-like connectivity and more asymmetry. Both the aging and the anxiety/depression asymmetry manifested as less rostral-like connectivity in the left LC than the right LC.

A strength of this study is that it is the first to attempt a voxel-based approach to quantifying functional connectivity in the LC. The results finding differences between rostral and caudal LC connectivity patterns are broadly consistent with prior work indicating differences between rostral/caudal LC and should help advance understanding of the LC's connectivity patterns with cortical regions.

A limitation of the study is the challenge of assessing activity not only from the small LC brainstem nucleus but also within it. Given the current spatial limitations of whole-brain functional imaging, the current findings are bolstered by including the 7T 1.6mm isotropic data. Spatial smoothing was applied with a 3mm FWHM isotropic kernel which may have reduced precision.

Another limitation was that the authors made conclusions about clustered functional organization but it was not clear how clustering was quantified.

Reviewer #2 (Public Review):

One of the major strengths in the current study is the implementation of the fully data-driven, gradient-based method for mapping connectopies of the LC. This approach is especially suited for brain structures that are difficult to localise because the resulted connectopic mapping is relatively robust to ROI definition (Fig. 7 in Haak et al., 2018). However, as a very inclusive definition of the LC (the "meta atlas") was adopted in the study, to what extent the gradient approach can tolerate changes of accuracy and specificity for LC ROI definition is unknown. Some comparative analyses would be helpful to provide assessments on the specificity and stability of the reported gradient pattern.

Haak et al. showed distinct reproducibility within and between subjects when comparing connectopic mappings between M1 and V1. M1 connectopic mapping showed very high consistency across subjects (ICCs > 0.9) compared with V1. This is very reasonable because the functional organisation within M1 is relatively homogeneous. Regarding the reliability of the LC rostro-caudal gradient, the authors only stated that "individual gradient estimation is often not consistent", but direct measurement on the consistency across subjects for the LC gradient was missing. This is important for future LC fMRI studies as more consistent pattern might warrant the application of an atlas-based method otherwise a more individualised pipeline is needed for investigating functional dissociation in LC subregions.

It puzzles me that why a dichotomous rostral vs caudal comparison was used to demonstrate the difference in connectivity patterns along the rostro-caudal gradient which might be an oversimplistic approach as described by the authors themselves? In fact, it might be more interesting to include the central "core" LC which is structurally organized in high density (Fernandes et al., 2012) and functionally distinguishable to the peri-LC "shell" region (Totah et al., 2018; Poe et al., 2022).

The composition of rostral vs caudal connectivity pattern changes over ageing, where the loss of rostral-like connectivity was consistent in bilateral LC whereas the gain of caudal-like connectivity in older subjects was only evident in the left LC. Do authors have any explanations on this left-lateralised ageing effect which is interestingly coincided with a lot of observations such as increased left LC contrast ratios was found during ageing (Betts et al., 2017) and in PD patients (Ye et al., 2022), reduced left LC-parahippocampal gyrus connectivity was reported in aMCI patients (Jacobs et al., 2015).

Reviewer #1 (Public Review):

People can perform a wide variety of different tasks, and a long-standing question in cognitive neuroscience is how the properties of different tasks are represented in the brain. The authors develop an interesting task that mixes two different sources of difficulty, and find that the brain appears to represent this mixture on a continuum, in the prefrontal areas involved in resolving task difficulty. While these results are interesting and in several ways compelling, they overlap with previous findings and rely on novel statistical analyses that may require further validation.

Strengths<br /> 1. The authors present an interesting and novel task for combining the contributions of stimulus-stimulus and stimulus-response conflict. While this mixture has been measured in the multi-source interference task (MSIT), this task provides a more graded mixture between these two sources of difficulty

2. The authors do a good job triangulating regions that encoding conflict similarity, looking for the conjunction across several different measures of conflict encoding

3. The authors quantify several salient alternative hypothesis and systematically distinguish their core results from these alternatives

4. The question that the authors tackle is of central theoretical importance to cognitive control, and they make an interesting an interesting contribution to this question

Concerns<br /> 1. It's not entirely clear what the current task can measure that is not known from the MSIT, such as the additive influence of conflict sources in Fu et al. (2022), Science. More could be done to distinguish the benefits of this task from MSIT.

2. The evidence from this previous work for mixtures between different conflict sources make the framing of 'infinite possible types of conflict' feel like a strawman. The authors cite classic work (e.g., Kornblum et al., 1990) that develops a typology for conflict which is far from infinite, and I think few people would argue that every possible source of difficulty will have to be learned separately. Such an issue is addressed in theories like 'Expected Value of Control', where optimization of control policies can address unique combinations of task demands.

3. Wouldn't a region that represented each conflict source separately still show the same pattern of results? The degree of Stroop vs Simon conflict is perfectly negatively correlated across conditions, so wouldn't a region that *just* tracks Stoop conflict show these RSA patterns? The authors show that overall congruency is not represented in DLPFC (which is surprising), but they don't break it down by whether this is due to Stroop or Simon congruency (I'm not sure their task allows for this).

4. The authors use a novel form of RSA that concatenates patterns across conditions, runs and subjects into a giant RSA matrix, which is then used for linear mixed effects analysis. This appears to be necessary because conflict type and visual orientation are perfectly confounded within the subject (although, if I understand, the conflict type x congruence interaction wouldn't have the same concern about visual confounds, which shouldn't depend on congruence). This is an interesting approach but should be better justified, preferably with simulations validating the sensitivity and specificity of this method and comparing it to more standard methods.

A chief concern is that the same pattern contributes to many entries in the DV, which has been addressed in previous work using row-wise and column-wise random effects (Chen et al., 2017, Neuroimage). It would also be informative to know whether the results hold up to removing within-run similarity, which can bias similarity measures (Walther et al., 2016, Neuroimage).

Another concern is the extent to which across-subject similarity will only capture consistent patterns across people, making this analysis very similar to a traditional univariate analysis (and unlike the traditional use of RSA to capture subject-specific patterns).

5. Finally, the authors should confirm all their results are robust to less liberal methods of multiplicity correction. For univariate analysis, they should report the effects from the standard p < .001 cluster forming threshold for univariate analysis (or TFCE). For multivariate analyses, FDR can be quite liberal. The authors should consider whether their mixed-effects analyses allow for group-level randomization, and consider (relatively powerful) Max-Stat randomization tests (Nichols & Holmes, 2002, Hum Brain Mapp).

Reviewer #2 (Public Review):

Summary, general appraisal

This study examines the construct of "cognitive spaces" as they relate to neural coding schemes present in response conflict tasks. The authors utilize a novel paradigm, in which subjects must map the direction of a vertically oriented arrow to either a left or right response. Different types of conflict (spatial Stroop, Simon) are parametrically manipulated by varying the spatial location of the arrow (a task-irrelevant feature). The vertical eccentricity of the arrow either agrees or conflicts with the arrow's direction (spatial Stroop), while the horizontal eccentricity of the arrow agrees or conflicts with the side of the response (Simon). A neural coding model is postulated in which the stimuli are embedded in a cognitive space, organized by distances that depend only on the similarity of congruency types (i.e., where conditions with similar relative proportions of spatial-Stroop versus Simon congruency are represented with similar activity patterns). The authors conduct a behavioral and fMRI study to provide evidence for such a representational coding scheme. The behavioral findings replicate the authors' prior work in demonstrating that conflict-related cognitive control adjustments (the congruency sequence effect) shows strong modulation as a function of the similarity between conflict types. With the fMRI neural activity data, the authors report univariate analyses that identified activation in left prefrontal and dorsomedial frontal cortex modulated by the amount of Stroop or Simon conflict present, and multivariate representational similarity analyses (RSA) that identified right lateral prefrontal activity encoding conflict similarity and correlated with the behavioral effects of conflict similarity.<br /> This study tackles an important question regarding how distinct types of conflict, which have been previously shown to elicit independent forms of cognitive control adjustments, might be encoded in the brain within a computationally efficient representational format. The ideas postulated by the authors are interesting ones and the utilized methods are rigorous. However, the study has critical limitations that are due to a lack of clarity regarding theoretical hypotheses, serious confounds in the experimental design, and a highly non-standard (and problematic) approach to RSA. Without addressing these issues it is hard to evaluate the contribution of the authors findings to the computational cognitive neuroscience literature.

The primary theoretical question and its implications are unclear.

The paper would greatly benefit from more clearly specifying potential alternative hypotheses and discussing their implications. Consider, for example, the case of parallel conflict monitors. Say that these conflict monitors are separately tuned for Stroop and Simon conflict, and are located within adjacent patches of cortex that are both contained within a single cortical parcel (e.g., as defined by the Glasser atlas used by the authors for analyses). If RSA was conducted on the responses of such a parcel to this task, it seems highly likely that an activation similarity matrix would be observed that is quite similar (if not identical) to the hypothesized one displayed in Figure 1. Yet it would seem like the authors are arguing that the "cognitive space" representation is qualitatively and conceptually distinct from the "parallel monitor" coding scheme. Thus, it seems that the task and analytic approach is not sufficient to disambiguate these different types of coding schemes or neural architectures.

The authors also discuss a fully domain-general conflict monitor, in which different forms of conflict are encoded within a single dimension. Yet this alternative hypothesis is also not explicitly tested nor discussed in detail. It seems that the experiment was designed to orthogonalize the "domain-general" model from the "cognitive space" model, by attempting to keep the overall conflict uniform across the different stimuli (i.e., in the design, the level of Stroop congruency parametrically trades off with the level of Simon congruency). But in the behavioral results (Fig. S1), the interference effects were found to peak when both Stroop and Simon congruency are present (i.e., Conf 3 and 4), suggesting that the "domain-general" model may not be orthogonal to the "cognitive space" model. One of the key advantages of RSA is that it provides the ability to explicitly formulate, test and compare different coding models to determine which best accounts for the pattern of data. Thus, it would seem critical for the authors to set up the design and analyses so that an explicit model comparison analysis could be conducted, contrasting the domain-general, domain-specific, and cognitive space accounts.<br /> Relatedly, the reasoning for the use of the term "cognitive space" is unclear. The mere presence of graded coding for two types of conflict seems to be a low bar for referring to neural activity patterns as encoding a "cognitive space". It is discussed that cognitive spaces/maps allow for flexibility through inference and generalization. But no links were made between these cognitive abilities and the observed representational structure. Additionally, no explicit tests of generality (e.g., via cross-condition generalization) were provided. Finally, although the design elicits strong CSE effects, it seems somewhat awkward to consider CSE behavioral patterns as a reflection of the kind of abilities supported by a cognitive map (if this is indeed the implication that was intended). In fact, CSE effects are well-modeled by simpler "model-free" associative learning processes, that do not require elaborate representations of abstract structures.

More generally, it seems problematic that Stroop and Simon conflict in the paradigm parametrically trade-off against each other. A more powerful design would have de-confounded Stroop and Simon conflict so that each could be separately estimation via (potentially orthogonal) conflict axes. Additionally, incorporating more varied stimulus sets, locations, or responses might have enabled various tests of generality, as implied by a cognitive space account.

Serious confounds in the design render the results difficult to interpret.

As much prior neuroimaging and behavioral work has established, "conflict" per se is perniciously correlated with many conceptually different variables. Consequently, it is very difficult to distinguish these confounding variables within aggregate measures of neural activity like fMRI. For example, conflict is confounded with increased time-on-task with longer RT, as well as conflict-driven increases in coding of other task variables (e.g., task-set related coding; e.g., Ebitz et al. 2020 bioRxiv). Even when using much higher resolution invasive measures than fMRI (i.e., eCoG), researchers have rightly been wary of making strong conclusions about explicit encoding of conflict (Tang et al, 2019; eLife). As such, the researchers would do well to be quite cautious and conservative in their analytic approach and interpretation of results.

This issue is most critical in the interpretation of the fMRI results as reflecting encoding of conflict types. A key limitation of the design, that is acknowledged by the authors is that conflict is fully confounded within-subject by spatial orientation. Indeed, the limited set of stimulus-response mappings also cast doubt on the underlying factors that give rise to the CSE modulations observed by the authors in their behavioral results. The CSE modulations are so strong - going from a complete absence of current x previous trial-type interaction in the cos(90) case all the way to a complete elimination of any current trial conflict when the prior trial was incongruent in the cos(0) case - that they cause suspicion that they are actually driven by conflict-related control adjustments rather than sequential dependencies in the stimulus-response mappings that can be associatively learned.

To their credit, the authors recognize this confound, and attempt to address it analytically through the use of a between-subject RSA approach. Yet the solution is itself problematic, because it doesn't actually deconfound conflict from orientation. In particular, the RSA model assumes that whatever components of neural activity encode orientation produce this encoding within the same voxel-level patterns of activity in each subject. If they are not (which is of course likely), then orthogonalization of these variables will be incomplete. Similar issues underlie the interpretation target/response and distractor coding. Given these issues, perhaps zooming out to a larger spatial scale for the between-subject RSA might be warranted. Perhaps whole-brain at the voxel level with a high degree of smoothing, or even whole-brain at the parcel level (averaging per parcel). For this purpose, Schaefer atlas parcels might be more useful than Glasser, as they more strongly reflect functional divisions (e.g., motor strip is split into mouth/hand divisions; visual cortex is split into central/peripheral visual field divisions). Similarly, given the lateralization of stimuli, if a within-parcel RSA is going to be used, it seems quite sensible to pool voxels across hemispheres (so effectively using 180 parcels instead of 360).

The strength of the results is difficult to interpret due to the non-standard analysis method.

The use of a mixed-level modeling approach to summarize the empirical similarity matrix is an interesting idea, but nevertheless is highly non-standard within RSA neuroimaging methods. More importantly, the way in which it was implemented makes it potentially vulnerable to a high degree of inaccuracy or bias. In this case, this bias is likely to be overly optimistic (high false positive rate).

A key source of potential bias comes from the fact that the off-diagonal cells are not independent (e.g., the correlation between subject A and B is strongly dependent on the correlation between subject A and C). For appropriate degrees of freedom calculation, the model must take this into account somehow. As fitted, the current models do not seem to handle this appropriately. That being said, it may be possible to devise an appropriate test via mixed-level models. In fact, Chen et al. have a series of three recent Neuroimage articles that extensively explore this question (all entitled "Untangling the relatedness among correlations") - adopting one of the methods described in the papers, seems much safer, if possible.

Another potential source of bias is in treating the subject-level random effect coefficients (as predicted by the mixed-level model) as independent samples from a random variable (in the t-tests). The more standard method for inference would be to use test statistics derived from the mixed-model fixed effects, as those have degrees of freedom calculations that are calibrated based on statistical theory.

No numerical or formal defense was provided for this mixed-level model approach. As a result, the use of this method seems quite problematic, as it renders the strength of the observed results difficult to interpret. Instead, the authors are encouraged using a previously published method of conducting inference with between-subject RSA, such as the bootstrapping methods illustrated in Kragel et al. (2018; Nat Neurosci), or in potentially adopting one of the Chen et al. methods mentioned above, that have been extensively explored in terms of statistical properties.

Reviewer #3 (Public Review):

Yang and colleagues investigated whether information on two task-irrelevant features that induce response conflict is represented in a common cognitive space. To test this, the authors used a task that combines the spatial Stroop conflict and the Simon effect. This task reliably produces a beautiful graded congruency sequence effect (CSE), where the cost of congruency is reduced after incongruent trials. The authors measured fMRI to identify brain regions that represent the graded similarity of conflict types, the congruency of responses, and the visual features that induce conflicts.

Using several theory-driven exclusion criteria, the authors identified the right dlPFC (right 8C), which shows 1) stronger encoding of graded similarity of conflicts in incongruent trials and 2) a positive correlation between the strength of conflict similarity type and the CSE on behavior. The dlPFC has been shown to be important for cognitive control tasks. As the dlPFC did not show a univariate parametric modulation based on the higher or lower component of one type of conflict (e.g., having more spatial Stroop conflict or less Simon conflict), it implies that dissimilarity of conflicts is represented by a linear increase or decrease of neural responses. Therefore, the similarity of conflict is represented in multivariate neural responses that combine two sources of conflict.

The strength of the current approach lies in the clear effect of parametric modulation of conflict similarity across different conflict types. The authors employed a clever cross-subject RSA that counterbalanced and isolated the targeted effect of conflict similarity, decorrelating orientation similarity of stimulus positions that would otherwise be correlated with conflict similarity. A pattern of neural response seems to exist that maps different types of conflict, where each type is defined by the parametric gradation of the yoked spatial Stroop conflict and the Simon conflict on a similarity scale. The similarity of patterns increases in incongruent trials and is correlated with CSE modulation of behavior. However, several potential caveats need to be considered.

One caveat to consider is that the main claim of recruitment of an organized "cognitive space" for conflict representation is solely supported by the exclusion criteria mentioned earlier. To further support the involvement of organized space in conflict representation, other pieces of evidence need to be considered. One approach could be to test the accuracy of out-of-sample predictions to examine the continuity of the space, as commonly done in studies on representational spaces of sensory information. Another possible approach could involve rigorously testing the geometric properties of space, rather than fitting RSM to all conflict types. For instance, in Fig 6, both the organized and domain-specific cognitive maps would similarly represent the similarity of conflict types expressed in Fig1c (as evident from the preserved order of conflict types). The RSM suggests a low-dimensional embedding of conflict similarity, but the underlying dimension remains unclear.

Another important factor to consider is how learning within the confined task space, which always negatively correlates the two types of conflicts within each subject, may have influenced the current results. Is statistical dependence of conflict information necessary to use the organized cognitive space to represent conflicts from multiple sources? Answering this question would require a paradigm that can adjust multiple sources of conflicts parametrically and independently. Investigating such dependencies is crucial in order to better understand the adaptive utility of the observed cognitive space of conflict similarity.

Taken together, this study presents an exciting possibility that information requiring high levels of cognitive control could be flexibly mapped into cognitive map-like representations that both benefit and bias our behavior. Further characterization of the representational geometry and generalization of the current results look promising ways to understand representations for cognitive control.

Reviewer #1 (Public Review):

Microglia are increasingly recognized as playing an important role in shaping the synaptic circuit and regulating neural dynamics in response to changes in their surrounding environment and in brain states. While numerous studies have suggested that microglia contribute to sleep regulation and are modulated by sleep, there has been little direct evidence that the morphological dynamics of microglia are modulated by the sleep/wake cycle. In this work, Gu et al. applied a recently developed miniature two-photon microscope in conjunction with EEG and EMG recording to monitor microglia surveillance in freely-moving mice over extended period of time. They found that microglia surveillance depends on the brain state in the sleep/wake cycle (wake, non-REM, or REM sleep). Furthermore, they subjected the mouse to acute sleep deprivation, and found that microglia gradually assume an active state in response. Finally, they showed that the state-dependent morphological changes depend on norepinephrine (NE), as chemically ablating noradrenergic inputs from locus coeruleus abolished such changes; this is in agreement with previous publications. The authors also showed that the effect of NE is partially mediated by β2-adrenergic receptors, as shown with β2-adrenergic receptor knock-out mice. Overall, this study is a technical tour de force, and its data add valuable direct evidence to the ongoing investigations of microglial morphological dynamics and its relationship with sleep. However, there are a number of details that need to be clarified, and some conclusions need to be corroborated by more control experiments or more rigorous statistical analysis. Specifically:

1. The number of branch points per microglia shown here (e.g., Fig. 2g) is much lower than the values of branch points in the literature, e.g., Liu T et al., Neurobiol. Stress 15: 100342, 2021 (mouse dmPFC, IHC); Liu YU et al., Nat. Neurosci. 22: 1771-81, 2019 (mouse S1, in vivo 2P imaging). The authors need to discuss the possible source of such discrepancy.<br /> 2. Microglia process end-point speed (Fig. 2h, o): here the authors show that the speed is highest in the wake state and lowest in NREM, which agrees with the measurement on microglia motility during wakefulness vs NREM in a recent publication (Hristovska I et al., Nat. Commun. 13: 6273, 2022). However, Hristovska et al. also reported lower microglia complexity in NREM vs wake state, which seems to be the opposite of the finding in this paper. The authors need to discuss the possible source of such differences.<br /> 3. Fig. 3: the authors used single-plane images to analyze the morphological changes over 3 or 6 hours of SD, which raises the concern that the processes imaged at the baseline may drift out of focus, leading to the dramatic reduction in process lengths, surveillance area, and number of branch points. In fact, a previous study (Bellesi M et al., J. Neurosci. 37(21): 5263-73, 2017) shows that after 8 h SD, the number of microglia process endpoints per cell and the summed process length per cell do not change significantly (although there is a trend to decline). The authors may confirm their findings by either 3D imaging in vivo, or 3D imaging in fixed tissue.<br /> 4. Fig. 4b: the EEG and EMG signals look significantly different from the example given in Fig. 2a. In particular, the EMG signal appears completely flat except for the first segment of wake state; the EEG power spectrum for REM appears dark; and the wake state corresponds to stronger low frequency components (below ~ 4 Hz) compared to NREM, which is the opposite of Fig. 2a. This raises the concern whether the classification of sleep stage is correct here.<br /> 5. Fig. 4 NE dynamics. How long is a single continuous imaging session for NE? When monitoring microglia surveillance, the authors were able to identify wake or NREM states longer than 15 min, and REM states longer than 5 min. Here the authors selected wake/NREM states longer than 1 min and REM states longer than 30 s. What makes such a big difference in the time duration selected for analysis? Also, the definition of F0 is a bit unclear. Is the same F0 used throughout the entire imaging session, or is it defined with a moving window?<br /> 6. Fig. 5b: how does the microglia morphology in LC axon ablation mice compare with wild type mice under the wake state? The text mentioned "more contracted" morphology but didn't give any quantification. Also, the morphology of microglia in the wake state (Fig. 5b) appears very different from that shown in Fig. S3C1 (baseline). What is the reason?<br /> 7. The relationship between NE level and microglia dynamics. Fig. 4C shows that the extracellular NE level is the highest in the wake state and the lowest in REM. Previous studies (Liu YU et al., Nat. Neurosci. 22(11):1771-1781, 2019; Stowell RD et al., Nat. Neurosci. 22(11): 1782-1792, 2019) suggest that high NE tone corresponds to reduced microglia complexity and surveillance. Hence, it would be expected that microglia process length, branch point number, and area/volume are higher in REM than in NREM. However, Fig. 2l-n show the opposite. How should we understand this?

Reviewer #2 (Public Review):

The manuscript describes an approach to monitor microglial structural dynamics and correlate it to ongoing changes in brain state during sleep-wake cycles. The main novelty here is the use of miniaturized 2p microscopy, which allows tracking microglia surveillance over long periods of hours, while the mice are allowed to freely behave. Accordingly, this experimental setup would permit to explore long-lasting changes in microglia in a more naturalistic environment, which were previously not possible to identify otherwise. The findings could provide key advances to the research of microglia during natural sleep and wakefulness, as opposed to anesthesia. The main findings of the paper are that microglia increase their process motility and surveillance during REM and NREM sleep as compared to the awake state. The authors further show that sleep deprivation induces opposite changes in microglia dynamics- limiting their surveillance and size. The authors then demonstrate potential causal role for norepinephrine secretion from the locus coeruleus (LC) which is driven by beta 2 adrenergic receptors (b2AR) on microglia. However, there are several methodological and experimental concerns which should be addressed.

The major comments are summarized below:

1. The main technological advantage of the 2p miniaturized microscope is the ability to track single cells over sleep cycles. A main question that is unclear from the analysis and the way the data is presented is: are the structural changes in microglia reversible? Meaning, could the authors provide evidence that the same cell can dynamically change in sleep state and then return to similar size in wakefulness? The same question arises again with the data which is presented for anesthesia, is this change reversible?<br /> 2. The binary comparison between brain states is misleading, shouldn't the changes in structural dynamics compared to the baseline of the state onset? The authors method describes analysis of the last 5 minutes in each sleep/wake state. However, these transitions are directional- for instance, REM usually follows NREM, so the description of a decrease in length during REM sleep could be inaccurate.<br /> 3. Sleep deprivation- again, it is unclear whether these structural changes are reversible. This point is straightforward to address using this methodology by measuring sleep following SD. In addition, the authors chose a method to induce sleep deprivation that is rather harsh. It is unclear if the effect shown is the result of stress or perhaps an excess of motor activity.<br /> 4. The authors perform measurements of norepinephrine with a recently developed GRAB sensor. These experiments are performed to causally link microglia surveillance during sleep to norepinephrine secretion. They perform 2p imaging and collect data points which are single neurons, and it is unclear why the normalization and analysis is performed for bulk fluorescence similar to data obtained with photometry.<br /> 5. The experiments involving b2AR KO mice are difficult to interpret and do not provide substantial mechanistic insight. Since b2AR are expressed throughout numerous cell types in the brain and in the periphery, it is entirely not clear whether the effects on microglia dynamics are direct. The conclusion and the statement regarding the expression of b2AR in microglia is not supported by the references the authors present, which simply demonstrate the existence and function of b2AR in microglia. In addition, these mice show significant changes in sleep pattern and increased REM sleep. This could account for reasons for the changes in microglia structure rather than the interpretation that these are direct effects.<br /> To summarize, the main conclusions of the paper require further support with analysis of existing data and experimental validation.

Reviewer #1 (Public Review):

This study demonstrates that vitamin D-bound VDR increased the expression of SIRT1 and that vitamin D-bound VDR interacts with SIRT1 to cause auto-deacetylation on Lys610 and activation of SIRT1 catalytic activity. This is an important finding that is relevant to the actions of VDR on colorectal cancer. The data presented to support the presented conclusion is convincing.

A strength of the study is that it is focused on a narrow group of conclusions.

The major weakness of the study is that the site of SIRT1 regulatory lysine acetylation is defined by mutational analysis rather than by direct biochemical analysis. This issue is partially mitigated by previous reports of K610 acetylation using mass spec (https://www.phosphosite.org/proteinAction.action?id=5946&showAllSites=true). However, Fig. 4E is reassuring because it shows that the apparent acetylation of the K610 mutant SIRT1 appears to be lower than WT SIRT1

A second weakness of the study relates to the use of shRNA-mediated knockdown of VDR for some studies in which a previously reported cell line was employed. The analysis presented would be more compelling if similar data was obtained using more than one shRNA. Similarly, only a single siRNA for SIRT1 is presented in Table 1.

A third weakness of the study is that the conclusion that the VDR interaction with SIRT1 is the cause of auto-deacetylation rather than an associated event mediated by another mechanism would be more strongly supported by mutational analysis of SIRT1 and VDR residues required for the binding interaction. Will VDR increase SIRT1 activity when mutations are introduced to block the interaction? While the finding that catalytically inactive SIRT1 does not interact with VDR is helpful, this does not address the role of the binding surface.

A fourth weakness of the study is that it would be improved by testing the proposed hypothesis through in vitro reconstitution with purified proteins. Does VDR cause auto-deacetylation and activation of Sirt1 in vitro?

Reviewer #2 (Public Review):

The authors decipher the signaling between vitamin D and proteins that are downstream of SIRT1. The importance of vitamin D in physiology is clear. However, the link between vitamin D and cancer is less clear. This study provides very interesting and solid information on the link between vitamin D and colorectal cancer. It is likely that this study will provide insight into the importance of vitamin D in other types of cancer. It may also lead to new therapeutic strategies for specific cases.

The authors focus on vitamin D-mediated signaling through VDR, SIRT1 and Ace H3K9. They highlight the importance of K610 in SIRT1 in this process. This article is convincing, although the authors can improve their study as outlined below:

* The authors should specify which cell line was used to perform the experiment in Figure 1E,F. What would be the result in the presence/absence of 1,25(OH)2D3? In Figure 1G, what is the meaning of # and ###?

* Figure 2C, it would have been ideal to show the VDR-SIRT1 interaction after a Sirt1 IP.

* I understand the authors' overall message for this figure, but it is far from clear. This section needs to be improved. For example, in Figure 3G, does this mean that the level of AceH3K9 is independent of the level of SIRT1? Is there a contradiction? The authors should indicate the color of the different stainings for Figure 3D. Do the authors mean that the secondary antibody marks in brown/red? If so, these results are inconsistent with the text considering that hematoxylin was used for non-tumor tissue. This part needs to be clarified. What about the level of FOXO3A in these tissues/tumors? What is the level of 1,25(OH)2D3 in these patients? In Figure 3D, the following information is missing: "A detailed amplification is shown in the lower left of each micrograph." In Figure 3E, it says p=0.325, in the legend p<0.01, and in the text there is a trend. Which is the correct version?

* Figure 4F. The quality of the presented blots is not optimal. It needs to be improved. In addition, the number of independent biological experiments is not indicated. In general, the authors should better indicate the number of independent biological experiments performed, at least for some of them. For example, see Figure 1G. Regarding Figure 2C, we understand that the WB was performed 3 times. Is this the case for the PI? etc...

Reviewer #1 (Public Review):

In this study, Jiamin Lin et al. investigated the potential positive feedback loop between ZEB2 and ACSL4, which regulates lipid metabolism and breast cancer metastasis. They reported a correlation between high expression of ZEB2 and ACSL4 and poor survival of breast cancer patients, and showed that depletion of ZEB2 or ACSL4 significantly reduced lipid droplets abundance and cell migration in vitro. The authors also claimed that ZEB2 activated ACSL4 expression by directly binding to its promoter, while ACSL4 in turn stabilized ZEB2 by blocking its ubiquitination. While the topic is interesting, there are several major concerns with the study and its conclusions are not convincing.

1. Figure 1A, the clinical relevance or biological significance of drug-resistant luminal breast cancer cell lines with metastatic cancer is questionable. Additionally, the RNA-seq analysis lacked multiple test correction for differential gene expression analysis, and no fold-change cut-off was used, leading to incorrect thresholds and wrongly identified significant signals.

2. Figure 1D-E, the clinical associations between ACSL4 and ZEB2 overexpression and poor patient survival are not justified. The authors used an old web tool, the Kaplan-Meier plotter database, based on microarray data, to perform the analysis. The reviewer repeated the analysis and found that multiple microarray probes for ZEB2 were available, leading to opposite results when different probes were selected. The reviewer also repeated the analysis using more reliable TCGA RNA-seq data and found no correlation between ASCL4 or ZEB2 expression and post-progression survival.

3. Figure 1I relied on IHC to support the negative correlation between ACSL4 and Erα expression, but the small sample size limits the power to establish the relationship and the results are not definitive without further replication or biological investigation. The authors should provide more detailed and comprehensive analysis, including appropriate statistical tests, to ensure the findings are robust and reliable.

4. Figure 3B-C lacks justification of the differences by showing only one field without any internal control for exposure. The reviewer suggests to show additional fields where cells with both efficiently and inefficiently knocked-down are present, to justify the robustness of the results. This can also be achieved by mixing control and knockdown cells.

5. Figure 4A-D, oleate-induced cell migration is a well-documented feature across different cancer types. To make it more relevant to the current study, the authors should examine multiple cell lines with high and low ZEB2/ACSL4 expression to determine the underlying relevance.

6. Figure 4E, it is difficulty to conclude that cancer cells utilize stored lipids during migration to fuel metastasis based on current data. Do you see any evidence of lipid signal decreasing in the leading edge of the scratch wound-healing migration assay? The authors should also compare signals between unmigrated and migrated cells in the transwell assay.

7. Figure 6 warrants a genome-wide ChIP-seq to justify direct regulation of ASCL4 promoter by ZEB2. The reviewer's analysis of publicly available ZEB2 ChIP-seq in multiple cell types detected no ZEB2 binding signaling within {plus minus} 5 kb of ASCL4 promoter.

8. Figure 7 presents a series of self-contradictory results. Figure 7C, why no significant change in ZEB2-MYC expression was observed in the presence of ACSL4 and/or HA-Ubi? In Figure 7 E&G, why robust ACSL4 expression is present in the control group in (E) but not in (G)? Additionally, why there is no degradation in ZEB2 baseline level over time in the shACSL4 group in (E)? These raise severe concerns about the data quality.

9. Figure 7D, the IP result of ACSL4 is not justified as there is no enrichment of ACSL4 in the IP compared to input. With the current data, it is hard to justify that there is any direct interaction. Moreover, based on IF data in Figure 3B-C, ACSL4 is exclusively localized in the cytoplasm, while ZEB2 is exclusively localized in the nucleus. It is hard to believe there is any direct interaction and mutual regulation.

Reviewer #2 (Public Review):

In this study, the authors validated a positive feedback loop between ZEB2 and ACSL4 in breast cancer, which regulates lipid metabolism to promote metastasis.

Overall, the study is original, well structured, and easy to read. Despite the reliability of the data discussed in this article, there are still some deficiencies that need to be addressed through further explanation.

Major issues:

1. The authors demonstrated that ACSL4 regulates ZEB2 not only via a post-transcriptional mechanism but also via a transcriptional mechanism. The authors have not provided a comprehensive explanation of the specific mechanism in this paper. Therefore, it is recommended that the author delve into the potential mechanisms in the discussion section. For example, related mechanisms affecting ZEB2 ubiquitination degradation, as well as factors affecting ZEB2 upstream transcriptional regulation, etc.

2. To further clarify the interaction of ZEB2 and ACSL4, it is best to perform in vitro glutathione-S-transferase (GST) pulldown assay and immunofluorescence assay.

3. In Figure 7B, the protein level of ZEB2 seems not to be altered in BT549 BCSC cell line after the depletion of ACSL4.

4. EMT is characterized by changes in cell morphology, so the staining of cytoskeletons with Phalloidin is needed.

5. Additional breast cancer cases or cohorts (such as TMA) should be used to validate the positive correlation between ACSL4 and ZEB2 expression through IHC analysis.

Reviewer #3 (Public Review):

The manuscript by Lin et al. reveals a novel positive regulatory loop between ZEB2 and ACSL4, which promotes lipid droplets storage to meet the energy needs of breast cancer metastasis. It is of interest, however, some concerns should be addressed to strengthen the finding.

Major concerns:

1. The effect of ZEB2 overexpression is not fully demonstrated in the whole study. This point should be addressed.

2. Does the addition of oleate restore the ability of migration or invasion in ACSL4 knockdown cells?

3. Which cellular compartment does ACSL4 localize in and interact with ZEB2 to stabilize ZEB2?

4. The ubiquitination assay and Co-IP assay are just performed in HEK293T cells. This result should be confirmed in MDA-MB-231 cells or Taxol-resistant MCF-7 cells.

5. How does ACSL4 regulate ZEB2 at the mRNA level？Please verify.

6. In Fig. 2F, the silencing efficiency for ACSL4 and ZEB2 should be shown. In addition, the protein level of ZEB2 or ACSL4 in shZEB2 and shZEB2+ACSL4 groups should also be addressed.

7. What is the survival status of patients with both high expression of ACSL4 and ZEB2 in TCGA. In addition, more survival data from databases especially patients with both high expression of ACSL4 and ZEB2 are needed to analyze to support the finding.

Reviewer #1 (Public Review):

The authors present a study of visuo-motor coupling primarily using wide-field calcium imaging to measure activity across the dorsal visual cortex. They used different mouse lines or systemically injected viral vectors to allow imaging of calcium activity from specific cell-types with a particular focus on a mouse-line that expresses GCaMP in layer 5 IT (intratelencephalic) neurons. They examined the question of how the neural response to predictable visual input, as a consequence of self-motion, differed from responses to unpredictable input. They identify layer 5 IT cells as having a different response pattern to other cell-types/layers in that they show differences in their response to closed-loop (i.e. predictable) vs open-loop (i.e. unpredictable) stimulation whereas other cell-types showed similar activity patterns between these two conditions. They analyze the latencies of responses to visuomotor prediction errors obtained by briefly pausing the display while the mouse is running, causing a negative prediction error, or by presenting an unpredicted visual input causing a positive prediction error. They suggest that neural responses related to these prediction errors originate in V1, however, I would caution against over-interpretation of this finding as judging the latency of slow calcium responses in wide-field signals is very challenging and this result was not statistically compared between areas. Surprisingly, they find that presentation of a visual grating actually decreases the responses of L5 IT cells in V1. They interpret their results within a predictive coding framework that the last author has previously proposed. The response pattern of the L5 IT cells leads them to propose that these cells may act as 'internal representation' neurons that carry a representation of the brain's model of its environment. Though this is rather speculative. They subsequently examine the responses of these cells to anti-psychotic drugs (e.g. clozapine) with the reasoning that a leading theory of schizophrenia is a disturbance of the brain's internal model and/or a failure to correctly predict the sensory consequences of self-movement. They find that anti-psychotic drugs strongly enhance responses of L5 IT cells to locomotion while having little effect on other cell-types. Finally, they suggest that anti-psychotics reduce long-range correlations between (predominantly) L5 cells and reduce the propagation of prediction errors to higher visual areas and suggest this may be a mechanism by which these drugs reduce hallucinations/psychosis.

This is a large study containing a screening of many mouse-lines/expression profiles using wide-field calcium imaging. Wide-field imaging has its caveats, including a broad point-spread function of the signal and susceptibility to hemodynamic artifacts, which can make interpretation of results difficult. The authors acknowledge these problems and directly address the hemodynamic occlusion problem. It was reassuring to see supplementary 2-photon imaging of soma to complement this data-set, even though this is rather briefly described in the paper. Overall the paper's strengths are its identification of a very different response profile in the L5 IT cells compared other layers/cell-types which suggests an important role for these cells in handling integration of self-motion generated sensory predictions with sensory input. The interpretation of the responses to anti-psychotic drugs is more speculative but the result appears robust and provides an interesting basis for further studies of this effect with more specific recording techniques and possibly behavioral measures.

Reviewer #2 (Public Review):

Summary:

This work investigates the effects of various antipsychotic drugs on cortical responses during visuomotor integration. Using wide-field calcium imaging in a virtual reality setup, the researchers compare neuronal responses to self-generated movement during locomotion-congruent (closed loop) or locomotion-incongruent (open loop) visual stimulation. Moreover, they probe responses to unexpected visual events (halt of visual flow, sudden-onset drifting grating). The researchers find that, in contrast to a variety of excitatory and inhibitory cell types, genetically defined layer 5 excitatory neurons distinguish between the closed and the open loop condition and exhibit activity patterns in visual cortex in response to unexpected events, consistent with unsigned prediction error coding. Motivated by the idea that prediction error coding is aberrant in psychosis, the authors then inject the antipsychotic drug clozapine, and observe that this intervention specifically affects closed loop responses of layer 5 excitatory neurons, blunting the distinction between the open and closed loop conditions. Clozapine also leads to a decrease in long-range correlations between L5 activity in different brain regions, and similar effects are observed for two other antipsychotics, aripripazole and haloperidol, but not for the stimulant amphetamine. The authors suggest that altered prediction error coding in layer 5 excitatory neurons due to reduced long-range correlations in L5 neurons might be a major effect of antipsychotic drugs and speculate that this might serve as a new biomarker for drug development.

Strengths:

- Relevant and interesting research question:

The distinction between expected and unexpected stimuli is blunted in psychosis but the neural mechanisms remain unclear. Therefore, it is critical to understand whether and how antipsychotic drugs used to treat psychosis affect cortical responses to expected and unexpected stimuli. This study provides important insights into this question by identifying a specific cortical cell type and long-range interactions as potential targets. The authors identify layer 5 excitatory neurons as a site where functional effects of antipsychotic drugs manifest. This is particularly interesting as these deep layer neurons have been proposed to play a crucial role in computing the integration of predictions, which is thought to be disrupted in psychosis. This work therefore has the potential to guide future investigations on psychosis and predictive coding towards these layer 5 neurons, and ultimately improve our understanding of the neural basis of psychotic symptoms.

- Broad investigation of different cell types and cortical regions:

One of the major strengths of this study is quasi-systematic approach towards cell types and cortical regions. By analysing a wide range of genetically defined excitatory and inhibitory cell types, the authors were able to identify layer 5 excitatory neurons as exhibiting the strongest responses to unexpected vs. expected stimuli and being the most affected by antipsychotic drugs. Hence, this quasi-systematic approach provides valuable insights into the functional effects of antipsychotic drugs on the brain, and can guide future investigations towards the mechanisms by which these medications affect cortical neurons.

- Bridging theory with experiments:

Another strength of this study is its theoretical framework, which is grounded in the predictive coding theory. The authors use this theory as a guiding principle to motivate their experimental approach connecting visual responses in different layers with psychosis and antipsychotic drugs. This integration of theory and experimentation is a powerful approach to tie together the various findings the authors present and to contribute to the development of a coherent model of how the brain processes visual information both in health and in disease.

Weaknesses:

- Unclear relevance for psychosis research:

From the study, it remains unclear whether the findings might indeed be able to normalise altered predictive coding in psychosis. Psychosis is characterised by a blunted distinction between predicted and unpredicted stimuli. The results of this study indicate that antipsychotic drugs further blunt the distinction between predicted and unpredicted stimuli, which would suggest that antipsychotic drugs would deteriorate rather than ameliorate the predictive coding deficit found in psychosis. However, these findings were based on observations in wild-type mice at baseline. Given that antipsychotics are thought to have little effects in health but potent antipsychotic effects in psychosis, it seems possible that the presented results might be different in a condition modelling a psychotic state, for example after a dopamine-agonistic or a NMDA-antagonistic challenge. Therefore, future work in models of psychotic states is needed to further investigate the translational relevance of these findings.

- Incomplete testing of predictive coding interpretation:

While the investigation of neuronal responses to different visual flow stimuli Is interesting, it remains open whether these responses indeed reflect internal representations in the framework of predictive coding. While the responses are consistent with internal representation as defined by the researchers, i.e., unsigned prediction error signals, an alternative interpretation might be that responses simply reflect sensory bottom-up signals that are more related to some low-level stimulus characteristics than to prediction errors. Moreover, This interpretational uncertainty is compounded by the fact that the used experimental paradigms were not suited to test whether behaviour is impacted as a function of the visual stimulation which makes it difficult to assess what the internal representation of the animal actual was. For these reasons, the observed effects might reflect simple bottom-up sensory processing alterations and not necessarily have any functional consequences. While this potential alternative explanation does not detract from the value of the study, future work would be needed to explain the effect of antipsychotic drugs on responses to visual flow. For example, experimental designs that systematically vary the predictive strength of coupled events or that include a behavioural readout might be more suited to draw from conclusions about whether antipsychotic drugs indeed alter internal representations.

- Methodological constraints of experimental design:

While the study findings provide valuable insights into the potential effects of antipsychotic drugs, it is important to acknowledge that there may be some methodological constraints that could impact the interpretation of the results. More specifically, the experimental design does not include a negative control condition or different doses. These conditions would help to ensure that the observed effects are not due to unspecific effects related to injection-induced stress or time, and not confined to a narrow dose range that might or might not reflect therapeutic doses used in humans. Hence, future work is needed to confirm that the observed effects indeed represent specific drug effects that are relevant to antipsychotic action.

Conclusion:

Overall, the results support the idea that antipsychotic drugs affect neural responses to predicted and unpredicted stimuli in deep layers of cortex. Although some future work is required to establish whether this observation can indeed be explained by a drug-specific effect on predictive coding, the study provides important insights into the neural underpinnings of visual processing and antipsychotic drugs, which is expected to guide future investigations on the predictive coding hypothesis of psychosis. This will be of broad interest to neuroscientists working on predictive coding in health and in disease.

Reviewer #3 (Public Review):

The study examines how different cell types in various regions of the mouse dorsal cortex respond to visuomotor integration and how antipsychotic drugs impacts these responses. Specifically, in contrast to most cell types, the authors found that activity in Layer 5 intratelencephalic neurons (Tlx3+) and Layer 6 neurons (Ntsr1+) differentiated between open loop and closed loop visuomotor conditions. Focussing on Layer 5 neurons, they found that the activity of these neurons also differentiated between negative and positive prediction errors during visuomotor integration. The authors further demonstrated that the antipsychotic drugs reduced the correlation of Layer 5 neuronal activity across regions of the cortex, and impaired the propagation of visuomotor mismatch responses (specifically, negative prediction errors) across Layer 5 neurons of the cortex, suggesting a decoupling of long-range cortical interactions.

The data when taken as a whole demonstrate that visuomotor integration in deeper cortical layers is different than in superficial layers and is more susceptible to disruption by antipsychotics. Whilst it is already known that deep layers integrate information differently from superficial layers, this study provides more specific insight into these differences. Moreover, this study provides a first step into understanding the potential mechanism by which antipsychotics may exert their effect.

Whilst the paper has several strengths, the robustness of its conclusions is limited by its questionable statistical analyses. A summary of the paper's strengths and weaknesses follow.

Strengths:

The authors perform an extensive investigation of how different cortical cell types (including Layer 2/3, 4 , 5, and 6 excitatory neurons, as well as PV, VIP, and SST inhibitory interneurons) in different cortical areas (including primary and secondary visual areas as well as motor and premotor areas), respond to visuomotor integration. This investigation provides strong support to the idea that deep layer neurons are indeed unique in their computational properties. This large data set will be of considerable interest to neuroscientists interested in cortical processing.

The authors also provide several lines of evidence that visuomotor information is differentially integrated in deep vs. superficial layers. They show that this is true across experimental paradigms of visuomotor processing (open loop, closed loop, mismatch, drifting grating conditions) and experimental manipulations, with the demonstration that Layer 5 visuomotor integration is more sensitive to disruption by the antipsychotic drug clozapine, compared with cortex as a whole.

The study further uses multiple drugs (clozapine, aripiprazole and haloperidol) to bolster its conclusion that antipsychotic drugs disrupt correlated cortical activity in Layer 5 neurons, and further demonstrates that this disruption is specific to antipsychotics, as the psychostimulant amphetamine shows no such effect.

In widefield calcium imaging experiments, the authors effectively control for the impact of hemodynamic occlusions in their results, and try to minimize this impact using a crystal skull preparation, which performs better than traditional glass windows. Moreover, they examine key findings in widefield calcium imaging experiments with two-photon imaging.

Weaknesses:

A critical weakness of the paper is its statistical analysis. The study does not use mice as its independent unit for statistical comparisons but rather relies on other definitions, without appropriate justification, which results in an inflation of sample sizes. For example, in Figure 1, independent samples are defined as locomotion onsets, leading to sample sizes of approx. 400-2000 despite only using 6 mice for the experiment. This is only justified if the data from locomotion onsets within a mouse is actually statistically independent, which the authors do not test for, and which seems unlikely. With such inflated sample sizes, it becomes more likely to find spurious differences between groups as significant. It also remains unclear how many locomotion onsets come from each mouse; the results could be dominated by a small subset of mice with the most locomotion onsets. The more disciplined approach to statistical analysis of the dataset is to average the data associated with locomotion onsets within a mouse, and then use the mouse as an independent unit for statistical comparison. A second example, for instance, is in Figure 2L, where the independent statistical unit is defined as cortical regions instead of mice, with the left and right hemispheres counting as independent samples; again this is not justified. Is the activity of cortical regions within a mouse and across cortical hemispheres really statistically independent? The problem is apparent throughout the manuscript and for each data set collected.

An additional statistical issue is that it is unclear if the authors are correcting for the use of multiple statistical tests (as in for example Figure 1L and Figure 2B,D). In general, the use of statistics by the authors is not justified in the text.

Finally, it is important to note that whilst the study demonstrates that antipsychotics may selectively impact visuomotor integration in L5 neurons, it does not show that this effect is necessary or sufficient for the action of antipsychotics; though this is likely beyond the scope of the study it is something for readers to keep in mind.

Reviewer #1 (Public Review):

The paper by Dr. Ter-Ovanesyan et. all discussing a very important topic in the field of extracellular vesicles: how to enrich EVs compare to more abundant other circulating particles like lipoproteins, especially VLDL and LDL, which overlap in size and density with EVs and make the purification process challenging. The authors discussed several approaches, including size exclusion chromatography, density-gradient centrifugation, and methods combining charge and size separation. They also proposed the Tri-Mode Chromatography (TMC) method as a good alternative to conventional SEC separation. However, the results provided for the TMC method do not fully support the claim. TEM images provided show the presence of lipoprotein particles at a higher rate than EVs. In addition, proteomics data suggest that lipoproteins and free proteins are still overrating ones associated with EVs.

The importance of this paper is the code available for an automated device for simultaneous fraction collection, which can be very useful for researchers with limited resources since commercial devices are quite expensive.

Reviewer #2 (Public Review):

The authors of the current study set out to improve the purity of extracellular vesicles obtained from plasma. A well-described problem is that various means of separating extracellular vesicles from other plasma constituents tend to leave residual impurities such as lipoproteins and free proteins in the final extracellular vesicles preparation. Van Deun and colleagues had previously improved on the size exclusion chromatography approach by adding a second form of chromatography using separation based on charge. The current authors have evaluated that method and another gold standard approach, iodixanol gradient ultracentrifugation, and they have extended the work with the addition of a third form of chromatography. They are building on their prior work on separating albumin from plasma extracellular vesicles.

A major strength of the paper is that the authors have used complementary methods including a digital immunoassay method and transmission electron microscopy to demonstrate the purity of their sample preparation method. In addition, they have used mass spectrometry to show that they are able to profile hundreds of proteins in their plasma extracellular vesicle sample preparations.

Another major strength of the work is that the authors have taken pains to aid others in reproducing and extending the work. The authors used commercially available human pooled plasma, which is a good decision in terms of reproducibility, compared with a single person's plasma. The authors have explained exactly how to make their new chromatography columns, and they've also explained how to make a manual or an automated apparatus to improve the parallel processing of samples. They explained exactly how to fabricate each apparatus, with computer-aided design files and Raspberry Pi software. I anticipate many others will be able to implement what the authors have done because they shared these resources.

Moreover, the authors have shared the essential data needed to understand and vet their work.

Meanwhile, they shared simple and practical information about the preparation of Sepharose columns to improve the yield of chromatography. They showed that in-column washing with PBS yielded more extracellular vesicles compared with washing Sepharose prior to making the column. This finding should help anyone using size-exclusion chromatography or the more sophisticated combinations of chromatography studied herein.

The major weakness of the method is that it remains unclear to what extent the results of proteomic profiling of these purer plasma extracellular vesicles continue to be confounded by free proteins. This is a problem that will take sustained efforts to resolve, but the authors have built the next piece of the road heading in that direction.

The authors have succeeded in their main aims, albeit without being able to completely rid the sample preparations of lipoproteins, which may or may not be possible.

The results support the authors' conclusions.

This work is going to be useful to the increasing number of researchers who find that circulating extracellular vesicles hold promise for the diagnosis of diseases. In order to find the "signal" within the noise of the complex admixture constituting human plasma, a suitable process for separating vesicles from what I would call impurities is essential. The ability to automate that process while also scaling it up are additional essential components for the extracellular vesicle biomarker field to develop into a clinically useful source of biomarkers. The authors have made progress in each of these areas.

Reviewer #1 (Public Review):

In this manuscript, the authors use purified human proteins to assess the factors required for the reglucosylation of MHC-I and describe an elegant, mass-spectrometry-based assay to assess reglucosylation. This process is an essential quality-control step for peptide-MHC-I complexes before they are trafficked to the cell surface. Earlier studies have established TAPBPR as a tapasin-like peptide editor of MHC-I outside the peptide loading complex. The ER chaperone UGGT1 has also been shown to interact with MHC-I loaded with a low-affinity peptide, reglucosylating it to allow re-interaction with the peptide loading complex via calreticulin. That TAPBPR facilitates the interaction of UGGT1 with MHC-I was described by Boyle and co-workers in 2017. In that study, a free cysteine on TAPBPR was shown to be essential for the interaction between TAPBPR and UGGT1, although there was no inter-molecular disulfide linkage formed. The data in the current in vitro study suggests that while TAPBPR is an essential facilitator of reglucosylation of the HLA-A*68:02 allele, the free Cys on TAPBPR is not required to bridge the interaction between MHC-I and UGGT1.

Reviewer #2 (Public Review):

In this manuscript, authors had to circumvent some challenges in protein design that included the generation of peptide-receptive MHCI and a defined Man9GlcNAc2 glycan tree on the MHC I recognizable by UGGT1. Production of peptide-receptive MHCI was achieved by forming a fos/jun dimerized single-chain MHC1-fos with TAPBPR-jun in the presence of the α-mannosidase I inhibitor kifunensine. Glucozylation of MHCI by UGGT1 was monitored on protease-cleaved MHCI/TAPBPR, and liquid chromatography-mass spectrometry was used to monitor reglucosylation. Authors have provided convincing evidence that TAPBPR is sufficient and necessary for glucosylation of MHC 1, hence TAPBPR in addition to serving as an accessory protein in regulating peptide selection has a second function in quality control and fitness of newly synthesized MHC I during maturation.

The strength of the study lies in the generation of a complete in vitro system where different steps and direct interactions between different components of MHCI maturation can be monitored, hence leading to a better mechanistic understanding of MHC I maturation. However, some potential weakness might be that the major finding of the manuscript describing the critical role of TAPBPR as a chaperon in optimizing peptide selection and regulation of MHC I glucosylation and reglucosylation has been previously reported. Nonetheless, the current study further establishes and better defines some prior findings, thus quite valuable.

Reviewer #1 (Public Review):

In this manuscript, the authors investigated the role of Elg1 in the regulation of telomere length. The main role of the Elg1/RLC complex is to unload the processivity factor PCNA, mainly after completion of synthesis of the Okazaki fragment in the lagging strand. They found that Elg1 physically interacts with the CST (Cdc13-Stn1-Ten1) and propose that Elg1 negatively regulates telomere length by mediating the interaction between Cdc13 and Stn1 in a pathway involving SUMOylation of both PCNA and Cdc13. Accumulation of SUMOylated PCNA upon deletion of ELG1 or overexpression of RAD30 leads to elongated telomeres. On the other hand, the interaction of Elg1 with Sten1 is SIM-dependent and occurs concurrently with telomere replication in late S phase. In contrast Elg1-Cdc13 interaction is mediated by PCNA-SUMO, is independent on the SIM of Elg1 but still dependent on Cdc13 SUMOylation. The authors present a model containing two main messages 1) PCNA-SUMO acts as a positive signal for telomerase activation 2) Elg1 promotes Cdc13/Stn1 interaction at the expense of Cdc13/Est1 interaction thus terminating telomerase action.

The manuscript contains a large amount of data that make a major inroad on a new type of link between telomere replication and regulation of the telomerase. Nevertheless, the detailed choreography of the events as well as the role of PCNA-SUMO remain elusive and the data do not fully explain the role of the Stn1/Elg1 interaction. The data presented do not sufficiently support the claim that SUMO-PCNA is a positive signal for telomerase activation.

Reviewer #2 (Public Review):

This paper purports to unveil a mechanism controlling telomere length through SUMO modifications controlling interactions between PCNA unloader Elg1 and the CST complex that functions at telomeres. This is an extremely interesting mechanism to understand, and this paper indeed reveals some interesting genetic results, leading to a compelling model, with potential impact on the field. The conclusions are largely supported by experiments examining protein-protein interactions at low resolution and ambiguous regarding directness of interactions like co-IP and yeast two-hybrid (Y2H) combined with genetics. However, some results appear contradictory and there's a lack of rigor in the experimental data needed to support claims. There is significant room for improvement and this work could certainly attain the quality needed to support the claims. The current version needs substantial revision and lacks the necessary experimental detail. Stronger support for the claims would add detail to help distinguish competing models.

Reviewer #3 (Public Review):

This paper reveals interesting physical connections between Elg1 and CST proteins that suggest a model where Elg1-mediated PCNA unloading is linked to regulation of telomere length extension via Stn1, Cdc13, and presumably Ten1 proteins. Some of these interactions appear to be modulated by sumolyation and connected with Elg1's PCNA unloading activity. The strength of the paper is in the observations of new interactions between CST, Elg1, and PCNA. These interactions should be of interest to a broad audience interested in telomeres and DNA replication.

What is not well demonstrated from the paper is the functional significance of the interactions described. The model presented by the authors is one interpretation of the data shown, and proposes that the role of sumolyation is temporally regulate the Elg1, PCNA and CST interactions at telomeres. This model makes some assumptions that are not demonstrated by this work (such as Stn1 sumolyation, as noted) and are left for future testing. Alternative models that envision sumolyation as a key in promoting spatial localization could also be proposed based on the data here (as mentioned in the discussion), in addition to or instead of a role for sumolyation in enforcing a series of switches governing a tightly sequenced series of interactions and events at telomeres. Critically, the telomere length data from the paper indicates that the proposed model depicts interactions that are not necessary for telomerase activation or inhibition, as telomeres in pol30-RR strains are normal length and telomeres in elg1∆ strains are not nearly as elongated as in stn1 strains. One possibility mentioned in the paper is the PCNAS and Elg1 interactions are contributing to the negative regulation of telomerase under certain conditions that are not defined in this work. Could it also be possible that the role of these interactions is not primarily directed toward modulating telomerase activity? It will be of interest to learn more about how these interactions and regulation by Sumo function intersect with regulation of telomere extension.

Reviewer #1 (Public Review):

This manuscript describes the identification of influential organisms on rice growth and an attempt of validation. The analysis of eDNA on rice pot and mimic field provides rice growth promoting organisms. This approach is novel for plant ecology field. However current results did not fully support whether eDNA analysis-based detection of influencing organism.

The strength of this manuscript is to attempt application of eDNA analysis-based plant growth differentiation. The weakness is too preliminary data and experimental set-up to make any conclusion. The trials of authors experiments are ideal. However, the process of data analysis did not meet certain levels. For example, eDNA analysis of different time points on rice growth stages resulted in two influential organisms for rice growth. Then they cultivate two species and applied rice seedlings. Without understanding of fitness and robustness, how we can know the effect of the two species on rice growth.

The authors did not check the fate of two species after introducing into rice. If this is true, it is difficult to link between the rice gene expression after treatments and the effectiveness of two species. I think the validation experiment in 2019 needs to be re-conducted.

Reviewer #2 (Public Review):

The manuscript "Detecting and validating influential organisms for rice growth: An ecological network approach" explores the influence of biotic and abiotic entities that are often neglected on rice growth. The study has a straightforward experimental design, and well thought hypothesis for explorations. Monitoring data is collected to infer relationships between species and the environment empirically. It is analyzed with an up-to-date statistical method. This allowed the manuscript to hypothesize and test the effects most influential entities in a controlled experiment.

The manuscript is interesting and sets up a nice framework for future studies. In general, the manuscript can be improved significantly, when this workflow is smoothly connected and communicated how they follow each other more than the sequence and dates provided. It is valuable philosophical thinking, and the research community can benefit from this framework.

I understand the length and format of the manuscript make it difficult to add more details, but I am sure it can refer to/clear some concepts/methods that might be new for the audience. How/why variables are selected as important parts of the system, a tiny bit of information about the nonlinear time series analysis in the early manuscript, and the biological reasoning behind these statistically driven decisions are some examples.

Reviewer #3 (Public Review):

Most farming is done by subtracting or adding what people want based in nature. However, in nature, crops interact with various objects, and mostly we are unaware of their effects. In order to increase agricultural productivity, finding useful objects is very important. However, in an uncontrolled environment, it coexists with so many biological objects that it is very inefficient to verify them all experimentally. It is therefore necessary to develop an effective screening method to identify external environmental factors that can increase crop productivity. This study identified factors presumed to be important to crop growth based on metabarcoding analysis, field sampling, and non-linear analysis/information theory, and conducted a mesocosm experiment to verify them experimentally. In conclusion, the object proposed by the author did not increase rice yield, but rather rice growth rate.

Strength<br /> In actual field data, since many variables are involved in a specific phenomenon, it is necessary to effectively eliminate false positives. Based on the metabarcoding technique, various variables that may affect rice growth were quantitatively measured, although not perfectly, and the causal relationship between these variables and rice growth was analyzed by using information transfer analysis. Using this method, two new players capable of manipulating rice growth were verified, despite their unknown functions until now. I found this process to be very logical, and I think it will be valuable in subsequent ecological studies.

Weaknesses<br /> CK treatment's effectiveness remains questionable. Rice's growth was clearly altered by CK treatment. The validation of the CK treatment itself is not clear compared to the GN treatment, and the transcriptome data analysis results do not show that DEG is not present. The possibility of a side effect caused by a variable that the author cannot control remains a possibility in this case. Even though this part is mentioned in Discussion, it is necessary to discuss various possibilities in more detail.

Reviewer #1 (Public Review):

This study investigates the context-specificity of facial expressions in three species of macaques to test predictions for the 'social complexity hypothesis for communicative complexity'. This hypothesis has garnered much attention in recent years. A proper test of this hypothesis requires clear definitions of 'communicative complexity' and 'social complexity'. Importantly, these two facets of a society must not be derived from the same data because otherwise, any link between the two would be trivial. For instance, if social complexity is derived from the types of interactions individuals have, and different types of signals accompany these interactions, we would not learn anything from a correlation between social and communicative complexity, as both stem from the same data.

The authors of the present paper make a big step forward in operationalising communicative complexity. They used the Facial Action Coding System to code a large number of facial expressions in macaques. This system allows decomposing facial expressions into different action units, such as 'upper lid raiser', 'upper lip raiser' etc.; these units are closely linked to activating specific muscles or muscle groups. Based on these data, the authors calculated three measures derived from information theory: entropy, specificity and prediction error. These parts of the analysis will be useful for future studies.

The three species of macaque varied in these three dimensions. In terms of entropy, there were differences with regard to context (and if there are these context-specific differences, then why pool the data?). Barbary and Tonkean macaques showed lower specificity than rhesus macaques. Regarding predicting context from the facial signals, a random forest classifier yielded the highest prediction values for rhesus monkeys. These results align with an earlier study by Preuschoft and van Schaik (2000), who found that less despotic species have greater variability in facial expressions and usage.

Crucially, the three species under study are also known to vary in terms of their social tolerance. According to the highly influential framework proposed by Bernard Thierry, the members of the genus Macaca fall along a graded continuum from despotic (grade 1) to highly tolerant (grade 4). The three species chosen for the present study represent grade 1 (rhesus monkeys), grade 3 (Barbary macaques), and grade 4 (Tonkean macaques).

The authors of the present paper define social complexity as equivalent to social tolerance - but how is social tolerance defined? Thierry used aggression and conflict resolution patterns to classify the different macaque species, with the steepness of the rank hierarchy and the degree of nepotism (kin bias) being essential. However, aggression and conflict resolution are accompanied by facial gestures. Thus, the authors are looking at two sides of the same coin when investigating the link between social complexity (as defined by the authors) and communicative complexity. Therefore, I am not convinced that this study makes a significant advance in testing the social complexity for communicative complexity hypothesis. A further weakness is that - despite the careful analysis - only three species were considered; thus, the effective sample size is very small.

Reviewer #2 (Public Review):

This is a well-written manuscript about a strong comparative study of diversity of facial movements in three macaque species to test arguments about social complexity influencing communicative complexity. My major criticism has to do with the lack of any reporting of inter-observer reliability statistics - see comment below. Reporting high levels of inter-observer reliability is crucial for making clear the authors have minimized chances of possible observer biases in a study like this, where it is not possible to code the data blind with regard to comparison group. My other comments and questions follow by line number:

38-40. Whereas I am an advocate of this hypothesis and have tested it myself, the authors should probably comment here, or later in the discussion, about the reverse argument - greater communicative complexity (driven by other selection pressures) could make more complicated social structures possible. This latter view was the one advocated by McComb & Semple in their foundational 2005 Biology Letters comparative study of relationships between vocal repertoire size and typical group size in non-human primate species.

72-84 and 95-96. In the paragraph here, the authors outline an argument about increasing uncertainty / entropy mapping on to increasing complexity in a system (social or communicative). In lines 95-96, though, they fall back on the standard argument about complex systems having intermediate levels of uncertainty (complete uncertainty roughly = random and complete certainty roughly = simple). Various authors have put forward what I think are useful ways of thinking about complexity in groups - from the perspective of an insider (i.e., a group member, where greater randomness is, in fact, greater complexity) vs from the perspective of an outside (i.e., a researcher trying to quantify the complexity of the system where is it relatively easy to explain a completely predictable or completely random system but harder to do so for an intermediately ordered or random system). This sort of argument (Andrew Whiten had an early paper that made this argument) might be worth raising here or later in the discussion? (I'm also curious where the authors sentiments lie for this question - they seem to touch on it in lines 285-287, but I think it's worth unpacking a little more here!)

115-129. See also:<br /> Maestripieri, D. (2005). "Gestural communication in three species of macaques (Macaca mulatta, M. nemestrina, M. arctoides): use of signals in relation to dominance and social context." Gesture 5: 57-73.<br /> Maestripieri, D. and K. Wallen (1997). "Affiliative and submissive communication in rhesus macaques." Primates 38(2): 127-138.<br /> On that note, it is probably worth discussing in this paragraph and probably later in the discussion exactly how this study differs from these earlier studies of Maestripieri. I think the fact that machine learning approaches had the most difficulty assigning crested data to context is an important methodological advance for addressing these sorts of questions - there are probably other important differences between the authors' study here and these older publications that are worth bringing up.

220-222. What is known about visual perception in these species? Recent arguments suggest that more socially complex species should have more sensitive perceptual processing abilities for other individuals' signals and cues (see Freeberg et al. 2019 Animal Behaviour). Are there any published empirical data to this effect, ideally from the visual domain but perhaps from any domain?

274-277. I am not sure I follow this - could not different social and non-social contexts produce variation in different affective states such that "emotion"-based signals could be as flexible / uncertain as seemingly volitional / information-based / referential-like signals? This issue is probably too far away from the main points of this paper, but I suspect the authors' argument in this sentence is too simplified or overstated with regard to more affect-based signals.

288 on. Given there are only three species in this study, the chances of one of the species being the 'most complex' in any measure is 0.33. Although I do not believe this argument I am making here, can the authors rule out the possibility that their findings related to crested macaques are all related to chance, statistically speaking?

329-330. The fact that only one male rhesus macaque was assessed here seems problematic, given the balance of sexes in the other two species. Can the authors comment more on this - are the gestures they are studying here identical across the sexes?

354-371. Inter-observer reliability statistics are required here - one of the authors who did not code the original data set, or a trained observer who is not an author, could easily code a subset of the video files to obtain inter-observer reliability data. This is important for ruling out potential unconscious observer biases in coding the data.

Reviewer #1 (Public Review):

This study aims to address the mechanism of eccDNA generation during spermatogenesis in mice. Previous efforts for cataloging eccDNA in mammalian germ cells have provided inconclusive results, particularly in the correlation between meiotic recombination and the generation of eccDNA. The authors employed an established approach (Circle-seq) to enrich and amplify eccDNA for sequencing analyses and reported that sperm eccDNA is not associated with miotic recombination hotspots. Rather, the authors reported that eccDNAs are widespread, and oligonucleosomal DNA fragments from sperm undergoing apoptosis, with the ligation of DNA ends by microhomology-mediated end-joining, would be a major source of eccDNA.

The strength of the study includes evaluating the eccDNA contents not only in sperm but also from earlier stages of cells in spermatogenesis. The differences in eccDNA size peaks between sperm and other progenitors, in particular, the unique peak in sperm around 360 bp, are intriguing. Results from sequencing data analysis were presented elegantly.

I also have critiques. First, the lack of eccDNA quality control step is a concern. Previous studies employed electron microscopy to ensure that DNA species are mostly circular before rolling-circle amplification. Phi29 polymerase is widely used for DNA amplification, including whole genome amplification of linear chromosomal DNA. Phi29 polymerase has a high processivity and strand displacement activity. When those activities occur within a molecule, it creates circular DNA from linear DNA in vitro. In vitro-created eccDNA from linear DNA would be randomly distributed in the genome, which may explain the low incidence of common eccDNA between replicates. Therefore, it will be crucial to show that DNA prior to amplification is dominantly circular. Electron microscopy would be challenging for the study because the relatively small number of cells were processed to enrich eccDNA. An alternative method for quality controls includes spiking samples with linear and circular exogenous DNA and measuring the ratios of circular/linear control DNA before and after column purification/exonuclease digestion. eccDNA isolation procedures can be validated by a very high circular/linear control DNA ratio.

Another critique is regarding the limitation of the study. It is important to remind the readers of the limitations of the study. As the authors mentioned, rolling circle amplification preferentially increases the copy numbers of smaller eccDNA. Therefore, the native composition of eccDNA is skewed. In addition, the candidate eccDNAs are identified by split reads or discordant read pairs. The details of the mapping process are unclear from the methods, but such a method would require reads with high mapping quality; the identification of eccDNA is expected to require sequencing reads that are mapped to genomic locations uniquely with high confidence, and reads mapped to more than one genomic location, such as highly similar repeat sequences or duplications, are eliminated. Such identification criteria would favor eccDNA formed by little or no homology at the junction sequences, and eliminate eccDNA formed by long homologies at the ends, such as eccDNA formed exclusively by satellite DNA. Therefore, it is not surprising that the authors found the dominance of microhomology-mediated eccDNA. It remains to be determined whether small eccDNA with microhomologies are the dominant species of eccDNA in the native composition. In this regard, it is noted that similar procedures of eccDNA enrichment (column purification, exonuclease digestion, and rolling circle amplification ) revealed variable sizes and characteristics of eccDNA in sperm (human from Henriksen et al. or mice from this study), dependent on the methods of sequencing (long-read or short-read sequencing). Considering these limitations, the last sentence of the introduction, "We conclude that germline eccDNAs are formed largely by microhomology mediated ligation of nucleosome protected fragments, and barely contribute to de novo genomic deletions at meiotic recombination hotspots" needs to be revised.

Small eccDNA (microDNA) data from various mouse tissues are available from the study by Dillion et al., (Cell Reports 2015). Authors are encouraged to examine whether the notable findings in this study (oligonucleosomal-sized eccDNA peaks and the association with apoptotic cell death) are unique to sperm or common in the eccDNA from other tissues.

Reviewer #2 (Public Review):

This study presents a useful investigation of eccDNAs in spermatogenesis of mouse. It provides evidence about the biogenesis of eccDNAs and suggests that eccDNAs are derived from oligonucleosmal DNA fragmentation during apoptosis by MMEJ and may not be the direct products of germline deletions. However, the method of data analyses were not fully described and data analysis is incomplete. It provides additional observations about the eccDNA biogenesis and can be used as a starting point for functional studies of eccDNA in sperms. However, many aspects about data analyses and data interpretations need to be improved.

• Most of the conclusions made by the work are only based on the bioinformatics analyses, the validation of these foundlings using other method (biochemistry/molecular biology method) are missing. For example, no QC results presented for the eccDNA purification, which may show whether contaminates such as linear DNA or mitochondria DNA have been fully removed. Additionally, it is also helpful to use simple PCR to test the existence of identified eccDNAs in sperm or other samples to validate the specificity of the Circle-seq method.

• The reliability of the data analysis methods is uncertain, as the authors constructed and utilized their own pipeline to identify eccDNAs, despite the availability of established bioinformatics tools such as ECCsplorer, eccFinder, and Amplicon Architect. Moreover, the lack of validation of the pipeline using either ground truth datasets or simulation data raises concerns about its accuracy. Additionally, the methodology employed for identifying eccDNA that encompasses multiple gene loci remains unclear.

• Although the author stated that previous studies utilizing short-read sequencing technologies may have incorrectly annotated eccDNA breakpoints, this claim requires careful scrutiny and supporting evidence, which was not provided in the manuscript.

• The similarity between the eccDNA profiles of human and mouse sperm remains uncertain, and therefore, analyses of human eccDNA data and comparisons between the two are necessary if the authors claim that their findings of widespread eccDNA formation in mouse spermatogenesis extend to human sperms.

Reviewer #1 (Public Review):

In their manuscript "Spindle assembly checkpoint-dependent mitotic delay is required for cell division in absence of centrosomes," Farrell and colleagues employ carefully chosen approaches to assay mitotic timeliness in the absence of centrosomes in mammalian culture cells, namely the mechanism behind it and its function. The authors acknowledge prior work well and present their data succinctly, clearly, and with a clear logical flow of experiments. The experiments are thoughtfully designed and presented, with appropriate controls in place.

The authors' model whereby centrosome separation and its early definition of poles mediates timely mitosis without relying on a SAC-dependent delay is compelling, and the authors' data are consistent with it. They show using two different MPS1 inhibitors that acentrosomal cell division fails, supporting their claims that a SAC-dependent delay is required in these instances. Furthermore, they show that reintroducing a time delay is sufficient to restore cell division, but inhibiting a different aspect of SAC function does not restore cell division. Next, the authors rule out polyploidy as a potential confounding factor for requiring a SAC-dependent delay, and instead demonstrate that inhibiting centrosome separation by Eg5 inhibition is sufficient to require this delay for mitotic progression. The authors' findings overall support their proposed model.

Probing what aspects of centrosomes protect against a requirement for SAC-dependent delays would strengthen the work and specifically the conclusion that centrosomes provide "two-ness". For example, the authors could examine division in a population of cells with only one centrosome. Seeing some restoration of mitotic progression in the absence of SAC-dependent delays would suggest that even one centrosome with uninhibited Eg5 is sufficient to negate SAC-dependent delays, and would limit models for what exactly centrosomes contribute. This would help disentangle the roles of actual centrosomes and their biochemical cues, Eg5-driven centrosome separation, and early definition of poles on mitotic progression in the absence of SAC-dependent delays. Making a high fraction of cells with one centrosome could be achieved by using centrinone for a shorter time.

Reviewer #2 (Public Review):

Centrosomes are an integral part of cell division in most animal cells. There are notable exceptions, however, such as oocytes and plants. In addition, some animal cells can be engineered to lack centrosomes yet they can still manage to complete mitosis. This paper uses a couple methods (PLK4 inhibition and deletion of SASS6) to remove centrosomes from an immortalized cell line. Indeed, a strength of the paper is that similar results are obtained using both protocols to generate acentrosomal cells. The authors find acentrosomal cells take longer to divide, mostly due to a longer metaphase. The paper is based on the finding that inhibition of MPS1 results in a failure to divide, and the hypothesis that a SAC - dependent delay is required for these acentrosomal cells to divide.

The finding that MPS1 inhibition results in a failure to division is interesting. This is investigated by analyzing cells where AurB, APC or Eg5 (to generate monastral spindles) have been inhibited. My concerns are that the results are not conclusive that the effect of MPS1 is on cell cycle regulation. There is not enough data to make a conclusion as to why inhibition of MPS1 results in cell division failure.

1) An example is how to interpret the effect of Aurora B inhibition, which does not block acentrosomal cell division. If Aurora B is required for SAC activity, it suggests this effect of MPS1 may be a function other than SAC. Given the complexity of the SAC, it would be informative to test other SAC components. Instead, the authors conclude that the mitotic delay caused by MPS is required for acentrosomal cell division. I don't think they have ruled out, or even addressed other functions of MPS1.

2) The authors find that when both the APC and MPS1 are inhibited, the cells eventually divide. These results are intriguing, but hard to interpret. The authors suggest that the failure to divide in MPS1-inhibited cells is because they enter anaphase, and then must back out. This is hard to understand and there is not data supporting some kind of aborted anaphase. Is the division observed with double inhibition some sort of bypass of the block caused by MPS1 inhibition alone? It is not clear why inhibition of APC causes increased cell division when MPS1 is inhibited.

3) The authors characterize MTOC formation in these cells, which is also interesting. MTOCs are established after NEB in acentrosomal cells. Indeed, forming these MTOCs is probably a key mechanism for how these cells complete a division, like mouse oocytes.

Following this, the results with inhibiting Eg5 are interesting. The double inhibition of MPS1 and Eg5 results in division failure, like MPS1 inhibition in acentrosomal cells. Thus, there is a cell division block when the centrioles fail to divide. This result raises the possibility that failure to make a bipolar spindle, or the presence of a monopolar spindle, is the problem. In the absence of a bipolar spindle, a SAC induced delay is required for spindle assembly. This is a possibility but there are multiple interpretations of these results. Primarily, these results do not show the MPS1 effect on acentrosomal cells is SAC related. That a SAC mediated delay is required for acentrosmomal spindle assembly is not the only conclusion.

Reviewer #1 (Public Review):

Based on a recent report of spontaneous and reversible remapping of spatial representations in the enthorhinal cortex (Low et al 2021), this study sets out to examine possible mechanisms by which a network can simultaneously represent a positional variable and an uncorrelated binary internal state. To this end, the authors analyse the geometry of activity in recurrent neural networks trained to simultaneously encode an estimate of position in a one-dimensional track and a transiently-cued binary variable. They find that network activity is organised along two separate ring manifolds. The key result is that these two manifolds are significantly more aligned than expected by chance, as previously found in neural recordings. Importantly, the authors show that this is not a direct consequence of the design of the model, and clarify scenarios by which weaker alignment could be achieved. The model is then extended to a two-dimensional track, and to more than two internal variables. The latter case is compared with experimental data that had not been previously analysed.

Strengths:<br /> - rigorous and careful analysis of activity in trained recurrent neural networks<br /> - particular care is taken to show that the obtained results are not a necessary consequence of the design of the model<br /> - the writing is very clear and pleasant to read<br /> - close comparison with experimental data<br /> - extensions beyond the situations studied in experiments (two-dimensional track, more than two internal states)

Weaknesses:<br /> - no major weaknesses<br /> - (minor) the comparison with previous models of remapping could be expanded

Altogether the conclusions claimed by the authors seem to be strongly supported and convincing.

Reviewer #2 (Public Review):

This important work presents an example of a contextual computation in a navigation task through a comparison of task driven RNNs and mouse neuronal data. Authors perform convincing state of the art analyses demonstrating compositional computation with valuable properties for shared and distinct readouts. This work will be of interest to those studying contextual computation and navigation in biological and artificial systems.

This work advances intuitions about recent remapping results. Authors trained RNNs to output spatial position and context given velocity and 1-bit flip-flops. Both of these tasks have been trained separately, but this is the first time to my knowledge that one network was trained to output both context and spatial position. This work is also somewhat similar to previous work where RNNs were trained to perform a contextual variation on the Ready-Set-Go with various input configurations (Remington et al. 2018). Additionally findings in the context of recent motor and brain machine interface tasks are consistent with these findings (Marino et al in prep). In all cases contextual input shifts neural dynamics linearly in state space. This shift results in a compositional organization where spatial position can be consistently decoded across contexts. This organization allows for generalization in new contexts. These findings in conjunction with the present study make a consistent argument that remapping events are the result of some input (contextual or otherwise) that moves the neural state along the remapping dimension.

The strength of this paper is that it tightly links theoretical insights with experimental data, demonstrating the value of running simulations in artificial systems for interpreting emergent properties of biological neuronal networks. For those familiar with RNNs and previous work in this area, these findings may not significantly advance intuitions beyond those developed in previous work. It's still valuable to see this implementation and satisfying demonstration of state of the art methods. The analysis of fixed points in these networks should provide a model for how to reverse engineer and mechanistically understand computation in RNNs.

I'm curious how the results might change or look the same if the network doesn't need to output context information. One prediction might be that the two rings would collapse resulting in completely overlapping maps in either context. I think this has interesting implications about the outputs of the biological system. What information should be maintained for potential readout and what information should be discarded? This is relevant for considering the number of maps in the network. Additionally, I could imagine the authors might reproduce their current findings in another interesting scenario: Train a network on the spatial navigation task without a context output. Fix the weights. Then provide a new contextual input for the network. I'm curious whether the geometric organization would be similar in this case. This would be an interesting scenario because it would show that any random input could translate the ring attractor that maintains spatial position information without degradation. It might not work, but it could be interesting to try!

I was curious and interested in the authors choice to not use activity or weight regularization in their networks. My expectation is that regularization might smooth the ring attractor to remove coding irrelevant fluctuations in neural activity. This might make Supplementary Figure 1 look more similar across model and biological remapping events (Line 74). I think this might also change the way authors describe potential complex and high dimensional remapping events described in Figure 2A.

Overall this is a nice demonstration of state-of-the-art methods to reverse engineer artificial systems to develop insights about biological systems. This work brings together concepts for various tasks and model organisms to provide a satisfying analysis of this remapping data.

Reviewer #3 (Public Review):

This important work provides convincing evidence that artificial recurrent neural networks can be used to model neural activity during remapping events while an animal is moving along a one-dimensional circular track. This will be of interest to neuroscientists studying the neural dynamics of navigation and memory, as well as the community of researchers seeking to make links between artificial neural networks and the brain.

Low et al. trained artificial recurrent neural networks (RNNs) to keep track of their location during a navigation task and then compared the activity of these model neurons to the firing rates of real neurons recorded while mice performed a similar task. This study shows that a simple set of ingredients, namely, keeping track of spatial location along a one-dimensional circular track, along with storing the memory of a binary variable (representing which of the two spatial maps are currently being used), are enough to obtain model firing rates that reproduce features of real neural recordings during remapping events. This offers both a normative explanation for these neural activity patterns as well as a potential biological implementation.

One advantage of this modeling approach using RNNs is that this gives the authors a complete set of firing rates that can be used to solve the task. This makes analyzing these RNNs easier, and opens the door for analyses that are not always practical with limited neural data. The authors leverage this to study the stable and unstable fixed points of the model. However, in this paper there appear to be a few places where analyses that were performed on the RNNs were not performed on the neural data, missing out on an opportunity to appreciate the similarity, or identify differences and pose challenges for future modeling efforts. For example, in the neural data, what is the distribution of the differences between the true remapping vectors for all position bins and the average remapping vector? What is the dimensionality of the remapping vectors? Do the remapping vectors vary smoothly over position? Do the results based on neural data look similar to the results shown for the RNN models (Figures 2C-E)?

I enjoyed that the authors leveraged the RNNs to model remapping in a 2D navigation task that is harder to understand from data alone, at least with current experimental capabilities. I would recommend clarifying that you're studying a 2D environment that consists of two circular variables. Currently, this is not clear from the text, and it is more natural to interpret the task schematic in Figure 4A as depicting an arena without periodic boundary conditions. Figure 4F depicts neural activity for this task as a standard torus, however, I suspect the neural activity might actually lie along the surface of a Clifford torus as Cueva, Ardalan et al. 2021 found when they trained a RNN to store two circular variables. As a disclaimer, I am one of the authors of that study.

There are many choices that must be made when simulating RNNs and there is a growing awareness that these choices can influence the kinds of solutions RNNs develop. For example, how are the parameters of the RNN initialized? How long is the RNN trained on the task? Are the firing rates encouraged to be small or smoothly varying during training? For the most part these choices are not explored in this paper so I would interpret the authors' results as highlighting a single slice of the solution space while keeping in mind that other potential RNN solutions may exist. For example, the authors note that the RNN and biological data do not appear to solve the 1D navigation and remapping task with the simplest 3-dimensional solution. However, it seems likely that an RNN could also be trained such that it only encodes the task relevant dynamics of this 3-dimensional solution, by training longer or with some regularization on the firing rates. Similarly, a higher-dimensional RNN solution may also be possible and this would likely be necessary to explain the more variable manifold misalignment reported in the experimental data of Low et al. 2021 as opposed to the more tightly aligned distribution for the RNNs in this paper. However, thanks to the modeling work done in this paper, the door has now been opened to these and many other interesting research directions.

Reviewer #1 (Public Review):

Meta-cognition, and difficulty judgments specifically, is an important part of daily decision-making. When facing two competing tasks, individuals often need to make quick judgments on which task they should approach (whether their goal is to complete an easy or a difficult task).

In the study, subjects face two perceptual tasks on the same screen. Each task is a cloud of dots with a dominating color (yellow or blue), with a varying degree of domination - so each cloud (as a representation of a task where the subject has to judge which color is dominant) can be seen an easy or a difficult task. Observing both, the subject has to decide which one is easier.

It is well-known that choices and response times in each separate task can be described by a drift-diffusion model, where the decision maker accumulates evidence toward one of the decisions ("blue" or "yellow") over time, making a choice when the accumulated evidence reaches a predetermined bound. However, we do not know what happens when an individual has to make two such judgments at the same time, without actually making a choice, but simply deciding which task would have stronger evidence toward one of the options (so would be easier to solve).

It is clear that the degree of color dominance ("color strength" in the study's terms) of both clouds should affect the decision on which task is easier, as well as the total decision time. Experiment 1 clearly shows that color strength has a simple cumulative effect on choice: cloud 1 is more likely to be chosen if it is easier and cloud 2 is harder. Response times, however, show a more complex interactive pattern: when cloud 2 is hard, easier cloud 1 produces faster decisions. When cloud 2 is easy, easier cloud 1 produces slower decisions.

The study explores several models that explain this effect. The best-fitting model (the Difference model is the paper's terminology) assumes that the decision-maker accumulates evidence in both clouds simultaneously and makes a difficulty judgment as soon as the difference between the values of these decision variables reaches a certain threshold. Another potential model that provides a slightly worse fit to the data is a two-step model. First, the decision maker evaluates the dominant color of each cloud, then judges the difficulty based on this information.

Importantly, the study explores an optimal model based on the Markov decision processes approach. This model shows a very similar qualitative pattern in RT predictions but is too complex to fit to the real data. It is hard to judge from the results of the study how the models identified above are specifically related to the optimal model - possibly, the fact that simple approaches such as the Difference model fit the data best could suggest the existence of some cognitive constraints that play a role in difficulty judgments.

The Difference model produces a well-defined qualitative prediction: if the dominant color of both clouds is known to the decision maker, the overall RT effect (hard-hard trials are slower than easy-easy trials) should disappear. Essentially, that turns the model into the second stage of the two-stage model, where the decision maker learns the dominant colors first. The data from Experiment 2 impressively confirms that prediction and provides a good demonstration of how the model can explain the data out-of-sample with a predicted change in context.

Overall, the study provides a very coherent and clean set of predictions and analyses that advance our understanding of meta-cognition. The field would benefit from further exploration of differences between the models presented and new competing predictions (for instance, exploring how the sequential presentation of stimuli or attentional behavior can impact such judgments). Finally, the study provides a solid foundation for future neuroimaging investigations.

Reviewer #2 (Public Review):

Starting from the observation that difficulty estimation lies at the core of human cognition, the authors acknowledge that despite extensive work focusing on the computational mechanisms of decision-making, little is known about how subjective judgments of task difficulty are made. Instantiating the question with a perceptual decision-making task, the authors found that how humans pick the easiest of two stimuli, and how quickly these difficulty judgments are made, are best described by a simple evidence accumulation model. In this model, perceptual evidence of concurrent stimuli is accumulated and difficulty is determined by the difference between the absolute values of decision variables corresponding to each stimulus, combined with a threshold crossing mechanism. Altogether, these results strengthen the success of evidence accumulation models, and more broadly sequential sampling models, in describing human decision-making, now extending it to judgments of difficulty.

The manuscript addresses a timely question and is very well written, with its goals, methods and findings clearly explained and directly relating to each other. The authors are specialists in evidence accumulation tasks and models. Their modelling of human behaviour within this framework is state-of-the-art. In particular, their model comparison is guided by qualitative signatures which are diagnostic to tease apart the different models (e.g., the RT criss-cross pattern). Human behaviour is then inspected for these signatures, instead of relying exclusively on quantitative comparison of goodness-of-fit metrics. This work will likely have a wide impact in the field of decision-making, and this across species. It will echo in particular with many other studies relying on the similar theoretical account of behaviour (evidence accumulation).

A few points nevertheless came to my attention while reading the manuscript, which the authors might find useful to answer or address in a new version of their manuscript.

1. The authors acknowledge that difficulty estimation occurs notably before exploration (e.g., attempting a new recipe) or learning (e.g., learning a new musical piece) situations. Motivated by the fact that naturalistic tasks make difficult the identification of the inference process underlying difficulty judgments, the authors instead chose a simple perceptual decision-making task to address their question. While I generally agree with the authors's general diagnostic, I am nevertheless concerned so as to whether the task really captures the cognitive process of interest as described in the introduction. As coined by the authors themselves, the main function of prospective difficulty judgment is to select a task which will then ultimately be performed, or reject one which won't. However, in the task presented here, participants are asked to produce difficulty judgments without those judgements actually impacting the future in the task. A feature thus key to difficulty judgments thus seems lacking from the task. Furthermore, the trial-by-trial feedback provided to participants also likely differ from difficulty judgments made in real world. This comment is probably difficult to address but it might generally be useful to discuss the limitations of the task, in particular in probing the desired cognitive process as described in introduction. Currently, no limitations are discussed.

2. The authors take their findings as the general indication that humans rely on accumulation evidence mechanisms to probe the difficulty of perceptual decisions. I would probably have been slightly more cautious in excluding alternative explanations. First, only accumulation models are compared. It is thus simply not possible to reach a different conclusion. Second, even though it is particularly compelling to see untested predictions from the winning model in experiment #1 to be directly tested, and validated in a second experiment, that second experiment presents data from only 3 participants (1 of which has slightly different behaviour than the 2 others), thereby limiting the generality of the findings. Third, the winning model in experiment #1 (difference model) is the preferred model on 12 participants, out of the 20 tested ones. Fourth, the raw BIC values are compared against each other in absolute terms without relying on significance testing of the differences in model frequency within the sample of participants (e.g., using exceedance probabilities; see Stephan et al., 2009 and Rigoux et al., 2014). Based on these different observations, I would thus have interpreted the results of the study with a bit more caution and avoided concluding too widely about the generality of the findings.

3. Deriving and describing the optimal model of the task was particularly appreciated. It was however a bit disappointing not to see how well the optimal model explains participants behaviour and whether it does so better than the other considered models. Also, it would have been helpful to see how close each of the 4 models compared in Figures 2 & 3 get to the optimal solution. Note however that neither of these comments are needed to support the authors' claims.

4. The authors compared the difficulty vs. color judgment conditions to conclude that the accumulation process subtending difficulty judgements is partly distinct from the accumulation process leading to perceptual decisions themselves. To do so, they directly compared reaction times obtained in these two conditions (e.g. "in other cases, the two perceptual decisions are almost certainly completed before the difficulty decision"). However, I find it difficult to directly compare the 'color' and 'difficulty' conditions as the latter entails a single stimulus while the former comprises two stimuli. Any reaction-time difference between conditions could thus I believe only follow from asymmetric perceptual/cognitive load between conditions (at least in the sense RT_color < RT_difficulty). One alternative could have been to present two stimuli in the 'color' condition as well, and asking participants to judge both (or probe which to judge later in the trial). Implementing this now would however require to run a whole new experiment which is likely too demanding. Perhaps the authors could instead also acknowledge that this a critical difference between their conditions, which makes direct comparison difficult.

Reviewer #3 (Public Review):

The manuscript presents novel findings regarding the metacognitive judgment of difficulty of perceptual decisions. In the main task, subjects accumulated evidence over time about two patches of random dot motion, and were asked to report for which patch it would be easier to make a decision about its dominant color, while not explicitly making such decision(s). Using 4 models of difficulty decisions, the authors demonstrate that the reaction time of these decisions are not solely governed by the difference in difficulties between patches (i.e., difference in stimulus strength), but (also) by the difference in absolute accumulated evidence for color judgment of the two stimuli. In an additional experiment, the authors eliminated part of the uncertainty by informing participants about the dominant color of the two stimuli. In this case, reaction times were faster compared to the original task, and only depended on the difference between stimulus strength.

Overall, the paper is very well written, figures and illustrations clearly and adequately accompanied the text, and the method and modeling are rigor.

The weakness of the paper is that it does not provide sufficient evidence to rule out the possibility that judging the difficulty of a decision may actually be comparing between levels of confidence about the dominant color of each stimulus. One may claim that an observer makes an implicit color decision about each stimulus, and then compares the confidence levels about the correctness of the decisions. This concern is reflected in the paper in several ways:

1. It is not clear what were the actual instructors to the participants, as two different phrasings appear in the methods: one instructs participants to indicate which stimulus is the easier one and the other instructs them to indicate the patch with the stronger color dominance. If both instructions are the same, it can be assumed that knowing the dominant color of each patch is in fact solving the task, and no judgment of difficulty needs to be made (perhaps a confidence estimation). Since this is not a classical perceptual task where subjects need to address a certain feature of the stimuli, but rather to judge their difficulties, it is important to make it clear.

2. Two step model: two issues are a bit puzzling in this model. First, if an observer reaches a decision about the dominant color of each patch, does it mean one has made a color decision about the patches? If so, why should more evidence be accumulated? This may also support the possibility that this is a "post decision" confidence judgment rather than a "pre decision" difficulty judgment. Second, the authors assume the time it takes to reach a decision about the dominant color for both patches are equal, i.e., the boundaries for the "mini decision" are symmetrical. However, it would make sense to assume that patches with lower strength would require a longer time to reach the boundaries.

3. Experiment 2: the modification of the Difference model to fit the known condition (Figure 5b), can also be conceptualized as the two-step model, excluding the "mini" color decision time. These two models (Difference model with known color; two-step model) only differ from each other in a way that in the former the color is known in advance, and in the second, the subject has to infer it. One may wonder if the difference in patterns between the two (Figure 3C vs. Figure 6B) is only due to the inaccuracies of inferring the dominant color in the two-step model.

An additional concern is about the controlled duration task: Why were these specific durations chosen (0.1-1.65 sec; only a single duration was larger than 1sec), given the much longer reaction times in the main task (Experiment 1), which were all larger on average than 1sec? This seems a bit like an odd choice. Additionally, difficulty decision accuracies in this version of the task differ between known and unknown conditions (Figure 7), while in the reaction time version of the same task there were no detectable differences in performance between known and unknown conditions (Figure 6C), just in the reaction times. This discrepancy is not sufficiently explained in the manuscript. Could this be explained by the short trial durations?

Reviewer #1 (Public Review):

This paper performed a functional analysis of the poorly characterized pseudo-phosphatase Styxl2, one of the targets of the Jak/Stat pathway in muscle cells. The authors propose that Styxl2 is essential for de novo sarcomere assembly by regulating autophagic degradation of non-muscle myosin IIs (NM IIs). Although a previous study by Fero et al. (2014) has already reported that Styxl2 is essential for the integrity of sarcomeres, this study provides new mechanistic insights into the phenomenon. In vivo studies in this manuscript are compelling; however, I feel the contribution of autophagy in the degradation of NM IIs is still unclear.

Major concerns:

1) The contribution of autophagy in the degradation of Myh9 is still unclear to this reviewer. It has been reported that autophagy is dispensable for sarcomere assembly in mice (Cell Metab, 2009, PMID; 1994508). In Fig. 7A, the authors showed that overexpressed Styxl2 downregulated the amount of ectopically expressed Myh9 in an ATG5-dependent manner in C2C12 cells; however, the experiment is far from a physiological condition. Therefore, the authors should test ATG5 knockdown and the genetic interaction between Styxl2 and ATG5 in vivo. That is, 1) loss of ATG5 on sarcomere assembly in zebrafish, and 2) the genetic interaction between Styxl2 and ATG5; co-injection of Styxl2 mRNA and ATG5-MO into the zebrafish embryos.

2) As referenced, Yamamoto et al. reported that Myh9 is degraded by autophagy. Mechanistically, Nek9 acts as an autophagic adaptor that bridges Atg8 and Myh9 through interactions with both. Inconsistent with the model, the authors mentioned on page 12, lines 365-367, "A recent report showed that Myh9 could also undergo Nek9-mediated selective autophagy (Yamamoto et al., 2021), suggesting that Myh9 is ubiquitinated". I think it is not yet explored whether autophagic degradation of Myh9 requires its ubiquitination. Moreover, I cannot judge whether Myh9 is ubiquitinated in a Styxl2-dependent manner from the data in Fig. 7C. The author should test whether Nek9 is required for Myh9 degradation in muscles. If Nek plays a role in the Myh9 degradation, it would be better to remove Fig. 7C.

3) In Fig. 5F, the protein level of Styxl2 and Myh10 should be checked because the efficiency of Myh10-MO was not shown anywhere in this manuscript.

Reviewer #2 (Public Review):

The authors investigated the role of the Jak1-Stat1 signaling pathway in myogenic differentiation by screening the transcriptional targets of Jak1-Stat1 and identified Styxl2, a pseudophosphatase, as one of them. Styxl2 expression was induced in differentiating muscles. The authors used a zebrafish knockdown model and conditional knockout mouse models to show that Styxl2 is required for de novo sarcomere assembly but is dispensable for the maintenance of existing sarcomeres. Styxl2 interacts with the non-muscle myosin IIs, Myh9 and Myh10, and promotes the replacement of these non-muscle myosin IIs by muscle myosin IIs through inducing autophagic degradation of Myh9 and Myh10. This function is independent of its phosphatase domain.

A previous study using zebrafish found that Styxl2 (previously known as DUSP27) is expressed during embryonic muscle development and is crucial for sarcomere assembly, but its mechanism remains unknown. This paper provides important information on how Styxl2 mediates the replacement of non-muscle myosin with muscle myosin during differentiation. This study may also explain why autophagy deficiency in muscles and the heart causes sarcomere assembly defects in previous mouse models.

Reviewer #3 (Public Review):

Wu and colleagues are characterising the function of Styxl2 during muscle development, a pseudo-phosphatase that was already described to have some function in sarcomere morphogenesis or maintenance (Fero et al. 2014). The authors verify a role for Styxl2 in sarcomere assembly/maintenance using zebrafish embryonic muscles by morpholino knock-down and by a conditional Styxl2 allele in mice (knocked-out in satellite cells with Pax7 Cre).

Experiments using a tamoxifen inducible Cre suggest that Styxl2 is dispensable for sarcomere maintenance and only needed for sarcomere assembly.

BioID experiments with Styxl2 in C2C 12 myoblasts suggest binding of nonmuscle myosins (NMs) to Styxl2. Interestingly, both NMs are downregulated when muscles differentiate after birth or during regeneration in mice. This down-regulation is reduced in the Styxl2 mutant mice, suggesting that Styxl2 is required for the degradation of these NMs.

Impressively, reducing one NM (zMyh10) by double morpholino injection in a Styxl2 morphant zebrafish, does improve zebrafish mobility and sarcomere structure. Degradation of Mhy9 is also stimulated in cell culture if Styxl2 is co-expressed. Surprisingly, the phosphatase domain is not needed for these degradation and sarcomere structure rescue effects. Inhibitor experiments suggest that Styxl2 does promote the degradation of NMs by promoting the selective autophagy pathway.

Strengths:

A major strength of the paper is the combination of various systems, mouse and fish muscles in vivo to test Styxl2 function, and cell culture including a C2C12 muscle cell line to assay protein binding or protein degradation as well as inhibitor studies that can suggest biochemical pathways.

Weakness:

The weakness of this manuscript is that the sarcomere phenotypes and also the western blots are not quantified. Hence, we rely on judging the results from a single image or blot.<br /> Also, Styxl2 role in sarcomere biology was not entirely novel.

Few high resolution sarcomere images are shown, myosins have not been stained for.

Reviewer #1 (Public Review):

C. elegans is a pre-eminent model for developmental genetics, and its invariant lineage makes it possible in theory to define molecular features such as gene expression comprehensively and at single cell resolution across the organism.

Previously published single-cell RNA-seq studies have mapped gene expression across the lineage through the 16-cell stage (Tintori et al 2017, Hashimshony et al 2016), and at later stages (Packer et al 2019, with good coverage starting at the 100-cell stage and some coverage at the ~50-cell stage). This left the critical period around gastrulation (~28-cell and ~50-cell) without comprehensive transcriptome data. This study covers this gap with a heroic effort involving the manual isolation and analysis of over 800 cells from embryos of known stage, combined with painstaking curation using known markers from small scale studies and larger imaging-based expression atlases. Importantly, the dataset overlaps at early and late stages with data from prior studies.

The data quality and overlap with Tintori and Packer datasets both appear high, but to make this inference required additional analysis from Supplemental Table 6 by this reviewer as it is not explored or described in the manuscript. Analyses demonstrating continuity with these datasets would greatly increase the value of the resource.

The authors show that specific lineages and stages preferentially express TFs with different classes of DNA binding domains. This extends previous work implicating homeodomains as preferentially involved in nervous system patterning and as enriched in neural and muscle progenitors in mid-stage embryos.

They also show that C. elegans homologs of Drosophila early embryonic regulators (which function based on spatial position in that system) tend to also be patterned in early C. elegans embryos, but with lineage-specific patterns. This conserved use of regulators would be fairly remarkable given the dramatically different developmental modes in these two species, although this observation is not backed up by quantitative analyses.

Finally, there is an argument that combinations of TFs expressed in lineage-specific patterns give rise to "stripe" patterns. This section is also not based on statistical analyses but suggests the possibility that lineage and positional regulation may be more convoluted than was previously thought.

Reviewer #2 (Public Review):

The C. elegans embryo has been model system of study for more than 30 years because of the ease of doing forward and reverse genetics, coupled with its nearly invariant lineage which allows a description of development at high resolution. 4D time lapse imaging coupled with spatially resolved gene expression has enabled identification of transcriptional signatures of cells in space and time, and in the past decade this has been advanced with single-cell transcriptomics methods, using individually isolated embryonic cells (which can retain their identity) or by deconvolving complex mixtures of early cells. Recent work using these methods has resolved spatiotemporal expression patterns for many genes, defining cells up to gastrulation stage, but then changing to more tissue-specific patterns during morphogenesis. A key paradigm of specification in C. elegans and other systems is that early maternal factors initiate or restrict patterns of transcription factor expression from the zygotic genome. Combinatorial expression patterns and some symmetries broken by autonomous or extrinsic cell inductions ultimately program lineages towards their fates. To date, only simple networks have been elucidated, as the increasing complexity of these networks and the high level of redundancy has made functional dissection of such pathways difficult. Hence, almost all of the work in recent years has been descriptive.

In this work the authors fill a knowledge gap from the early embryo (~16 cells) to the ~100-cell stage and describe new patterns of gene expression. They reconcile their findings with that of others who have defined expression patterns using other methods, such as scRNA-Seq from complex mixtures of cells, and from transcription factor expression analyses. The resulting description of embryonic develop is the most precise to date, and offers a potentially useful resource for other researchers.<br /> The authors attempt to use their results to find patterns of gene expression that could hint at phylogenetic conservation of specification mechanisms. They find some supporting evidence that expression of homeobox genes occurs in anterior-posterior stripes, which recalls the elaborate A/P patterning system elucidated in the Drosophila embryo, which belongs to the sister phylum Arthropoda in the Ecdysozoan clade of molting animals. It felt as if the authors chose the Hox genes they need to support this conclusion.

Some caveats exist to the work. The expression patterns seem to be well-validated, and following prior work from the Yanai group are likely to be strongly correlated with expression in living embryos. When cells are separated, they could lose some expression patterns that require cell-cell interactions, so it is expected that there might be a small minority of expression patterns that are more complex than what has been documented here.

A major caveat is the idea of the stripes of Hox expression. I just did not find these arguments to be compelling. Seeing these 'stripes' requires organizing the data in a way that maximizes their appearance, for one. Since there is not a lot of movement of cells away from their birth in the early embryo, the AB descendants are anterior to those of MS, anterior to those of E, anterior to those of C, D, and P4. Lineage-specific expression will just naturally fall into 'stripes'. Second, the conservation of Hox expression patterns typically comes with collinearity of the genes along the length of a chromosome (i.e. the so-called Hox clusters) with expression along the body axis, as well as posterior-to-anterior fate transformations when Hox specification is disrupted.

A minor note is the detection of an enrichment of GATA factors in the early E lineage. This has now been found to be a derived condition even within the genus (see Broitman-Maduro et al. Development 149 (21): dev200984, as other species like C. angaria show only a simpler network of elt-3 -> elt-2. This suggests that many of the other patterns of gene expression, particularly in the early embryo, could be highly derived as well; some caution is warranted in generalizing the results as being conserved with arthropods as some of this could be convergent.

Given what the authors are proposing about Hox stripes, some omissions of prior work were surprising (or maybe I missed them). For example, a comprehensive study of Hox genes in C. elegans by Hench et al. (2015) (PLoS One 10(5): e0126947) evaluated all the homeobox genes and examined their genomic locations and expression patterns in the embryo at high spatiotemporal resolution. Work from the Hobert lab (Nature 2020, 584(7822):595-601) showed how homeobox codes specify classes of C. elegans neurons, and the Murray lab (PLoS Genet. 18(5):e1010187) examined Hox control of posterior lineage specification at high resolution, with functional assays.

The Discussion section of the paper is brief, consistent with the descriptive nature of the work overall, but it would have been nice to see the findings related to other published studies as indicated above.

Reviewer #3 (Public Review):

The authors claim that this dataset covers a timepoint of embryogenesis that is not well covered in the other published single cell datasets (Tintori et al 2016 and Packer et al 2019). The Tintori data indeed do not cover the 28-102-cell stages sufficiently, but it is unclear how the data presented here compare to the Packer et al data. It is true that the Packer et al data have fewer cells at earlier timepoints than at later ones, but given that they sequenced tens of thousands of cells, they report that they still have ~10,000 cells <210 min of embryogenesis. If the authors want to make any claims about how their data enables exploration of a stage that was previously not accessible, this would require a better comparison to the available data.

The authors provide thorough support for how they assigned cell identities in their data. It is surprising though that at the 102-cell stage they only identify 37 unique cell identities. They suggest that this is because there are many equivalence groups at this stage. However, I would strongly encourage the authors to perform a similar analysis or otherwise compare their obtained identities with the data from Packer et al. 2019. It seems possible that given the low number of cells in this dataset, the authors are missing certain identities and it would be important to know this.

The main analysis the authors perform is to look at expression patterns of various classes of TFs and ask whether they are enriched in particular lineages or at specific timepoints. This analysis is interesting but would be more informative if the authors provided in Figure 3d the numbers of each class of TFs. The authors then focus on the homeodomain class of TFs as they display interesting lineage-specific expression patterns, which when mapped on the embryo form stripes. The stripe pattern however is not that obvious, at least not as shown in Figure 4b (for example all three darker shades of blue looks indistinguishable). Perhaps separate embryo schematics showing the different TF expression patterns would show this more clearly. Moreover, given the relatively small number of cell identities found in this dataset (particularly at the 102-cell stage), a similar analysis using the Packer data would provide further support to these patterns. The localization of cells with shared expression patterns does show a stripe pattern at the 28-cell stage, but also not so clearly beyond this timepoint.

I am also unsure about the validity/value of the comparison of the stripes to Drosophila and the centrality of homeodomain TFs to anterior-posterior positional identity. First, it would be important to map other TFs, very likely there are several other TFs that correlate with positional identity. Also, even if the expression of the homeodomain TFs in C. elegans form stripes, there are still several cells within that stripe that do not express these TFs, it is thus unclear whether these TFs encode positional information or the identity of cells with different positions in the embryo.

Reviewer #4 (Public Review):

This is an admirable piece of work. The authors build on a previous dataset they assembled, but expand it to include all stages of early development in the nematode Caenorhabditis elegans. Cell collection was done manually, which is very impressive, and is clearly far better than pooled unidentified cells. I will not comment on the specific sequencing and analysis, since this is not my expertise, but will comment on the general conclusions and comparative framework in which the authors place their results.

While the Introduction and Discussion sections are actually fairly short, much of the presentation of the results is based on a certain comparative framework, which is explicitly a comparison between C. elegans and Drosophila melanogaster. This is an important perspective, but I feel the authors' interpretation is in some places exaggerated and in other places almost trivial.

Drosophila and C. elegans are two of the main models for developmental biology. However, it has been clear for over two decades that both species are highly derived and specialized and therefore, treating them as representative for their taxa is problematic. Much of the authors' discussion hinges on the question of comparing syncytial and lineage-dependent development. The syncytial early development of Drosophila is very specific and is clearly a recent innovation within a restricted group of flies. The canonical Drosophila segmentation cascade is mostly a novelty and most elements within the cascade are recent (the authors are invited to browse my 2020 review in Curr. Top. Dev. Biol.) Specifically, the expression of gap genes in regional stripes is not found very broadly. Conversely, the polarizing role of Caudal is very ancient and is probably found in all Bilateria. When making comparisons with a distantly related species, it is important to keep this in mind. Not as much is known about development of other nematodes, but the little that is known indicates that C. elegans is also unusual, and specifically, the eutelic development (conserved cell lineages in development) is not found in all nematodes.

The authors suggest that regional expression of transcription factors in stripes is a conserved characteristic of development. This is true for Hox genes and has been known for decades. The regional expression they show for other genes is not convincing as "stripes". It is no surprise that developmental transcription factors are regionalized, but linking this to the stripes of Drosophila gap genes and even more so to Drosophila pair-rule and segment-polarity genes is a bit far-fetched. Yes, many genes are expressed in restricted domains along the A-P axis, but that is all that can be said based on the data. Calling them "Drosophila-like" is unfounded.

Beyond these broad homology statements, the rest of the presentation is fine and I have no major comments.

Reviewer #1 (Public Review):

The authors of this well-described publication provided strong evidence that current DNA-based microbial genomics methodologies have an inherent constraint. These approaches cannot detect the source of sequenced DNA, and they fail to demonstrate the origin of sequenced DNA from live or non-viable bacteria. Moreover, scientists proved in people and mice that live bacteria for the most part remained within hair follicles rather than on the skin's surface. Overall, this study is of excellent quality and has broad implications beyond a particular subject.

Strengths:

The study is well-designed, and the experimental methods are well-described.<br /> The results are presented clearly and are supported by statistical analyses.<br /> The study's findings are novel and have important implications for understanding the skin microbiome and the biology of the skin.

Weakness:

RNA-based NGS could parallelly study the results of this DNA-based microbiome study. The bulk RNA-Seq can sequence thousands of transcripts from each viable bacterium and match them with the bacterial genome and transcriptome references. It is one of the best confirmatory methods for showing the diversity of viable cutaneous bacteria.

Reviewer #2 (Public Review):

The study by Acosta et al. is very interesting as it presents a simple and easy method for identifying live and dead bacteria DNA in the skin - PMA labeling, verified by FISH. This study provides several meaningful conclusions that could inform future skin microbiome studies:

Firstly, the 16s rRNA gene sequencing of skin microbial samples collected by cotton swabs may include DNA from a large number of dead bacteria, leading to an over-representation of skin bacteria in the analysis.

Secondly, the study found that there were fewer live bacteria on the skin surface than the detected bacterial DNA predicted, with most skin bacteria harboring in the hair follicles. This conclusion aligns with the physiological properties of the skin, as the hair follicle epithelium creates a moist, nutrient-rich, low-UV, and immune-privileged environment, which is conducive to the growth, colonization, and development of microorganisms.

Finally, the authors propose that the bacteria on the skin surface originate from the proliferation and replenishment of hair follicle resident bacteria, which could be one reason for the short-term instability and long-term stability of the skin microbiome.

Overall, this study provides valuable insights into the composition and distribution of skin bacteria and highlights the importance of using appropriate methods to identify live bacteria in skin microbiome studies.

Reviewer #1 (Public Review):

Previous reports suggested an association between ceramide accumulation in skeletal muscle and disruption of insulin signaling and metabolic dysregulation. Mechanistically, however, how intracellular ceramide attenuates insulin action and reduces metabolism is not fully understood. It was suggested that insulin receptor (IR) signaling to PI3-K/AKT is inhibited by elevated intracellular ceramide. However, other studies failed to demonstrate an inhibitory effect of ceramide on PI3K/AKT. More recently, a study was published describing that intracellular localization of diacylglycerols and sphingolipids influences insulin sensitivity and mitochondrial function in human skeletal muscle (PMID: 29415895). In the present study, Diaz-Vegas and colleagues used an in vitro system to investigate this topic further and better understand how intracellular ceramide accumulation causes cellular insulin resistance and metabolic dysregulations in cultured myocytes.

The authors applied multiple methods to achieve this goal. Among these procedures are:

1) The overexpression of enzymes involved in mitochondrial ceramide synthesis and degradation;

2) Treatments of myocytes with different pharmacological tools to validate their findings;

3) Mitochondrial proteomics and lipidomics analyses.

The effects of these experimental conditions and treatment on intracellular lipids contents, mitochondrial functions, and insulin signaling in myocytes were then evaluated.

Findings:

The authors' findings indicate that incubation of myocytes with palmitate increases mitochondrial ceramide and reduces the insulin-stimulated GLUT4-HA translocation to the myocyte surface without affecting AKT activation. The elevation in mitochondrial ceramide lowers the coenzyme Q levels e depletes the electron transport chain (ETC) components, impairing mitochondrial respiration. Such mitochondrial dysfunction appears to attenuate the translocation of GLUT4-HA to the plasma membrane of the L6-myotubule. Also, mitochondrial proteomic analysis revealed an association of insulin sensitivity with mitochondrial ceramide and ETC expression levels in human muscle.

Based on these findings, the authors propose a mechanism whereby the building up of ceramide inside mitochondria depletes CoQ and compromise mitochondrial respiratory complexes, raising ROS. The resulting mitochondrial dysfunction causes insulin resistance in cultured myocytes. They postulate that CoQ depletion links ceramides with insulin resistance and define the respirasome as a critical connection between ceramides and mitochondrial dysfunction.

Relevance and critiques:

This original study provides direct evidence that mitochondrial ceramide accumulation depletes CoQ and downregulates multiple ETC components in myocytes. Consequently, elevation in the levels of reactive oxygen species (ROS) and mitochondrial dysfunctions occur. The authors proposed that such mitochondrial dysregulation attenuates insulin-stimulated GLUT4 translocation to the plasma membrane of L6-myotubules. Moreover, mitochondrial ceramide accumulation does not affect insulin action on AKT activation.

Overall, this is a well-done study, showing that in obesity, elevated mitochondrial ceramide suppresses mitochondrial function and attenuates insulin action on glucose transporter GLUT4 translocation into the myocyte surface. The main conclusion is supported by the results presented. The study also applied multiple methods and described several experiments designed to test the author's central hypothesis.

Importantly, these new findings shed light on possible cellular mechanisms whereby ectopic fat deposition in skeletal muscle drives insulin resistance and metabolism dysregulation. The results demonstrating that alterations in mitochondrial ceramide are sufficient to attenuate insulin-stimulated GLUT4 trafficking in cultured myocytes are very interesting. Well-done.

Comments for further discussion and suggestions:

Although the authors' results suggest that higher mitochondrial ceramide levels suppress cellular insulin sensitivity, they rely solely on a partial inhibition (i.e., 30%) of insulin-stimulated GLUT4-HA translocation in L6 myocytes. It would be critical to examine how much the increased mitochondrial ceramide would inhibit insulin-induced glucose uptake in myocytes using radiolabel deoxy-glucose.

Another important question to be addressed is whether glycogen synthesis is affected in myocytes under these experimental conditions. Results demonstrating reductions in insulin-stimulated glucose transport and glycogen synthesis in myocytes with dysfunctional mitochondria due to ceramide accumulation would further support the authors' claim.

In addition, it would be critical to assess whether the increased mitochondrial ceramide and consequent lowering of energy levels affect all exocytic pathways in L6 myoblasts or just the GLUT4 trafficking. Is the secretory pathway also disrupted under these conditions?

Reviewer #2 (Public Review):

The findings reported by Diaz-Vegas et al. extend those described in a previous paper from the same group establishing a role for mitochondrial CoQ depletion in the development of insulin resistance in muscle and adipose tissue (Fazakerley, 2018). In this new report, investigators sought to determine whether CoQ depletion contributes to insulin resistance caused by palmitate exposure and/or intracellular ceramide accumulation. To this end, researchers employed a widely used in vitro model of insulin resistance wherein L6 myocytes develop impaired Glut4 translocation upon exposure to palmitate (in this case, 150 uM for 16 hours). This model was combined with a variety of pharmacologic and genetic manipulations aimed at augmenting or inhibiting CoQ biosynthesis and/or ceramide biosynthesis, specifically in mitochondria. This series of experiments produced a valuable and provocative body of evidence positioning CoQ depletion downstream of mitochondrial ceramide accumulation and necessary for both palmitate- and ceramide-induced insulin resistance in L6 myocytes. Investigators concluded that mitochondrial ceramides, CoQ depletion and respiratory dysfunction are part of a core pathway leading to insulin resistance.

Strengths:

The study provides exciting, first-time evidence linking palmitate-induced insulin resistance to ceramide accumulation within the mitochondria and subsequent depletion of CoQ. Ceramide accumulation specifically in mitochondria was found to be necessary and sufficient to cause insulin resistance in cultured L6 myocytes.

The in vitro experiments featured a set of mitochondrial-targeted genetic manipulations that permitted up/down-regulation of ceramide levels specifically in the mitochondrial compartment. Genetically induced mitochondrial ceramide accumulation led to CoQ depletion, which was accompanied by increased ROS production and diminution of ETC proteins and OXPHOS capacity and impaired insulin action, thereby establishing cause/effect.

Analysis of mitochondria isolated from human muscle biopsies obtained from individuals with disparate metabolic phenotypes revealed a positive correlation between complex I proteins and insulin sensitivity and a negative correlation with mitochondrial ceramide content. While it is likely that many factors contribute to these correlations, the results support the possibility that the ceramide/CoQ mechanism might be relevant to glucose control in humans.

These important findings offer valuable new insights into mechanisms that connect ceramides to insulin resistance and mitochondrial dysfunction, and could inform new therapeutic approaches towards improved glucose control.

Weaknesses:

The mechanistic aspect of the work and conclusions put forth rely heavily on studies performed in cultured myocytes, which are highly glycolytic and generally viewed as a poor model for studying muscle metabolism and insulin action. Nonetheless, the findings provide a strong rationale for moving this line of investigation into mouse gain/loss of function models.

One caveat of the approach taken is that exposure of cells to palmitate alone is not reflective of in vivo physiology. It would be interesting to know if similar effects on CoQ are observed when cells are exposed to a more physiological mixture of fatty acids that includes a high ratio of palmitate, but better mimics in vivo nutrition.

While the utility of targeting SMPD5 to the mitochondria is appreciated, the results in Figure 5 suggest that this manoeuvre caused a rather severe form of mitochondrial dysfunction. This could be more representative of toxicity rather than pathophysiology. It would be helpful to know if these same effects are observed with other manipulations that lower CoQ to a similar degree. If not, the discrepancies should be discussed.

The conclusions could be strengthened by more extensive studies in mice to assess the interplay between mitochondrial ceramides, CoQ depletion and ETC/mitochondrial dysfunction in the context of a standard diet versus HF diet-induced insulin resistance. Does P053 affect mitochondrial ceramide, ETC protein abundance, mitochondrial function, and muscle insulin sensitivity in the predicted directions?

Reviewer #1 (Public Review):

In this manuscript, Elkind et al. use a deep learning segmentation algorithm trained on detecting putative cell nuclei in mouse brains to count cells in the Allen Mouse Brain Connectivity Atlas. The Allen Mouse Brain Connectivity Atlas is a dataset compromising hundreds of mice brains. The authors use this increased statistical power for detecting differences in volume, cell count, and cell density between strains (C57BL/6J and FVB.CD1) as well as sex differences.

Both volume, cell count, and cell density are regularly used in neuroanatomy to normalize or benchmark results so having a large available dataset for others to compare their data would be a useful resource. The trained segmentation algorithm might also find utility in assays where investigators for one reason or another can't dedicate an entire labeled channel to count cell nuclei.

Nevertheless, because of technical reasons, I find the current work problematic.

Major:

The authors make use of the "red" channel from the Allen Mouse Brain Connectivity Project (AMBCP). The AMBCP was acquired using two-photon tomography with the TissueCyte 1000 system (http://help.brain-map.org/download/attachments/2818171/Connectivity_Overview.pdf?version=2&modificationDate=1489022310670&api=v2). The sample is illuminated at 925 nm wavelength and the channel the authors describe as autofluorescence is collected through a 593/40 nm bandpass filter. The authors go on to describe their rationale for using this channel for quantifying cell nuclei:<br /> "We noticed that the red (background) channel of STPT images, taken for the purpose of atlas alignment, typically features dark, round-like objects resembling cell nuclei. We had observed this phenomenon in our own imaging of mouse brains but found little more than anecdotal mentions of it in the literature8,9,10,11".<br /> The authors here cite a Scientific Reports paper from 2021 with 11 citations, a Journal of Clinical Pathology paper from 2005 with 87 citations, and lastly a paper in Laboratory Investigation from 2016 with 41 citations. The authors completely fail to cite the work from Watt Webb's group (co-inventor of 2p microscopy) in PNAS from 2003 that entirely described the phenomena of native fluorescence by multiphoton-excitation (https://www.pnas.org/doi/10.1073/pnas.0832308100 ), citations so far: 1959 citations. This is either indicative of poor scholarship or an attempt to describe something as novel. Either way, the native fluorescence and second harmonic generation from multiphoton illumination are perfectly characterized by Webb and colleagues and they clearly show the differential effect on nucleosides, retinol, indoleamines, and collagen. This is also where the authors should have paid more attention to discrepancies in their own data when correlated to well-established cell nuclei markers (Murakami et al). The authors will note "black large spots" in the data at specific anatomical regions and structures, like the fornix and stria medullaris:<br /> https://connectivity.brain-map.org/projection/experiment/siv/263780729?imageId=263780960&imageType=TWO_PHOTON,SEGMENTATION&initImage=TWO_PHOTON&x=15702&y=18833&z=5

which is not reproduced in for example the Allen Reference Atlas H&E staining:<br /> http://atlas.brain-map.org/atlas?atlas=1&plate=100960284#atlas=1&plate=100960284&resolution=4.19&x=5507.4000244140625&y=5903.39990234375&zoom=-2

In connection here notice the poor signal in the 2p "autofluorescence" within the paraventricular nucleus:<br /> https://connectivity.brain-map.org/projection/experiment/siv/263780729?imageId=263780960&imageType=TWO_PHOTON,SEGMENTATION&initImage=TWO_PHOTON&x=15702&y=17833&z=6

and then compare it to the H&E staining:<br /> http://atlas.brain-map.org/atlas?atlas=1&plate=100960280#atlas=1&plate=100960276&resolution=1.50&x=5342.476283482143&y=5368.023856026786&zoom=0

These multiphoton-specific signals are especially pronounced in the pons and medulla which makes quantification especially dubious, which is even apparent simply from looking at Figure 1c in the manuscript. The authors here use the correlation on log-log coordinates between their data and that of Murakami et al to argue that the method has validity. However, the variance explained here is R^2 = 0.74 which is very poor given the log-log coordinates. A more valid metric would use linear coordinates and computing the ICC and interpret it according to established guidelines (e.g. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4913118/).

In addition to the above concern, the authors argue that the large sample size of the AMBCP is what would enable them to find statistically significant small effect sizes that might have gone undetected in the literature. However, this argument falls flat once we examine some of the main findings the authors report. Although the authors do not directly report measures of dispersion we can estimate it from the figures and then arrive at the sample size needed to find the reported effect size. For example, the effect that describes ORBvl2/3 volume is larger in female mice compared to males would only require n=13 mice at the desired power of 0.8. Likewise, the sample size needed to detect the increased BST volume in male mice looks to be roughly n=16 mice at the desired power of 0.8. Both of these estimates are well within what is a reasonable sample size to expect in an ordinary study. This begs the question: why did the authors simply not verify some of their main findings in an independent sample obtained through traditional ways to quantify volume and cell density since it is well within reach? Such validation would strengthen the arguments of the paper.

Reviewer #2 (Public Review):

This report describes a large-scale analysis of cell counts in mouse brains. The authors found that the Allen Mouse Connectivity project has a rich dataset for cell counting that is yet to be analyzed, and they developed methods to quantify cells in different nuclei. They go on to compare males vs females and two different strains. From this analysis, they found specific differences between male versus female brains, left versus right hemispheres, and C57BL/6 versus FVB.CD1 mice, especially with regard to cell counts and density.

Overall, the methodology is sound and the quality of the data seems high. In fact, this study uses >100 brains for the statistics, and this is one of the major strengths of this study. For researchers who are interested in interrogating the differences at the macroscopic level in brain structures, this study will be a great resource. For example, the manuscript contains an interesting finding that for most brain areas, females have larger volumes but fewer cell numbers.

Reviewer #3 (Public Review):

Elkind et al. have devised a strategy to detect cells in whole brain samples of the large, publicly accessible Allen Mouse Brain Connectivity database. They put together an analysis pipeline to quantify cell numbers and -density as well as volumes for all annotated brain areas in these samples. This allowed them to make several important discoveries such as (1) strain-, sex- and hemisphere-specific differences in cell densities, (2) a large interindividual variability in cell numbers, and (3) an absence of linear scaling of cell count with volume, among others. The key strength of this work lies in its comprehensive analysis, the large sample size that the authors have drawn from (making their conclusions particularly robust), and the fact that they have made their analysis tools accessible. A weakness of the current manuscript is the dense layout and overplotting of several of the figures, and the lack of necessary information to understand them more easily. Another, conceptual weakness of using the autofluorescence channel for cell detection is that the identity (neuronal vs non-neuronal) of the underlying cells remains unresolved. Overall, however, I believe that this study has the potential to serve as a valuable reference point, and I would expect this work to have a lasting impact on quantitative studies of mouse brain cytoarchitecture.

Reviewer #1 (Public Review):

Alignment between high dimensional data which express their dynamics in a subspace is a challenge which has recently been addressed both with analytic-based solutions like the Procrustes transformation, and, most interestingly, via deep learning approaches based on adversarial networks. The authors have previously proposed an adversarial network approach for alignment which relied on first dimensionally-reducing the binned neural spikes using an autoencoder. Here, they use an alternative approach to align data without use of an initial dimensional-reduction step.

The results are fairly clear - the Cycle-GAN approach works better than their previous ADAN approach and one based on dimensionality reduction followed by the Procrustes transform. In general, a criticism of this entire field is to understand what alignment teaches us about the brain or how it specifically will be used in a BCI context.

There are a few issues with the paper.

1.) To increase the impact of their work, the investigators have now used it to align data in multiple types of tasks. There was an unanswered question about this related to neuroscience - does alignment in one task predict alignment for another?

2) Investigators use decoding as a way of comparing alignment performance. The description of the cycle GAN was not super detailed, and it wasn't clear whether there was any dynamic information stored in the network that might create questions of causality in actual use. It seems that input is simply the neural activity at a current time point rather than neural activity across the trial, which would alleviate this concern. However, they mention temporal alignment but never describe in detail whether all periods of spikes are properly modeled by the system or if only subsets of data (specific portions of task or non-task time) will work. Perhaps this is more a question of the Wiener filter, for which precise details are missing.

3) In general, precise details of the algorithms should have been provided.

4) Cross validation for day-0 alignment is not explained.

5) Details of statistical tests is not provided.

6) (minor) The idea that for neurons that have disappeared that the CycleGAN can "infer their response properties", seems an incorrect description. A proper description should be that it "hallucinates" their response properties?

Reviewer #2 (Public Review):

In this manuscript, the authors use generative adversarial networks (GANs) to manipulate neural data recorded from intracortical arrays in the context of intracortical BCIs so that these decoders are robust. Specifically, the authors deal with the hard problem where signals from an intracortical array change over time and decoders that are trained on day 0 do not work on day K. Either the decoder or the neural data needs to be updated to achieve the same performance as initially. GANs try to alter the neural data from day K to make it indistinguishable to day 0 and thus in principle the decoder should perform better. The authors compare their GAN approach to an older GAN approach (by an overlapping group of authors) and suggest that this new GAN approach is somewhat better.

Major Strengths are multiple datasets from behaving monkeys performing various tasks that involve motor function. Comparison between two different GAN approaches and a classical approach that uses factor analysis. The weakness is insufficient comparison to another state-of-the-art approach that has been applied on the same dataset (NoMAD, Karpowicz et al. BioRxiv 2022).

The results are very reasonable and they show their approach, Cycle GANs, does slightly better than the traditional GAN approach. However, the Cycle GANs have many more modules and also as I understand it performs a forward backward mapping of the day - 0 and day - k and thus theoretically better. But, it seems quite slow.

I think the results are interesting but as such, I am not sure this is such a fundamental advance compared to the Farashcian et al. paper, which introduced GANs to improve decoding in the face of changing neural data. There are other approaches that also use GANs and I think they all need to be compared against each other. Finally, these are all offline results and what happens online is anyone's real guess. Of course, this is not just a weakness of this study but many such studies of its ilk.

Reviewer #1 (Public Review):

The study by Yang et al. reports a new mechanistic role of vinculin in inhibiting the Mef2c nuclear translation and sclerostin expression in osteocytes and promoting bone formation. The authors showed the reduction of vinculin in aged bone human bone samples. A 10kb DMP-1-Cre mouse model was generated that deleted vinculin in osteocytes. They found that vinculin deletion caused bone loss and decreased bone formation associated with increased sclerostin expression. This increase does not affect the protein level of transcription factor Met2c but interestingly enhances nuclear translocation. Vinculin is interested in Mef2c and appears to retain Mef2c in the cytosol. As expected, as a component of the mechanosensory focal adhesion complex, bone formation via tibial loading was decreased in vinculin deletion. Intriguingly, the bone loss associated with estrogen deficiency through ovariectomy was attenuated. Overall, the study unveiled an important role concerning a key player of focal adhesion and the study was well designed and executed. The paper would be strengthened by including a more thorough discussion including variables such as male vs. female, and cortical vs. trabecular bone as the vinculin deletion appeared to primarily affect trabecular bone while mechanical loading exerts anabolic effects on both bone types. The effect of estrogen deficiency effect is interesting and is worth some discussion.

Strengths:<br /> The paper shows a novel mechanism that vinculin retains Mef2c in the cytosol via protein interaction to prevent it from migrating to the nucleus and increases transcription of sclerostin, an inhibitory factor for Wnt/β-catenin signaling, a critical pathway for osteoblast activity and bone formation.<br /> They employed various in vivo and in vitro models as well as human tissue samples including generating conditional knockout of vinculin in osteocytes in vivo and vinculin gene knockdown in MLO-Y4 cells. They also used physiological/pathological relevant models, tibial loading, and ovariectomy to study the role of vinculin under mechanical loading and estrogen deficiency. The adopted standard techniques to study bone properties include microCT, bone formation, bone histomorphometry, histochemistry as well as biochemical assays such as immunoprecipitation, ChIP assays, etc.

The study is comprehensive and thorough and the noticeable uniqueness is that after observing the phenotypes from in vivo data, they further explored the underlying mechanisms using cell models. The experiments in general are well-designed and presented with adequate repeats and statistical analysis. The paper is also logically written and the figures were clearly labeled.

Minor weaknesses:<br /> More discussion is necessary concerning the potential difference in responses between male and female. Most of the studies were conducted in male mice except ovariectomy mice.<br /> It is interesting that the cKO of vinculin in osteocytes primarily affects trabecular bones with limited effect on cortical bones. However, sclerostin is increased in cortical bones. The promotion of bone formation by mechanical loading appears to affect both cortical and trabecular bones. If focal adhesion is a key mechanosensory complex, how to reconcile the different responses in the cKO model?<br /> The OVX response is interesting and it is worthwhile to elaborate more regarding the potential underlying mechanism and what's the relationship between estrogen and mechanical loading and if the action of estrogen on vinculin shares any similar mechanisms with mechanical loading, etc.

Reviewer #2 (Public Review):

In this interesting study, Wang et al. demonstrated a critical role of the key focal adhesion protein vinculin in the control of bone mass in mice. Specifically, the authors deleted vinculin expression by using the mouse 10-kb Dmp1-Cre transgenic mice that were reported to primarily target osteocytes and mature osteoblasts. The authors found that vinculin loss in these cells caused severe osteopenia in mice due to impairment of osteoblast and bone formation with minimal impact on osteoclast formation and bone resorption. Interestingly, the vinculin loss also reduced the mechanical loading induction of bone formation in mice. Mechanically, the authors found that vinculin knockdown increased, while vinculin overexpression decreased, sclerostin expression in osteocytes without affecting that of Mef2c, a major transcriptional regulator of the Sost gene, which encodes sclerostin. Mechanistically, the authors found that vinculin protein bound to Mef2c and vinculin loss increased Mef2c nuclear translocation and binding to the Sost enhancer ECR5. Deleting Sost expression largely reversed the osteopenic phenotypes caused by vinculin deletion. Finally, the authors demonstrated that estrogen promoted vinculin expression in osteocytes and that vinculin loss abolished the estrogen deficiency induction of bone loss in mice. In this study, with a tremendous amount of convincing in vitro and in vivo data, the authors have established a critical role of vinculin in bone and defined a novel mechanism that regulates bone mass. The findings from this study are important and interesting.

Reviewer #1 (Public Review):

This study represents in exciting collaboration between two young independent scientists in Uruguay and Japan. Trigo and Kawaguchi provide evidence for the presynaptic modulation of the opening-probability of calcium channels as a major mechanism of digital-analog coupling in immature cerebellar molecular layer interneurons (MLI). Applying a combination of electrophysiological methods including direct axonal whole-cell patch-clamp recordings and glutamate photolysis in acute brain slices and dissociated cultured neurons, the authors provide the following empirical findings: 1) Spontaneous and evoked EPSPs are reliably transmitted into the presynaptic compartment. The amplitude of the spontaneous EPSPs decayed with a length constant of 180 µm in the axon. 2) Physiologically relevant short and subthreshold (< 10 mV) depolarizations before action potentials ('pre-AP') increase the release probability and subsequently short-term depression at the MLI-Purkinje cell synapse without changing the duration of APs and just a minor reduction in amplitude of APs (< 10%). 3) The pre-AP subthreshold depolarizations subsequently increase the amplitude of AP-induced presynaptic calcium currents and GABAergic postsynaptic currents. 4) A short interval of only 3 ms duration between the pre-AP depolarization and the AP blocks the analog coupling. 5) A biophysical model of presynaptic calcium channel gating is proposed, which involves depolarization-induced intermediate gating steps that increase the probability of activating the channels during the AP.

A particular strength of this study is the large data set of technically very challenging direct recordings from small presynaptic terminals. The proposed mechanism provides an innovative explanation for the experimental findings. The most innovative experiments might be those with a 3-ms-gap between the pre-APs and APs. At this synapse, elevated residual intracellular calcium concentration was previously shown to mediate analog coding (https://doi.org/10.1523/JNEUROSCI.5127-10.2011). However, the elevated residual calcium cannot explain the surprising block of analog coding by a 3-ms-gap in the depolarization, because intracellular calcium signals decay with kinetics in the range of 100 ms. Both mechanisms (residual calcium and priming of calcium channels) are probably operating in parallel and future studies should resolve the exact interplay of both mechanisms. A potential weakness of the study is that the proposed priming of calcium channels is not shown explicitly to be able to explain the experimental data. Quantitive simulations of calcium channel gating states were only performed in steady-state but not in a time-dependent manner during pre-APs and APs.

Reviewer #2 (Public Review):

This study used direct recording from the soma, the terminal and the postsynaptic cell in cerebellar inter-neuron- Purkinje cell synapses. The authors nicely showed that action potentials travel reliably from the soma to the axon. In addition, they showed that the postsynaptic responses elicited at the dendrites reliably traveled along the axon. Such sub-threshold potential could potentiate transmitter release in short-term (for tens of ms at most), by "priming" Ca channels and accelerating activation kinetics of Ca channels. Results are based on the technically demanding electrophysiological technique and are in general. The study directly solves the mechanism of short-term facilitation induced by sub-threshold depolarization.

Reviewer #3 (Public Review):

Trigo & Kawaguchi study how small somatic subthreshold depolarizations that do not trigger full blown APs can propagate to presynaptic endings and modulate transmitter release. To this end they directly recorded from small cerebellar MLI boutons. In paired somatic and presynaptic recordings, they demonstrate that small synaptic potentials can travel within 2 to 3 ms to the bouton and arrive there with an amplitude attenuated by 20 to 70% with respect to the somatically recorded potential. As expected, this amplitude attenuation depends on axon length. In recordings of MLI-Purkinje cell pairs the authors further demonstrate that small somatic subthreshold depolarizations of about 20 mV size can enhance AP-triggered IPSCs recorded in the Purkinje cells and change synaptic plasticity during AP trains. In order to address mechanisms of such presynaptic modulation, the authors measure presynaptic AP waveforms via cell attached recordings and found these very stable. On the other hand, presynaptic ICa(V) directly recorded in voltage-clamped MLI boutons facilitated in response to small pre-depolarizations and such facilitated ICa(V) produced larger IPSCs in paired recordings of MLI boutons and coupled Purkinje cells. The authors propose that an accumulation of partially gated channels during small presynaptic depolarizations is able to produce more rapid gating of VGCCs during the AP waveform on arrival of an invading presynaptic AP.

Electrotonic coupling between soma and presynaptic endings to the extent that small subthreshold depolarizations such as synaptic potentials can travel to the bouton has been demonstrated before. However direct quantification of such coupling is difficult because of the small size of presynaptic compartments. Trigo & Kawaguchi have now pioneered such very challenging direct presynaptic recordings in the form of recordings of MLI soma and bouton pairs or paired pre- and postsynaptic recordings.

The data is convincing and I do not see a need for additional experiments. But the manuscript in its present form falls short with respect to the presentation and discussion of the data. The authors conclusion about the mechanism of presynaptic ICa(V) facilitation should be verified with proper kinetic simulations using a kinetic scheme such as that proposed by Li, Bischofberger & Jonas (2007) J.Neurosci. which should be adapted to the presynaptic ICa(V) in MLI boutons. This would strengthen the manuscript which otherwise, regarding mechanisms, remains somewhat speculative.

Reviewer #1 (Public Review):

This manuscript investigates the mechanisms of 'summiting disease' using a previously characterised Drosophila model. The authors also show that E. muscae infiltrates the brain likey through a defective blood-brain barrier and populates regions of the brain in the medial protocerebrum. It likely releases metabolites into the haemolymph of summiting flies that has the ability to induce summiting in uninfected flies. They also show that a burst of locomotor activity precedes death. To understand the circuit basis of this, they perform a screen of more than a hundred neuronal lines and genes to identify an active DPN1>pars intercerebralis neurons> corpora allata>JH axis as being invovled in the summiting behaviour while not affecting death.

Reviewer #2 (Public Review):

In this study, the authors aim to uncover the neuroanatomical and metabolite underpinnings of an intriguing phenomenon observed in some insects due to the infection of fungal pathogens. They very cleverly develop a high-throughput assay to examine and quantify this behaviour in a tractable model organism - Drosophila melanogaster which the authors have previously shown to also exhibit this phenomenon. They characterize the details of this behaviour and clearly show the temporal gating of this summiting-followed-by-death behavior to occur shortly before the dusk transition. They go on to examine using a candidate (over 200) screen approach potential neuronal circuits and genes based on the hypothesis that they may be related to 'arousal and gravitaxis'. They narrow down to a line that is restricted to the PI based on the fact that it has a significant effect on the summiting behaviour and that it is known to affect locomotion. They can demonstrate that flies when a subset of PI neurons (R19G10) are transiently activated, they will show summiting even without exposure to the pathogen. Based on Syt-eGFP staining they conclude that PI communicates with the carpora cardiaca (CA). They also show that CA itself is necessary for this behavior, but cannot demonstrate the role of Juvenile hormones using their pharmacological methods.

The authors then describe an automated classifier to identify an upcoming summiting behaviour. Further, they use this real-time classifier to stage different steps of the summiting and match it to the extent of pathology observed by microscopy. They also ask whether the constituents of the hemolymph differ between the summiting and not-yet summiting flies for which they conduct metabolome analysis of the hemolymphs. They are also able to show that cross-injection of uninfected or infected but not summiting flies can be induced to show summiting-like behaviour upon injection with the hemolymph.<br /> Finally, they propose the sequence by which the fungal pathogen may modulate the behaviours of the host fly so as to execute this highly gated act of increased locomotion prior to death.

Strengths<br /> • The detailed characterization of the behaviour in D melanogaster and development of the high-throughput behavioural arena.<br /> • Development of the automated classifier which appears to accurately predict this behaviour.<br /> • Narrowing down to a small group of PI neurons having a strong impact on this behaviour although sufficiency is not clearly demonstrated.

Weaknesses<br /> • The evidence of temporal (circadian) gating is weak despite the proposed DN1p - PI - CA connections.<br /> • The eventual modification of the behavior to enable enhanced locomotion and negative geotaxis to occur appears to be mediated by yet unknown factors<br /> • The metabolite analysis did not help to narrow down to candidates that can be speculated to cause this behaviour.

Reviewer #3 (Public Review):

The fungus Entomophthora muscae infects flies and in turn manipulates the flies to produce a summiting behavior that is believed to enhance spore dispersal that happens upon the eventual death of the fly. In this study, the authors undertake a Herculean effort to identify the neural pathways that are manipulated by the fungus to cause summiting. In a major advance, the authors develop techniques that allow them to track behaviors of infected flies over the course of several days. This allows them to investigate summiting behaviors that occur just prior to death with unprecedented detail. In their analysis, the authors find that summiting flies show a burst of increased locomotion just prior to death. Importantly, they show that this burst of locomotion is not seen in flies that are dying from other causes (starvation or desiccation). The burst of locomotion is also found to coincide with an increase in elevation that occurs with summiting, but other results indicate that a change in elevation may be an indirect consequence of increased locomotion. With this new knowledge in hand, the authors screen for genes and neuronal pathways that either disrupt or enhance the burst of locomotion that is characteristic of summiting. These experiments clearly indicate that neurons and genes controlling circadian rhythms play a major role in summiting behaviors. The authors focus their attention on a particular subset of clock neurons (DN1p) as potentially mediating summiting behavior. It is worth noting that DN1p neurons have been implicated in a variety (and in some cases contradictory) of circadian processes and that the interpretation of manipulations of these neurons may be an oversimplification. In particular, prior studies have implicated these cells in temperature entrainment/compensation so interpreting thermogenetic manipulations of these cells might be complicated. The authors also zoom in on a specific region of the brain containing neurons of the pars intercebralis, since they find infiltration by the fungus in this region and the effects of drivers targeting the PI. Converging and convincing lines of evidence to suggest that the PI neurons output to the corpora allata and effects of summing may be mediated by the CA. The already impressive series of experiments are further clinched by the development of a machine vision-based classifier that allows the authors to automatically identify summiting flies so that they may be collected for metabolomic analyses. The authors are automatically emailed and seemingly roused themselves in the middle of the night in order to obtain the precious flies they needed. They find a bunch of compounds that appear in summiting flies and even inject hemolymph from the infected animals into naive flies to find that circulating compounds can affect behaviors. Overall, this paper is a tour de force that addresses a system of long-standing interest and brings it into the modern age. Many new questions are now raised for the future by this fascinating study.

Reviewer #1 (Public Review):

The manuscript by Gochman and colleagues reports the discovery of a very strong sensitization of TRPV2 channels by the herbal compound cannabidiol (CBD) to activation by the synthetic agonist 2-aminoethoxydiphenyl borate (2-APB). Using patch-clamp electrophysiology the authors show that the ~100-fold enhancement by micromolar CBD of TRPV2 current responses to low concentrations of 2-APB reflects a robust increase in apparent affinity for the latter agonist. Cryo-EM structures of TRPV2 in lipid nanodiscs in the presence of both drugs report two-channel conformations. One conformation resembles previously solved structures whereas the second conformation reveals two distinct CBD binding sites per subunit, as well as changes in the conformation of the S4-S5 linker. Interestingly, although TRPV1 and TRPV3 are highly homologous to TRPV2 and both CBD binding sites are relatively conserved, the CBD-induced sensitization towards 2-APB is observable only for TRPV3 but not for TRPV1. Moreover, the simultaneous substitution of non-conserved residues in the CBD binding sites and the pore region of TRPV1 with the amino acids present in TRPV2 fails to confirm strong CBD-induced sensitization. The authors conclude that CBD-dependent sensitization of TRPV2 channels depends on structural features of the channel that are not restricted to the CBD binding site but involve multiple channel regions.

These are important findings that promote our understanding of the molecular mechanisms of TRPV family channels, and the data provide convincing evidence for the conclusions.

Reviewer #2 (Public Review):

In this manuscript, Gochman et al. studied the molecular mechanism by which cannabidiol (CBD) sensitizes the TRPV2 channel to activation by 2-APB. While CBD itself can activate TRPV2 with low efficacy, it can sensitize TRPV2 current activated by 2-APB by two orders of magnitude. The authors showed, via single-channel recording, that the CBD-dependent sensitization arises from an increase in Po when the channel binds to both CBD and 2-APB. The authors then used cryo-EM to investigate how CBD binds to TRPV2 and identified two CBD binding sites in each subunit, with one site being previously reported and the other being newly discovered.

TRPV1 and TRPV2 are two channels closely related to TRPV2. All three channels can be activated by CBD and 2-APB, but only TRPV2 and 3 are strongly sensitized by CBD. To understand the molecular basis of the different sensitivity to CBD, the authors compared the residues within the CBD binding sites and generated mutants by swapping non-conserved residues between TRPV1 and TRPV2. They then performed patch-clamp recordings on these mutants and found that mutations on non-conserved residues indeed influenced the CBD-dependent sensitization, thereby supporting the observed CBD binding sites.

Unexpectedly, the authors did not identify the binding site of 2-APB, despite its robust effect in electrophysiology recordings, especially when combined with CBD. Although previous structural studies of TRPV2 have reported 2-APB binding sites, the associated densities in these studies were not well-resolved. Therefore, the authors called on the field to re-examine published structural data with regard to the 2-APB binding sites.

Overall, this is an important study with well-designed and well-conducted experiments.

Reviewer #3 (Public Review):

In this paper, Gochman et al examine TRPV1-3 channel sensitization by CBD, specifically in the context of 2-APB activation. The authors primarily used classic electrophysiological techniques to address their questions about channel behavior but have also used structural biology in the form of cryo-EM to examine drug binding to TRPV2. The authors have carefully observed and quantified sensitization of the rat TRPV2 channel to 2-APB by CBD. While this sensitization has been reported previously (Pumroy et al, Nat Commun 2022), the authors have gone into much more detail here and carefully examined this process from several angles, including a comparison to some other known methods of sensitizing TRPV2. Additionally, the authors have also revealed that CBD sensitizes rat TRPV1 and mouse TRPV3 to 2-APB, which has not been reported previously. Up to this point, the work is well thought through and cohesive.

The major weakness of this paper is that the authors' efforts to track down the structural and molecular basis for CBD sensitization neither give insight into how sensitization occurs nor provide a solid footing for future work on the topic. The structural work presented in this paper lacks proper controls to interpret the observed states and the authors do nothing to follow up on a potentially interesting second binding site for CBD. Overall, the structural work feels detached from the rest of the paper. The mutations chosen to examine sensitization are based on setting up TRPV1 in opposition to TRPV2 and TRPV3, which makes little sense as all three channels show sensitization by CBD, even if to different extents. The authors chose their mutations based on the assumption that response to CBD is the key difference between the channels for sensitization, yet the overall state of each channel or the different modes of activation by 2-APB seem to be more likely candidates. As a result, it is not particularly surprising that none of the mutations the authors make reduce CBD sensitization in TRPV2 or increase CBD sensitization in TRPV1.

A difficulty in examining TRPV1-3 as a group is that while they are highly conserved in sequence and structure, there are key differences in drug responses. While it does seem likely that CBD would bind to the same location in TRPV1-3, there is extensive evidence that 2-APB binds at different sites in each channel, as the authors discuss in the paper. Without more basic information about where 2-APB binds to each channel and confirmation that CBD does indeed bind TRPV1-3 at the same site, it may not be possible to untangle this particular mode of channel sensitization.

Reviewer #2 (Public Review):

The manuscript is well written, the data are based on well-performed experiments, and the conclusions are supported by the data. The authors study thoroughly the global phenotype of T and NK cells and also analyze antigen-specific T cell frequencies. The data confirm that individuals who had severe COVID-19 disease (required ventilation and/or ITU admission) have slightly more activated CD4 and CD8 T cells at 3 months post-infection and report more frequently long COVID symptoms, yet the novelty of this manuscript is to show that these two are not linked to each other. Moreover, the manuscript confirms that patients across all disease severities mount and maintain memory T cell and antibody responses to SARS-CoV-2.

In the introduction, the authors want to highlight the extent of patients who suffer from long COVID symptoms, yet it should be noted that these high frequencies (8-21%) are coming from unvaccinated and hospitalized patients (like those included in this study), while a large group of individuals experience asymptomatic SARS-CoV-2 infection, and these individuals are not integrated into these studies.

The authors find that patients who recovered from severe COVID-19 3 months ago have more activated CD4+ and CD8+ T cells than patients who recovered from the mild disease. Although the difference is significant, the frequency of CD4+ T cells with an activated phenotype is increased only by about 2-fold (~2% vs ~1%), while the frequency of activated CD8+ T cells is about 6% vs 4%, which should be added to the results to better describe the extent of the activation.

As the authors mention in the discussion, it cannot be excluded that the more activated T cell phenotype in patients who recovered from severe COVID-19 is not rather a consequence of the increased comorbidities associated with this group. However, their Luminex analysis of the serum shows that the levels of cytokines TNF-a, IL-4, IL-12, IL-15, and IL-17A decline by 8 and 12 months, suggesting that the immune activation by 3 months is most likely a consequence of the previous severe viral infection.<br /> To strengthen this point, PBMC is probably not available at a later time point, to see if the increased T cell activation decreases in line with the serum cytokines. Yet, the authors should at least try to repeat the experiments of coculturing CD3+ T cells from healthy volunteers with the serum of mild/severe patients at 8-12 months post-recovery (Fig. 3 D-E).

The authors tried to find if the activated T cell phenotype or increased serum cytokines at 3 months post-infection is linked with increased long COVID symptoms. The study does not find any direct association when the data are adjusted for age, sex, and severity. This is the only novelty of this study, yet it is an important piece of information in the attempt to broaden our understanding of the underlying causes of long COVID symptoms.

Overall, it would be important to understand if increased frequencies of T cell activation (~2-fold) and increased levels of serum cytokines at 3 months following severe COVID-19 that resulted in ventilation and/or ITU admission is specific to severe SARS-CoV-2 infection, or if similar consequences are resulting also from other severe acute viral infections. Addressing this question is beyond the scope of the manuscript, yet it should be discussed.

Reviewer #3 (Public Review):

In this paper, the authors used a cohort study to link immune signatures in blood 30 days after COVID-19 infection as possible predictors of prolonged symptomatology. The paper partially achieves its aims. While the selected analyses are comprehensive, the cohort design is appropriate and the mechanistic ex vivo work is clever and convincing, the strength of conclusions is somewhat limited by the selection of imprecise clinical endpoints, and the lack of analyses examining T regulatory signatures.

Strengths of the paper are:<br /> • The paper includes a comprehensive and structured immune analysis.<br /> • The paper is extremely clearly written.<br /> • The use of manual gating and unsupervised analysis in Fig 1 is complementary and helpful.<br /> • Bystander T cell experiments with IL-15 are useful and attempt to explore mechanisms from human samples which are traditionally very challenging.<br /> • The experiments shown in Figure 4 documenting equal Cov2 T cell responses in all 3 cohorts are an extremely important result.

Major concerns are:<br /> • The significance of the study is somewhat limited by the small sample size.<br /> • The symptomatic outcome scale for PASC is blunt and poorly captures severity. More state-of-the-start scales of symptomatic severity and heterogeneity exist for PASC. I suggest this and other papers as an example: https://pubmed.ncbi.nlm.nih.gov/36454631/<br /> • The omission of analyses examining T regulatory functions is a missed opportunity and these may be impaired in this population.<br /> • This is a challenging question that can be applied to many exploratory studies of this nature: how can we rule out the possibility that statistically significant differences in Figs 1, 2 & 3 are statistically significant but biologically meaningless? All cellular and cytokine measures of immune responses shown in these figures are not routinely measured in the clinic. Are there studies that can be cited to show that these differences are sufficient to have a causal impact on prolonged symptoms and tissue damage rather than just correlations with these outcomes?

Reviewer #1 (Public Review):

The authors' objectives were to identify the features of uORFs that determine their effects on the translation of the main ORF found in the same transcript. The major strengths of the paper are the creative and powerful experimental platforms used to measure translation, the computational approaches used to identify the key features that determine the effect of uORFs on translation and the comparative analysis of two closely related species to understand how uORF activity evolves. The authors successfully and convincingly identify features associated with the regulatory effects of uORFs and have results suggesting that uORFs that would have strong repressive effects would be selected against. Although these insights regarding evolution are very interesting and may contribute to our understanding of regulatory evolution, at a level that is rarely explored, this section could benefit from additional analyses of existing data to fully support the conclusions. Another aspect that would need to be considered is the possible interaction between the uORFs and the main ORFs. Here, all experiments are performed with the same main ORFs (YFP) for practical and essential reasons, but it would be useful to know whether some uORF features would have effects whose sign and magnitude may depend on which main ORFs they associate with. Overall, there are several areas in which the authors' claims or conclusions are not fully justified and require either additional statistical analysis or new experimentation.

Reviewer #2 (Public Review):

This report uses massively parallel reporter assays to examine the impact on gene expression of >2000 uORFs found in yeast mRNAs with 5'UTR lengths <181nt, by comparing expression of two YFP reporters for each uORF, one containing the WT 5'UTR and the other with the uORF AUG codon mutated to a near-cognate AAG triplet. All of the mRNAs were expressed from the same promoter from the ENO2 gene, which is expected to produce the predicted 5' ends for all of the mRNAs being sampled. The results indicated that most AUG uORFs are repressive, while most nonAUG (near-cognate) uORFs have little effect on reporter expression; and a small fraction of AUG uORFs are stimulatory to YFP expression. They corroborated these results by sequencing the reporter library mRNAs in polysome vs monosome fractions and showing a good correlation (R=0.78) between the effects of the uORF AUG mutations on YFP expression versus fraction of the mRNA in polysomes. The reporter library was assayed in in both WT and upf1 mutants to evaluate the impact of NMD on uORF regulation of reporter expression and polysome association, which allowed them to determine that, on average, NMD accounts for ~35% of the uORF-mediated repression of reporter expression, ie. the magnitude of the repression is blunted in the upf1 mutant. Consistent with this, the reductions in YFP expression are frequently associated with reductions in reporter mRNA levels, measured by RNA-seq. Moreover, the repressive effects of the uORFs calculated from YFP expression versus polysome association of reporter mRNAs are more congruent in the upf1 mutant where NMD effects are absent versus the WT. Their bioinformatic analyses provide some evidence that NMD control is lessened by inefficient termination at uORFs with UGAC stop codons, for long vs. short uORFs, and by decreasing the distance of the uORF stop codon from the mRNA cap. Their large dataset allowed them to conduct machine learning to identify features of uORFs that are associated with their effects on YFP expression, finding that repression by the uORF is associated about equally with a good Kozak context for the start codon, a shorter distance of the uORF from the cap, and shorter distance of the uORF stop codon to the downstream CDS, with a somewhat weaker association with a longer uORF CDS. These findings for Kozak context were predictable from prior work, as were the associations with uORF length and distance to the YFP AUG in the context of known effects of these parameters on reinitiation. However, the association with distance of the uORF from the cap is more novel. They provide some additional support for the latter by analyzing the influence of different TSSs/5'UTR lengths on uORF repressive function for a subset of 333 uORFs, finding that the repressive effect can vary depending on the TSS, with several instances in which the uORF was less inhibitory when the TSS is located further upstream from the uORF AUG. Finally, they provide some evidence that uORFs conserved between closely related yeast species are generally less repressive and have poorer AUG contexts, leading to the conclusion that they are under purifying selection to make them less inhibitory.

This study is valuable in providing an unprecedented, comprehensive analysis of the regulatory effects of naturally occurring AUG and near-cognate uORFs on gene expression in a manner that distinguishes between repression of translation versus repression of mRNA stability via NMD. Owing to the large number of uORFs analyzed in a system that eliminates variations in transcription rate, it was possible to identify certain statistically significant associations between uORF features and the extent to which they repress translation or evoke NMD.

There are several areas in which the authors' claims or conclusions are not fully justified and require either additional statistical analysis or new experimentation to support the claims. In particular, additional experiments are needed to confirm that the reporter mRNAs initiate at the predicted TSS; to bolster the novel conclusion that moving a uORF farther from the cap reduces its inhibitory effect on translation initiation downstream, independently of the inclusion of other uORFs in the intervening interval; and to test their interpretations concerning the differences in uORF function between S. cerevisiae and S. paradoxus for particular mRNAs.

Reviewer #1 (Public Review):

The manuscript by Muthana et al. describes the effect of injection of an antibody specific for human CTLA4 conjugated to a cytotoxic molecule (Ipi-DM1) in knock-in mice expressing human CTLA4. The authors show that Ipi-DM1 administration causes a partial decrease (about 50% in absolute number) of mature B cells in blood and bone marrow 9-14 days after the beginning of treatment. Ipi-DM1 also results in a partial decrease in Foxp3+ Tregs (about 40% in absolute number) and a slight increase in activation of conventional T cells (Tconvs) in the blood at D9. Tconv depletion, CTLA4-Ig or anti-TNF mAb partially prevents the effect of ipi-DM1 on B cells. This work is interesting but has the following major limitations:

1- This work could have been of more interest if the Ipi-DM1 molecule would be used in the clinic. As this is not the case, the intimate mechanism of the effect of this molecule in mice is of reduced interest.<br /> 2- The fact that a partial deletion of Tregs is associated with activation of Tconvs and a decrease in B cells has been published several times and is therefore not new. According to the authors, their work would be the first to show that activation of Tconvs would lead to B cell depletion. However, this is shown in an indirect way and the mechanisms are not really elucidated. Indeed, this work shows a correlation between an increase in Tconv activation and a decrease in the number of B cells in the blood. The experiments to try to show a causal link are of 2 types: deletion of T cells (Fig 4) and blocking T cell activation with CTLA4-Ig (Fig 5) (neutralization of TNF addresses another question). Neither of these 2 experiments is totally convincing. Indeed, the absence of B cell depletion when T cells are deleted can be explained by other mechanisms than the preservation of B cell destruction by activated T cells. The phenomenon could be explained by B cell recirculation to lymphoid tissues or an effect of massive T cell death for example. The experiment shown in Fig. 5 with Belatacept is more convincing because this time the effect is targeted to activated T cells only. However, the prevention of B cell ablation is only partial. Again, since only blood is analyzed, other mechanisms could explain the B cell loss, such as their recirculation in lymphoid tissues.<br /> 3- It is disappointing that only the blood (and sometimes the bone marrow) was studied in this work. The interest of doing experiments in mice is to have access to many tissues such as the spleen, lymph nodes, colon, lung, and liver. To conclude that there is B cell deletion without showing lymphoid organs (where the majority of B cells reside) is insufficient. As discussed above, the drop in B cells in the blood could be due to their recirculation in lymphoid organs. In addition, there is no measurement of functional B cells activity. Do mice treated with Ipi-DM1 have a decreased ability to develop an antibody response following immunization?<br /> 4- Although it is difficult to study in vivo, there is not a single evidence of increased B cell death after injection of Ipi-DM1.<br /> 5- In most of the experiments, B cells are quantified with the B220 marker alone, but this marker, in some cases, can be expressed by other cells. It would have been preferable to use a marker more specific to B cells such as CD19 for example.

In conclusion, the concept that T cell activation can lead to B cell deletion is interesting but this study shows it only in an indirect and incomplete way.

Reviewer #2 (Public Review):

Despite the fact that CTLA-4 is a critical molecule for inhibiting the immune response, surprisingly individuals with heterozygous CTLA-4 mutations exhibit immunodeficiency, presenting with antibody deficiency secondary to B cell loss. Why the loss of a molecule that regulates T cell activation should lead to B cell loss has remained unclear. In this study, Muthana and colleagues use an anti-CTLA-4 antibody drug conjugate (aCTLA-4 ADC) to delete cells expressing high levels of CTLA-4, and show that this leads to a reduction in B cells. The aCTLA-4 ADC is found to delete a subset of Tregs, leading to hyperactivation of T cells that is associated with B cell depletion. Using blocking antibodies, the authors implicate TNFa in the observed B cell loss.

The reciprocal regulation of T and B cell homeostasis is an important research area. While it has been shown that Treg defects are associated with B cell loss, the mechanisms at play are incompletely understood. CTLA-4 is not normally expressed in B cells so an indirect mechanism of action is assumed. The authors show that the decrease in Treg following aCTLA-4 ADC treatment is associated with activation of T cells, and that B cell loss is blunted if T cells are depleted. A role for both CD4 and CD8 T cells is identified by selective CD4/CD8 depletion. T cells appear to require CD28 costimulation in order to mediate B cell loss, since the response is partially inhibited in the presence of the costimulation blockade drug belatacept (CTLA-4-Ig). Finally, experiments using the anti-TNFa antibody adalimumab suggest a potential role for TNFa in the depletion of B cells.

While the manuscript makes a useful contribution, a number of questions remain. Perhaps most important is the extent to which this model mimics the natural situation in individuals with CTLA-4 mutations (or following CTLA-4-based clinical interventions). aCTLA-4 ADC treatment permits acute deletion of Treg expressing high levels of CTLA-4, whereas in patients the Treg population remains but is specifically impaired in CTLA-4 function. Secondly, although the requirement for T cells to mediate B cell loss is convincingly demonstrated, the incomplete reversal by TNFa blockade suggests additional unidentified factors contribute to this effect. Finally, although the manuscript favours peripheral killing of mature B cells over alterations to B cell lymphopoiesis, one concern is that this may simply reflect the model employed: the short-term (6 day) treatment used here may be too acute to alter B cell development, but this may nevertheless be a feature of prolonged immune dysregulation in humans.

Reviewer #3 (Public Review):

The co-suppressive molecule CTLA-4 has a critical role in the maintenance of peripheral tolerance, primarily by Treg mediated control of the co-stimulatory molecules CD80 and CD86. As stated by the authors, previous studies have found a variety of effects of anti-CTLA-4 antibody treatment or genetic loss of CTLA-4 on B-cells. These include increased B-cell activation and antibody production, autoantibody production, impairment of B-cell production in the bone marrow and loss of peripheral B-cells. In this article Muthana et al use a CTLA-4 humanized mouse model and examine the effects of drug conjugated CTLA-4 on the immune system. They observe a transient loss of B-cells in the blood of the treated mice. They then use a range of immune interventions such as T-cell depletion and blocking antibodies to demonstrate that this effect is dependent on T-cell activation.

Since anti-CTLA-4 immunotherapy is in active clinical use exploration of its effects are welcome, this is helped by the use of a humanized CTLA-4 system which should be considered a strength of the paper. However, currently, the central premise of this paper, that B-cells are depleted, seems underexplored. Direct evidence of T-cell killing of B-cells is never presented, rather it is inferred from the reduced numbers of B-cells in the blood. The status of B-cells in sites that contain a large proportion of B-cells such as the spleen and lymph nodes is not examined. Additionally, no examination of B-cell antibody production is performed.

Reviewer #1 (Public Review):

This study was designed to examine the bypass of Ras/Erk signaling defects that enable limited regeneration in a mouse model of hepatic regeneration. The authors show that this hepatocyte proliferation is marked by expression of CD133 by groups of cells. The CD133 appears to be located on intracellular vesicles associated with microtubules. These vesicles are loaded with mRNA. The authors conclude that the CD133 vesicles mediate an intercellular signaling pathway that supports cell proliferation. These are new observations that have broad significance to the fields of regeneration and cancer.

The primary observation is that the limited regeneration observed in livers with Ras/Erk signaling defects is associated with CD133 expression by groups of cells. The functional significance of CD133 was tested using Prom1 KO mice - the data presented are convincing.

The major weakness of the study is that some molecular mechanistic details are unclear - this is, in part, due to the extensive new biology that is described. Nevertheless, the data used to support some key points in this study are unclear:

a) What is the evidence that the observed CD133 groups of cells are not due to clonal growth. Is this conclusion based on the time course (the groups appear more rapidly than proliferation) or is this based on the GFP clonal analysis?

b) What is the evidence that the CD133 vesicles mediate intercellular communication. This is an exciting hypothesis, but what is the evidence that this happens? Is this inferred from IEG mRNA diversity? or some other data. Is there direct evidence of transfer - for example, the does the GFP clonal analysis show transfer of GFP that is not mediated by clonal proliferation? Moreover, since the hepatocytes are isogenic, what distinguishes the donor and recipient cells?

Increased clarity concerning what is hypothesis and what is directly supported by data - would improve the presentation of this study.

Reviewer #2 (Public Review):

The manuscript by Kaneko set out to understand the mechanisms underlying cell proliferation in hepatocytes lacking Shp2 signals. To do this, the authors focused on CD133 as the proliferating clusters of cells in the Shp2 knockout (SKO) livers are CD133 expressing. After excluding the contribution of progenitors that are CD133 to this cell population, the authors focused on the intrinsic regulation of CD133 by Met/Shp2 regulated Ras/Erk parthway and showed upregulation of CD133 to be a compensatory signal to overcome loss of Ras/Erk signal and suggested Wnt10a in the regulation of CD133 signal. The study then focused on the observed filament localization of CD133 in the CD133+ cluster of cells. The study went on to identify the CD133+ vesicles that contain primarily mRNA vs. microRNA like other EVs. Specifically, the authors identified several mRNA species that encode IEGs, indicating a potential role for these CD133+ vesicles in cell proliferation signal transmission to neighboring cells via delivery of the IEG mRNAs as cargos. Finally, they showed that the induction of CD133 (and by derivative, the CD133+ vesicles) are necessary for maintaining cell proliferation in the cell cluster with high proliferation capacities in the SKO livers; and in intestinal crypt organoids treated with Met inhibitors to block Ras/ERk signal.

1) The identification of CD133+ vesicles is largely based on staining and costainings. Though the experiments are very well done with many controls and approaches, the authors may want to perform one or two key experiments with EM to definitively demonstrate the colocalization. For example, the mCherry experiment in Fig6H and the colocalization experiments for CD133 and HuR in Fig 7.

2) Since CD133+ marks the 50nM intracellsome defined by the authors, it is unclear what the CD133- vesicles used as controls are. Are they regular EVs that are larger in size? This needs better clarification as they are used as a control for many experiments such as Fig 7A.

Reviewer #1 (Public Review):

This is an interesting manuscript that proposes a new approach to for accounting for viral diversity within hosts in phylogenetic analyses of pathogens. Concretely, the authors consider sites for which a minor allele exist as an additional base in the substitution model. For example, if at a particular site 60% of reads have an C and 40% have a G, then this site is assigned Cg, as opposed to an C which is typical of analysing consensus sequences. Because we typically model sequence evolution as a Markovian process, as is the case here, the data become naturally more informative, given that there are more states in the Markov chain when adding these bases. As a result, phylogenetic trees estimated using these data are better resolved than those from consensus sequences. The branches of the trees are probably also longer, which is why temporal signal becomes more apparent.

I commend the authors on their rigorous simulation study and careful empirical data analyses. However, I strongly suggest they consider whether treating minor alleles as an additional base is biologically realistic and whether this may have implication for other analyses, particularly when there is very high within-host diversity and the number of states in becomes very large.

Reviewer #2 (Public Review):

I agree that minor genetic variation could potentially be used to more accurately infer who-infected- whom in an outbreak scenario. Indeed, the use of minor genetic variation has proven very useful in reconstructing transmission chains for chronic infections such as HIV (e.g., see applications using Phyloscanner). To me, it seems that considering the full spectrum of viral genetic diversity within infected hosts would necessarily do the same if not better than considering only consensus-level viral sequence data. This is because there is a necessarily a loss of data and potentially a loss of information when going from considering the genetic composition of viral populations within a host to only considering the consensus sequences of those viral populations. As such, Ortiz et al.'s hypothesis stated on lines 66-70 is a reasonable one, and I was looking forward to seeing this hypothesis evaluated in detail in this manuscript.<br /> There are several parts of this manuscript I really like. In particular, encoding within-sample diversity as character states and using that alternative representation of sequence data for phylogenetic inference (as shown in Figure 3) is a very interesting idea, I think. There are some limitations that are not explicitly mentioned, however. For example, when using this 16-character state representation for phylogenetic inference, they assume independence between nucleotide sites. This is a major assumption that can be violated when considering longitudinal intrahost data and transmission dynamics in an outbreak setting, given genetic linkage between sites.

I have several major concerns about the work as it stands, particularly in the context of the SARS-CoV-2 application.

Concerns not related to the SARS-CoV-2 application:<br /> Concern #1: Figure 4 shows that a model using within-sample diversity can more accurately reconstruct evolutionary histories than a model that uses only consensus-level genetic data. This is really interesting. The Materials and Methods section (particularly lines 351-354) indicates that the sequence data were generated using certain specified substitution rates. The rates specified seem to be chosen in such a way to facilitate finding an improvement when using within-sample diversity. I don't know whether the relative rates of these 'substitutions' at all mirror "real-life". It would be very useful to have a broader set of analyses here to examine the effect of these 'substitution' rates on the utility of incorporating within-sample diversity into phylogenetic inference. (Also, 1, 100, 200 (line 353) inconsistent with 1, 20, 200 in Supp Table 3)

Concern #2: Figure 5 is very interesting, particularly the results at bottleneck sizes of 1-10. What are the 'substitution' rates that are inferred here from using this simulated dataset? The Material and Methods section also does not mention the within-host viral generation time anywhere, as far as I can see (~line 384 states the mutation rate per base per generation cycle but not the length of the generation cycle anywhere).

Concerns related to the SARS-CoV-2 application:<br /> Concern #3: I am very concerned about the testing of this hypothesis on the SARS-CoV-2 data presented. First, 1% is a very low variant calling threshold. Second, analysis of the 17 samples that were resequenced (out of 454) indicated that on average, 39% of iSNVS (intrahost single nucleotide variants) called between duplicate runs were only observed in one of the two runs (line 117). Their analysis in Figure 1 indicates that these discrepant (and seemingly spurious) variants occur at higher levels in high Ct samples (which makes sense; Figure 1b). They therefore decide to limit their analyses to samples with Ct values <= 30. This results in 249 samples. However, if we look at Figure 1b, only ~10% of iSNVs called across duplicate runs with Ct = 30 are shared! That means that 90% of iSNVs in the set appear to be spurious. If we assume that each duplicate run of a sample has approximately the same number of spurious iSNVs, then approximately 82% of iSNVs called in a sample with a Ct of 30 would be spurious. This fraction decreases with samples that have lower Ct values, but even at a Ct of 27, only ~60% of iSNVs called across duplicate runs are shared. All the downstream SARS-CoV-2 analyses based on within-host sample diversity therefore are based on samples where the large majority of considered sample diversity is not real. This leads to me necessarily discounting all of those downstream SARS-CoV-2 results.

Concern #4: Lines 153-167: I can't figure out how to square the quantitative results given in this paragraph with what is shown in Figure 2. To me, Figure 2 shows only that Technical Replicates have higher probabilities of sharing a variant than with 'No' relationship. What would also be helpful here so that the reader can get a better feel for the data would be to see the iSNV frequencies plotted over time for the longitudinal replicate samples in the supplement and, for the 'epidemiological' samples to show 'TV plots' in the supplement (as in Fig 3c in McCrone et al. eLife)

Concern #5: Figure 6 and associated text: (a) root-to-tip distance: what units is this distance in? (b) That the authors find a temporal signal in these transmission clusters (where all consensus sequences within a cluster are the same) is interesting but also a bit baffling to me. Given the inference of very small transmission bottlenecks in previous studies (e.g., Martin & Koelle - reanalysis of Popa et al.; Lythgoe et al.; Braun et al.), I don't understand where the temporal signal comes in. Do the samples become more genetically diverse over the outbreak (this seems to be indicated in lines 260-262 but never shown and unlikely given bottleneck sizes)? Additional analyses to help the reader understand WHY within-sample diversity allows for the identification of temporal signal is important. This could involve plotting genetic diversity of the samples by collection date or some other, similar analyses.

Concern #6: Paragraph consisting of lines 229-238 and Figure 7: This analysis stops abruptly. What are the conclusions here? Figure 7a (right) seems inconsistent to me with Figure 7b and 7C results. Also, the main hypothesis put forward in this paper is that within-sample sequence data can better resolve who-infected-whom in an outbreak setting. Figure 7b and 7c however are never compared against analogous panels that use just consensus sequences. (Even though the consensus sequences are the same, according to Figure 7a, the inferences shown in Figures 7b and 7c could use additional data such as collection times, etc. that would provide information even when using exclusively consensus-level data). Also, do the analyses in Figures 7b and 7c use the 16-character state model at all? I think Supp Figure 9 is relevant here but not sure how?)

Additional concerns:<br /> Concern #7: Some of the stated conclusions, particularly in the Discussion section and in the Abstract, do not seem to be supported by the presented results. For example, line 27: 'within-sample diversity is stable among repeated serial samples from the same host': Figure 2 does not show this conclusively. Line 28: 'within-sample diversity... is transmitted between those cases with known epidemiological links': Figure 2 also does not show this conclusively. Line 29: 'within-sample diversity... improves phylogenetic inference and our understanding of who infected whom': Figure 7b/c results using within-sample diversity is never compared against results that use only consensus, so improvement not demonstrated. Line 272-273: 'samples with shorter distance in the consensus phylogeny were more likely to share low frequency variants'. Line 287: 'We demonstrated that phylogenies... were heavily biased'.

Concern #8: The manuscript at times does not cite previous work that is highly relevant and thus overstates the novelty of the current work. For example: lines 21-23: '..conventional whole-genome sequencing phylogenetic approaches to reconstruct outbreaks exclusively use consensus sequences...' Phyloscanner uses within-sample diversity, for example, as does SCOTTI. These are finally cited in the discussion section (~line 310), but because this previous work is not acknowledged earlier in the manuscript, the novelty of the work presented here is somewhat overstated.

In sum, I think that the 16 character-state model is a very interesting model. More analyses on simulated data would be helpful to expand on when below-the-consensus level genetic data would truly be informative of phylogenetic relationships and who-infected-whom in outbreak settings. The SARS-CoV-2 analyses are very worrisome to me, given the inclusion of samples where the majority of considered within-sample genetic diversity is very likely not real. Some of the stated conclusions appear to either be at odds with the results presented or not directly evaluated.

Reviewer #1 (Public Review):<br /> <br /> Beta-hemoglobinopathies, such as sickle cell disease and beta-thalassemia, are common and debilitating genetic diseases caused by mutations in the adult beta-globin gene. Many in the field are pursuing various strategies to therapeutically upregulate fetal gamma-globin to treat these diseases. In this paper, the authors aimed to instead edit the promoter of the delta-globin gene to cause upregulation of delta-globin expression. Delta-globin is highly homologous to adult beta-globin and is pan-cellularly expressed in adult red blood cells, albeit at low levels due to the low activity of its promoter. Gene editing to activate the promoter of delta-globin could allow delta-globin expression to be elevated which could compensate for defective beta-globin in patients with beta-hemoglobinopathies. This is an underexplored and very interesting approach, and this study represents the first time that delta-globin upregulation has been attempted using gene editing in adult-like human erythroid immortalised and primary cells.

The first major finding from this study was that gene editing to insert KLF1, beta-DRF, and TFIIb sites into the delta-globin promoter was sufficient to cause upregulation of delta-globin expression at the mRNA and protein levels in immortalized HUDEP-2 cells. Modest upregulation was seen in pooled populations of HUDEP-2 cells (where ~25% of cells were HDR edited). Robust expression of delta-globin was seen in homozygously edited clonal populations of HUDEP-2 cells, with delta-globin constituting ~25% of total beta-like globin expression at the mRNA level in these cells. The results presented thus strongly support this finding.

The second major finding was that gene editing to insert KLF1, beta-DRF, and TFIIb sites into the delta globin promoter was sufficient to cause upregulation of delta-globin in primary human CD34+ cells. Despite HDR editing efficiencies of ~25% in these primary cells, and possibly due to only two donor cell populations being used, significant upregulation of delta-globin was not detected in pooled populations of edited primary CD34+ cells. Encouraging evidence of upregulation was seen in the clonal population of edited cells from the two donors. As such the results provide moderate support for this finding.

In combination, the HUDEP-2 cell and CD34+ cell data provide compelling evidence that gene editing of the delta-globin promoter is a promising line of enquiry for the treatment of beta-hemoglobinopathies.

This important study establishes and provides a proof-of-principle for this alternative therapeutic approach for those with beta-hemoglobinopathies. Future studies based on this work may enable delta-globin to be upregulated to therapeutically relevant levels in patient cells, including in cells from patients with beta-hemoglobinopathies. The therapeutic benefits of delta-globin upregulation will then be able to be assessed. This finding will be of interest to those in the globin switching and gene editing fields.

Reviewer #2 (Public Review):

Targeted genetic engineering with programmable nucleases and other targetable enzymes (aka "genome editing") has emerged as a technology with curative potential in hemoglobinopathies, sickle cell disease, and beta-thalassemia. Multiple ongoing clinical trials are evaluating such editing using distinct approaches: elevation of fetal hemoglobin (HbF), direct repair of the mutation causing SCD, and engineering of a Hb variant. The present work explores a different strategy: the targeted engineering of the promoter of a paralog of adult beta-globin known as HBD. This is a timely effort because there has emerged, over the past decade, a clear and charted path for advancing any such approach to human clinical trials. The study identifies three transcription factor binding sites as divergent in the HBD promoter vs the HBB one. A homology-directed repair (HDR)-based scheme using oligonucleotide repair templates in combination with a CRISPR-Cas9-induced double-strand break (DSB) is designed and used to generate pools of human immortalized cells bearing one, two, or all three such de novo introduced TF binding sites at the HBD promoter. Only the latter scheme is shown to measurably increase HBD (following erythroid differentiation) in pools of cells and single-cell-derived clones as gauged by qPCR and HPLC. A similar analysis is performed on pools of erythroid-like cells generated from genome-edited human hematopoietic stem and progenitor cells (HSPCs), as well as genetically clonal erythroid colonies bearing the edits of interest; trends in these data support the observations made on the immortalized cells. Overall the data support the notion that HBD promoter genome editing has the potential as a strategy to normalize hemoglobin synthesis in hemoglobinopathies. Further, the data support an advance of this approach down a well-established path of preclinical development in such cases: increasing the efficiency of genome editing in HSPCs to what would be deemed therapeutically useful, assessing the genotoxic burden from the editing, evaluating the potential negative impact on stemness, and determining whether this approach would normalize hemoglobin synthesis in the erythroid progeny of patient HSPCs.

The genome editing scheme for the "KDT" strategy in Fig 1B involves the introduction of three binding sites for transcription factors at progressively increasing distances from the site of the DSB induced by Cas9. It would be of interest to determine from the next-generation-sequencing data whether partial gene conversion tracks are observed at the edited locus (Elliott and Jasin MCB 18: 93), and if yes, whether these affect in some way the pool-level measurement by qPCR on HBD mRNA levels (Fig 1D).

The data in Fig 2A show an analysis of transcription factor and RNA pol II occupancy following genome editing at HBD. The figure legend refers to these data as having been obtained on single-cell-derived clones bearing the edits in homozygous or heterozygous form, but it is unclear from fig 2A, which clones were used for which analysis.

The data in Fig 3C present an analysis of HBD levels in erythroid colonies derived from genome-edited HSPCs. It would be helpful to clarify whether an individual dot represents a single such colony (this would seem to be the case from the cognate figure legend). If so, what number of such colonies would one need to obtain to gain a clearer sense of the effect on HBD levels from the various genome editing strategies used?

It would be helpful to comment, in the Discussion, on potential genome editing strategies to obtain high-efficiency pool-level uniform long-track gene conversion that is necessary to obtain high HBD levels in the progeny of edited CD34 cells. Would this be a good application of the AAV6 strategy developed by the Sangamo and Porteus groups? Would prime editing as developed by Liu be an option here?

It would be equally helpful, in the Discussion, to place the level of HbA2 obtained via the strategy shown in the manuscript in the context of other genome-editing-based approaches for normalizing Hb synthesis in the hemoglobinopathies (ie HbF elevation by editing the BCL11A enhancer, or the gamma-globin promoter; or direct repair of the SCD mutation; or engineering of Hb Makassar).

Reviewer #3 (Public Review):

This is a well-written and referenced paper from the laboratory of an outstanding senior investigator. Dr. Corn and colleagues demonstrate convincingly that correction of three transcription factor binding sites in the delta-globin gene promoter results in high levels of delta-globin expression in HUDEP-2 clonal cell populations (Fig. 2B and C) and in CD34+ HSPC (hematopoietic stem and progenitor cells) clonal cell expansions (Fig. 3C). Although correction of the mutant KLF1 binding site has previously been shown to upregulate delta-globin gene transgenes, this new data demonstrate that correction of multiple factor binding sites is required to achieve high-level expression of the delta-globin gene in the endogenous beta-globin gene locus. The results are important because high delta-globin protein levels inhibit the formation of sickle hemoglobin (HbS) polymers that cause sickle cell disease.

Unfortunately, high levels of delta-globin gene expression were not observed after editing of pooled (non-clonal) populations of HUDEP-2 cells (Fig. 1D) or CD34+ HSPC pooled cell populations (Fig. 3B). This result suggests that correction of all 3 promoter elements on individual alleles in CD34+ HSPC populations is far below the level required to be clinically relevant. Also, NHEJ is high in CD34+ HSPC (Fig. 3A); therefore, promoter deletions will inactivate many alleles, and total hemoglobin levels in erythrocytes derived from populations of edited CD34+ HSPC will be much less than normal (29 pg/cell). These cells would be extremely beta-thalassemic.

Reviewer #1 (Public Review):

In this interesting manuscript, Nasser et al explore long-term patterns of behavior and individuality in C. elegans following early-life nutritional stress. Using a rigorous, highly quantitative, high-throughput approach, they track patterns of motor behavior in many individual nematodes from L1 to young adulthood. Interestingly, they find that early-life food deprivation leads to decreased activity in young larvae and adults, but that activity between these times, during L2-L4, is largely unaffected. Further, they show that this "buffering" of stress requires dopamine signaling, as L2-L4 activity is significantly reduced by early-life starvation in cat-2 mutants. The paper also provides evidence that serotonin signaling has a role in modulating sensitivity to stress in L1 larvae and adults, but the size of these effects is modest. To evaluate patterns of individuality, the authors use principal components analysis to find that three temporal patterns of activity account for much of the variation in the data. While the paper refers to these as "individuality types," it may be more reasonable to think of these as "dimensions of individuality." Further, they provide evidence that stress may alter the strength and/or features of these dimensions. Though the circuit mechanisms underlying individuality and stress-induced changes in behavior remain unknown, this paper lays an important foundation for evaluating these questions. As the authors note, the behaviors studied here represent only a small fraction of the behavioral repertoire of this system. As such, the findings here are an interesting and very promising entry point for a deeper understanding of behavioral individuality, particularly because of the cellular/synaptic-level analysis that is possible in this system. This paper should be of interest to those studying C. elegans behavior and also more generally to those interested in behavioral plasticity and individuality.

Reviewer #2 (Public Review):

This paper set out to understand the impact of early life stress on the behavior and individuality of animals, and how that impact might be amplified or masked by neuromodulation. To do so, the authors built on a previously established assay (Stern et al 2017) to measure the roaming fraction and speed of individuals. This technique allowed the authors to assess the effects of early life starvation on behavior across the entire developmental trajectory of the individual. By combining this with strains with mutant neuromodulatory systems, this enabled the authors to produce a rich dataset ripe for analysis to analyze the complicated interactions between behavior, starvation intensity, developmental time, individuality, and neuromodulatory systems.

The richness of this dataset - 2 behavioral measures continuous across 5 developmental stages, 3 different neuromodulatory conditions (with the dopamine system subject to decomposition by receptor types) and 4 different levels of starvation, with ~50-500 individuals in each condition-underlies the strength of this paper. This dataset enabled the authors to convincingly demonstrate that starvation triggers a behavioral effect in L1 and adult animals that is largely masked in intermediate stages, and that this effect becomes larger with increased severity of starvation. Furthermore, they convincingly show that the masking of the effect of starvation in L2-L4 animals depends on dopaminergic systems. The richness of the dataset also allowed a careful analysis of individuality, though only neuromodulatory mutants convincingly manipulated individuality, recapitulating earlier research. Nonetheless, a few caveats exist on some of their findings and conclusions:

1. Lack of quantitative analysis for effects within developmental stages. In making the argument for buffered effects of starvation on behavior during periods of larval development, the authors make claims regarding the temporal structure of behavior within specific stages. However, no formal analysis is performed and and the traces are provided without confidence intervals, making it difficult to judge the significance of potential deviations between starvation conditions.

2. Incorrect inferences from differences in significance demonstrating significant differences. The authors claim that there is an increase in PC1 inter-individual variation in tph-1 individuals, however the difference in significance is not evidence of a significant difference between conditions (see Nieuwenhuis et al. 2011). This undermines claims about an interaction of starvation, neuromodulators, and individuality.

3. Sensitivity of analysis to baseline effects and assumptions of additive/proportional effects. The neuromodulatory and stress conditions in this paper have a mixture of effects on baseline activity and differences from baseline. The authors normalize to the roaming fraction without starvation, making the reasonable assumption that the effect due to starvation is proportional to baseline, rather than an additive effect. This confound is most visible in the adult subpanel of figure 5d, where an ~2-3 fold difference in relative roaming due to starvation is clearly noted, however, this is from a baseline roaming fraction in tph-1 animals that are ~2 fold higher, suggesting that the effect could plausibly be comparable in absolute terms.

Unavoidably, any such assumptions on the expected interaction between multiple effects will be a gross simplification in complicated nonlinear systems, and the data are largely shown with sufficient clarity to allow the reader to make their own conclusions. However, some of the interpretations in the paper lean heavily on an assumption that the data support a direct interpretation (e.g. "neuronal mechanisms actively buffer behavioral alterations at specific development times") rather than an indirect interpretation (e.g. that serotonin reduces baseline roaming fraction which makes a fixed sized effect more noticeable). Parsing the differences requires either more detailed mechanistic study or careful characterization of the effect of different baselines on the sensitivity of behavior to perturbation-barring that it's worth noting that many of these interactions may be due to differences in biological and experimental sensitivity to change under different conditions, rather than a direct interaction of stress and neuromodulatory processes or evidence of differing neuromodulatory activity at different stages of development.

Reviewer #3 (Public Review):

In this study, Nasser et al. aim to understand how early-life experience affects 1) developmental behavior trajectory and 2) individuality. They use early life starvation and longitudinal recording of C. elegans locomotion across development as a model to address these questions. They focus on one specific behavioral response (roaming vs. dwelling) and demonstrate that early life (right after embryo hatching) starvation reduces roaming in the first larval (L1) and adult stages. However, roaming/dwelling behavior during mid-larval stages (L2 through L4) is buffered from early life starvation. Using dopamine and serotonin biosynthesis null mutant animals, they demonstrated that dopamine is important for the buffering/protection of behavioral responses to starvation in mid-larval stages, while in contrast, serotonin contributes to early-life starvation's effects on reduced roaming in the L1 and adult stages. While the technique and analysis approaches used are mostly solid and support many of the conclusions made in the manuscript for part 1), there are some technical limitations (e.g., whether the method has sufficient resolution to analyze the behaviors of younger animals) and confounding factors (e.g., size of the animal) that the authors do not yet sufficient address, and can affect interpretation of the results. Additionally, much of the study is descriptive and lacks deep mechanistic insight. Furthermore, the focus on a single behavioral parameter (dwelling vs. roaming) limits the broad applicability of the study's conclusions. Lastly, the manuscript does not provide clear presentation or analysis to address part 2), the question of how early life experience affect individuality.

Reviewer #1 (Public Review):

The DNA damage checkpoint is a cellular signalling pathway that responds to DNA damage and replication stress. This manuscript by Ho et al. systematically investigates an aspect of the checkpoint response in budding yeast that has been previously understudied, namely which proteins change subcellular and how these changed depend on the checkpoint kinases Mec1 and Rad53. By nice detective work the authors find a new mode of activation of Rad53, which is Mec1-independent, but rather depends on factors of so called retrograde signalling. Currently, we view checkpoint signalling as hierarchical, with Mec1 and Tel1 activating Rad53, despite both Mec1 and Rad53 having independent targets. This manuscript challenges that view by finding a Mec1 (and Tel1) independent mode of activation. It is very clear from survival and mass spectrometry data that in the absence of Mec1 this activation pathway and Rtg3 has a key role in activating Rad53. In the current form of the manuscript, it remains however difficult to assess what is the contribution of these factors on Rad53 activation in an otherwise WT background.

Reviewer #2 (Public Review):

Using an approach that combines synthetic genetic array (SGA) analysis with high-throughput microscopic analysis of the GFP-tagged yeast ORF collection in the budding yeast, Saccharomyces cerevisiae, this study has examined the contribution of the critical checkpoint kinases Mec1 and Rad53 to the subcellular relocalization of 322 candidate proteins in response to HU- and MMS-induced replication stress. Previous studies have established that Mec1 is required for Rad53 activation during replication stress and that Mec1 also serves checkpoint functions independent of Rad53. Unexpectedly, this study identifies groups of proteins whose stress-induced relocalization is dependent on Rad53 but not Mec1. This data indicates that Rad53 mediates some replication stress responses in a non-canonical manner that is independent of Mec1.

The authors confirm their initial observations from the screening approach by focusing on the Rad53-dependent and Mec1-independent focus formation of GFP-Rad54. Moreover, using mass-spec analysis the authors demonstrate that some Rad53 phosphorylation sites known to be critical for Rad53 activation, including a consensus Mec1 phosphorylation site, are phosphorylated after replication stress even in the absence of Mec1. Motivated by this finding the authors screen for potential kinase and phosphatase pathways that may regulate Rad53 function during MMS-induced replication stress. Top hits identified include members of the retrograde signaling pathway, which is confirmed by conventional genetic assays while mass spec analysis supports the involvement of Rtg3 in mediating Rad53 phosphorylation during replication stress in the absence of Mec1.

Overall this is a solid study reporting unexpected new findings that significantly advance our view of the global replication checkpoint response. The data are generally of high quality, well presented and quantified, and overall support the authors' claims. The mass spec approach used here to identify Rad53 phosphorylation sites offers an unbiased alternative to the simpler and more widely employed gel-shift method to monitor Rad53 activation. The hits identified in the various screens presented here provide a platform for potential follow-up studies by the community. The main drawback is that it remains unclear how Rtg3 promotes Rad53 activtation. However, this could be considered to be beyond the scope of this study.

Reviewer #3 (Public Review):

The work by Ho et al describes the identification of Mec1/Tel1 independent activation of Rad53 after MMS treatment, which could lead to changes of GFP fusion signals for several dozens of proteins and this was partly dependent on Rtg3. Starting from an unbiased, targeted screen, the authors identified proteins whose GFP fusion signals changed intensity in rad53∆ but not in mec1∆ cells using live cell imaging, including Rad54. Using Rad54 as a readout for the subsequent experiments, a second screen amongst kinases/phosphatases and their regulators found that rtg2-3 mutants reduced Rad54-GFP intensity. Mass spectrometry data identified Rad53 phosphorylate sites in mec1∆ tel1∆ cells, consistent with the cell biological data described above. Overall, the work was well done and supported the main conclusions. The concept of Mec1/Tel1-independent and Rtg3-dependent Rad53 activation connects checkpoint signaling with the retrograde pathway.

Reviewer #1 (Public Review):

In this paper, Reato, Steinfeld et al. investigate a question that has long puzzled neuroscientists: what features of ongoing brain activity predict trial-to-trial variability in responding to the same sensory stimuli? They record spiking activity in the auditory cortex of head-fixed mice as the animals performed a tone frequency discrimination task. They then measure both overall activity and the synchronization between neurons, and link this 'baseline state' (after removing slow drifts) of cortex to decision accuracy. They find that cortical state fluctuations only affect subsequent evoked responses and choice behavior after errors. This indicates that it's important to take into account the behavioral context when examining the effects of neural state on behavior.

Strengths of this work are the clear and beautiful presentation of the figures, and the careful consideration of the temporal properties of behavioral and neural signals. Indeed, slowly drifting signals are tricky as many authors have recently addressed (e.g. Ashwood, Gupta, Harris). The authors are well aware of the difficulties in correlating different signals with temporal and cross-correlation (such as in their 'epoch hypothesis'). To disentangle such slow trends from more short-lived state fluctuations, they remove the impact of the past 10 trials and continue their analyses with so-called 'innovations' (a term that is unusual, and may more simply be replaced with 'residuals').

I do wonder if this throws out the baby with the bathwater. If the concern is statistical confound, the 'session permutation' method (Harris) may be better suited. If the concern is that short-term state fluctuations are more behaviorally relevant (and obscured by slow drifts), then why are the results with raw signals in the supplement (Suppfig 8) so similar?

While the authors are correct that go-nogo tasks have drawbacks in dissociating sensitivity from response bias, they only cursorily review the literature on 2AFC tasks and cortical state. In particular, it would be good to discuss how the specific method - spikes, EEG (Waschke), widefield (Jacobs) and algorithm for quantifying synchronization may affect outcomes. How do these population-based measures of cortical state relate to those described extensively with slightly different signals, notably LFP or EEG in humans (e.g. work by Saskia Haegens, Niko Busch, reviewed in https://doi.org/10.1016/j.tics.2020.05.004)? This review also points out the importance of moving beyond simple measures of accuracy and using SDT, which would be an interesting improvement for this paper too.

Reviewer #2 (Public Review):

The relationship between measures of brain state, behavioral state, and performance has long been speculated to be relatively simple - with arousal and engagement reflecting EEG desynchronization and improved performance associated with increases in engagement and attention. The present study demonstrates that the outcome of the previous trial, specifically a miss, allows these associations to be seen - while a correct response appears less likely to do so. This is an interesting advance in our understanding of the relationship between brain state, behavioral state, and performance.

While the study is well done, the results are likely to be specific to their trial structure and states exhibited by the mice. To examine the full range of arousal states, it needs to be demonstrated that animals are varying between near-sleep (e.g. drowsiness) and high-alertness such as in rapid running. The fact that the trials occurred rapidly means that the physiological and neural variables associated with each trial will overlap with upcoming trials - it takes a mouse more than a few seconds to relax from a previous miss or hit, for example. Spreading the rapidity of the trials out would allow for a broader range of states to be examined, and perhaps less cross-talk between adjacent trials. The interpretation of the results, therefore, must be taken in light of the trial structure and the states exhibited by the mice.

Reviewer #2 (Public Review):

The manuscript by Mohebi et al. examines a critical open question regarding the interaction of cholinergic interneurons of the striatum and transmitter release from dopaminergic axons in behaving animals. Activation of cholinergic interneurons in the striatum can evoke dopamine release in brain slices and in vivo as measured with voltammetry. However, it remains an open question in what context and to what extent this acetylcholine-mediated dopamine occurs in behaving animals. Here, the authors argue that CIN activity triggers dopamine release in the nucleus accumbens which encodes the motivation to obtain a reward through increasing "ramps" of dopamine release. Their data suggest that the ramps are not reflected in the firing of dopaminergic neurons. Rather, they provide compelling evidence that the ramps of dopamine release correlate with ramps in cholinergic interneuron activity as measured with GCaMP6. What's more, the authors show that ACh-mediated dopamine release has no paired-pulse depression, a striking result that differs from all prior ex vivo brain slice data. The manuscript is extremely well written and the data are of very high quality. Overall, this study represents an important step forward in our understanding of how ACh-mediated dopamine release regulates behavior, and more broadly how axons can generate behaviors independently from somatic activity.

Major comments<br /> 1. The complete absence of any short-term plasticity in CIN-mediated dopamine release is a striking result that is important for the field. The authors should strengthen this result with additional quantitative analysis demonstrating the lack of STP. They have analyzed paired-pulse ratios, but they should analyze this for stimuli at the higher frequencies (4 Hz, etc) that are more physiologically relevant. For example, Fig 1e shows a CIN-evoked DA release at many optically-stimulated frequencies. The authors should quantify short-term plasticity by generating fits of the single stimulus signal and comparing the mathematical sum predicted from 4 stim DA signals at different frequencies to the recorded data. A similar analysis has been done with Ca signals (Koester and Sakmann, 2000).

2. The authors show that optical activation of CINs results in DA release as measured by dLight. To clearly establish that these signals are generated by DA release driven by nicotinic receptors (and not a partial effect of some unknown artifact), it would be useful to show that the optical CIN-evoked dLight signals shown in Fig. 1 are inhibited by nicotinic receptor antagonists such as DHbE. This control experiment would significantly strengthen the result shown here.

3. Similarly, the authors show clear correlations between CIN activity and DA release during behavior. The authors should consider determining whether CINs play a causal role in triggering DA release during behavior. For example, does infusion of DHbE in the NAc prevent the light-mediated DA release during behavior? As an alternative hypothesis, some groups have been suggesting that CIN activity has almost no direct influence over DA. Therefore, testing whether a causal relationship exists between CINs and DA release would be an important experiment in addressing these two opposing viewpoints.

4. The ramps that are described in this manuscript are an order of magnitude faster (increasing over 100s of milliseconds) than ramps described in other studies that occur over seconds. In fact, the two signals may be completely different functionally. Discussion of this topic would be helpful.

Public Review:

Barreat and Katzourakis analyze the evolutionary history of eukaryotic viruses (and related mobile elements) in the Bamfordvirae kingdom, and discuss potential scenarios regarding the origin of different viral taxa in this group. This version of their manuscript includes a larger number of sequences to better represent diversity in these viral groups, and explored new evolutionary scenarios, including a "virophage-first" hypothesis now presented as the one best supported by phylogenetic analyses. The authors also present compelling analyses suggesting that the "nuclear escape" hypothesis in which these different viral groups separately "escaped" from nuclear (integrated) elements is not consistent with the current genomic and phylogenetic information available.

This work is thus an important step in our collective understanding of the ancient evolutionary history of eukaryotic viruses, and more generally of the constraints and main drivers of virus evolution.

Reviewer #1 (Public Review):

Owen D et al. investigated the protein partners and molecular functions of ZMYM2, a transcriptional repressor with key roles in cell identity and mutated in several human diseases, in human U2OS cells using mass spectrometry, siRNA knockdown, ChIP-seq and RNA-seq. They tried to identify chromatin bound complexes containing ZMYM2 and identified known and novel protein partners, including ADNP and the newly described partner TRIM28. Focusing mainly on these two proteins, they show that ZMYM2 physically interacts with ADNP or TRIM28, and co-occupies an overlapping set of genomic regions with ADNP and TRIM28. By generating a large set of knockdown and RNA-seq experiments, they show that ZMYM2 co-regulates a large number of genes with ADNP and TRIM28 in U2OS cells. Interestingly, ZMYM2-TRIM28 do not appear to repress genes directly at promoters, but the authors find that ZMYM2/TRIM28 repress LTR elements and suggest that this leads to gene deregulation at distance by affecting the chromatin environment within TADs.

A strength of the study is that, compared to previous studies of ZMYM2 protein partners, it investigates binding partners of ZMYM2 using the RIME method on chromatin. The RIME method makes it possible to identify low-affinity protein-protein interactions and proteins interactions occurring at chromatin, therefore revealing partners most relevant for gene regulation at chromatin. This allowed the identification of novel ZMYM2 partners not identified before, such as TRIM28.

The authors present solid interaction data with appropriate controls and generated an impressive amount of datasets (ChIP-seq for TRIM28 and ADNP, RNA-seq in ZMYM2, ADNP and TRIM28 knockdown cells) that are important to understand the molecular functions of ZMYM2. These datasets were generated with replicates and will be very useful for the scientific community. This study provides important novel insights into the molecular roles of ZMYM2 in human U2OS cells.

The authors could have been more precise in the manuscript title and abstract to emphasize that these findings apply to human cells, as indeed there is no demonstration yet that the findings presented here can be transposed to mouse cells.

The manuscript's main conceptual advance is that the authors propose a novel model of gene regulation whereby transcriptional repressors of transposable elements could regulate genes at distance by modulating the local chromatin environment within TADs. Additional experiments would be needed to strengthen this model. For example the authors could have performed TRIM28 ChIP in ZMYM2-kd cells to test if ZMYM2 favors the recruitment of TRIM28 to its genomic targets, as well as ChIP-seq of repressive chromatin marks (such as H3K9me3) in ZMYM2-kd cells to investigate if the loss of ZMYM2 leads to reduced H3K9me3 in ERVs and over large regions surrounding the ERVs.

Reviewer #2 (Public Review):

In this study the authors investigate functional associations made by transcription factor ZMYM2 with chromatin regulators, and the impact of perturbing these complexes on the transcriptome of the U2OS cell line. They focus on validating two novel chromatin-templated interactions: with TRIM28/KAP1 and with ADNP, concluding that via these distinct chromatin regulators, ZMYM2 contributes to transcriptional control of LTR and SINE retrotransposons, respectively.

Strengths and weakness of the study:

- The co-localization of ZMYM2 with ADNP and TRIM28 is validated through RIME, ChIP-seq and co-IP. (Notably, since both RIME and ChIP-seq rely on crosslinking, and the co-IP with TRIM28 required crosslinking due to being SUMO-dependent, only the ZMYM2-ADNP co-IP experiment demonstrates an interaction in the absence of crosslinking).

- It is good that uniquely-mapped reads are used in the ChIP-seq analysis given the interest in repetitive elements. Likewise, though the RT-qPCR data in Fig5 should be complemented by analysis of the RNA-seq data that the authors already have, it seems that the primers are carefully designed such that a single retrotransposon copy is amplified.

- The top-scoring interactors are highly-abundant nuclear proteins: for example, data from the contaminant repository for affinity purification mass-spec data (https://reprint-apms.org/) show that TRIM28 is identified in 466 / 716 AP-MS experiments with a mean spectral count of 16. While this does not indicate that the ZMYM2-TRIM28 interaction is not 'true', it would have been helpful to further dissect the interaction to strengthen this conclusion. For example, it would be nice to see the co-IP (fig 3A) repeated from the cells expressing the ZMYM2 mutant that is no longer competent to bind SUMO (used in the ChIP-seq data of Fig 2). Alternatively - if the model is that ZMYM2 recruits SUMOylated TRIM28 - with well-characterized TRIM28 mutants that lack SUMOylation.

- The transcriptional response using bulk RNA-seq in ZMYM2-depleted cells is rather gene-centric despite the title of the paper being about TE transcription. In fact the only panels about TE transcription are the RT-qPCR data in Fig 5D,F. I may be missing something (and there aren't many details given about the RNA-seq experiments) but why not look at TE transcription in an unbiased way with the transcriptomic data at hand? I appreciate potential hazards of multi-mapping etc but it would be interesting to see at least some subfamily analysis (e.g. using the TEtranscripts tool). On a similar point, why not show some RNA-seq in the genome browser snapshots of the epigenomics - together with a RepeatMasker annotation track of TEs...

While the results broadly support the authors' conclusions, I have the overall impression that the central claim of TE transcriptional regulation by ZMYM2 could be strengthened a lot with some fairly straightforward additional experiments and analyses.

Reviewer #3 (Public Review):

ZMYM2 is a transcriptional repressor known to bind to the post-translational modification SUMO2/3. It has been implicated in the silencing of genes and transposons in a variety of contexts, but lacking sequence-specific DNA binding, little is known about how it is targeted to specific regions. At least two reports indicate association with TRIM28 targets (Tsusaka 2020 Epigenetics & Chromatin, Graham-Paquin 2022 bioRxiv) but no physical association with TRIM28 targets had been observed. Tsusaka 2020 theorizes an indirect, potentially SUMO-independent, interaction via ATF7IP and SETDB1.

Here, Owen and colleagues show that a subset of ZMYM2-binding sites in U2OS cells are clearly TRIM28 sites, and further find that hundreds of genes are silenced by both ZMYM2 and TRIM28. They next demonstrate that ZMYM2 homes to chromatin, and interacts with TRIM28, in a SUMOylation-dependent manner, suggesting that ZMYM2 is recognizing SUMOylation on TRIM28 itself. ZMYM2 separately homes to SINE elements bound by the ChAHP complex, in an apparently SUMOylation independent manner. Although this is not the first report to show physical interaction between ZMYM2 and ChAHP, it is the first to show that ZMYM2 homes to ChAHP-binding sites and functions as a corepressor at these sites.

The mode by which ZMYM2 and TRIM28 coregulate genic targets remains somewhat unclear. TRIM28/ZMYM2 bind to LTR elements, loss of these proteins results in upregulation of genes distal to (but in the same TAD as) these binding sites.

Overall, the manuscript is well-written, convincing, and fills a significant hole in our understanding of ZMYM2's mechanistic function.

Reviewer #1 (Public Review):

In the manuscript there is not much comparison between the crystal and cryoEM structures provided, and on inspection they appear to be very similar. The crystal structures also reveal parts of the CC domains in Las1, which is not present in the cryoEM structures. It is interesting the CC domains in Sc and Cj are quite different as illustrated in Figure 4B. They also seem to be somewhat disconnected from the rest of the complex (more so for Cj), even though that's not apparent in Figures 2-4. Despite this, it would be very useful to show the cryoEM densities when describing the catalytic site and C-terminal domain interactions, for example, as this can be very useful to increase confidence in the model and proposed mechanisms.

The description of the complex as a butterfly is engaging, and from a certain angle it can be made to look as such; this was also described previously in (Pillon et al., 2019, NSMB) for the same complex from a different organism (Ct). However, it is a bit misleading, because the complex is actually C2 symmetric. Under this symmetry, the 'body' would consist of two 'heads' one pointing up, one down facing towards the back, and one wing would have its back toward the viewer, the other the front. The structures presented here in Sc and Cj seem quite similar to the previous structure of the same complex in Ct, though the latter was only solved with cryoEM, and was also lacking the structure of the CC domain in Las1.

For the model suggested in Figure 8, perhaps in the 'weak activity' state, the LCT in Las1 could still be connected to Grc3, via the LCT, rather than disconnected as shown. This could facilitate faster assembly of the 'high activity' state. The complex is described as 'compact and stable', but from the structure and this image, it appears more dynamic, which would serve its purpose and the illustrated model better. The two copies of HEPN appear to have more connective area, meaning they are indeed more likely to remain assembled in the 'weak activity' state. On the other hand, HEPN in one protein appears to have less binding surface with PNK in Grc3, and even less so with the CTD (both PNK and CTD being from the other associated protein), meaning these bindings could release easily to form the 'weak activity' state.

There is also the potential to speculate that the GCT is bound to HEPN near the catalytic area in the 'weak activity' state. The reduced activity when the GCT residues are replaced by Alanine could then be explained by the complex not being able to assemble as quickly upon binding of the substrate, as it could if the GCT remained bound, rather than by a conformational change that it induces upon binding. The conformational change is also likely to be influenced by the combined binding of PNK and CTD in the assembled state, which also contact HEPN, rather than by GCT alone.

When comparing the structure of the HEPN domain in the lone Las1 protein to the structure of Las1-HEPN in the Las1-Grc3 complex, it is mentioned that 'large conformational changes are observed'. These could be described a bit better. The conformational change is ~3-4Å C-alpha RMSD across all ~150 residues in the domain (~90 residues forming a stable core that only changes by ~1Å). There is also a shift in the associated HEPN domain in Las1B domain compared to the bound HEPN in the Las1-Grc3 complex, as shown in Figure 7D: ~1Å shift and ~12degrees rotation. This does point to the conformation of HEPN changing upon complex formation, as does the relative positions of the HEPN domains in Las1A and Las1B. The conformational change and relative shift could indeed by key for the catalysis of the substrate as mentioned.

Overall, the structures presented should be very useful in further study of this system, even though the exact dynamics and how the substrate is bound are aspects that are perhaps not fully clear yet. The addition of the structures of the CC domain in two different organisms and the Las1 HEPN domain (not in complex with Grc3) as new structural information should allow for increasing our understanding of the overall complex and its mechanism.

Reviewer #2 (Public Review):

In this manuscript, Chen et al. determined the structural basis for pre-RNA processing by Las1-Grc3 endoribonuclease and polynucleotide kinase complexes from S. cerevisiae (Sc) and C. jadinii (Cj). Using a robust set of biochemical assays, the authors identify that the sc- and CjLas1-Grc3 complexes can cleave the ITS2 sequence in two specific locations, including a novel C2' location. The authors then determined X-ray crystallography and cryo-EM structures of the ScLas1-Grc3 and CjLas1-Grc3 complexes, providing structural insight that is complimentary to previously reported Las1-Grc3 structures from C. thermophilum (Pillon et al., 2019, NSMB). The authors further explore the importance of multiple Las1 and Grc3 domains and interaction interfaces for RNA binding, RNA cleavage activity, and Las1-Grc3 complex formation. Finally, evidence is presented that suggests Las1 undergoes a conformational change upon Grc3 binding that stabilizes the Las1 HEPN active site, providing a possible rationale for the stimulation of Las1 cleavage by Grc3.

Several of the conclusions in this manuscript are supported by the data provided, particularly the identification and validation of the second cleavage site in the ITS2. However, several aspects of the structural analysis and complimentary biochemical assays would need to be addressed to fully support the conclusions drawn by the authors.

• There is a lack of clarity regarding the number of replicates performed for the biochemical experiments throughout the manuscript. This information is critical for establishing the rigor of these biochemical experiments.

• The authors conclude that Rat1-Rai1 can degrade the phosphorylated P1 and P2 products of ITS2 (lines 160-162, Figure 1H). However, the data in Fig. 1H shows complete degradation of 5'Phos-P2 and 5'Phos-P4 of ITS2, while the P1 and 5'Phos-P3 fragments remain in-tact. Additional clarification for this discrepancy should be provided.

• The authors determined X-ray crystal structures of the ScLas1-Grc3 (PDB:7Y18) and CjLas1-Grc3 (PDB:7Y17) complexes, which represents the bulk of the manuscript. However, there are major concerns with the structural models for ScLas1-Grc3 (PDB:7Y18) and CjLas1-Grc3 (PDB:7Y17). These structures have extremely high clashscores (>100) as well as a significant number of RSRZ outliers, sidechain rotamer outliers, bond angle outliers, and bond length outliers. Moreover, both structures have extensive regions that have been modeled without corresponding electron density, and other regions where the model clearly does not fit the experimental density. These concerns make it difficult to determine whether the structural data fully support several of the conclusions in the manuscript. A more careful and thorough reevaluation of the models is important for providing confidence in these structural conclusions.

• The presentation of the cryo-EM datasets is underdeveloped in the results section drawing and the contribution of these structures towards supporting the main conclusions of the manuscript are unclear. An in-depth comparison of the structures generated from X-ray crystallography and cryo-EM would have greatly strengthened the structural conclusions made for the ScLas1-Grc3 and CjLas1-Grc3 complexes.

• The authors conclude that truncation of the CC-domain contributes to Las1 IRS2 binding and cleavage (lines 220-222, Fig. 4C). However, these assays show that internal deletion of the CC-domain alone has minimal effect on cleavage (Fig 4C, sample 3). The loss in ITS2 cleavage activity is only seen when truncating the LCT and LCT+CC-domain (Fig 4C, sample 2 and 4, respectively). Consistently, the authors later show that Las1 is unable to interact with Grc3 when the LCT domain is deleted (Fig. 6 and Fig. 6-figure supplement 2). These data indicate the LCT plays a critical role in Las1-Grc3 complex formation and subsequent Las1 cleavage activity. However, it is unclear how this data supports the stated conclusion that the CC-domain is important for LasI cleavage.

• The authors conclude that the HEPN domains undergo a conformational change upon Grc3 binding, which is important for stabilization of the Las1 active site and Grc3-mediated activation of Las1. This conclusion is based on structural comparison of the HEPN domains from the CjLas1-Grc3 complex (PDB:7Y17) and the structure of the isolated HEPN domain dimer (PDB:7Y16). However, it is also possible that the conformational changes observed in the HEPN domain are due to truncation of the Las1 CC and CGT domains. A rationale for excluding this possibility would have strengthened this section of the manuscript.

Reviewer #1 (Public Review):

Membrane receptor guanylyl cyclases are important for many physiological processes but their structures in full-length and their mechanism are poorly understood. Caveney et al. determined the cryo-EM structure of a highly engineered GC-C in a complex with endogenous HSP90 and CDC37. The structural work is solid and the structural information will be useful for the membrane receptor guanylyl cyclases field and the HSP90 field. However, a detailed characterization of the protein sample is lacking. Moreover, the physiological significance of this structure is not fully exploited by supporting experiments and the mechanistic insight is currently limited.

1. The characterization of the protein sample is lacking. SDS-PAGE would be useful to identify potential proteolysis, leading to the dissociation of GC dimer. Further size-exclusion chromatography would be helpful to estimate the molecular weight of the complex and to determine if only GC-C monomer is purified.

2. The orientation distribution of the particles is not homogenous in Fig. S1D. It would be helpful to present the 3DFSC curve to evaluate the effect of preferred orientation on the reconstruction.

3. Description of protein expression details is lacking. Did the author use transient transfection, stable cell line or virus-mediated transduction?

4. HSP90 binds ATP and is often co-purified with endogenous ATP/ADP. Is there ATP or ADP present in the sample/cryo-EM maps? Is the conformation of NBD similar to ATP-bound HSP90? The author needs to include the description/figures about the nucleotide state of HSP90.

5. The catalytic domains of GC have to be dimerized to perform cyclase function. The presence of only one GC-PK monomer in the cryo-EM structure indicates the structure does not represent an active state of GC. These results suggest the GC expressed in this way is not functional. The authors need to explain why most of the GC protein is trapped in this inactive form.

6. The GC-C construct used here is a highly engineered "artificial" construct, which has not been fully characterized in this work. Does this construct have similar activity as the activated wt GC-C? Does the protein (this engineered construct) expressed in CHO cells show activity?

7. Are the residues on the interface between GC and HSP conserved in other members of membrane receptor guanylyl cyclases? Would mutations on this interface affect the activity of GC?

8. The authors propose that targeting HSP90 would tune the activity of GC. Is there any experimental data supporting this idea?

9. The model in Fig. S3 is largely speculative due to the lack of supporting functional data. In addition, it would be better to change the title to "structure of the protein kinase domain of guanylyl cyclase receptor in complex with HSP90 and cdc37" because the mechanistic insight is limited.

Reviewer #2 (Public Review):

Caveney et al have overexpressed an engineered construct of the human membrane receptor guanyl cyclase GC-C in hamster cells and co-purified it with the endogenous HSP90 and CDC37. They have then determined the structure of the resultant complex by single particle cryoEM reconstruction at sufficient resolution to dock existing structures of HSP90 and CDC37, plus an AlphaFold model of the pseudo-kinase domain of the guanylyl cyclase. The novelty of the work stems from the observation that the pseudo-kinase domain of GC-C associates with CDC37 and HSP90 similarly to how the bona fide protein kinases CDK4, CRAF and BRAF have been previously shown to interact.

The experimentation is limited to the cryoEM analysis, and is lacking additional studies that would give deeper insight into the oligomeric nature - if any - of the GC-C when bound to HSP90-CDC37 as compared to the free protein. This is relevant, as the dimerization domain downstream of the pseudokinase, is evident in the maps - albeit not well resolved - and it is not clear whether it is still able to mediate dimerization with a second free or HSP90-CDC37-bound GC-C. It would also be good to see some experimentation that asks whether association with HSP90-CDC37 inhibits the guanyl cyclase activity. It is clear from previous work that HSP90-CDC37 silence the kinase activity of their bound client kinases, but in this case the catalytic guanyl cyclase is not directly associated with the chaperone complex and may still be able to function.

Although the sequence alignment presented in SuppFig 2 shows that GC-C conserves the classic DFG motif that plays a critical role in the regulation of most kinases, the numbering of the sequence is absent, making it very difficult to relate this to the structural detail shown in Fig 2B. This needs to be clarified, as the interaction of CDC37-Trp31 with the DFG motifs and downstream activation loops in CRAF and BRAF have been proposed as important features of the selectivity of these kinases for the HSP90-CDC37 system, and it would be good to be able to see clearly how much of this is also conserved in the GC-C pseudokinase domain interaction. For example, is the much shorter activation segment (DFG -> APE) ordered in the complex or disordered?

It was not easy to follow what was in the sample used for cryoEM. The cloning of the guanylyl cyclase (GC) component is described in the methods and they have shown some illustrations in fig 1 but a proper numbered figure of the domain organisation clearly showing domain boundaries and linker segments is really needed for a reader not familiar with the structure of GCs, especially since they have replaced the ECD with a leucine zipper in their construct. It is important to show a domain figure of what this construct looks like as well, as from the illustrations in fig 1 for examples its hard to see what's PK, DD, GC domains. It would also be helpful to see in the supplementary a gel of complex they put on the grids, to make it clearer what exactly the sample is and to reassure that the GC-C domains that are not resolved in the cryoEM are nonetheless present in the sample.

Overall there is only minimal proposal of mechanism or biological function based on the structure. The speculation in the Discussion of two fates - PP5 dephosphorylation or E3 ligase recruitment, is not supported by any experimentation, which is reasonable for speculation, but is also not underpinned by reference to any previously published work suggesting that these additional processes may be important. In the absence of any work by the authors can they put these speculations more in context with previously published work that supports the importance of these processes specifically for GC regulation?

Reviewer #3 (Public Review):

A detailed understanding of how membrane receptor guanylyl cyclases (mGC) are regulated has been hampered by the absence of structural information on the cytoplasmic regions of these signaling proteins. The study by Caveney et al. reports the 3.9Å cryo-EM structure of the human mGC cyclase, GC-C, bound to the Hsp90-Cdc37 chaperone complex. This structure represents a first view of the intracellular functional domains of any mGC and answers without doubt that Hsp90-Cdc37 recognizes mGCs via their pseudokinase (PK) domain. This is the primary breakthrough of this study. Additionally, the new structural data reveals that the manner in which Hsp90-Cdc37 recognizes the GC-C PK domain C-lobe is akin to how kinase domains of soluble kinases docks to the chaperone complex. This is the second major finding of this study, which provides a concrete framework to understand, more broadly, how Hsp90-Cdc37 recruits a large number of other diverse client proteins containing kinase or pseudokinase domains. Finally, the Hsp90-Cdc37-GC-C structure offer clues as to how GC-C may be regulated by phosphorylation and/or ubiquitinylation by serving as a platform for recruitment of PP5 and/or E3 ligases.

Comments:

1. The authors used an interesting approach to obtain the GC-C-Hsp90-Cdc37 complex. Flag-tagged human GC-C was overexpressed in CHO cells with the expectation of co-purifying endogenous hamster homologs of Hsp90 and Cdc37. There are several points worth noting:<br /> a. It is not clear from the data presented (Figure 1C, Suppl Fig 1A) or the Methods the percentage of particles in the cryo-EM specimen that represent the GC-C-Hsp90-Cdc37 complex. Presumably, some fraction of GC-C isolated will not be associated with Hsp90-Cdc37. If a very large portion of GC-C is associated with Hsp90-Cdc37, it would be good to explain why this is to be expected. Are 2D/3D classes corresponding to the activated GC-C dimer found? If not, why?<br /> b. Figure 1A suggests that GC-C is phosphorylated before recruitment of Hsp90-Cdc37. What is the phosphorylation status of the GC-C specimen that was imaged by cryo-EM?<br /> c. The resolution of the cryo-EM map (3.9 Å) is too low for unambiguous identification of proteins. Please provide more precise justification for the claim that the densities observed do in fact correspond to hamster Hsp90 and Cdc37.<br /> d. The authors state that human GC-C pulls down hamster Hsp90-cdc37 but soluble kinases cannot, despite the high sequence identity between human and hamster Hsp90-cdc37. Is this because GC-C recognition is more promiscuous? Can this difference be understood in light of the new structural information presented?

2. A large portion of the enforced GC-C dimer was not visible in the cryo-EM maps. It is not easy to learn from Figure 1 exactly which parts of the GC-C construct was sufficiently ordered and observed structurally. Please improve Figure 1.

3. On page 4, the authors claim that they are able to orient the GC-C-Hsp90-Cdc37 complex "as it would sit on a membrane" and referred to Figure 1B. It is not clear what is implied here. Does Hsp90-Cdc37 binding constrain the complex to face the inner leaflet of the membrane in a specific orientation as shown in Figure 1B? If true, this could potentially have important functional implications. Please illustrate how this was deduced based on the information available.

4. Also on page 4, it is stated that it is sterically unlikely an additional Hsp90-Cdc37 complex is associated with the other copy of GC-C in the leucine zippered dimer. It is not obvious to the reader how this may be the case. An additional figure could help make this more clear. Additional biochemical evidence will also help. The absence of GC-C-Hsp90-Cdc37 dimers in cryo-EM micrographs can also support the argument.

5. Some comments on Figure 2:<br /> a. NTD and CTD are mislabeled in Figure 2A.<br /> b. The authors should show cryo-EM density to support their modeling of GC-C in Figures 2B and C.

6. The authors claim that Hsp90-Cdc37 clients are more similar structurally near the cdc37 interface. Please illustrate this with additional figures. Suppl. Figure 2 is inadequate for this purpose. The authors can also consider adding a more detailed discussion comparing the interactions between the pseudokinase/kinase C-lobe and Cdc37 in known structures. Is shape/charge complementarity a universal feature of cdc37-dependent kinase/pseudokinase recruitment? It would be interesting to also consider if it would be possible to predict which of the ~60 human pseudokinases are possible Hsp90-Cdc37 clients. New structural findings from this study and publicly available AI-predicted protein structures could help.

Reviewer #1 (Public Review):

This manuscript conducts a classic QTL analysis to identify the molecular basis of natural variation in disease resistance. This identifies a pair of glycosyltransferases that contribute to steroidal glycoalkaloid production. Specifically altering the final hexose structure of the compound. This is somewhat similar to the work in tomatine showing that the specific hexose structure mediates the final potential bioactivity. Using the resulting transgenic complementation lines that show that the gene leads to a strong resistance phenotype to one isolate of Alternaria solani and the Colorado potato beetle. This is solid work showing the identification of a new gene and compound influencing plant biotic interactions. While the experiments are solid, the introduction, discussion and associated claims don't accurately reflect my reading of what is known and said in the current literature.

The sentence on line 53-54 is misleading. It provides only three citations on specific links between specialized metabolism and disease resistance. However, there are actually at least 40 on specific links of camalexin and indolic phytoalexins to disease resistance. Similarly there are dozens of uncited papers on benzoxazinoids, indolic glucosinolates, aliphatic glucosinolates and tomatine to both non-host and host based resistance mechanisms. This even goes as far as showing how the pathogens resist an array of these compounds. The choices in the introduction make it appear that little is known about specialized metabolism to disease resistance but I would suggest that this is not an allusion supported by the literature. I would agree that given the breadth of specialized metabolism we have a lot of knowledge about a set of them but that there are hundreds to thousands of untested compounds but to indicate that little is known is unfair to the specialized metabolism community. This is especially true as the introduction and discussion give no image of the large body of literature on specialized metabolism to insect interactions even though this is a major component of this manuscript.

I would also agree that specialized metabolism is not a conscious target of breeding programs but the work on benzoxazinoids in maize and glucosinolates in the Brassica's has shown that these compounds have been influenced by breeding programs. Similarly work on de novo domestication of multiple crops is focused on the adjustment of specialized metabolism in these crops.

I would disagree with the hint on line 49-50 and again on lines 236-239 that specialized metabolism may have less pleiotropy. This is not supported by recent work on benzoxazinoids and glucosinolates showing that they have numerous regulatory links to the plant and can be highly pleiotropic. Even the earliest avenicin work in oat showed that the deficient lines had altered root development.

My main message from the above three paragraphs is to point out that there are a number of places in the manuscript where the current state of the specialized metabolite literature is not accurately portrayed. To properly place the manuscript in the broader context, I would suggest a more even handed introduction and discussion that takes into account the current state of the specialized metabolism literature.

Is it accurate to say complete resistance to A. solani if only a single isolate of the pathogen is used? Is there evidence that I am unaware of that there are no isolates of this pathogen with saponin resistance? There are pathogens with natural tomatine resistance and this is a common feature of plant pathogens that they have genetic variation in the resistance to specialized metabolism. For example, it should be noted that Botrytis BO5.10 is a tomatine sensitive isolate and the van Kan and Hahn groups have published on isolates that are resistant to saponins. I would suggest caveating across the manuscript that this is a single isolate and that it is possible that there may be isolates with natural resistance to the steroidal glycoalkaloid?

In Figure 4b, is the infection site about 3.5 mm in size such that 3.5 mm means absolutely no infection? If not, that would mean there is some outgrowth by Alternaria and the resistance isn't complete.

Reviewer #2 (Public Review):

The study focuses on a mechanism of pest/pathogen resistance identified in Solanum commersonii, which appears to offer dominant resistance to Alternaria solani through the activity of specific glycosyltransferases which facilitate the production of tetraose glycoalkaloids in leaf tissue. The authors demonstrated that these glycoalkaloids are suppressive to the growth of multiple pathogenic ascomycetes and furthermore, that transgenic plants expressing these glycosyltransferases in susceptibleS. commersonii clones demonstrate improved resistance to a specific strain of A. solani and a genotype of Colorado Potato Beetle. The study design is straightforward, yet thorough, and does a good job demonstrating the importance of these genes in resistance. While the research findings are significant there are statements throughout the manuscript that overstate both the novelty and utility of the findings.

Title: While the protection is impressive, the title suggests that these glycoalkaloids provide protection against all fungi and insects, which is both unlikely and essentially impossible to prove. This should be changed to something more measured. This is especially true given that only a single fungus and insect were tested against transgenic plants, but would be an overstatement even with more robust evaluation.

Throughout the paper: A single isolate of A. solani and a single genotype of CPB were used in this study. While this is in line with the typical limitations of such a study, the authors need to be careful about claiming broad resistance to either of the species. Variability in fungicide tolerance and detoxification activity have been noted in both fungi and CPB, so more specific language should be used throughout (such as L213 and L221).

Reviewer #1 (Public Review):

In this article, Cacioppo et al., report on a previously unappreciated mechanism of the regulation of Aurora Kinase A (AURKA) protein levels that is orchestrated via coordinated action of alternative polyadenylation of AURKA mRNA and hsa-let-7a miRNA. Moreover, it is proposed that this mechanism may play a major role in neoplasia. In support of their model, the authors demonstrate that short-to-long 3'UTR AURKA mRNA isoform ratio is elevated in triple negative breast cancer patients where it correlates with poor prognosis. The authors further generated reporters suitable for single cell live imaging that express different 3'UTR variants, which revealed highly variable ratios of short and long 3'UTR AURKA isoforms across different cell lines. This was followed by actinomycin D chase and nascent chain immunoprecipitation assays in U2OS osteosarcoma cells to demonstrate that while short and long 3'UTR AURKA isoforms have comparable stability, short 3'UTR AURKA isoforms appear to exhibit higher ribosome association which is indicative of higher translation activity. Furthermore, using an additional reporter assay which takes advantage of trimethoprim-based stabilization of highly unstable E. Coli dihydrofolate reductase mutants Cacioppo et al., provide evidence that in contrast to the short 3'UTR AURKA mRNA isoform which appears to be constitutively translated throughout the cell cycle, long 3'UTR AURKA mRNA isoform is preferentially translated in the G2 phase. Further evidence is provided that suppression of long 3'UTR AURKA mRNA isoform is at least in part mediated by hsa-let-7a miRNA. Finally, the authors provide evidence that disrupting the expression of long 3'UTR AURKA mRNA isoform using CRISPR-based strategy, leads to overexpression of AURKA driven by the short 3'UTR isoform which is paralleled by an increase in cancer-related phenotypes.

Strengths: Overall it was thought that this study is of potentially broad interest inasmuch as it delineates a hitherto unappreciated mechanisms of regulation of AURKA protein levels, whereby AURKA is emerging as one of the major factors in neoplasia, including resistance to anti-cancer treatments. In general, it was thought that the author's conclusions were sufficiently supported by provided data. It was also thought that this study incorporates innovative methodology including single-cell expression sensors coupled with live cell microscopy and an assay to study translation in different phases of cell cycle without need for cell synchronization.

Weaknesses: Several relatively minor issues were observed regarding methodology and data interpretation. Namely, some inconsistencies between the models and/or cell lines that were used throughout the manuscript were noted. For instance, key experiments were performed almost exclusively in U2OS osteosarcoma cells, whereby triple negative breast cancer patient data were used to set the scientific foundation of the study. Considering potential differences in alternative polyadenylation between cell and tissue types, it was thought that investigation across the broader compendium of cell lines may be required for generalization of findings observed in U2OS cells. It was also found that the precise mechanisms underpinning the role of hsa-let-7a miRNA in regulation of AURKA protein levels remain largely obscure.

Reviewer #2 (Public Review):

Cacioppo et al describe a mechanism of translation regulation of Aurora A, which is dependent on alternative polyadenylation. They suggest that altered expression of the resulting isoforms in cancers is at least partly responsible for elevated Aurora A levels, which in turn is known to indicate poor prognosis.

The authors exploit publicly available databases and patient data to highlight the correlation of increased abundance of the SHORT isoform (relative to the LONG one) and poor patient survival in TNBC, as well as breast and lung cancer.

In their thorough mechanistic study they use a number of reporters to assess the impact of alternative polyadenylation on mRNA stability and translation efficiency and explore whether this process accounts for cell-cycle-regulated expression of Aurora A. These reporters are carefully controlled and well explained. I particularly commend the authors for the clear graphical presentations of the reporters (eg fig 2A, fig 3D, fig 4A). Rigorous control experiments are performed to make sure that the reporters work and "report" what they are meant to do, and to show that previous findings can be reproduced in experiments based on the reporters (eg higher protein expression from the short 3' UTR APA isoform of CDC6 mRNA, targeting of MZF1 3'UTR by hsa-let-7a).

They show that translation of the longer isoform is subject to suppression by hsa-let-7a, while the shorter isoform is not. They attribute cell-cycle regulated expression of Aurora A at least in part to the suppression of translation of the LONG isoform in G1 and S.<br /> In Figure 6 they address whether the APA-based regulatory mechanism alters Aurora A levels sufficiently to confer features associated with oncogenic transformation and overexpression of Aurora A. These data nicely tie together the observations in databases and the mechanistic part of the study.

The logic is clear and the conclusions are well supported by the data.

The authors state themselves that the impact of translation regulation on Aurora A levels in the cell cycle is an important but unanswered question. The evidence that suppression of translation of the LONG transcript contributes to the cell-cycle regulation of Aurora A is convincing, but the extent could be explored further. I wonder whether published genome-wide studies (eg PMCID 4548207, PMC3959127) have relevant data on the translation rate of Aurora A in the cell cycle.

In the paper this question is addressed in cells enriched in G1/S (Fig 6) and using the reporters (Fig 5). Having generated the ΔdPAS mutants, Aurora A levels could be easily assessed in each cell-cycle phase. The best way to do this would be sorting followed by immunoblotting.

The fact that Aurora A levels are reduced by a 6h treatment with 0.1 mg/ml CHX (Fig 6D) is interpreted as "AURKA expression in G1/S was reduced in the mutated cell lines when treated with CHX, indicating that translation of the short isoform is active in this phase" It is rather expected that using a translation inhibitor will stop the accumulation of a protein and so this experiment does not add much. A better approach to address the effect of the mutations on translation would be to add a proteasome inhibitor and follow accumulation of Aurora A, preferably not only in G1/S but also in other cell-cycle phases. Accumulation of the protein in this experiment would better reflect translation rates.

Reviewer #3 (Public Review):

Summary:

This manuscript sheds light on the cell cycle-dependent post-transcriptional regulation of the oncogenic kinase AURKA. AURKA mRNA is subjected to alternative polyadenylation (APA), resulting in a short and a long 3'UTR isoform. While the ratio long/short isoform is important for AURKA expression and might impact cancer development, it is not unclear how this is regulated throughout the cell cycle. Translation and decay rate of the long isoform only are targeted by let-7a miRNA and in a cell-cycle dependent manner. In contrast, the short isoform is translated highly and constantly throughout interphase. Finally, depletion of the long isoform led to an increase in proliferation and migration rates of cells. In Triple Negative Breast Cancer, where AURKA is typically overexpressed, the short isoform is predominant and its expression correlates with faster relapse times of patients, suggesting that this mechanism might play an important role in this cancer.

Originality and novelty:

The originality of this work is to show the cooperation between APA and miRNA-targeting in controlling gene expression dynamics of AURKA during cell cycle. To investigate this mechanism, the authors have developed an interesting transient single-cell and biochemical assay to rapidly study mRNA-specific gene expression in a way that measures post-transcriptional events. This manuscript puts an emphasis on the cell cycle dependent expression control of AURKA at the translation level. However, the magnitude of the changes in mRNA levels throughout the cell cycle is even greater than that of the changes in translation. Therefore, it remains unclear whether translation really is that important in controlling AURKA expression during the cell cycle. Moreover, (i) AURKA regulation by miRNA is already known (Fadaka et al., Oncotarget 2020, Zhang et al., Arch Med Sci 2020, Yuan et al., Technol Cancer Res Treat 2019, Ma et al., Oncotarget 2015), (ii) the concept of cooperation between APA and translation already is not new (Sandberg et al., Science 2008, Mayr and Bartel, Cell 2009, Masamha et al. Nature 2015), and (iii) previous transcriptome-wide studies already suggested a cell-cycle dependent control of AURKA at the translation level (Tanenbaun et al., eLife 2015, translation efficiency ratio G2/G1 = 1.59) as well as the mRNA level (Krenning et al., eLife 2022). The impact of this manuscript could be increased by investigating (i) the mechanism of cell cycle-dependent regulation by let7a expression (i.e is there changes in let7a expression or activity during the cell cycle in this model) and (ii) the origin of AURKA APA dysregulation in cancer (could it be modulated by CFIm25? (Masamha et al. Nature 2015, Tamaddon et al. Sci Rep 2020)).

Reviewer #1 (Public Review):

In this manuscript, the authors aimed to compare, from testis tissues at different ages from mice in vivo and after culture, multiple aspects of Leydig cells. These aspects included mRNA levels, proliferation, apoptosis, steroid levels, protein levels, etc. A lot of work was put into this manuscript in terms of experiments, systems, and approaches. However, as written the manuscript is incredibly difficult to follow. The Introduction and Results sections contain rather loosely organized lists of information that were altogether confusing. At the end of reading these sections, it was unclear what advance was provided by this work. The technical aspects of this work may be of interest to labs working on the specific topics of in vitro spermatogenesis for fertility preservation but fail to appeal to a broader readership. This may be best exemplified by the statements at the end of both the Abstract and Discussion which state that more work needs to be done to improve this system.

Reviewer #2 (Public Review):

Preserving and restoring the fertility of prepubertal patients undergoing gonadotoxic treatments involves freezing testicular fragments and waking them up in a culture in the context of medically assisted procreation. This implies that spermatogenesis must be fully reproduced ex vivo. The parameters of this type of culture must be validated using non-human models. In this article, the authors make an extensive study of the quality of the organotypic culture of neonatal mouse testes, paying particular attention to the differentiation and endocrine function of Leydig cells. They show that fetal Leydig cells present at the start of culture fail to complete the differentiation process into adult Leydig cells, which has an impact on the nature of the steroids produced and even on the signaling of these hormones.

The authors make an extensive study of the different populations of Leydig cells which are supposed to succeed each other during the first month of life of the mouse to end up with a population of adult and fully functional cells. The authors combine quantitative in situ studies with more global analyzes (RT-QtPCR Western blot, hormonal assays), which range from gene to hormone. This study is well written and illustrated, the description of the methods is honest, the analyses systematic, and are accompanied by multiple relevant control conditions.

Since the aim of the study was to study Leydig cell differentiation in neonatal mouse testis cultures, the study is well conceived, the results answer the initial question and are not over-interpreted.

My main concern is to understand why the authors have undertaken so much work when they mention RNA extractions and western blot, that the necrotic central part had to be carefully removed. There is no information on how this parameter was considered for immunohistochemistry and steroid measurements. The authors describe the initial material as a quarter testis, but they don't mention the resulting size of the fragment. A brief review of the literature shows that if often the culture medium is crucial for the quality of the culture (and in particular the supplementations as discussed by the authors here), the size of the fragments is also a determining factor, especially for long cultures. The main limitation of the study is therefore that the authors cannot exclude that central necrosis can have harmful effects on the survival and/or the growth and/or the differentiation of the testis in culture. In this sense, the general interpretation that the authors make of their work is correct, the culture conditions are not optimized.

Organotypic culture is currently trying to cross the doors of academic research laboratories to become a clinical tool, but it requires many adjustments and many quality controls. This study shows a perfect example of the pitfall often associated with this approach. The road is still long, but every piece of information is useful.

Reviewer #3 (Public Review):

Moutard, Laura, et al. investigated the gene expression and functional aspects of Leydig cells in a cryopreservation/long-term culture system. The authors found that critical genetic markers for Leydig cells were diminished when compared to the in-vivo testis. The testis also showed less androgen production and androgen responsiveness. Although they did not produce normal testosterone concentrations in basal media conditions, the cultured testis still remained highly responsive to gonadotrophin exposure, exhibiting a large increase in androgen production. Even after the hCG-dependent increase in testosterone, genetic markers of Leydig cells remained low, which means there is still a missing factor in the culture media that facilitates proper Leydig cell differentiation. Optimizing this testis culture protocol to help maintain proper Leydig cell differentiation could be useful for future human testis biopsy cultures, which will help preserve fertility and child cancer patients.

Methods: In line 226, there is mention that the central necrotic area was carefully removed before RNA extraction. This is particularly problematic for the inference of these results, especially for the RT-qPCR data. Was the central necrotic area consistent between all samples and variables (16 and 30FT)? How big was the area? This makes the in-vivo testis not a proper control for all comparisons. Leydig cells are not evenly distributed throughout the testis. A lot of Leydig cells can be found toward the center of the gonad, so the results might be driven by the loss of this region of the testis.

What did the morphology of the testis look like after culturing for 16 and 30 days? These images will help confirm that the culturing method is like the Nature paper Sato et al. 2011 and also give a sense of how big the necrotic region was and how it varied with culturing time.

There are multiple comparisons being made. Bonferroni corrections on p-value should be done.

Results: In the discussion, it is mentioned that IGF1 may be a missing factor in the media that could help Leydig cell differentiation. Have the authors tried this experiment? Improving this existing culturing method will be highly valuable.

Add p-values and SEM for qPCR data. This was done for hormones, should be the same way for other results.

Regarding all RT-qPCR data-There is a switch between 3bHSD and Actb/Gapdh as housekeeping genes. There does not seem to be as some have 3bHSD and others do not. Why do Igf1 and Dhh not use 3bHSD for housekeeping? If this is the method to be used, then 3bHSD should be used as housekeeping for the protein data, instead of ACTB. Also, based on Figure 1B and Figure 2A (Hsd3b1) there does not seem to be a strong correlation between Leydig cell # and the gene expression of Hsd3b1. If Hsd3b1 is to be used as a housekeeper and a proxy for Leydig cell number a correlation between these two measurements is necessary. If there is no correlation a housekeeping gene that is stable among all samples should be used. Sorting Leydig cells and then conducting qPCR would be optimal for these experiments.

Figure 2A (CYP17a1): It is surprising that the CYP17a1 gene and protein expression is very different between D30FT and 36.5dpp, however, the immunostaining looks identical between all groups. Why is this? A lower magnification image of the testis might make it easier to see the differences in Cyp17a1 expression. Leydig cells commonly have autofluorescence and need a background quencher (TrueBlack) to visualize the true signal in Leydig cells. This might reveal the true differences in Cyp17a1.

Figure 3D: there are large differences in estradiol concentration in the testis. Could it be that the testis is becoming more female-like? Leydig and Sertoli cells with more granulosa and theca cell features? Were any female markers investigated?

Figure 3D and Figure 5A: It is hard to imagine that intratesticular estradiol is maintained for 16-30 days without sufficient CYP19 activity or substrate (testosterone). 6.5 dpp was the last day with abundant CYP19 expression, so is most of the estrogen synthesized on this first day and it sticks around? Are there differences in estradiol metabolizing enzymes? Is there an alternative mechanism for E production?

Reviewer #1 (Public Review):

In this study the authors sought to address the issue of whether the Steller's sea cow -- a massive extinct sirenian ("sea cow") species that differs from its living relatives (manatees and dugongs) not only in body mass but also in having inhabited cold climates in the northern Pacific -- had hemoglobin adaptations that enhanced the species' thermoregulatory capacities relative to those of the extant species, which are restricted to relatively warm waters. To do so, the authors synthesized recombinant hemoglobin proteins of all the major sea cow lineages and used these data to assess differences in O2 binding, Hb solubility, responses to allosteric effectors, and thermal sensitivity. The work presented is very innovative and in my opinion convincingly demonstrates that the Steller's sea cow had remarkable hemoglobin adaptations that allowed for an extreme range extension into cool waters despite several physiological constraints that are inherent to the sirenian (and paenungulate, afrotherian, etc.) clade. I did not detect any obvious weaknesses of the paper, whereas the use of ancient DNA to resurrect 'extinct' hemoglobins, and the various analyses of these extinct hemoglobins alongside those of extant relatives is very exciting and are major strengths of the paper that make this study a very important advance for our understanding of Steller's sea cow's paleophysiology, as well as our understanding of the potential for extreme hemoglobin phenotypes that have not been documented among living species. Moving forward, these methods can be used to study aspects of the paleophysiology of other recently extinct mammals. I applaud the authors on an excellent and innovative study that significantly augments our understanding of the Steller's sea cow.

Reviewer #2 (Public Review):

This manuscript is an impressive "resurrection" of physiology regarding an enigmatic though unfortunately extinct species, and their potential adaptation to cold-water environments. I am largely convinced of their findings, which I feel are very straightforward and thorough.

One place where the authors perhaps fell a bit short was regarding some conclusions associated with maternal/fetal oxygen delivery. The sirenian versions of fetal & embryonic hemoglobin genes have been identified and assessed to some degree in previously published work the same research group. I feel the manuscript would have benefited from actual analysis of the fetal & embryonic hemoglobin (epsilon, gamma, zeta) to strengthen their assertions.

Reviewer #3 (Public Review):

Signore et al. the synthesized and functionally characterized the recombinant adult hemoglobin (Hb) proteins of extant, extinct, and ancestral sirenians to explore the putative role of Hb in helping Steller's sea cows adapt to life in extremely cold waters. The functional comparisons show that the Hb of the subarctic Steller's sea cows differs in multiple biochemical properties relative to the Hbs of the two extant sirenians in the study, the Florida manatee, and the dugong and also from the Hb inferred for the common ancestor of Steller's sea cow and dugong. Specifically, the Steller's sea cow shows reduced oxygen binding affinity, reduced sensitivity to the allosteric co-factors DPG, Cl-, and H+, increased solubility, and reduced thermal sensitivity. DPG plays an important role in regulating Hb oxygen affinity in mammals, and the lack of sensitivity to it is unique to the Hb of Steller's sea cow. Sequence comparisons show that the Hb of the Steller's sea cow differs at 11 amino acids from that of its sister group, the dugong, one of which is intriguing because it occurs in a position that is invariable among mammals at a site that is critical for DPG binding, a change from Lys to Ans in position 82 of the mature β/δ globin chain. To test the significance of this change, the authors use site directed mutagenesis to insert back a Lys in the Steller's sea cow Hb background (β/δ82Asn→Lys) and test its biochemical properties. The functional assays with the β/δ82Asn→Lys mutant indicate that reverting this position to its ancestral state drastically altered the biochemical properties of the Steller's sea cow Hb, making it functionally similar to the Hbs of manatee, dugong, and the Hb inferred for the common ancestor of Steller's sea cow and dugong.

The study's strength lies in comparing the different recombinant Hbs in an explicit evolutionary framework. The conclusions are supported by the analyses, and the results are relevant in the fields of evolutionary biology, physiology, and biochemistry because they suggest that a single amino acid substitution in a protein can have profound biochemical consequences that impact whole organism physiology.

Reviewer #1 (Public Review):

This study utilizes scRNA-seq to generate a detailed map of transcriptional changes that occur in asynchronously replicating the Trypanosoma brucei insect (PCF) and mammalian (BSF) stages. The analyses were performed on both fresh and cryo-preserved parasites, and transcriptional changes in PCF compared to existing proteomic datasets at the same stage. This is the first study to comprehensively map cell cycle-related transcriptional changes in T. brucei BSF and to undertake a side-by-side analysis of the two major parasite developmental stages. The study identified >1,500 transcripts that exhibit dynamic changes during the cell cycle across the two stages, substantially increasing the number of cell cycle-regulated (CCR) genes compared to previous analyses. Analysis of the data revealed common as well as stage-specific CCR transcripts and identified transcripts with known/suspected functions in cell cycle regulation as well as hypothetical proteins. The findings also support and quantify previous observations suggesting that most transcript changes (83-86% of CCR transcripts) are not reflected by similar changes in corresponding proteins, and where there is a correlation, protein expression levels expectedly lag behind transcripts. Overall, the study provides the most comprehensive transcriptome atlas of the T. brucei cell cycle undertaken to date, highlighting a large number of genes and cellular processes that are linked to cell cycle progression, while further confirming the importance of post-transcriptional regulatory processes in these divergent eukaryotes. The work represents a significant technical advance, particularly in the validation of the use of cryo-preserved parasites for single-cell RNS-seq, and nicely integrates results from previous proteomics and gene-knockout studies.

Reviewer #2 (Public Review):

Parasitic African trypanosomes are agents of devastating diseases in humans and animals. Currently, no vaccines exist, with control of human disease being realized thru vector suppression and elimination of infected hosts while animal diseases remain rampant on the continent. The molecular aspects of the multiple developmental stages the parasite undergoes thru its mammal and tsetse hosts, and the unique aspects of parasite gene expression regulation and host evasion mechanisms have been extensively investigated. Recent applications of single-cell transcriptomics (scRNA) to these approaches have expanded knowledge gained from total RNA and revealed new insights.

In this paper, Briggs et al., set out to determine the cell cycle-related genes (CCR) of T. brucei, which follows the typical eukaryotic progression through G1, S, G2, and M phases followed by cytokinesis, although trypanosomes are unusual in that both nuclear and mitochondrial genome replication and segregation are orchestrated during cell division. while many regulators remain unidentified, are absent, or have been replaced by trypanosomatid-specific factors. For these studies, they apply scRNA methodology using asynchronous mixed populations of cultured 'monomorphic' slender mammalian (BSF) and insect stage (PCF) cells and then determine their cell cycle phases computationally. Of interest, performing similar analysis with fresh and cryopreserved cells made minimal difference to the outcome, thus enabling future investigations with preserved cells.

The study identified 1,550 genes with dynamic transcript level changes reflective of the cell cycle, 1,151 of which had not been previously identified by bulk analysis. These revealed a common set of highly conserved CCR genes as well as unique gene transcript levels expressed thru the cell cycle for BSF and PCF cells. Expression patterns of the G1 and S phase genes are highly comparable between BSF and PCF forms, whereas, after the S phase, the timing of gene expression for the S-G2 transition is far less synchronized. Comparison between transcript expression patterns and previously published protein abundance changes identified a relative delay in peak levels for transcript and protein for at least 50% of the genes that could be compared. Collectively, this foundational analysis generates transcript atlases for BSF and PCF cell cycles, which can be further mined for downstream functional investigations.

Reviewer #3 (Public Review):

In this article, Briggs et al. used scRNAseq to get high-resolution cell cycle-regulated transcriptomes of both replicative forms of Trypanosoma brucei (PCF and BSF) without prior synchronization. Briggs et al. also demonstrated that performing the scRNAseq library immediately after thawing cryopreserved samples did not show significant differences. The authors used computational reconstruction of the cell cycle to get the dynamic expression patterns of cycling genes in both life cycle forms. They identified a core cycling transcriptome highly conserved between forms. However, some slight differences were found between them, e.g.: a switch in gene expression associated with the S-G2 transition is much more discrete in PCFs than BSFs. Moreover, as proteomics data across the cell cycle is not available for BSFs, the authors tagged the top most significant genes with transcripts peaking in G1, S, and G2/M phases with a fluorescent epitope. After comparing the transcript expression patterns with protein abundance, the authors found that the majority of genes with periodic cycling transcript and protein levels exhibited a relative delay between peak transcript and protein expression, which was expected. In summary, this work provides a valuable public tool for further investigation into gene expression dynamics throughout the cell cycle in T. brucei.

Reviewer #1 (Public Review):

The nuclear receptor Nurr1 is a target of interest in neurodegenerative diseases like Parkinson's and Alzheimer's, but its mechanism of activation on NBRE-containing promoters and potential druggability is unknown. A heterodimer of Nurr1 with RXRa can be activated by a subset of ligands that bind to the RXRa ligand binding domain (LBD). Here, the authors provide evidence that transcriptional activation occurs through ligand-induced dissociation of the heterodimer, leading to an active Nurr1 monomer.

NMR spectroscopy and other biophysical, biochemical, and cell-based assays provide a strong foundation for the work. The manuscript is well-written and easy to follow, and for the most part, it thoughtfully addresses experimental results and data interpretation with reasonable caveats. However, a reliance on simple correlative analyses, including some with rather modest correlations (R2 values {less than or equal to} 0.5), may fail to account for some potentially interesting outlier ligands and oversimplify conclusions. Despite this possible oversimplification, this manuscript provides solid evidence of their discovery of an interesting mechanism by which a subset of RXRɑ ligands leads to transcriptional activation of Nurr1 at NBRE promoters--this is an exciting finding that could be potentially relevant in the development of neuroprotective therapies.

Reviewer #2 (Public Review):

Nurr1 is a nuclear receptor and is important for mammalian brain development and homeostasis. Dysfunctional Nurr1 transcriptional activities are implicated in neurodegenerative diseases like Parkinson's. This exquisite ligand-dependent and specific transcriptional reprogramming make nuclear receptors ideal drug targets. However, the design of Nurr1-selective ligands has been confounded by the fact that Nurr1's ligand binding pocket appears to collapse in x-ray crystal structures. Interestingly, RXRalpha-targeted ligands, Nurr1's obligate heterodimer binding partner, show differential effects on Nurr1's transcriptional activities. In this study, the authors aimed to address how RXRalpha ligands lead to Nurr1 transcriptional activation. By combining biochemical approaches, NMR spectroscopy, and transcriptional reporter gene assays in neuronal cells, the authors convincingly show that these select RXRalpha ligands elicit an allosteric effect that reduces Nurr1 binding affinity. They further show that monomeric Nurr1 is a highly effective enhancer of the promoter that is repressed in the presence of RXRalpha. Overall, this is a well-presented and robust study as presented and the conclusions are supported by their evidence. This study should have a profound impact on the field as it provides a clear structural mechanism for ligand-dependent Nurr1 activation in neuronal cells.

Reviewer #3 (Public Review):

Nurr1 is an orphan nuclear receptor that may be a significant target for the treatment of neurodegenerative disorders. Targeting Nurr1 with small molecule ligands has been challenging, but there has been some progress in the identification of synthetic ligands that appear to increase Nurr1 activity. Nurr1 functions as a monomer, but may also heterodimerize with RXR. Heterodimerization appears to repress Nurr1 transcriptional activation via NBRE-driven reporters. Importantly, small molecule ligands that appear to selectively activate the Nurr1/RXR heterodimer complex (and not the Nurr1 or RXR homodimers, individually) have been identified. Exactly how these ligands function in this manner is unclear.

Here, the authors demonstrate that Nurr-1/RXR agonists actually function by perturbing the heterodimer formation providing Nurr1 monomers that are much more active in driving transcription. The authors demonstrate this with a range of biochemical, biophysical, and cell-based methodologies. Cotransfection assays examining the activity of Nurr1 on an NBRE reporter illustrate that RXRalpha is a repressor of Nurr1 transcriptional activity and that this is mediated by the RXR LBD. Using this experimental model as well as RXR coactivator interaction assays (biochemical) and RXR/DR1 reporter cotransfection assays, the authors examined multiple classes of RXR ligands (RXR agonists, modulators, antagonists, and Nurr1/RXRa selective agonists) to compare their activities. The Nurr1/RXR heterodimer agonists were quite effective at inducing transcription in the Nurr1/RXR assay but relatively ineffective in the RXR - coactivator binding assay or the RXR cotransfection assay. Using the array of ligands, the authors show that the Nurr1/RXR activity does not correlate to the ability of compounds to induce RXR to recruit a coactivator or activate RXR-mediated transcription. This suggests that Nurr1/RXR heterodimer agonists may not be mediating transcriptional activation via the "standard" mechanism. One weakness here is that some compounds used in the Nurr1/RXR transcription assay are not included in the other assays and may not have been included in the correlation studies. Assessment of RXR/Nurr1 dimerization in the presence of the ligands was assessed by ITC and demonstrated a correlation between the weakening of heterodimer formation and Nurr1/transcriptional activity suggesting that modulation of dimerization may be a mechanism by which the Nurr1/RXR heterodimer specific ligands function. NMR and analytical SEC data support this hypothesis as well. With regard to the physiological significance of these observations, no studies were completed on actual Nurr1 target genes addressing this type of mechanism offering limitations on the applicability of the hypothesis. However, the mechanism proposed is strongly supported by the data and offers a novel paradigm for the development of drugs targeting this receptor and possibly other nuclear receptors as well.

Reviewer #1 (Public Review):

In this manuscript, the authors describe a novel HA20-causing missense mutation, p.(Leu236Pro), in three patients from one family with periodic fevers, GI symptoms, urogenital ulcers, arthritis, and pustular rash. The patients had elevated levels of multiple proinflammatory cytokines, including IL-1b, IL-6, and TNFa. Patients had reduced A20 expression levels, and in silico analysis suggested that the p.Leu236Pro mutation destabilized the A20 protein. Using transfection assays, the authors determined that steady state protein expression of mutant A20 protein was lower, and that the half-life of the mutant protein was shorter. Treatment with MG132 increased the half life of mutant A20, suggesting that the mutant protein underwent degradation through the proteasome. Further experiments in the transfection system revealed that mutant A20 failed to suppress TNFα-induced NF-κB activity.

This paper will be of great interest to the field. HA20 is a novel disease (first described in 2016), and although the effects of frameshift/truncating mutations are quite evident, there is quite a lot of debate about the potential effects of missense mutations. It is not really clear which missense mutations cause disease and why, and clinicians who treat this disease are frequently faced with the dilemma of evaluating a patient with a rare missense variant of unknown significance. Thus, a paper that can explain the potential mechanisms by which missense mutations cause disease is highly relevant -- and this is an area of active investigation by several groups.

The strengths of this study include the thorough functional assessment of this novel mutation: the authors have collected quite a lot of data to show the effects of their mutation on protein stability and function. Another strength is the comparison with other similar mutations in the OTU and other domains. However, the data are not currently sufficient to support the conclusions of the authors about the effects of their mutation on protein folding. Similarly, the data do not sufficiently support the generalizability of this mechanism to other mutations in the OTU domain.

Reviewer #2 (Public Review):

The authors identified a novel TNFAIP3 variation Leu236Pro located in the A20 OUT domain and demonstrated its pathogenicity. Proinflammatory cytokines were substantially elevated in the patients. In vitro study showed decreased stability of the Leu236Pro A20 protein and Leu236Pro mutant failed to suppress TNF induced NF-κB activity. Review of previously reported TNFAIP3 missense variations revealed that only 3/7 are pathogenic. Truncating A20 mutations are easy to determine the pathogenicity, while missense TNFAIP3 variants require more functional studies to determine the pathogenicity. The results of this study can help interpretation of TNFAIP3 missense variations.

Reviewer #3 (Public Review):

The authors described the one family showing autoinflammatory phenotypes with L236P variant of TNFAIP3 gene. The variant has not been reported on and they evaluated the function of this variant using in vitro and in silico methods. I think this is well-written manuscript and I agree with their interpretation about the pathogenicity of this variant, but the new finding is poor. The variant information was only a new finding.

I recommend the revision of the following points.

In Table 2, T647P seemed to be pathogenic which was evaluated with in vitro assay by Kadowaki.

Two other missense variants, V377I (Niwano, Rheumatology 2022) and T602S (Jiang W, Cellular Immunol 2022) were recently reported. These should be included in the discussion.

Reviewer #1 (Public Review):

Yamanaka et al.'s research investigates into the impact of volatile organic compounds (VOCs), particularly diacetyl, on gene expression changes. By inhibiting histone acetylase (HDACs) enzymes, the authors were able to observe changes in the transcriptome of various models, including cell lines, flies, and mice. The study reveals that HDAC inhibitors not only reduce cancer cell proliferation but also provide relief from neurodegeneration in fly Huntington's disease models. Although the findings are intriguing, the research falls short in providing a thorough analysis of the underlying mechanisms.

HDAC inhibitors have been previously shown to induce gene expression changes as well as control cell division and demonstrated to work on disease models. The authors demonstrate diacetyl as a prominent HDAC inhibitor. Though the demonstration of diacetyl is novel, several similar molecules have been used before.

Reviewer #2 (Public Review):

Sachiko et al. study presents strong evidence that implicates environmental volatile odorants, particularly diacetyl, in an alternate role as an inhibitors HDAC proteins and gene expression. HDACs are histone deacetylases that generally have repressive role in gene expression. In this paper the authors test the hypothesis that diacetyl, which is a compound emitted by rotting food sources, can diffuse through blood-brain-barrier and cell membranes to directly modulate HDAC activity to alter gene expression in a neural activity independent manner. This work is significant because the authors also link modulation of HDAC activity by diacetyl exposure to transcriptional and cellular responses to present it as a potential therapeutic agent for neurological diseases, such as inhibition of neuroblastoma and neurodegeneration.

The authors first demonstrate that exposure to diacetyl, and some other odorants, inhibits deacetylation activity of specific HDAC proteins using in vitro assays, and increases acetylation of specific histones in cultured cells. Consistent with a role for diacetyl in HDAC inhibition, the authors find dose dependent alterations in gene expression in different fly and mice tissues in response to diacetyl exposure. In flies they first identify a decrease in the expression of chemosensory receptors in olfactory neurons after exposure to diacetyl. Subsequently, they also observe large gene expression changes in the lungs, brain, and airways in mice. In flies, some of the gene expression changes in response to diacetyl are partially reversable and show an overlap with genes that alter expression in response to treatment with other HDAC inhibitors. Given the use of HDAC inhibitors as chemotherapy agents and treatment methods for cancers and neurodegenerative diseases, the authors hypothesize that diacetyl as an HDAC inhibitor can also serve similar functions. Indeed, they find that exposure of mice to diacetyl leads to a decrease in the brain expression of many genes normally upregulated in neuroblastomas, and selectively inhibited proliferation of cell lines which are driven from neuroblastomas. To test the potential for diacetyl in treatment of neurodegenerative diseases, the authors use the fly Huntington's disease model, utilizing the overexpression of Huntingtin protein with expanded poly-Q repeats in the photoreceptor rhabdomeres which leads to their degeneration. Exposing these flies to diacetyl significantly decreases the loss of rhabdomeres, suggesting a potential for diacetyl as a therapeutic agent for neurodegeneration.

The findings are very intriguing and highlight environmental chemicals as potent agents which can alter gene expression independent of their action through chemosensory receptors.

Reviewer #1 (Public Review):

Asthma is a syndromic disease with multiple subtypes with different pathogenetic paths to a final wheezing phenotype. This limits the insights gleaned from genetic investigations of asthma. One of the most important phenotypes is early life onset wheezing, which persists. Here, the authors use data from multiple birth cohorts and by coupling latent class analysis of clinical phenotypic data with GWAS discovery, identify a novel locus close to annexin 1 (ANXA1) associated exclusively with early-onset persistent wheeze. The methodology is a major strength of the work and highlights the importance of acquiring and analysing phenotypic over simple use of doctor labels for complex diseases.

The authors went on to demonstrate a putative mechanism such that the risk allele (T) may confer a reduction in ANXA1 expression. Altered ANXA1 expression was additionally recapitulated in a murine model of house dust mite (HDM)-induced allergic airway disease. In this model, ANXA1 increased, rather than decreased, which may be attributable to its role in resolving inflammation. ANXA1-deficient mice had a more severe phenotype. This strengthens the evidence for causality in the novel link between ANXA1 and asthma and opens the door for further investigations. While novel for this link, the finding is well supported by prior knowledge about ANXA1-related pathways and inflammation. ANXA1 is known to participate in phospholipase A2-dependent reduction of inflammatory mediator production. Glucocorticoids increase ANXA1 levels. ANXA1 deficiency leads to airway hyperreactivity in mice. Overall, ANXA1 appears to be suitable as a therapeutic target and this may spur further investigations into the pathway.

Reviewer #2 (Public Review):

Granell et al. investigated genetic factors underlying wheezing from birth to young adulthood using a robust data-driven approach with the aim of understanding the genetic architecture of different wheezing phenotypes. The association of 8.1 million single nucleotide polymorphisms (SNPs) with wheeze phenotypes derived from birth to 18 years of age was evaluated in 9,568 subjects from five independent cohorts from the United Kingdom. This meta-genome-wide association study (GWAS) revealed the suggestive association of 134 independent SNPs with at least one wheezing subtype. Among these, 85 genetic variants were found to be potentially causative. Indeed, some of these were located nearby well-known asthma loci (e.g., the 17q21 chromosome band), although ANXA1 was revealed for the first time to play an important role in early-onset persistent wheezing. This was strongly supported by functional evidence. One of the top ANXA1 SNPs associated with wheezing was found to be potentially involved in the regulation of the transcription of this gene due to its location at the promoter region. This polymorphism (rs75260654) had been previously evidenced to regulate the ANXA1 expression in immune cells, as well as in pulmonary cells through its association as an eQTL. Protein-protein network analyses revealed the interaction of ANXA1 with proteins involved in asthma pathophysiology and regulation of the inflammatory response. Additionally, the authors conducted a murine model, finding increased anxa1 levels after a challenge with house dust mite allergens. Mice deficient in anxa1 showed decreased lung function, increased eosinophilia, and Th2 cell levels after allergen stimulation. These results suggest the dysregulation of the immune response in the lungs, eosinophilia, and Th2-driven exacerbations in response to allergens as a result of decreased levels of anxa1. This coincides with evidence of lower plasmatic ANXA1 levels in patients with uncontrolled asthma, suggesting this locus is a very promising candidate as a target of novel therapeutic strategies.

Limitations of this piece of work that need to be acknowledged: (1) the manual and visual inspection of Locus Zoom plots for the refinement of association signals and identification of functional elements does not seem to be objective enough; (2) the sample size is limited, although the statistical power was improved by the assessment of very accurate disease sub-phenotype; (3) association signals with moderate significance levels but with strong functional evidence were found; (4) no direct replication of the findings in independent populations including diverse ancestry groups was described. Nonetheless, the robustness and consistency of the findings supported by different analytical and experimental layers is the major strength of this study.

The authors successfully achieved the aims of the study, strongly supported by the results presented. This study not only provides an exciting novel locus for wheezing with potential implications in the development of alternative therapeutic strategies but also opens the path for better-powered research of asthma genetics, focused on accurate disease phenotypes derived by innovative data-driven approaches that might speed up the process to disentangle the missing heritability of asthma, making use of still useful GWAS approaches.

Reviewer #3 (Public Review):

Genome-wide association studies on asthma have been challenging, due to the accuracy of phenotyping and potential gene-environment interactions. Thus, the authors aimed to identify genetic loci associated with subtypes of childhood wheezing. One main strength of this investigation is that the data availability of wheeze from birth to adolescence among multiple birth cohorts allowed the sub-phenotyping on a large scale and high statistical power. The study is properly designed and the conclusions are well-supported. Understanding the heterogeneity in subtypes of childhood wheezing is of great clinical interest and may help inform future directions in disease prediction and prevention.

Reviewer #1 (Public Review):

In their study, Osorio-Valeriano and colleagues seek to understand how bacterial-specific polymerizing proteins called bactofilins contribute to morphogenesis. They do this primarily in the stalked budding bacterium Hyphomonas neptunium, with supporting work in a spiral-shaped bacterium, Rhodospirillum rubrum. Overall the study incorporates bacterial genetics and physiology, imaging, and biochemistry to explore the function of bactofilins and cell wall hydrolases that are frequently encoded together within an operon. They demonstrate an important, but not essential, function for BacA in morphogenesis of H. neptunium. Using biochemistry and imaging, they show that BacA can polymerize and that its localization in cells is dynamic and cell-cycle regulated. The authors then focus on lmdC, which encodes a putative M23 endopeptidase upstream of bacA in H. neptunium, and find that is essential for viability. The purified LmdC C-terminal domain could cleave E. coli peptidoglycan in vitro suggesting that it is a DD-endopeptidase. LmdC interacts directly with BacA in vitro and co-localizes with BacA in cells. To expand their observations, the authors then explore a related endopeptidase/bactofilin pair in R. rubrum; those observations support a function for LmdC and BacA in R. rubrum morphogenesis as well.

An overall strength of this study is the breadth and completeness of approaches used to assess bactofilin and endopeptidase function in cells and in vitro. The authors establish a clear function for BacA in morphogenesis in two bacterial systems, and demonstrate a physical relationship between BacA and the cell wall hydrolase LmdC that may be broadly conserved. The eventual model the authors favor for BacA regulation of morphogenesis in H. neptunium is that it serves as a diffusion barrier and limits movement of morphogenetic machinery like the elongasome into the elongating stalk and/or bud. However, there is no data presented here to address that model and the role of LmdC in H. neptunium morphogenesis remains unclear.

The data presented illuminate aspects of bacterial morphogenesis and the physical and functional relationship between polymerizing proteins and cell wall enzymes in bacteria, a recurring theme in bacterial cell biology with a variety of underlying mechanisms. Bactofilins in particular are relatively recently discovered and any new insights into their functions and mechanisms of action are valuable. The findings presented here are likely to interest those studying bacterial morphogenesis, peptidoglycan, and cytoskeletal function.

Reviewer #2 (Public Review):

This is an excellent study. It starts with the identification of two bactofilins in H. neptunium, a demonstration of their important role for the determination of cell shape and discovery of an associated endopeptidase to provide a convincing model for how these two classes of proteins interact to control cell shape. This model is backed up by a quantitative characterisation of their properties using high-resolution imaging and image analysis methods.

Overall, all evidence is very convincing and I do not have many recommendations on how to improve the manuscript.

In my opinion, there are only two issues that I have with the paper:

1. The single particle dynamics of BacA is presented as analysed and I would like to give some suggestions how to maybe extract even more information from the already acquired data:

1.1. Presentation: Figure 5A is only showing projections of single particle time-lapse movies. To convince the reader that it was indeed possible to detect single molecules it would be helpful if the authors present individual snapshots and intensity traces. In case of single molecules these will show step wise bleaching.

1.2. Analysis: Figure 5B and Supplement Figure 1 are showing the single particle tracking results, revealing that there are two populations of BacA-YFP in the cell. However, this data does not show if individual BacA particles transition between these two populations or not. A more detailed analysis of the existing data, where one can try to identify confinement events in single particle trajectories could be very revealing and help to understand the behaviour of BacA in more detail.

2. The title of Fig. 3 says that BacA and BacD copolymerise, however, the data presented to confirm this conclusion is actually rather weak. First, the Alphafold prediction does not show the co-polymer, and second, the in vitro polymerisation experiments were only done with BacA in the absence of BacD. Accordingly, the only evidence that supports this is their colocalization in fluorescence microscopy. I suggest either weakening the statement or changing the title adds more evidence.

Finally, did the authors think about biochemical experiments to study the interaction between the cytoplasmic part of LmdC and the bactofilins? These could further support their model.

Reviewer #1 (Public Review):

The manuscript provides analyses on a very complete dataset on weight and length growth, as well as several physiological markers related to growth, in bonobos. Moreover, there is a good overview of the presence of adolescent growth spurts in non-human primates, by reviewing published data, in comparison to their own dataset. They discuss the need to consider scaling laws when interpreting and comparing growth curves of different species and variables.

The manuscript is very well written, the sample is large, and the methods are well explained. It seems they have analyzed a very complete dataset. Also, the discussion and the references supporting the findings are complete.

The main weakness of this manuscript is that they do not provide a direct comparison with previous analyzed datasets in other species, using their own method (in part maybe because there is not available data, but just published figures).

On the other side, conclusions are well supported by the results, and the previous published datasets are discussed in the manuscript, although not in detail.

Reviewer #2 (Public Review):

This work sheds new light on the growth trajectory of Bonobo and contributes heavily to the discussion of the exclusivity of certain aspects of growth in modern humans. These results are also interesting as long as they are based on the study of the largest sample ever considered in the study of the growth of this species by including morphometric measurements as well as endocrinological factors.

The authors approach the study of the presence of growth spurs (GS) in Bonobo on the basis that GS are exclusive to the growth in modern humans. This idea is fairly widespread, however studies on non-human primates have shown an acceleration of growth during adolescence in several species, these works are recalled, presented and discussed by the authors. The originality of this work lies in highlighting the importance of scaling in studies of growth trajectories. The absence of GS in Bonobo but also in other primate species may result from not considering the conjunction of weight and height in the analysis of growth, because the pronounced changes in the speed of the height are in relation to the speed of changes in weight and this is modified according to the size/age. The authors apply scaling corrections to their results and the GS become evident (or more obvious) in Bonobo. Thus, the exclusivity of GS in growth in modern humans may in fact result only by the application of analytical approach not very appropriate in non-human primates.

Reviewer #1 (Public Review):

For many years it has been understood that transposable elements (TEs) are an important source of natural variation. This is because, in addition to simple knockouts of genes, TEs carry regulatory sequences that can, and sometimes do, affect the expression of genes near the TEs. However, because TEs can be difficult to map to reference genomes, they have generally not been used for trait mapping. Instead, single nucleotide polymorphisms are widely used because they are easy to detect when using short reads. However, improvements in sequencing technology, as well as an increased appreciation of the importance of TEs to both linked to favorable alleles and are more likely to be causing the changes that make those alleles beneficial in a given environment. Further, because TE activity can occur after bottlenecks, they can provide polymorphisms in the absence of variation in point mutations.

In this manuscript, the authors carefully examine insertion polymorphisms in rice and demonstrate linkage to differences in expression. To do this, they used expression quantitative trait locus (eQTL) GWAS using TIPs as genetic markers to examine variation in 208 rice accessions. Because they chose to focus on genes that were expressed in at least 10% of the accessions, presumably because more rare variants would end up lacking statistical power. This is an understandable decision, but it says that recent insertions, such as the MITE elements detailed in a previous paper, would not be included. Importantly, although TIPs associated with differentially expressed genes are far less common than SNPs' traditional eQTLs, there were a significant number of eQTLs that showed linkage to TIPs but not to QTL.

The authors then show that of the eQTLs associated with both TIPs and SNPs, TIPs are more tightly linked to the eQTL, and are more likely to be associated with a reduction in expression, with variation in the effects of various TEs families supporting that hypothesis. Here and throughout, however, the distance of the TEs could be an important variable. It is also worth noting the relative numbers in order to assess the claim in the title of the paper. The total number of eQTL-TIPs is ten-fold less than the number of eQTL-SNPs, and, of the eQTLs that have both, there are a significant number of eQTL-TIPs that are not more tightly linked to the expression differences than the eQTL.

The authors show that eQTL-TIPs are more likely to be in the promoter-proximal region, but this may be due to insertion bias, which is well documented in DNA-type elements. Here and throughout the authors are careful to state that the data is consistent with the hypothesis that TEs are the cause of the change, but do not claim that the data demonstrate that they are.

Throughout the rest of the manuscript, the authors systematically build the case for a causal role for TEs by showing, for instance, that eQTL-TIPs show much stronger evidence for selection, with increased expression being more likely to be selected than decreased expression. The authors provide examples of genes most likely to have been affected by TE insertions.

Overall, the authors build a convincing case for TEs being an important source of regulatory information. I don't have any issues with the analysis, but I am concerned about the sweeping claims made in the title. Once you get rid of eQTLs that could be altered by either SNPs or TIPs and include only those insertions that show strong evidence of selection, the number of genes is reduced to only 30. And even in those cases, the observed linkage is just that, not definitive evidence for the involvement of TEs. Although clearly beyond the scope of this analysis, transgenic constructs with the TEs present or removed, or even segregating families, would have been far more convincing.

The fact that many of the eQTL-TIPs were relatively old is interesting because it suggests that selection in domesticated rice was on pre-existing variation rather than new insertions. This may strengthen the argument because those older insertions are less likely to be purged due to negative effects on gene expression. Given that the sequence of these TEs is likely to have diverged from others in the same family, it would have been interesting to see if selection in favor of a regulatory function had caused these particular insertions to move away from more typical examples of the family.

Reviewer #2 (Public Review):

In this manuscript, Castanera et al. investigated how transposable elements (TEs) altered gene expression in rice and how these changes were selected during the domestication of rice. Using GWAS, the authors found many TE polymorphisms in the proximity of genes to be correlated to distinct gene expression patterns between O. sativa ssp. japonica and O. sativa ssp. indica and between two different growing conditions (wet and drought). Thereby, the authors found some evidence of positive selection on some TE polymorphisms that could have contributed to the evolution of the different rice subspecies. These findings are underlined by some examples, which illustrate how changes in the expression of some specific genes could have been advantageous under different conditions. In this work, the authors manage to show that TEs should not be ignored when investigating the domestication of rise as they could have played an important role in contributing to the genetic diversity that was selected. However, this study stops short of identifying causations as the used method, GWAS, can only identify promising correlations. Nevertheless, this study contributes interesting insights into the role TEs played during the evolution of rice and will be of interest to a broader audience interested in the role TEs played during the evolution of plants in general.

Reviewer #1 (Public Review):

With this work, the authors address a central question regarding the potential consequences of post-translational modifications for the pathogenesis of neurodegenerative diseases. Phosphorylation and mislocalization of the RNA binding protein TDP43 are characteristic of ~50% of frontotemporal lobar degeneration (FTLD), as well as >95% of amyotrophic lateral sclerosis (ALS). To determine if acetylation is a primary, disease-driving event, they generated a TDP-43 mutant harboring an acetylation-mimicking mutation (K145Q). Animals carrying the acetylation-mimic mutation (K145Q) displayed key pathological features of disease, including more cytoplasmic TDP43 and impaired TDP43 splicing activity, together with behavioral phenotypes reminiscent of FTLD.

This is a well-written and well-illustrated manuscript, with clear and convincing findings. The observations are significant and emphasize the importance of post-translational modifications to TDP-43 function and disease phenotypes. In addition, the TDP43(K145Q) mice may prove to be a valuable model for studying TDP-43-related mechanisms of neurodegeneration and therapeutic strategies.

However, as it stands it is challenging to determine if any or all of the phenotypes are a direct consequence of interrupted RNA binding by TDP-43, rather than acetylation per se. Furthermore, all the results are obtained using an acetylation-mimic mutation that may simply be disrupting a key residue involved in RNA binding by TDP43, instead of mirroring acetylation itself, which in theory is a reversible modification. Lastly, it remains unknown why TDP43(K145Q) mice developed features of FTLD, but not ALS, despite the fact that TDP-43 acetylation was found in ALS tissue and not FTLD.

Reviewer #2 (Public Review):

This paper extends prior work demonstrating the importance of K145 acetylation of TDP-43 as a post-translational modification that impacts its RNA-binding capacity and may contribute to pathology in FTLD-ALS. The main strengths of this paper are the generation of a novel mouse model, using CRISPR gene editing, in which an acetylation-mimetic mutation (K to Q) is introduced at position 145. Behavioral, biochemical, and genetic analyses indicate that these mice display phenotypes relevant to TDP-43-associated disease and will be a valuable contribution to the field. While most of the data are rigorous and clearly presented, several weaknesses should be addressed to strengthen the manuscript and further characterize the phenotype of mutant mice.

Reviewer #3 (Public Review):

Numerous experimental models are phenotyped in this manuscript including mouse neurons, iPSC-derived human neurons, knock-in mice, and knock-in iPSCs. Expression of acetylation-mimic or acetylation-null TDP-43 protein is achieved either with overexpression or CRISPR-Cas9-based knock-in. A complex phenotype is observed including loss of TDP-43 function (reduced autoregulation, increased cryptic splicing) and a gain of TDP-43 (increased insoluble TDP-43 protein). These correlate with downstream neurobehavioral changes which are most consistent with a cortical/hippocampal phenotype without a motor phenotype. Post-translational modifications of disease-associated proteins are thought to contribute to neurodegenerative disease pathogenesis, and this study succeeds in demonstrating that TDP-43 acetylation results in downstream molecular and behavioral phenotypes.

There are numerous additional strengths. TDP-43 acetylation is a post-translational modification that is known to be associated with TDP-43 inclusions that are characteristic of human diseases. An important strength is the rigorous use of multiple different experimental models (rodent cells, iPSC-derived neurons, mice, overexpression, knock-in) with overall consistent results. Moreover, multiple orthogonal endpoints are presented including histology/cytology/immunostaining, biochemistry, molecular biology, and neurobehavioral assays. As TDP-43 acetylation is known to block RNA binding, these novel cellular and mouse models represent interesting albeit complex tools to study the functional consequences of a partial loss of function. As TDP-43 regulates its own expression (i.e. autoregulation), the complexity lies at least in part due to the loss of RNA binding leading to a functional loss of TDP-43 function which includes the increased expression of the TARDBP transcript and TDP-43 protein.

Conceptually, there is a disconnect in that the mouse model exhibits primarily a cortical/hippocampal phenotype more akin to frontotemporal lobar degeneration with TDP-43 inclusions (FTLD-TDP), while TDP-43 acetylation is only seen in ALS tissues and not in FTLD-TDP tissues because most of the pathologic protein in the latter is N-terminally truncated (i.e. the acetylation site is not present). That being said, there is no mouse model which completely and faithfully recapitulates the human disease, and this mouse model avoids overt overexpression (increased TDP-43 protein expression stemming from altered autoregulation) and avoids the use of synthetic/artificial mutation (such as mutation of the TDP-43 nuclear localization signal).

In terms of the CNS phenotype, it is difficult to interpret the reduced density of NeuN-positive neurons in the mouse model as a neurodegenerative phenotype. The reduction in NeuN density but not overall cellular density is only suggestive of neurodegeneration (as opposed to, for example, a developmental phenotype) without more rigorous stereological approaches that take into account potential volumetric changes. Indeed, the absence of astrocytosis and microgliosis argues against neurodegeneration.

Some of the loss of function measures (CFTR construct splicing, shift in SORT1 protein) are subtle, although RNA sequencing clearly shows many splicing aberrations including cryptic splicing events which overall supports that a loss of TDP-43 function is observed.

Finally, there are multiple instances where multiple measurements are made on a few biological replicates. ANOVA or t-tests are not appropriate in these instances (lack of independence).

Reviewer #3 (Public Review):

The current manuscript describes the expression of multiple Natriuretic peptides and their receptor during the early embryo development in the amphibian Xenopus laevis. This signaling pathway is well known to control a broad range of physiological processes but its role in embryogenesis has not been studied before. Thus, the study presents some important novel findings. After defining the combination of ligands and receptors expressed during embryogenesis, they used loss of function experiment to test the requirement of this signaling pathway to the development of neural crest cells.

The loss of function experiments are well controlled as they use both chemical inhibitors and Morpholino that block the translation of the protein. They also rescue the phenotypes by using either mRNA from the human protein (receptor), or purified peptides (Ligands).

The results clearly show that loss of either Npr1 or Npr3 affects the development of both neural crest and placodes, while Npr2 had no visible effect. Similarly, they found that the loss of the ligands Nppa and Nppc affected neural crest and placode development while Nppb had no effect. Again, the loss of function was achieved with both Morpholino KD and inhibitors. In general, the loss of neural crest and placodal marker are associated with an expansion of neural marker (Sox2) and a corresponding decrease of epidermal marker (Keratin).

For Npr3 the author show that the loss of the protein is associated with an increase in cell death and decrease in cell proliferation which match some previous work on the role of the receptor in other cell type. It is unclear how much this can account for the striking difference in patterning observed and experiment to test this have not been performed.<br /> Overall, this work is important for the field as it shows novel genes that are critical for craniofacial and sensory development. It is likely that mutations in any genes involved in this pathway could result in birth defect which could be corrected pharmacologically.

Reviewer #1 (Public Review):

This manuscript reports the unique finding that specific ligands and receptors in the natriuretic peptide signaling pathway act during early embryogenesis to discriminate between neural crest (NC) and cranial placode (CP) fates. This is a significant finding for two reasons: 1) the developmental role of this pathway has not been studied in any detail; and 2) how cells located in the border between the neural ectoderm and non-neural ectoderm decide on NC versus CP fates is of broad interest and being actively pursued by a number of laboratories. The authors present logical and experimentally convincing support for their conclusions. They report the expression patterns by in situ hybridization and qPCR of the various ligands and receptors of the natriuretic peptide signaling pathway, clearly demonstrating that several of these molecules are expressed in the right place at the right time to influence NC and/or CP formation. They establish that Npr3 is a target of Pax3 and Zic1, two transcription factors previously shown to be required for NC and CP formation, further illustrating that it is part of the appropriate regulatory network. Next, the authors use morpholino knock down of Npr3 to show that the resulting embryos have deficient expression of two NC genes and two CP genes. The controls used for the knock-down are the correct ones and were confirmed by treatment with a high-affinity and selective Npr3 antagonist. The function of Npr3 was further explored by discriminating between its known two functions - clearance of natriuretic peptides and inhibition of adenylyl cyclase - by expressing either WT or mutant versions of human NPR3 in Npr3 morphant embryos. That WT rescued both NC and CP genes but the mutant version only rescued NC genes leads to the appropriate conclusion that Npr3 regulates NC and CP fates via different mechanisms. This conclusion was confirmed by treating Npr3 morphants with a specific adenylyl cyclase inhibitor, which restored CP gene expression, and treating CP promoting explants with an adenylyl cyclase activator, which repressed CP gene expression. Using similar knock-down approaches the authors convincingly demonstrate that Npr2 does not participate in NC/CP formation, but Npr1 does; again, the knock-down results were confirmed by treating embryos with a specific Npr1 antagonist. Finally, the authors complete the story by determining by equally well-controlled knock-down experiments which of the three natriuretic peptides participate in this process. In short, the many different experiments strongly support the conclusions, and the experiments are well controlled and include large numbers of embryos to provide exceptional rigor.

Reviewer #2 (Public Review):

This study reports a novel role of the natriuretic receptors Npr3 and Npr1 in the formation of neural crest (NC) and cranial placode (CP) progenitor populations in frog embryos. The authors discovered this receptor family in a screen for genes activated during NC development. They show the relevant expression of these receptors and the corresponding ligands in the NC and CP populations. Knockdown and rescue experiments combined with pharmacological drug treatment demonstrated that Npr3 clearance activity is required for NC progenitor formation. Surprisingly, adenylyl cyclase inhibition was required for cGMP production and the effect on CP development. The authors conclude that the two second messengers downstream participate in the segregation of the NC and CP progenitors in embryonic development.

The significance of this study is in the demonstration of two distinct developmental programs that are separately controlled by different activities of the same receptor. The study is well designed and executed with proper controls. Nevertheless, the data suggesting that Npr3 regulates NC and CP fates via different mechanisms are limited and need further support, such as the analysis of additional markers for CP progenitors, to be unambiguously interpreted. The work is likely to impact two different areas: early embryonic development and natriuretic peptide signaling.

Reviewer #1 (Public Review):

Rapan et al. analyzed the cytoarchitectonic of the prefrontal cortex based on observer-independent analysis, confirming previous parcellations based on cyto-, myelo-, and immunoarchitectonic approaches, but also defining novel subdivisions of areas 10, 9, 8B, and 46 and identified the receptor density "fingerprint" of each area and subdivision. Furthermore, they analyzed the functional connectivity of the prefrontal cortex with caudal frontal, cingulate, parietal, and occipital areas to identify specific features for the various prefrontal subdivisions. Altogether, this study corroborates previous parcellations of the prefrontal cortex, adds new cortical subdivisions, and provides a neurochemical description of the prefrontal areas useful for comparative considerations and for guiding functional and clinical studies.

Strengths:<br /> - This study provides a detailed cytoarchitectonic map of the prefrontal cortex enriched with receptor density and functional connectivity data.<br /> - The authors shared the data via repositories and applied their map to a macaque MRI atlas to further facilitate data sharing.

Weaknesses:<br /> - The temporal cortex should be included in the functional connectivity analysis as it is known from anatomical studies that most prefrontal areas display rich connectivity with temporal areas. The aim of creating a comprehensive view of the frontal cortex makes the manuscript data-rich but cursory in discussing the relevant anatomical and functional literature.

Reviewer #2 (Public Review):

Rapan and colleagues did perform an impressive multi-modal parcellation of the macaque frontal cortex. In addition to qualitative cytoarchitectonic and resting-state functional fMRI data analyses, the authors based their parcellation on quantitative receptor density analysis of 14 receptors. Compared with the classic Walker map of the macaque frontal cortex, the authors produced a more refined map. Those results should be discussed in light of previous work on the same topic (Petrides et al. 2012 Cortex; Reveley et al. 2017 Cerebral Cortex; Saleem and Logothetis 2012).

Reviewer #3 (Public Review):

The function of a brain area is defined by its interaction with other regions. Accordingly, two areas communicate via axons and dendrites, but the language is plurimodal along the neurotransmitter-receptor dimension. Consequently, reading its neurochemical constituents is the most advanced way to characterize the brain into functional territories.

Along this theoretical line of research, Rapan et al. produced an exceptional report on the structure of the macaque frontal lobes based on cytoarchitectural division complemented with functional connectivity and neurochemical data. Results are lavishly illustrated. They report 35 cytoarchitectural areas in the prefrontal lobe with precise, different connectivity and neurotransmitter profiles together with practical anatomical landmarks. All data is openly available to the community and will constitute a cornerstone for future neuroscientific research in the macaque frontal lobes.

I congratulate the authors for this already extraordinary work.

Reviewer #1 (Public Review):

The authors have approached the study of the mechanism of maturation of retroviruses lattice, where Gag polyprotein is the main component. The Gag polyprotein is common to all retroviruses and makes up most of the observed lattice underlying the virion membrane. Within the lattice, 95% of the monomers are Gag, and 5% are Gag-Pol, which has the 6 domains of Gag followed by protease, reverse transcriptase and integrase domains (coming from Pol) embedded within the same polyprotein. For the maturation and infectivity of HIV retrovirus, the Gag proteins within the immature lattice must be cleaved by the protease formed from a dimer of Gag-Pol. Importantly, the lattice covers only 1/3 to 2/3 of the available space on the membrane. The incompleteness of the lattice results in a periphery of Gag monomers with unfulfilled intermolecular contacts. Recently, the structure of the immature lattice has been partially resolved using sub-tomogram averaging cryotomography (cryET) and it has been shown that the incompleteness of the lattice provides more accessible targets for the protease (Tan A. et al. 2021). Based on these, the authors have wondered: does the incompleteness of the lattice allow for dynamic rearrangements that ensure that protease domains embedded within the lattice can find one another to dimerize and activate? To answer this, they started from experimental cryoET data and used reaction-diffusion simulations of assembled Gag lattices with varying energies and kinetic rates to test how lattice structure and stability can support the dimerization of the Gag-Pols. They found that although they represent only 5% of the monomers that assemble into the lattice, the stochastic assembly ensure that at least a pair of them are adjacent within the lattice. They next showed that if the molecules are distant from one another, they would need to detach, diffuse, and reattach stochastically at the site of another Gag-Pol molecule.

I consider the work very interesting, which could contribute to a very important aspect of retroviruses maturation such as their infectivity. However, the observations made by the authors do not necessarily answer their initial question which seemed to be focused on studying the possible role of the incompleteness of the lattice on the protease activation rather than the mechanism of Pol activation itself. Maybe this is only a nuance to be polished in the writing.

The weakness of the work comes from both the fact their entire study has been done by computational methods and the exclusion in their computational approaches of well-known cellular components with a role in retrovirus maturation, which might obey to the fact of keeping their models into the simplest possible since handling atomistic models is already a heavy task. Maybe complementary molecular or structural studies would strengthen their results.

Reviewer #2 (Public Review):

Immature lattice assembly remains an arcane topic, and these simulations provide high resolution data such as assembly kinetics and large-scale lattice rearrangement. Further, the authors extend their model to compare directly with experiments, e.g. SNAP-HALO dimerization, which provides a basis to interpret their conclusions. The manuscript is difficult to read, as it is a technical manuscript that overuses jargon; overall, it seems written for a specialized audience. Additionally, there are several aspects of the model design that remain opaque, such as the implicit lipid method and the suppression of multi-site nucleation. Further, analyses such as time auto-correlation and mean first passage time are not given much context by the authors. Altogether, it is the opinion of this reviewer that several revisions to the manuscript should be incorporated to improve clarity and strengthen the significance of the authors' efforts.

Reviewer #3 (Public Review):

The manuscript concerns the cleavage of the Gag polyprotein lattice from the HIV virion membrane, a key stage in HIV lifecycle, and one that is required for HIV to become infectious. Since cleavage requires homodimerization between the small fraction (5%) of such Gag polyproteins that carry a protease domain, referred to as Gag-Pol, this raises questions regarding how such homodimerization can take place, and whether it can happen on the required timescales, given that Gag-Pol is typically embedded in a lattice that is observed to form one large connected component.

The authors address these questions in silico, using particle-based reaction diffusion simulations. Such simulations are rigid-body and "structure-resolved" meaning that they rigidly incorporate the geometry of the polyproteins, and their various binding interfaces, based on existing structural data. Other aspects of the simulations are also in-line with available data, including copy numbers, lattice curvature, and dissociation rates. This focused approach is a strength of the work and allows the authors to make credible claims that their simulations have relevance to HIV (as does their commitment to comparison with HALO-SNAP-based measurements of dimerization kinetics as well as iPALM experiments that characterize lattice dynamics).

A central part of the model is that it allows for the "possibility of imperfect alignment of molecules in the lattice", presumably due to the incompatibility of regular hexagonal tiling and surfaces with non-zero Gaussian curvature, such as a sphere. This is implemented via the ad-hoc imposition of a free-energy penalty when complete hexamers are formed, implying that hexamers are less stable than six ideal bonds. By varying this strain penalty, the authors can change the stability of the lattice independently of individual binding affinities, allowing its use as an effective fitting parameter when comparing to HALO-SNAP data. In the latter case, agreement between simulation and experiment can only be found at moderate levels of lattice stability.

However, such energetic penalties are present whenever the polyprotein structure must undergo deformations which, on surfaces with nonzero Gaussian curvature, should be the case for partial tilings as well as complete ones (where all six interfaces form bonds). This, therefore, appears to be a weakness of the work. An elastic implementation of polyprotein structure, for example, would permit strain to accumulate (and therefore stresses to propagate) throughout the lattice naturally, irrespective of whether complete hexamers were formed, and might reasonably be expected to impact the likelihood of different lattice structures. Whilst it is not clear how or whether this would lead to qualitatively or quantitatively different results, it is nevertheless worth remarking upon since the authors high-level claim is that lattice structure is an important determinant of the mean-first-passage times to dimerization.

Overall, I find this to be a valuable study, carried out in a solid and comprehensive manner. The primary impact of the work appears to be twofold: the unification of different experimental measurements under a single model, and the further identification of the salient parts of that model that most impact biological function. The results advance the understanding of one of the steps of the HIV lifecycle, via a better description of the mechanisms underpinning Gag-Pol dimerization. Notably, the authors stop short of drawing parallels to many related concepts and models in statistical physics, such as those concerning percolation and diffusion limited aggregation as well as the notions of dislocations and defects in crystalline matter on curved surfaces. These might reasonably have provided a basis for better understanding and quantification of the authors' simulations, as well as improving the scope for extensions and conceptual clarity.

Reviewer #1 (Public Review):

The paper by Mohebi, Collins, and Berke describes the interactions between cholinergic interneurons and dopamine (DA) release in the core of the nucleus accumbens (NAc) in rats. The cholinergic triggering of DA release has been a debated issue in recent years, and this study provides data supporting cholinergic-dependent DA release.

The authors first show that optogenetic activation of cholinergic interneurons (CINs) induces DA release in the NAc, increasing with pulse width, frequency, and train pulse duration. They next show using simultaneous imaging of CIN calcium activity and DA release using RdLight that both are correlated in their response to sensory stimuli and entry to reward port in freely moving rats. They show that while CIN activity and DA release show ramping activity before entry to the center and food ports, such ramping is not seen in the spiking activity of DA cells. lastly, the authors show that blocking nicotinic receptors in the NAc by injection of DHBE impairs task performance, with similar (albeit weaker) effects as the DA antagonist flupenthixol. The uncoupling between DA release and DA cell firing, under certain conditions, has been shown by the authors in a previous paper (Mohebi et al, 2019). Here, the authors add the CINs calcium activity during the same task, showing that the dynamics of CIN activity resemble that of DA release. The results presented show correlations between CIN activity and DA release during behavior, however, the role of CINs in controlling DA release is not tested directly. The data presented in the paper are clear and it is well written. However, there are a few issues that need to be addressed, including some key experiments that could directly test the functional role of CIN-induced DA release.

Reviewer #3 (Public Review):

This report by Mohebi et al. provides new answers to old questions by showing that the activity of striatal cholinergic interneurons (CINs) escalates progressively during specific reward-related behaviors and that this correlates with previously observed ramps in dopamine (DA) release in the nucleus accumbens core. The report is strong and provides evidence for the authors' hypothesis that DA ramps are independent of DA neuron activity, but are instead the result of CIN activity and corresponding acetylcholine (ACh) release. The authors further demonstrate that the fidelity of CIN activation and consequent driving of DA release is even more robust in vivo than observed ex vivo slice preparations, which is fundamental for understanding the role of ACh-DA interactions in behavior. The findings complement the authors' previous evidence ventral tegmental area (VTA) DA neuron firing patterns do not show a ramping pattern; the previously reported VTA data are appropriately included here (in Fig. 3) to illustrate the absence of VTA firing during the time-locked increases in CIN activity and DA release. The present studies stop short of showing a direct link between CIN activity and DA release, however, which would require examining DA release during behavior in the presence of an antagonist of nicotinic ACh receptors. The authors also extend the understanding of the regulation of DA release by acetylcholine (ACh) by showing that optical activation of CINs in vivo promotes DA release responses that do not attenuate with repetitive stimulation. This contrasts with previous results in ex vivo striatal slices in which ACh-evoked DA release has been found to decline progressively from rundown and/or receptor desensitization. The authors propose that in vivo, AChE may be more effective in curtailing local ACh levels than in slices because of the slightly lower temperature typically used for slice studies, as well as the use of superfusion that might facilitate some AChE washout (AChE inhibitors are still effective in slices, of course). Overall, the report not only provides evidence for the cellular substrate for DA ramps but also shows the robustness of ACh-driven DA release in vivo. A few points to strengthen the report are listed below.

1) The authors give a few details about how CINs were activated at the beginning of the results, but say only that DA dynamics were monitored using fiber photometry. Given that the methods are at the end, a brief summary should be given here to indicate whether this means direct monitoring of DA or indirect via GCaMP, for example. It would be helpful to note the sensor used in the abstract, as well. In this light, as it were, RdLight1 should be described upon the first mention.

2) The authors show that infusion of DHbE in the NAc likelihood of decisions to approach the center port, as did antagonism of DA receptors. This supports the authors' argument that ramping of CIN activity and consequent ACh release underlies observed ramps in DA release. However, to show a causal interaction requires testing whether the observed DA ramps are absent after DHbE infusion in the NAc, under the same conditions that attenuated behavior.

3) In Fig. 3, the y-axis title for the upper panels should specify VTA, not simply "rate". This is stated in the legend, but should also be specified in the figure panel.

4) A recent preprint in BioRxiv by AC Krok, NX Tritsch et al. shows a related correlation between ACh and DA release in vivo in a reward task, as well as differences in other conditions. This report shows also that cortical input to CINs indeed plays a role, as suggested in the concluding sections of the present report. Consideration of the data in the preprint in the context of the present results could be valuable for the field.

Reviewer #1 (Public Review):

The authors compared the neural mechanisms of calling song in five Xenopus species. Two (X. laevis and X. petersii) were previously shown to produce fictive calls. This paper developed the techniques to evoke fictive calls for three additional species: X. cliivi, X. amieti, and X. tropicalis. The authors compared fast and low components of the calls and determined that the fast components in all species required bilateral coordination in the parabrachial nucleus (PBN), but the slow components were produced in the nucleus ambiguous (presumably with bilateral control, but that was not tested.

The abstract does not adequately summarize the content of the paper. There is no mention of stimulation, or bilateral connectivity, which is a large part of the paper. The names of all five species should appear in the abstract, not just X. laevis.

The conclusion that the "fast and slow CPGs identified in male X. laevis are conserved across species." is contradicted by the last paragraph, which states, "Fast trill-like CPGs are likely present only in fast clickers..." This inherent contradiction needs to be resolved.

The abstract also over-emphasizes the testosterone results. It states, "the development of fast CPGs [central pattern generators] depends on testosterone in a species-specific manner: testosterone facilitates the development of fast CPGs in a species with a courtship call containing fast clicks, but not in a species with a courtship call made entirely of slow clicks." The use of the word "development" implies embryology. Here, adults were treated and looked at 13 weeks later. There is no data presented about development. The effects of T could be simply to upregulate certain receptors of a circuit that was already present.

The concluding sentence of the abstract is, "The results suggest that species-specific calls of the genus Xenopus have evolved by utilizing conserved fast or slow CPGs that are broadly tuned to generate fast or slow trains of clicks, the development of which appear to be regulated by a strategic expression of testosterone receptors in the brain of each species." However, testosterone treatment was only applied to X. laevis females. The conclusion is based on plasma levels of testosterone in X. tropicalis. The conclusion that there is differential expression of testosterone receptors in the brain of each species is completely speculative and not supported by the data presented here.

Reviewer #2 (Public Review):

This study by Yamaguchi and Peltier provides a detailed investigation of the brainstem CPG functional organization that rules vocal behaviors in several Xenopus species, from an evolutionary perspective. The main conclusion of the paper reveals that vocal CPGs, located in the brainstem, generating fast and slow clicks in Xenopus male courtship calls are conserved across various Xenopus species. But the development of the fast CPG depends on testosterone only in species producing fast-click courtship calls.

Reviewer #1 (Public Review):

By the in vitro DNA damage response (DDR) assay with a defined DNA substrate using Xenopus extracts and in vitro binding assays with purified proteins, the authors nicely showed the role of APE1 (APEX1) in ATRIP recruitment for DDR activation, particularly a non-enzymatic (structural) role of APE1 in the binding to both ssDNAs and ATRIP. The results described in the paper are very convincing to support the authors' claim. However, these studies lack the quantification with proper statistics (and/or mentioning the reproducibility of the results). And, given the important discovery of APE1 in the DDR activation in vitro, it would be nice to demonstrate the role of APE1(APEX1) in ATR activation in vivo using siRNA-mediated knockdown of mammalian cells or yeast cells.

Reviewer #2 (Public Review):

ATM and Rad3-related (ATR) interact with ATRIP and plays a central role in DNA damage response. Previous studies have established the idea that ATR is recruited to RPA-covered ssDNA via ATRIP-RPA interaction. In this paper, the authors propose a new RPA-independent mechanism for ATR recruitment.

Reviewer #3 (Public Review):

In this manuscript, the authors explore the mechanism of ATRIP recruitment to single-stranded DNA (ssDNA), which is important for ATR activation and the subsequent control of DNA repair and cell cycle progression. Using Xenopus egg extracts, in vitro interaction assays, and ssDNA constructs, the authors found that AP endonuclease 1 (APE1) plays a role in the recruitment of ATRIP to ssDNA independently of RPA. Moreover, APE1 domains are characterized for ssDNA, ATRIP, and RPA interaction, determining that the nuclease activities of APE1 are not required for this new mode of ATRIP recruitment. Overall, the work presented makes a compelling case for a novel role for APE1 in ATRIP recruitment that seems crucial for ATR activation (at least in the Xenopus system). The results are likely to have an important impact on our understanding of the determinants for activation of ATR signaling and cellular responses to DNA damage and replication stress. It remains unclear whether the findings will be extended to other organisms and be relevant for different types of DNA lesions. Also, there are several points of concern in the manuscript that require further clarification, especially regarding some of the quantitative analyses presented and the claimed importance of the RPA-independent mode of ATRIP recruitment for ATR activation.

Reviewer #1 (Public Review):

In their study, Aman et al. utilized single cell transcriptome analysis to investigate wild-type and mutant zebrafish skin tissues during the post-embryonic growth period. They identified new epidermal cell types, such as ameloblasts, and shed light on the effects of TH on skin morphogenesis. Additionally, they revealed the important role of the hypodermis in supporting pigment cells and adult stripe formation. Overall, I find their figures to be of high quality, their analyses to be appropriate and compelling, and their major claims to be well-supported by additional experiments. Therefore, this study will be an important contribution to the field of vertebrate skin research. Although I have no major concerns, I would like to offer a few minor comments for the authors to consider.

1) The discovery of ameloblasts in the zebrafish skin is a fascinating finding that could potentially provide a new research model for understanding the development and regeneration of vertebrate teeth. It would be beneficial if the authors could provide further elaboration on this aspect and discuss how the zebrafish scale model could be utilized by researchers to better understand the morphogenesis of vertebrate teeth and/or hair.

2) While the overexpression-rescue experiments (i.e., fgf20a and pdafaa) provide crucial evidence to support the author's conclusions, it is important to note that overexpression driven by the heat-shock promoter is not spatially regulated. Therefore, it should be acknowledged that the rescue effects may not be cell-autonomous, as suggested in the current version.

3) Figure 7D. The authors used the ET37:EGFP lines to visualize hypodermis. Based on the absence of EGFP signal in the deep dermis of bnc2 mutants, the authors concluded that the hypodermis may be missing, suggesting the importance of the hypodermis in pigment cell formation. However, since the EGFP evidence is indirect, it is crucial to confirm the absence of the hypodermis structure with histology.

4) As the dataset is expected to be a valuable asset to the field, please provide Excel tables summarizing the key genes and their corresponding expression levels for each major cluster that has been identified.

Reviewer #2 (Public Review):

The authors used single cell transcriptome analysis of zebrafish skin cells and characterized various types of cells that are involved in scale formation and stripe patterning. The methods employed in this study is highly powerful to provide mechanistic explanation of these fundamental biological issues and will be a good example for many researchers studying other biological issues. Furthermore, the results characterizing differences in gene expression patterns among various types of cells will be informative for other researchers in the field.

For scale formation, it is known that mineralized tissues may significantly differ in rayfins and lobefins since sox9, col2a1, and col10a1 are all expressed in osteoblasts, in addition to chondrocytes, in zebrafish and gar (Eames et al., 2012, BMC Evol. Biol.). Furthermore, in mammals, Col10 is expressed in chondrocytes in mature cartilage that undergoes ossification. Thus, unlike the authors argue, col10a1 expression is not apparently relevant to the elasticity of scales.

The authors also state that the expression of dlx4a, msx2a, and runx2b characterize cells homologous to mammalian ameloblasts. However, dlx4, runx2, and msx2 are all duplicated in zebrafish, and the function of duplicated genes in teleost fishes may differ from that of single ancestral gene. Moreover, none of Dlx4, Msx2, and Runx2 is expressed specifically by ameloblasts in mammals. Indeed, both Msx2 and Runx2 are expressed in osteoblasts, while the expression of Dlx4 in ameloblasts is not reported. These results, together with the expression of an enamel gene, enam, in dermal cells (SFC), do not appear to support the homology of the surface tissue of mammalian teeth and zebrafish scales.

Reviewer #3 (Public Review):

This work describes transcriptome profiling of dissected skin of zebrafish at post-embryonic stages, at a time when adult structures and patterns are forming. The authors have used the state-of-the-art combinatorial indexing RNA-seq approach to generate single cell (nucleus) resolution. The data appears robust and is coherent across the four different genotypes used by the authors.

The authors present the data in a logical and accessible manner, with appropriate reference to the anatomy. They include helpful images of the biology and schematics to illustrate their interpretations.

The datasets are then interrogated to define cell and signalling relationships between skin compartments in six diverse contexts. The hypotheses generated from the datasets are then tested experimentally. Overall, the experiments are appropriate and rigorously performed. They ask very interesting questions of interactions in the skin and identify novel and specific mechanisms. They validate these well.

The authors use their datasets to define lineage relationships in the dermal scales and also in the epidermis. They show that circumferential pre-scale forming cells are precursors of focal scale forming cells while there appeared a more discontinuous relationship between lineages in the epidermis.

The authors present transcriptome evidence for enamel deposition function in epidermal subdomains. This is convincingly confirmed with an ameloblastin in situ. They further demonstrate distinct expression of SCPP and collagen genes in the SFC regions.

The authors then demonstrate that Eda and TH signalling to the basal epidermal cells generates FGF and PDGF ligands to signal to surrounding mesenchyme, regulating SFC differentiation and dermal stratification respectively.

Finally they exploit RNA-seq data performed in parallel in the bnc2 mutants to identify the hypodermal cells as critical regulators of pigment patterning and define the signalling systems used.

Whilst these six interactions in the skin are disparate, the stories are unified by use of the sci-RNA-seq data to define interactions. Overall, it's an assembly of work which identifies novel and interesting cell interactions and cross-talk mechanisms. There are some aspects that require clarification:

With respect to the discontinuous relationship noted in Figure 2I in the epidermis, the authors did not make mention of the fact that there are in fact two independent sources of periderm in the zebrafish. The first periderm derives from the EVL, is segregated a gastrulation, and gradually replaced from the basal epidermis during post-embryonic stages. Could this residual EVL-derived periderm have reduced sensitivity of the trajectory mapping from basal to periderm? The authors should comment whether their transcriptome dataset likely had residual EVL-derived periderm and if this might have impacted their trajectory continuity interpretation.

The authors ask if dermal SFCs express proteins associated with cartilage formation and use Col10a1 orthologues as markers (Fig 3B, I). I wonder if these are the best transcripts to answer this question as this has also been described to label osteoblasts in certain contexts in the fish and the authors might want to refer to Li et al 2009 or Avaron et al 2005. Were other markers of cartilage formation present such as collagen2 genes? These may be more definitive. The authors might want to reinterrogate their datasets for true cartilage markers or reframe their question.

Finally, of interest, were there any clear clusters on the UMAP plots (Fig 1 Supp3A) of unassigned identity? Even comment on these and how they were dealt with would be of significant interest to the field, as it is highly unlikely all cell types in the skin have been defined. This dataset promises to be a critical reference for defining these in the future.

Minor clarification:

Fig 2E top. The authors interpret that fate-mapped SFCs at the posterior margin are progressively displaced towards the scale focus. This is confusing as the margin SFC in Fig 2E seems to show them staying largely at the margin. Please clarify if this is what you meant.

Reviewer #3 (Public Review):

This paper addresses an apparent contradiction related to the interaction between spontaneous neural activity and neural plasticity during circuit development. It is well established that spontaneous activity contains instructive information for developmental downstream circuit refinement, for instance for the formation of retinotopic projections in the visual system. These developmental cues are contained in activity correlations, and thus can be picked up by Hebbian mechanisms. However, previous work has shown that informative correlations in spontaneous activity can be found on slow time scales (100s of milliseconds). Here it is shown that in this case correlations on fast time scales arise simultaneously from the integration of unrefined inputs and that these likely interfere with developmental plasticity. The paper shows that such fast, "parasitic" correlations can appear during retinal wave activity, and provides evidence that NMDA receptors can suppress them to avoid their influence during developmental plasticity.

This work is based on detailed biophysical models of thalamic relay neurons, which were fit to data from in-vitro whole-cell recordings. A genetic algorithm was used to fit these models, which provides a family of suitable models instead of a point estimate of good parameters. This approach is a real strength as it shows that the effects generalise well to many plausible models, and do not depend on specific parameter choices. The model neurons are then placed in simulated networks (without or with anatomically informed recurrent connections) and driven by retinal wave activity recorded from the mouse. Together the simulation and analysis show under which conditions fast, undesirable correlations do and do not appear. Specifically, the key model ingredient this work identifies for the suppression of fast correlations is the presence of NMDA receptors on the recipient synapses of the relay neurons.

An open question is how the parasitic correlations are actually suppressed by NMDA receptors. Is it correct that a stronger NMDAR contribution to the transmitted activity simply low-pass filters the incoming spike trains, and that fast correlations are smoothed out as a result? So are developing circuits tuned to slower activity? This could also explain why AMPA receptors subunits with slower kinetics are expressed during development in many circuits.

Taken together, I think this paper presents a very interesting set of results. The issue with parasitic correlations is quite obvious in retrospect, and clearly, a problem developing circuits will generally face. Additionally, the presence of NMDA receptors is often linked to plasticity and is seen as less important in shaping postsynaptic integration. Although no developmental plasticity has been modelled, would it be possible to predict the possible effects of experimental manipulations?

Reviewer #1 (Public Review):

The authors address an important and understudied problem: how precise temporal properties of synaptic transmission might impact the kinds of neuronal correlations that instruct development. The methods used to characterize and simulate retino-thalamo-cortical development are carefully carried out and yield convincing results. Based on these simulations, the authors argue that features such as slow NMDA receptor-mediated currents are able to prevent aberrant development which might otherwise result from rapid timescale correlations that lack meaningful information about visual topography.

Reviewer #2 (Public Review):

The paper starts with the premise that given the broad immature connectivity between the retina and thalamus during development, locally homogeneous synaptic currents should generate precise spike correlations (on a millisecond timescale) which are not seen in development and could be bad for developmental refinements and "network diversity". Rather, the correlations during development are over much longer timescales. The authors propose that two main factors, the dominance of NMDA (over AMPA) currents and the absence of recurrent connections prevents these precise correlations and preserves diversity.

The paper consists of three parts: (I) develop a biophysical model for a thalamic neuron, (II) use the model to determine which factors govern precise correlations, and then (III) simulate a cortical network and demonstrate loss of network diversity when precise correlations are used. While all parts are interesting, there are several claims in each (and the links between them) that are not fully justified.

What is commending about the paper is that it is one of few theoretical/modeling papers that focuses on neural circuit development and it manages to link experimental results to principles of circuit function. The authors apply quite a few modeling approaches ranging from single-neuron models (including building a database of thalamic neurons based on experimental data) and network models. Some of the claims regarding the timescales of correlations (long in development) are unjustified because the authors use a fixed-timescales kernel to compute these correlations and mainly investigate their amplitude or level, not their timescales. It is also not clear how important is the heterogeneity among thalamic neurons. Are the effects on the correlations the result of NMDA currents or the neuronal diversity from their database? Are precise correlations generated because of the diverse/heterogeneous neurons, or because of the levels of convergence? What happens to homogeneous neurons? The authors also propose that precise correlations impair network diversity but never show this impairment directly beyond a diversity of excitatory-to-excitatory connection strengths. If the authors were to clarify these issues then the paper could be a valuable contribution to the field of developmental systems and computational neuroscience.

Reviewer #1 (Public Review):

T2D in youth has been reported to reduce bone mass due to impaired bone anabolism, but the underlying mechanisms are not fully understood. The authors study the relationship between T2DM (Type 2 Diabetes Mellitus) and "skeletal fragility." Specifically, they look at glucose metabolism defects in osteoblasts during T2DM and their impacts on osteoblast activity. The results are novel as they elucidate the effects of low-dose STZ models of T2DM on osteoblast function and the function of osteoblasts from those mice in terms of glycolysis, glucose uptake, and function. Additionally, it covers recovery of glucose metabolic effects through overexpression of Hif1a or Pfkfb3 (targeted to osteoblasts) and metformin treatment. The role of Hif1a and Pfkfb3 in osteoblasts with regard to the rescue of T2DM bone effects is critical to the novelty of the paper and may benefit from being included and emphasized in the title and/or abstract. The study of osteoblasts and their glucose metabolism has been studied but not extensively at the mechanism level. The approach of using a mouse model is good for youth-onset T2D. It would be helpful if the author could include a bit more in the abstract about the critical role of Hif1a and Pfkfb3 in osteoblasts in recovery from T2DM treatment's bone effects in vivo.

Reviewer #1 (Public Review):

In their manuscript, Wang et al. investigate the changes occurring at the CNS borders upon neonatal bacterial meningitis. Both the dural meninges and the leptomeninges display changes. Using single nuc RNAseq and imaging approaches, they show that fibroblasts, endothelial cells and macrophages get inflamed, with an increase vascular leakage. Mechanistically, TLR4 KO but not CCR2 KO or liposome treatment (to deplete leptomeningeal macrophages) was able to rescue the vascular impairment. This is an interesting study that provides useful datasets for the community. However, we recommend several additions regarding data analysis (definitions, single cell, imaging) as well as additional studies (bacterial load, protein validation).

Reviewer #2 (Public Review):

In a neonatal model of bacterial meningitis induced by s.c. injection of E. coli, transcriptional changes were found across all major cell types including endothelial cells, fibroblasts and macrophages. Among macrophages, they describe 2 resident subsets and 2 inflammatory subsets. By immunohistochemistry of arachnoid and dura flatmounts, they show vascular changes upon infection, including clustering of CLDN5 and PECAM1, and disorganized capillary morphology, which was dependent on Tlr4 signaling but independent of arachnoid macrophages.

The manuscript would benefit from rewriting, it is not written in a concise manner and the rationale for experiments, time points for analyses and their conclusions are not clear. The model of s.c. bacterial infection is not well introduced and overall changes in the periphery, survival curves or bacterial counts (in the KO models) in the meninges/brain are not mentioned.

Reviewer #1 (Public Review):

This work puts forward a comprehensive characterisation of colorectal cancer (CCCRC), by classifying it into 4 subtypes with distinct TME features. It uses 10 public databases: 8 microarray datasets for the training of molecular classification and 2 RNAseq for validation (CRC-RNAseq) to identify the 4 subtypes using unsupervised machine learning (consensus clustering). These 4 subtypes were found to be somewhat distinct in terms of immune response and the possibilities for effective treatments. They found that one subtype may be more sensitive to chemotherapy, two to WNT pathway inhibitor SB216763 and Hedgehog pathway inhibitor vismodegib, and one to ICB treatment. They show an association with patient outcome in terms of PFS, validated in the validation cohort. They used histology to correspond the subtypes to known pathological types, as well as investigating their T cell makeup. They also investigated the genetic tumour evolution that may occur between the subtypes. A single-sample gene classifier was put forward as a way of identifying the class of cancer.

The evidence for the main results of the work is convincing, but a few areas need to be clarified and extended.

In the determination of the 4 subtypes (C1-C4) the methodology is clear, and the definition of the training and validation data are clear and well presented. The techniques used are well suited to the problem. The performance of the classification as a predictor of prognosis is presented as KM curves of PFS and OS for the training and validation sets. The training data shows a significant log-rank p-value in both PFS and OS. The validation data shows a significant effect in PFS.

What follows is quite an exhaustive process of finding differences between the cohorts using a multitude of techniques and datasets, including genomics, epigenetics, transcriptomics, and proteomics. These sections are mainly descriptive and do add understanding to the classification, especially with regard to the T-cell populations that are invasive.

Improvements could be made to the latter sections of the main paper. The basis for the potential clinical responses of the subtypes is arrived at via a "pre-clinical model" based on 81 genes. It would benefit from clarification on what genes were used in model training and details of the final model. Similarly the description of the "Single-sample gene classifier" could be enhanced similarly with a better description of which genes are in the final classifier.

Reviewer #2 (Public Review):

This study aimed to classify colorectal cancer (CRC) samples based on the expression of genes in selected gene lists, where the gene lists were chosen to represent aspects of the tumour microenvironment, tumour-associated immune cells, and tumour cells. The resulting clusters were then used to define a classifier, followed by a detailed description of molecular features of the tumours and tumour microenvironments assigned to each cluster. The authors claim this study is more "holistic" than previous work on CRC clustering/classifiers because they aimed to explicitly include additional components of the tumour microenvironment in both the clustering/classifier definition and in the subsequent description of molecular characteristics.

The CCCRC clustering and the resulting classifier presented in this paper are derived from published RNAseq studies. The multi-omics aspect of the work is restricted to smaller sample numbers for which both transcriptomic and another omics dataset were available in public resources and comprises a description or correlative analysis of each omics data type within each of the assigned CCCRC subtypes.

By applying solid computational methods to a compendium of published RNAseq datasets (n~1500 tumours), they found that tumour samples from colorectal cancers clustered into 4 subtypes ("CCCRC" subtypes) on the basis of 61 pre-defined gene expression signatures. These subtypes correlated with but did not correspond to, the previously described Consensus Molecular Subtypes (CMS) of colorectal tumours.

Other types of molecular data were available for some tumours, obtained from the same published resources: whole-slide images, mutations, tumour proteomics, and/or scRNAseq. The authors reanalysed these datasets using standard methods and drew correlations with the CCCRC subtypes they had assigned in this work. To (semi-)quantify immune infiltration characteristics from whole-slide images (WSI), they additionally performed automated segmentation in addition to review by pathologists, which in combination produced a convincing WSI-derived dataset.

In combination with existing CRC classifications, this study could facilitate future biomarker discoveries. This appears to be the authors' main claim, and the data and methods broadly support this claim.

Some aspects of the work need to be clarified:

This work relies on the definition of 4 clusters of CRC tumours based on consensus clustering of the 61 gene lists, which in turn depends on the choice of clustering method and the choice of gene lists. Sufficient detail is provided about the gene lists and resulting clusters, but this paper does not show how robust the 4 clusters are to these choices; for example, the "Energy" gene list appears to be a relatively strong component of clusters C2 and C3.

The authors examined whether their CCCRC classification showed differential disease progression in available retrospective cohorts of people treated with anti-PDL1 therapy. The authors presented this work as "significance of CCCRC in guiding the clinical treatment of colorectal cancer", but the data presented in this section cannot support clinical treatment decisions, which would require prospective studies and clinical trial designs. However, this section is potentially useful for generating hypotheses about potential biomarkers related to the CCCRC subtypes, and might, in the future with additional evidence, contribute to the design of a trial. The authors point out that additional experimental evidence would be required.

Other prognostic or predictive clinicopathological variables for colorectal cancer are not discussed in detail in the present work but are important for further work on the prognostic and predictive value of CRC molecular subtypes and biomarker derivation. Discrepancies in treatment response have previously been observed in separate CRC trials of biologically targeted agents with different chemotherapy backbones and other authors have suggested that treatment interactions with the tumour microenvironment might in part explain these discrepancies (e.g. Aderka (2019) PMID:31044725, and others).

Reviewer #3 (Public Review):

In their study: Comprehensive characterization of tumor microenvironment in colorectal cancer via histopathology-molecular analysis, Wu et al., aim to examine the contribution of the tumour microenvironment (TME) on biological and clinical heterogeneity in colorectal cancer (CRC).

To achieve this the authors use a vast array of publicly available datasets across a variety of biological modalities (transcriptomic, epigenetic, mutational). Using thoughtfully curated genesets the authors classify CRC into 4 holistic comprehensive characterised CRC (CCCRC) subtypes which comprise immune, stromal, and tumour features of CRC biology.

The authors investigate the association of their novel CCCRC subtypes with current "gold standard" classification schemes.

The authors' integration of deep learning methods for HE classification and subsequent association with "Tumor level" CCCRC subtypes is a refreshing addition to the study. Comment on the degree of heterogeneity observed in HE samples and correlation to the heterogeneity of CCCRC subtypes would be a welcomed addition. It is likely publicly available datasets from such platforms as 10X Genomic Visium would be available for this type of analysis.

Whilst one of the main outcomes of the study is the addition of another classification scheme to the field of colorectal cancer, the CCCRC scheme represents a holistic perspective on CRC classification.

The authors provide a welcomed graphical overview of the complex narrative of the study in Figure 7.

The authors focus on the classification of inter-patient heterogeneity and its associated predictive and prognostic utility. There appears to be a significant degree of overlap between immunosuppressive and immune excluded, and proliferative and immuno-modulatory signatures in Figure 1A. One of the major limitations of patient response to treatment is intra-patient heterogeneity, it would be nice for the authors to comment briefly on the degree of intra-patient heterogeneity of the CCCRC subtypes.

Overall the authors succeed in providing a holistic deep characterisation of CRC from the perspective of a variety of biological modalities. The authors provide a novel classification scheme for the field of CRC which demonstrates prognostic and predictive utility, which would benefit from further validation from external datasets. The authors demonstrate a pathway for integration and interpretation of complex high-dimensional data into clinically translatable currency such as the H&E.

Reviewer #1 (Public Review):

This paper investigates the metabolic basis of a node, posterior cingulate cortex (PCC), in the default node network (DMN). They employed sophisticated MRI-PET methods to measure both BOLD and CMRglc changes (both magnitude and dynamics) during attention-demanding and working memory tasks. They found uncoupling of BOLD and CMRglc in PCC with these different tasks. The implications of these findings are poorly interpreted, with a conclusion that is purely based on other work independent of this study. Various suggestions could allow them to place some speculations in line with a stronger interpretation of their results.

This is one of several papers in recent years investigating the metabolic underpinnings of activated (or task-positive) and deactivated (or task-negative) cortical areas in the human brain. In this study, they used BOLD fMRI and glucose PET scan to examine the metabolic distinction of the default node network (DMN), which is known to be deactivated during attention-demanding tasks, with different types of cognitively demanding tasks. Unlike the BOLD response in posteromedial DMN which is consistently negative, they found that CMRglc of the posteromedial DMN (a task-negative network) is dependent on the metabolic demands of adjacent task-positive networks like the dorsal attention network (DAN) and frontoparietal network (FPN). With attention-demanding tasks (like Tetris) the BOLD and CMRglc are both downregulated in DMN (specifically the posterior cingulate cortex, PCC, a task-negative node of DMN), but working memory induces CMRglc increase in PCC and which is decoupled from the negative BOLD response in PCC.

1. These complicated results are the main findings, and to provide a biological basis to these data they rather surprisingly, but without their own experimental evidence, conclude that the negative BOLD and negative CMRglc in PCC during attention-demanding tasks is due to decreased glutamate signaling (which was not measured in this study) and the negative BOLD and positive CMRglc in PCC during working memory is due to increased GABAergic activity (which was not measured in this study). It is rather surprising that without measurement, a conclusion is made which would at best be considered a hypothesis to be tested. Thus, independent of these hypothesized mechanisms, they need to summarize their results based on their own measurements in this study (see 3 for a hint).

2. It is mentioned that the FDG-PET scans allow quantitative CMRglc, both in terms of units of glucose use but also with high time resolution. Based on the method described, it isn't clear how this is possible. Important details of either prior work or their own work have been excluded that show how the time course of CMRglc (regardless of whether it's absolute or relative) can be compared with the BOLD time course. Furthermore, it is extremely difficult to conceive that quantitative CMRglc can be estimated without additional measurements (e.g., blood samples, etc). Significant methodological details have to be provided, which even should make their way to results given the importance of their BOLD-CMRglc coupling and decoupling in the same region.

3. It is surmised that the glutamatergic/GABAergic involvement of these metabolic differences in PCC is from another study, but what mechanism causes the BOLD signal to decrease in both stimuli? This is where the authors have to divulge the biophysical basis of the BOLD response. At the most basic level, the BOLD signal change (dS) can be positive or negative depending on the degree of coupling with changed blood flow (dCBF) and oxidative metabolism (dCMRO2) from resting condition. Unfortunately, neither CBF nor CMRO2 was measured in this study. In the absence of these additional measurements, the authors should at least discuss the basis of the BOLD response with regard to CBF and CMRO2. If we assume that both attention-demanding and working memory tasks decreased BOLD response in PCC in the same way, we have identical dCBF/dCMRO2 in PCC with both tasks, i.e., their results seem to suggest an alteration in aerobic glycolysis with different tasks. With attention-demanding tasks, CMRglc decreases similarly to CMRO2 decreases in PCC, whereas with working memory tasks, CMRglc increases differently from CMRO2 decreases. This suggests PCC may the oxygen to glucose index (OGI=CMRO2/CMRglc) would rise in PCC attention-demanding tasks, but fall in PCC with working memory tasks. This is obviously an implication rather than a conclusion as CBF or CMRO2 were not measured.

4. Given the missing attention that gives rise to the BOLD contrast mechanism, it is almost necessary to discuss the biophysical basis of BOLD contrast and specifically how metabolic changes have been linked to both increases and decreases in neuronal activity in the past. Although this type of work has largely been conducted in animal models, it seems that this topic needs to be discussed as well.

Reviewer #2 (Public Review):

This paper provides an important and insightful investigation into patterns of activations that emerge in external task states. The authors use state-of-the-art methods and novel analytic approaches to establish that deactivations in the default mode network during external tasks are driven by activity in brain regions that are important in the current tasks (such as the visual or dorsal attention networks). It will be important in the future to understand whether this is a symmetrical phenomenon by studying this behaviour in states that maximize activity within the default mode network and also drive reductions in networks that are not relevant to these situations.

Reviewer #3 (Public Review):

The authors report a study where, using multiple datasets with [18F]FDG PET bolus + continuous infusion ("functional PET") and BOLD fMRI data, they re-evaluate the metabolic and hemodynamic properties of the default mode network (DMN) in a task-evoked context, with a focus on posteromedial DMN due to its relevance for across-network integration.<br /> They show how posterior DMN is differently engaged depending on the chosen task: while visual and motor tasks lead to BOLD deactivations and glucose metabolic decrease, specifically in the dorsal posterior cingulate cortex (PCC) area, working memory tasks produce BOLD deactivations but metabolic increases, specifically in ventral PCC, as shown in their previous paper (Stiernman et al. 2021, https://doi.org/10.1073/pnas.2021913118). This aims to solve the controversies elicited by findings of both increased and decreased glucose consumption in the presence of BOLD deactivation in the DMN.

Additionally, they show how task-evoked glucose metabolism in posterior DMN seems to be shaped by that of the corresponding task-positive networks, with a positive link with dorsal attention and a negative link with frontoparietal network metabolism. This is explored using a type of directional connectivity analysis called "metabolic connectivity mapping", drawn from their previous work (Riedl et al. 2016, https://doi.org/10.1073/pnas.1513752113; Hahn et al. 2020, https://doi.org/10.7554/eLife.52443). They go on to speculate that concomitant BOLD deactivation and reductions in glucose expense might relate to decreased glutamatergic signaling, while BOLD deactivations accompanied by increased glucose consumption might depend on increased GABAergic neuronal activity.

This is a relevant topic because it not only shows how the DMN is flexibly engaged in different tasks but also allows us to better understand the complex relationships between BOLD fMRI and [18F]FDG PET signals, which are still not fully characterized to this day. Of course, while in resting state the situation is further complicated by the more uncertain physiological meaning of the resting BOLD signal, task-evoked states are expected to provide a more interpretable intermodal link between metabolism and hemodynamics, due to the known major changes in blood flow, blood volume, and glucose metabolism - which underlie BOLD and [18F]FDG signal changes - in response to neural activation. However, even in task states, there is not always a strong association between the two responses, as previously shown by the authors themselves (Rischka et al. 2018, https://doi.org/10.1016/j.neuroimage.2018.06.079). This is something I think the authors should stress out a little more, as they have previously done (Rischka et al. 2018, https://doi.org/10.1016/j.neuroimage.2018.06.079), both in the introduction and in reference to Figure 1, which shows clear differences between BOLD and [18F]FDG activations/deactivations (e.g., widespread negative responses in the cerebellum for [18F]FDG).

Overall, the analyses reported in the manuscript are simple and seem mostly sound, drawing from well-established methods in PET and fMRI activation studies, with additional approaches previously developed by some of the authors themselves (e.g., "metabolic connectivity mapping", Riedl et al. 2016, https://doi.org/10.1073/pnas.1513752113). Moreover, a clear strength of the paper is the high number of subjects, at least from a PET perspective, i.e., n = 50 for the Tetris task, plus group averages of previously published data for working memory (Stiernman et al. 2021, https://doi.org/10.1073/pnas.2021913118) and motor tasks (Hahn et al. 2018, https://doi.org/10.1007/s00429-017-1558-0).

The conclusions are in line with the results, and, though a little speculative, are potentially relevant for further exploration aimed at characterizing the neurotransmitter pathways underlying positive and negative BOLD and [18F]FDG responses. Moreover, the language is sufficiently clear to allow a proper understanding of the aims and the results, as well as the details of the analyses. As a side note, the title should probably be adjusted to "Task-evoked metabolic demands of the posteromedial default mode network are shaped by dorsal attention and frontoparietal control networks", to emphasize that the findings do not necessarily generalize to the resting state.

In conclusion, I am overall quite positive about this manuscript, which seems to nicely position itself within the existing literature, making some additional contributions.

Reviewer #1 (Public Review):

The authors assessed the association between exposures and obesity by environment-wide and epigenome-wide association studies. The strength of this study is that exposures, body mass index, and waist-hip ratio were measured three times from adolescence to early adulthood, and the associations were repeatedly evaluated. A weakness of this study is that a loose significance threshold was used for the epigenome-wide association study and only a small number of study subjects were measured in early adulthood. Since this is an observational study, the confounding effect should be considered when interpreting the exposures associated with obesity reported in this study.

Reviewer #2 (Public Review):

Since this study is a long-term cohort study in children and adolescents, it is advisable to decide whether to highlight differences by age group or to show consistent effect after exposure. In particular, obesity and related diseases are closely related to socio-economic environmental factors, and its impact might be different according to age (group) at exposure.

The part described in comparison with previous studies is a good attempt. However, some results are consistent with those of previous studies and some are not. This may be related to the time difference in socio-economic environmental factors rather than simply the difference between the West and China (Hong Kong). According to modernization/urbanization, changes in living environment, changes in family relationships, and changes in the care environment can also be factors especially in children.

In studying the effect of environment on gene expression, it can be thought that the influence of genes and the degree of expression might be different depending on the age of the subject (newborn, infant, infant, adolescent, adult) duration of exposure and these still need to be elucidated.

Reviewer #1 (Public Review):

This manuscript presents an exciting set of experiments on the mechanisms through which PSD proteins induce actin bundle formation. The study builds on a previous observation from the Zhang laboratory that phase condensates of six PSD proteins lead to the formation of actin bundles. Here, deep mechanistic analyses determine the necessity of upper vs. lower level PSD proteins for actin bundle formation, identify the domains and interactions of these proteins that are necessary and sufficient to induce actin bundles, and provide a first assessment in neurons of potential roles of the newly discovered mechanisms. The authors find that a patch of arginines in the Homer EVH1 domain plays a central role. Strikingly, no adaptors are needed for PSD condensates to induce actin bundles. This work is important for the understanding of roles and mechanisms of interactions between postsynaptic receptor scaffolds and cytoskeletal elements in dendritic spines. The mechanisms that are uncovered are likely mediators of structural and functional synaptic plasticity.

Overall, the data are rigorously acquired and convincing, the presentation of the findings is logical and clear, and the manuscript is well-written. In my view, a few adjustments in data presentation (quantitative assessment of in vitro experiments, statistical analyses) and additional analyses of existing data (on the localization and roles of transfected Homer proteins in neurons) will improve the paper, but new experiments are not necessary.

Reviewer #2 (Public Review):

In the manuscript, Chen and colleagues reconstituted the minimal system that indicates the coupling of PSD condensates with actin polymerization. While the functional connection between the assembly and dynamics of PSD and actin was known, the molecular mechanism remained elusive. Using a series of elegant biochemical reconstitutions and in-vitro assays complemented with analysis in living cells and primary neurons, the authors characterized whether PSD condensates of Homer-1, Shank-3 and SAPAP/GKAP are sufficient to induce F-actin bundling. Furthermore, they dissected the positively-charged Arg patch within EVH1 domain of Homer to be crucial for the F-actin bundling. Postsynaptic CaMKII and a short isoform of Homer, Homer1a, can both attenuate this process, suggesting various mechanisms neurons can regulate this process. Overall, the topic is timely, the study is well-designed, and the assays are clearly executed. However, several aspects need to be experimentally addressed, including some important controls:

1. It is well established that molecular crowding plays a crucial role in F-actin bundling. For example, in the reconstitution assays in Fig.1, the authors use 10 µM of each component of PSD (total of 60 µM), to which 5 µM actin is added. Yet, in their control assays (Supp. Fig. 1), only 10 µM of each protein was checked with the same amount of actin. A control is missing where the total protein crowding would be preserved, for example, by adding BSA or protein to mimic non-specific protein crowding.<br /> 2. Is the F-bunding observed under these physiological ratios of PSD proteins and actin? For instance, a recent quantitative study (PMID: 34168338) suggests actin:Homer-1 is 200:1 or 100:1, which is in stark difference from the 1:2 molar ratio used in the study. The protein concentrations (molar ratios) need to match the physiological.<br /> 3. In the cell migration assays, it is somewhat unclear to what extent the interaction is direct. For instance, co-sedimentation at ultra-speed (100,000 g) was used to suggest a direct binding of EVH1-GNC4 fusions (Homer1, Enah) with F-actin. The control that needs to be included is a protein known not to bind to F-actin incubated under the same conditions (salt concentration, duration of incubation) and spun down at 100,000xg. This is important to exclude that the tested proteins non-specifically entangle into F-actin without specifically binding to it, particularly at such high speed.<br /> 4. The imaging assay in hippocampal neurons uses an increased spine head size as a proxy for F-actin bundling. However, one needs to be careful as the baseline includes soluble mCherry, which is both much smaller in size and does not specifically enrich the spines. The image of Homer 1 R3E shows overall lower localization at the spines. Thus, one cannot exclude that the spine enlargement upon overexpression of Homer 1 wt and R3E+EN is not primarily driven by their overall enrichment in the PSD phase. A suitable control for this assay would be mCherry-tagged PSD95, which would localize to the spines yet is not directly involved in F-actin bundling.

Reviewer #1 (Public Review):

This study presents a valuable comparison of fibre orientation estimates from three different modalities: diffusion MRI, scattered light imaging, and x-ray scattering. The comparison is interesting as each modality is sensitive to different aspects of tissue microstructure - water anisotropy, micron-scale structural coherence, and myelin lamella respectively. Where scattered light and x-ray imaging can be only applied ex vivo, diffusion MRI has in vivo applications but suffers from being an indirect estimate of the microstructure of interest. By acquiring all modalities in both a vervet monkey and human brain sample, the authors provide quantitative, pixel/voxel-wise comparisons of fibre orientation estimates within the same tissue samples. The authors show convincing agreement in fibre orientations from all three methods, giving confidence in the fidelity of the methods for neuroanatomical investigations. Differences are also observed: SLI is shown to have less reliable estimates of fibre inclination, and the CSD analysis presented overestimates the number of crossing fibre populations when compared to the microscopy methods, particularly in single fibre regions such as the corpus callosum, a known artefact in some diffusion analyses.

In the current PDF, it is very difficult to see fibre orientations in figures due to low resolution, limiting the reader's ability to assess the results. Higher-resolution images would provide more information and easier comparisons.

The methods are generally clear though some additional information is needed: 1) to specify the resolution that the orientations are compared in each figure and how data was up-/down-sampled for these comparisons respectively. For example, each SAXS pixel contains many SLI pixels. It is currently unclear whether the mean SLI orientation from a neighbourhood is equivalent to the SLI compared, or whether a comparison was made for each SLI pixel. Similarly, for the dMRI-microscopy comparisons. 2) I also could not follow why two SLI methods are presented in the methods: SLI scatterometry relating to Figure 2, and angular SLI relating to all other results. Further clarification is needed. 3) Since the quality of the data co-registration can strongly impact pixel/voxel-wise comparisons, quantification of the registration accuracy or overlays demonstrating the quality of the co-registration would be valuable.

A primary weakness of the work as a diffusion MRI validation study is that though diffusion MRI supports many different models to extract fibre orientations with different outputs, here only a single model is compared to the microscopy data, which may affect the generalisability of the results. Further, it only compares the primary orientations from the diffusion MRI and does not consider each fibre population's magnitude (density of fibres) or the orientation dispersion, both of which can influence downstream analyses.

The paper could be strengthened by a more detailed discussion on the differences between the imaging modalities - e.g. in terms of imaging resolution, signal-generating mechanisms, and sensitivity to specific aspects of the tissue microstructure - and how these differences may limit their application to specific neuroanatomical investigations, or ability to validate one another. For example, the microscopy sections are 80 microns thick whilst the diffusion voxel is 200 microns. I expect this could contribute to the difference in the number of fibre populations per voxel.

The hypothesis that dMRI signal contributions from extra-axonal water result in additional fibre populations could be investigated by running CSD on both low and high-b-value data (for example using the openly available MGH dataset, Fan 2016) where fewer secondary fibre populations should be observed at high b-value.

Reviewer #2 (Public Review):

This work is a cross-validation of an x-ray tomography technique (SAXS) and an optical microscopy technique (SLI) for imaging axonal orientations ex vivo. These innovative methods were introduced in recent papers by the authors, who have teamed up here to compare them side-by-side on the same tissue samples for the first time. The two methods are both label-free (do not require staining) and they are quite complementary. SAXS can provide full 3D orientation measurements on intact tissue, but it operates at a mesoscopic resolution and requires access to a synchrotron. SLI can measure the orientations of multiple fascicles per voxel at a microscopic resolution and relies on more widely accessible equipment, but its accuracy suffers for fiber orientations perpendicular to the imaging plane and it requires tissue to be sectioned before it is imaged. Therefore it makes a lot of sense to explore the complementary strengths of these two techniques, and to use one to "fill in the blanks" of the other. The paper also compares the orientation measurements obtained with SAXS and SLI to those obtained with diffusion MRI. The latter provides only indirect measurements based on water diffusion, at a mesoscopic resolution somewhat lower than that of SAXS, but has the benefit of being feasible in vivo.

A limitation of this study is that conclusions on the comparison between SAXS and SLI are drawn from only 2 sections of a partial monkey brain sample and 2 sections of a partial human brain sample. Conclusions on diffusion MRI are drawn only on the 2 human sample sections. This is particularly an issue for the comparison to diffusion MRI, as the diffusion MRI voxels are wider than the section thickness, hence one cannot preclude that any orientations detected with diffusion MRI but not with SAXS and SLI come from the portion of the voxel that is missing from the corresponding SAXS/SLI section.

The stated aim of the paper is to provide a framework for combining the complementary benefits of SAXS and SLI, rather than simply presenting the results of a cross-validation study. This is a significant and ambitious aim. However, in order for this to serve as a framework, there would have to be clear prescriptions for how researchers interested in obtaining ground-truth measurements of axonal orientations would do so by using these two methods in tandem. This is not adequately developed in the paper in its present form. For example, the results show reasonable agreement between SAXS and SLI orientations when fibers lie within the SLI imaging plane and decreasing agreement for fibers with increasing through-plane inclination. How would the two methods be combined in voxels where they disagree? Would one use SLI orientations in voxels with fewer through-plane fibers and SAXS orientations in voxels with more through-plane fibers? How would voxels be assigned to each category? How would the orientation vectors from the two modalities be composed and how would the resolution difference between the two be handled? When the through-plane measurement of SLI is unreliable, is its in-plane measurement still reliable? That is if there were one mainly in-plane and one mainly through-plane fiber population, would the orientation of the former still be measured correctly by SLI? There is also considerable agreement reported here between through-plane orientations obtained with SAXS and diffusion MRI. Would this mean that diffusion MRI itself could be used to supplement SLI with through-plane orientations? Any clear set of prescriptions along these lines would represent a framework for imaging orientations by combining modalities. This, however, would require detailed steps for how to perform the combination and use the multi- vs. uni-modal framework to reconstruct connectional anatomy.

A key advantage of SAXS is that it can be performed on intact samples, i.e., before any nonlinear distortions of the tissue are introduced by sectioning. Thus it can provide an undistorted reference, with contrast on axonal orientations that would be absent in, say, a structural MRI of comparable resolution. This contrast could be used to drive registration of the distorted SLI sections to an undistorted SAXS volume, and therefore is a key way in which the two techniques can complement each other. Here, however, this is not explored, as SAXS is performed after sectioning. It is not clear if this is the authors' prescription for how a combined SAXS/SLI framework would be implemented, or if it was done specifically for this study. First, it would seem that SAXS on the intact sample would be lower maintenance, requiring less setup time and hence potentially less overall beamtime than performing SAXS on each section separately. This would make it more practical for routine deployment beyond a few sections. Second, because the SAXS data are now nonlinearly distorted, they cannot be affinely aligned to the MRI volumes. While, in principle, performing both SAXS and SLI on the sections may facilitate the comparison between the two, having to unmount, rehydrate, and remount the sections in between may negate this advantage, as now there is no guarantee that SAXS and SLI can be affinely registered to each other. Here all these registration steps are performed affinely, so it is unclear to which extent the computed errors between modalities are characterizing the inherent limitations of the respective contrasts, or limitations of the registration technique. Some of the alignment is performed manually, for example, specific regions of the images are realigned by hand, and the slice of the diffusion MRI volume that is aligned to the SAXS/SLI sections is chosen by hand. Again, for this to serve as a framework that can be deployed on whole samples, there would have to be clear prescriptions for how to perform these steps robustly, how to ensure that the MRI can be acquired in a coordinate frame parallel to the sections, etc.

Finally, the paper puts forth a general conclusion that diffusion MRI overestimates the number of fiber populations per voxel, on the basis of small ODF peaks appearing perpendicular to the main ODF peaks. Of all conclusions in the paper, this is the least convincingly supported by evidence. First, these small perpendicular peaks are a known artifact, which would be typically eliminated by ignoring ODF peaks below a certain amplitude, a common practice in diffusion tractography algorithms. The authors refrain from using an amplitude threshold, with the rationale that it may also remove true diffusion orientations. However, they apply a threshold when they detect SLI peaks (a rather stringent 8% of the maximum). Second, the explanation that these artifactual peaks may appear due to vessel walls is not convincing. Vasculature is sparse. A single vessel wall will not impact the diffusion signal in the same way as a bundle of parallel axons. In an axon bundle, water molecule displacements are restricted in all directions except parallel to the axons. A single vessel wall in a voxel will not have the same effect on displacements (which are much smaller than the size of the voxel). From Figure 5, it looks like there would be at most 1-2 of these vessels in a diffusion MRI voxel, and they would not be in all voxels. This cannot explain the widespread appearance of these small artifactual peaks. Third, many ODF reconstruction methods have parameters that can be adjusted to make these artifactual peaks more or less prominent. The default parameters may be optimal for in vivo but not ex vivo data, due to the effects of fixation. In light of these concerns, I would caution against making such a general statement about all diffusion MRI in the human brain, especially on the basis of a single diffusion reconstruction method applied to a single location in one brain.

Reviewer #1 (Public Review):

The study tackles the topic of male harm (sexual selection favoring male reproductive strategies that incur a reduction of female fitness) from an interesting angle. The authors put emphasis on using wild-collected populations and studying them within their normal thermal range of reproductive conditions. Where previous studies have used temperature variation as a proxy for stressful environmental change, this approach should instead clarify what can be the role of male harm on female fitness in natural conditions. A minor caveat regarding this point is the fact the polygamy treatment also has a heavily male-biased sex ratio (3:1). The authors argue that this sex ratio is within the range of normal variation in that species, but it is likely that the average is still (1:1) in natural populations and using a male-biased sex ratio could magnify the intensity of male harm. This does not undermine the conclusions regarding the temperature sensitivity of sexual conflict but should be acknowledged.

The authors find that varying temperature within a range found in natural conditions affects the reproductive interactions between males and females, particularly through male-harm mechanisms. Male harm, measured as a reduction in lifetime reproductive success (LRS) from monogamy to polygamy settings is present at 20C, stronger at 24, and absent or undetectable at 28C. Female senescence is always faster in the polygamy mating systems as compared to monogamy, but the effect appears strongest at 20C. Mating behaviors of males and females in these different settings are used to attempt to uncover underlying mechanisms of the sensitivity of male harm to temperature.<br /> A weakness of the manuscript in its current form is the lack of clarity about the experimental design, which makes understanding the results a long and involved procedure, even for someone who is familiar with the field. If the authors consider revising the manuscript, I suggest giving a better overview of the experimental design(s) earlier in the manuscript, perhaps supported by a diagram or flowchart. I also suggest structuring the results better to aid the reader (e.g., make clearer distinctions between results that come from the different experiments). Finally, some additional figures and statistical tests corrected for multiple testing would help get a better feel of some aspects of the dataset.

I believe that the conclusions are generally justified and the results overall convincing. Overall, this is an impressive study with a lot of dimensions to it. Its complexity is a challenge and may require additional effort from the authors to make it easier to access. The core of the question is answered by LRS measures, but the authors have also provided a wealth of behavioral data as well as other fitness components. The manuscript could be greatly improved by putting more effort into linking the different metrics together to track down potential mechanisms for the observed variation in male-harm-induced reduction in female LRS. The discussion would also benefit from considering the female side of the sexual conflict coevolution arms race.

Reviewer #2 (Public Review):

Londoño-Nieto et al. investigated the influence of temperature on the form and intensity of sexual conflict in Drosophila melanogaster. They aimed to test the effect of naturally occurring temperature fluctuations on a wild population of Drosophila while disentangling pre- and post-copulatory episodes of sexual conflict. To this end, they exposed females to males under monogamy or polyandry, hence manipulating the degree of male harm experienced by females. The effect of temperature was explored by exposing these groups to 20, 24, or 28{degree sign}C. They found that female fitness suffered from male harm most at 24{degree sign}C and less at the other two temperatures. Interestingly, pre- and postcopulatory episodes of sexual conflict were affected differently by temperature. Overall, these data suggest that the relationship between sexual conflict and temperature can be strong and complex. Hence, these results can have important implications for the impact of sexual conflict on population viability, especially in light of the climate crisis.

This paper tackles a highly relevant question using an established model organism for sexual conflict and contains a rich dataset obtained using a series of carefully planned experiments and analysed in an appropriate way. Importantly, the authors used biologically meaningful temperatures and mating treatments, which increases the relevance of the data. The main conclusions are well supported by the data. Nevertheless, the devil is in the detail, and given the way the authors frame their study (i.e. testing a natural population under naturally occurring temperature fluctuations) and their results (i.e. sexual conflict is buffered by temperature effects in the wild) there are some limitations to be considered:

1) The authors frame their study as addressing the question of how sexual conflict reacts to naturally occurring temperature fluctuations in the wild. Nevertheless, the population used in this experiment had been kept for nearly 3 years in the laboratory prior to the experiment. Importantly, the authors ensured that the laboratory population maintained genetic diversity, by regularly crossing wild lines into it. Nevertheless, this population remained for some time in the laboratory under standardized conditions. The applied temperature fluctuations are in a biologically meaningful range (though only during the reproductive season), but it remains unclear if the applied fluctuations were in a standardized way (i.e. pre-programmed) or included random fluctuations (i.e. a more natural setting). This laboratory setup has certainly clear advantages, for example, it enables the exclusion of any effects other than the temperature on sexual conflict. Nevertheless, how these will then ultimately play out in the wild could be a different story.

2) The authors highlight clearly that temperature fluctuations in the wild might play an important part in how sexual conflict plays out in natural populations. This very interesting and highly relevant point might lead the reader to assume that this is what was actually tested in the experiment. Nevertheless, in the experiments, different constant temperatures were applied to the flies, while only the stock population was kept at a fluctuating temperature regime. Hence, the influence of fluctuations during episodes of sexual conflict remains untested. While the present data show that sexual conflict can be modulated by temperature, the effect of naturally occurring fluctuations on the net cost of sexual conflict to a population remains unclear.

3) The authors conclude that the effect of sexual conflict can be buffered by temperature in the wild. In general, I agree with this, although a more conservative way of framing this would be to say that temperature modulates or moderates sexual conflict instead of buffers it. If there really is a buffering effect of temperature in the wild remains to be tested, I believe. This will depend on how actual changes in temperature affect this dynamic (see point 2). In addition, I think another interesting open question is what the mechanism behind the observed differences might be. Are male and female interests really more aligned at different temperatures (i.e. males plastically reduce harm)? This would really buffer the harm of sexual conflict at those temperatures. Nevertheless, alternatively, males might not be perfectly adapted to manipulate the female optimally at lower or higher temperatures. This would mean that if the temperatures change, males might evolve to increase the manipulation of females, and hence the scope for sexual conflict might not change in the end under this scenario. Nevertheless, as the authors themselves state: 'An intriguing possibility is thus that SFPs are more effective at lowering female re-mating rates at warm temperatures, thereby buffering these costs.' Therefore, a temperature-dependent increase in the effectiveness of male manipulation might counterintuitively reduce sexual conflict in this species.

4) In the end the authors argue that the climate crisis might have 'unexpected positive consequences via its effect on male harm'. Sexual conflict is indeed widespread, but it takes many different forms (as has been nicely described in the introduction of this paper). Because the studied system seems to be quite a specific example, it is questionable how far spread this phenomenon is in nature. In addition, it remains unclear how male harm will evolve in response to the climate crisis (see point 3). Finally, the relative fitness of females increased in the present experiment, as the tested range was within the reproductive optimum of the species. Nevertheless, the relative importance of the positive effect of sexual conflict on fitness outside of optimal temperatures seems questionable.

Nonetheless, I believe these results to be of exceeding interest to the scientific community and of importance to the field. It opens up many potential research directions and adds further data to the fascinating field of sexual conflict, SFPs, and male harm in Drosophila.

Reviewer #3 (Public Review):

In this paper, the authors explore the effects of the environment, specifically temperature, on male harm to females. Male harm is the phenomenon where males reduce female fitness in polyandrous systems, where a single female may mate with multiple males. The selection of males to increase their reproductive success in male-male competition can lead to genetic conflict that increases male fitness at the expense of female fitness. Typically, male harm has been studied in single environments under optimal conditions. However, there is an increasing focus on the effect of the environment on fitness costs of male harm to females, as a way to better understand the effect of male harm on population fitness in more realistic ecological contexts. In this paper, the authors add to these studies by exploring the effect of temperature on male harm and female fitness, using the fruit fly Drosophila melanogaster, as a model system. They find that temperature affects the impact of male harm on female fitness, with male harm having the greatest effect at 24˚C relative to 20˚C and 28˚C. The authors then go on to disentangle how temperature affects the various components of male harm that impact female fitness (e.g. harassment, ejaculate toxicity). The paper demonstrates that male harm depends on ecological context, which has implications for understanding its impact on population fitness under realistic ecological scenarios, particularly with respect to climate change.

The strength of the paper is that it demonstrates that male harm (presented as differences in female life reproductive success between monogamous and polyandrous matings) changes with temperature. The authors dissect this general observation by showing that different aspects of pre-copulatory reproductive behavior, for example, male-male aggression, copulation rate, and female rejection rate, also change with temperature. Further, they demonstrate that correlates for male ejaculate quality also change with temperature, suggesting that temperature also affects post-copulatory mechanisms of male harm.

The weakness of the paper is that the method and results section are difficult to follow, which negatively impacts the interpretation of the data. The experiments are complex and need to be for what the authors are studying. Nevertheless, the paper is written in a way that makes it challenging for the reader to fully understand how precisely the experiments were conducted. Further, the authors do not explain clearly how some of the experiments relate to the phenomenon ostensibly being assayed. For example, a more detailed explanation of why mating duration and remating latency are assays for ejaculate quality in the context of sperm competition would be very helpful in interpreting the data. Further, a clearer explanation of the statistical analyses conducted

Reviewer #1 (Public Review):

The authors generated a detailed single-cell RNAseq dataset for the microfilariae stage of the human nematode parasite Brugia malayi. This is an impressive and important achievement, given that it is difficult to obtain sufficient material from human parasites and the microfilariae are protected by a chitin sheath. The authors collected microfilariae from jirds and carefully worked out a protocol of digestion, dissociation and filtering, to obtain single-cell material for sequencing.

The single-cell resource was complemented with a dataset derived from FACS-sorted large secretory cells, allowing the identification of several specific proteins expressed in this unique microfilarial cell-type important for immune evasion.

The authors also generated new data for secretory cells of Caenorhabditis elegans and concluded that there is limited similarity between the composition of Brugia and C. elegans secretory cell types.

In a further set of experiments, the authors analysed gene expression changes in dissociated Brugia cells to the commonly used anthelminthic drug ivermectin. This revealed specific gene expression changes across various cell types, providing new insights into how the drug effects the parasite.

Finally, the authors developed a method to keep dissociated Brugia cells alive in culture for two days. This method will aid cellular studies of this parasite.

The authors may want to explore the new resource in more detail to reach more specific biological conclusions. For example, the authors mention that the large secretory cells are critical to parasite survival and immune evasion. With a more complete list of genes expressed in these cells the authors could try to reach more specific conclusions or predictions. Are there newly identified secreted factors that could contribute to immune evasion? It would be important to read in more detail about such proteins (including an analysis of the sequences and phylogenies), especially if the authors could identify new candidates as potential vaccine or diagnostic targets. Likewise, can the data be used to understand in more detail the mechanism of immune evasion or ivermectin action?

The authors searched for known secreted proteins, including antigens, vaccine targets, and diagnostic markers and mapped the expression of these to the single-cell atlas. It is not clear from the paper how comprehensive previous studies to identify secretory proteins were. With the new resource in hand, the authors could look at all secreted proteins (with a signal peptide) expressed in the ES and other cells. The paper would benefit from a more comprehensive overview of the classes of secretory proteins and their expression.

The authors show that an abundance of C2H2 transcription factors is localizing almost exclusively to the secretory cells. It would be useful to see a classification of these proteins and phylogenetic analysis relating them to C2H2 from C. elegans and other animals.

In general, a more detailed bioinformatic analysis of secretory products and more discussions of potential functions (e.g. serpins etc.) would make the paper more interesting and could stimulate more mechanistic thinking.

Reviewer #2 (Public Review):

The overall objective of this paper is to characterize the cells that are responsible for producing the secretions of the parasitic larvae, Brugia malayi. This parasite is a human pathogen that is one of three responsible for lymphatic filariasis/elephantiasis a disease that threatens half of the world's population. The specific focus of this work is protein secretions made by the parasites. In general, it is well-known that parasitic worms can manipulate and evade host immune immunity via secreted products. Studies have focused on the activities of these secretions and specific molecules. What is lacking is a detailed description of the identity and anatomical location of the cells that produce them. This is especially important as these cells are the target of different classes of anthelminthic drugs. This knowledge could allow new strategies to target these pathogens and to better understand the mechanism of actions.

To better understand this important topic, this manuscript describes a method to dissociate cells of the pre-larval stage (microfilariae) of the human parasitic filarial nematode, Brugia malayi. This method is then used to create an atlas of cells based on the expression profiles of individual dissociated cells. Cells are grouped into clusters with similar patterns of expression using single-cell mRNA sequencing analysis pipelines and the clusters are defined by using a combination of known functionalities based on the well-established, free living, soil nematode, C. elegans, and different functional classifications based on genes of interest. These include known antigens as well as targets of 3 classes of anthelmintic molecules. Using the scRNA-seq data, clear hypotheses can be made about ion channel and structural protein composition, the putative targets of the anthelminthics. Finally, it is proposed that the dissociated cells can be cultured which can facilitate future studies since cell lines or primary cultures of cells from filarial worms are not available.

This paper represents a huge undertaking on an important and understudied area. The authors have taken on a major challenge to gain novel insights, and to provide data and protocols for the field to use. The data are well-presented and support the conclusions of the work. The authors have broadly achieved their goals and the data generated and methodology will be important for the community.

Reviewer #3 (Public Review):

Henthorn and coworkers obtain a single cell atlas of the parasite nematode Brugia malayi to search for excretory secretory products. These are involved in therapeutic responses but it is unknown what are the cell types that express them. In fact this seems as an ideal question to be addressed with single cell transcriptomics. The authors analyse their dataset, coming to the conclusion that many of these ES products are expressed broadly throughout the parasite, including secretory and non secretory types. This would be a nice conclusion if supported by the data. Then they go on to compare responses to exposure of ivermectin at the single cell level.

I must praise the attempt of using single cell transcriptomics to examine this question. These relatively novel methods have been so far used to collect information of cell types, but have immense potential for the investigation of important questions in neglected diseases like this. Fundamental knowledge about the biology of Brugia malayi and the tissue and cell types present are key to understanding their pathogenesis and advancing new therapeutic options. The authors start this research project with the right model and right technique.

My major concern is the quality of their single cell data. The authors perform no FACS or other methods to clear their suspension from cellular debris. This arises from all cell types, and then gets encapsulated with single cells in the droplet-based single cell transcriptomic process. Then, all cell barcodes receive genes from a single cell but also from a collection of cellular debris particles (arising from all other cell types). This, and only this, can in principle explain one of the major findings in the abstract: secreted antigens are expressed broadly in many cell types. This same caveat might explain their finding of pan neuronal markers broadly expressed - conceptually very similar to what the authors hold for secreted antigens, but that the authors only mention briefly and do not explain.

Reviewer #1 (Public Review):

The manuscript Role of cytoneme-like structures and extracellular vesicles in Trichomonas vaginalis parasite: parasite communication by Salas N et al is an interesting manuscript with novel findings, clear strategies, and fine design of experiments. Despite the quality of the manuscript, it must be improved in order to deliver the best message in the area of cellular biology and molecular parasitology.

Reviewer #2 (Public Review):

The manuscript is rigorous and clearly written; the experiments are well described, and the conclusions are consistent with the experimental results. Particularly interesting are the data demonstrating the role of cytoneme-like structures. The microscopy images supporting the experimental data are clear and fascinating.<br /> The work is, in my opinion, well conducted.

Reviewer #3 (Public Review):

In this manuscript, the authors (Salas et al.) have investigated the communication strategies among various strains or species of Trichomonas vaginalis. T. vaginalis is a parasite that is responsible for non-viral sexually transmitted infections, worldwide. The authors have demonstrated that highly adherent parasite uses cytoneme-like membrane structures and extracellular vesicles (EVs) to communicate with poorly adherent isolates and mount a stronger response to hosts.

The major strength of this work is the use of state-of-the microscopic techniques to analyze cytonemes and EVs. However, the weakness is the experiments shown in the manuscript are more descriptive than mechanistic. The significance of this work is high because it demonstrates the presence of a unique communication strategy in Trichomonas vaginalis. Trichomonas uses cytoneme-like elongated membrane structures and extracellular vesicles to interact with each other and induce a robust pathogenic response in host cells.

The authors have used state-of-the-art cell biology techniques to conduct the study and the data analysis is solid.

Overall, the experiments are solid and the authors were able to accomplish their objectives of demonstrating parasite communication in T. vaginalis.

Reviewer #1 (Public Review):

The authors of this study exerted a variety of laboratory experiment methods and in silico analysis of expression data, and showed the differentiated aspects of the protein functions of the product of the duplicated genes eS27 and eS27L as well as their redundant aspects. These proteins are components of the cellular machinery for translation, namely 'readout' of the genome, in eukaryotes. This study provides a valuable test case of examining why seemingly redundant genes that underwent gene duplication during evolution have been retained in the genomes of many present-day organisms.

Reviewer #2 (Public Review):

In this manuscripts, the authors investigated differential role of two closely related proteins, S27 and S27L , which are one of the subunits of ribosome. Ribosomes containing each protein associate with a distinct set of mRNAs, suggesting that ribosomes in the cells play distinct roles depending on which subtype of S27 subunits they contain. The authors also performed functional analyses using mutant mice, and demonstrated that functions of S27-containing ribosome can be rescued by S27L-containing ribosome and vice versa. These findings provide new experimental insights into the origin of family genes fixed during the course of evolution.

Reviewer #3 (Public Review):

A current topic in the translational control field revolves around the idea that "the ribosome" is not a singular monolith machine, but rather that there are a variety of ribosomes, some with specialized functions. The presence of evolutionarily conserved ribosomal protein gene paralogs provides a platform for testing this idea. Presumably, if a paralog is required to translate a specific mRNA or class of mRNAs in a cell or organ type specific manner, it's loss should generate an observable phenotype. In this study, Xu and colleagues exploit the evolutionarily conserved eS27 and eS27L proteins to probe this hypothesis. Technically, the work is on the cutting edge of the field. Advanced genetic engineering techniques were used to generate mice lacking either paralogous gene, to create reciprocal swaps of each coding sequence into the other locus, and even to create genetically homogenous mice. The authors also use state of the art molecular biology methods, e.g. paralog-specific ribosome profiling, to search for differences in the mRNAs translated by ribosomes containing either of the two homologs.

Some phylogenetic evidence was presented suggesting that the paralogs first appeared during a gene duplication event in vertebrates: however, only and bird and one amphibian are represented. It is recommended that this analysis go deeper, parsing the amphibians and fish more finely. Although not identifying evidence for specialized ribosomes, they did find that it is essential that at least two copies of eS27 or eS27L are retained. Interestingly, the embryonic lethality of truncation alleles of either of the two paralogs result manifested at different stages of development, pointing to some kind of functional differences during development. The finding that eS27L containing ribosomes are more prevalent in lactating mammary gland and liver is an interesting observation, and that such ribosomes are preferentially associated with mRNAs involved in the cell cycle. From this the authors conclude that the data support subfunctionalization model of eukaryotic ribosomal protein S27 evolution rather than a specialized ribosome model. I also note that this is the most comprehensive and technologically advanced study of its kind in the translational control field and that it represents a significant contribution to the field of evolutionary biology.

Reviewer #1 (Public Review):