Reviewer #1 (Public Review):

In this study, the authors confirm and extend their previous work demonstrating that ApoE4, a major risk for for Alzheimer's disease, impairs endocytic recycling of membrane receptors, leading to synaptic dysfunction. Previously, the authors demonstrated in vitro that upon binding to ApoER2 at the plasma membrane and internalization, ApoE4 along with ApoER2 and glutamate receptors become trapped in the early endosome due to the similarity between the isoelectric point of ApoE4 and the pH of early endosomes. Enhancing acidification by inhibiting NHE6, a proton leak channel in the early endosome, restored vesicle recycling and improved synaptic plasticity in AD extract-treated hippocampal slices from ApoE4-KI mice. In the current study, the authors create and use novel NHE6 germline knockout and conditional knockout mouse lines to reduce NHE6 expression and enhance acidification of early endosomes. They confirm their previous findings and also extend their work by crossing NHE6 KO or cKO mice to a knockin, humanized APP mouse that expresses mutant human amyloid precursor protein under control of the endogenous APP promoter, alone or crossed with ApoE4-KI mice. In both cases, reduction of NHE6 resulted in increased Iba1-expressing microglia and GFAP-expressing astrocytes as well as a reduction in plaques. Together these findings highlight the importance of ApoE4's detrimental effect on endosomal recycling in vivo, with consequences for accumulation of AD-related pathology. While the studies presented are well-done and robust, some mechanistic links are missing, which make it difficult to fully support the conclusions drawn.

Major strengths of this paper include:<br /> 1. The creation of novel germline KO and conditional KO NHE6 mice allow for a number of in vivo investigations that would be difficult to complete otherwise. These mice represent a valuable resource to the field.

2. Use of the NHE6-KO and cKO mice to confirm the previous findings (that used pharmacological inhibition of NHE6) that enhancing endosomal acidification ameliorates ApoE4-induced deficits in vesicle cycling and synaptic plasticity in vivo. In addition, the finding that NHE6 ablation in APP and APP/ApoE4KI mice robustly reduced plaque accumulation is striking.

3. The demonstration that BACE-mediated production of APP CTFs is unaltered by NHE6 ablation supports the conclusion that the reduction in plaques is unlikely due to reduction in Abeta generation, but more likely due to clearance of Abeta.

Weaknesses of this paper include:<br /> 1. The authors conclude that reducing NHE6 clears plaques by activating resident microglia, shifting them from a dormant state to a damage-associated activated state that phagocytoses Abeta plaques. However, there is no data presented to demonstrate this. In a supplemental figure, the authors show there are more Iba1-expressing microglia and GFAP-expressing astroctyes in APP mice and in APP/ApoE4KI mice in which NHE6 has been ablated, but this does not prove that this is the mechanism by which plaques are cleared.

2. The mechanisms underlying the increase in Iba1 and GFAP are not clear. The authors cite a previous paper from another group that demonstrated in their own NHE6 KO mice, there was an increase in GFAP and in activated microglia expressing CD68, which may relate to the cell loss in hippocampus and other brain regions documented in those mice. However, in the current study, the authors indicate that in their NHE6 KO lines, there is no overt cell loss. It is therefore unclear how reductions in NHE6 expression lead to microglial/astrocyte activation. This is an important point to work out, since the authors conclude that it is microglial activation that is responsible for the reduction in Abeta plaques.

3. What might be some of the underlying explanations be for the differences between the published NHE6-KO mice, which has fairly widespread cell loss, and the current KO mice generated in this paper, which did not exhibit noticeable cell loss in brain regions other than the cerebellum?

4. There are a number of mechanistic links that have not been worked out, as indicated above. Until these links are identified and characterized, a number of the conclusions drawn by the authors are not yet supported.

Reviewer #1 (Public Review):

The authors sought to establish a standardized quantitative approach to categorize the activity patterns in a central pattern generator (specifically, the well-studied pyloric circuit in C. borealis). While it is easy to describe these patterns under "normal" conditions, this circuit displays a wide range of irregular behaviors under experimental perturbations. Characterizing and cataloguing these irregular behaviors is of interest to understand how the network avoids these dysfunctional patterns under "normal" circumstances.

The authors draw upon established machine learning tools to approach this problem. To do so, they must define a set of features that describe circuit activity at a moment in time. They use the distribution of inter-spike-intervals ISIs and spike phases of the LP and PD neuron as these features. As the authors mention in their Discussion section, these features are highly specialized and adapted to this particular circuit. This limits the applicability of their approach to other circuits with neurons that are unidentifiable or very large in number (the number of spike phase statistics grows quadratically with the number of neurons).

The main results of the paper provide evidence that ISIs and spike phase statistics provide a reasonable descriptive starting point for understanding the diversity of pyloric circuit patterns. The authors rely heavily on t-distributed stochastic neighbor embedding (tSNE), a well-known nonlinear dimensionality reduction method, to visualize activity patterns in a low-dimensional, 2D space. While effective, the outputs of tSNE have to be interpreted with great care (Wattenberg, et al., "How to Use t-SNE Effectively", Distill, 2016. http://doi.org/10.23915/distill.00002). I think the conclusions of this paper would be strengthened if additional machine learning models were applied to the ISI and spike phase features, and if those additional models validated the qualitative results shown by tSNE. For example, tSNE itself is not a clustering method, so applying clustering methods directly to the high-dimensional data features would be a useful validation of the apparent low-dimensional clusters shown in the figures.

The authors do show that the algorithmically defined clusters agree with expert-defined clusters. (Or, at least, they show that one can come up with reasonable post-hoc explanations and interpretations of each cluster). The very large cluster of "regular" patterns -- shown typically in a shade of blue -- actually looks like an archipelago of smaller clusters that the authors have reasoned should be lumped together. Thus, while the approach is still a useful data-driven tool, a non-trivial amount of expert knowledge is baked into the results. A central challenge in this line of research is to understand how sensitive the outcomes are to these modeling choices, and there is unlikely to be a definitive answer.

Nonetheless, the authors show results which suggest that this analysis framework may be useful for the community of researchers studying central pattern generators. They use their method to qualitatively characterize a variety of network perturbations -- temperature changes, pH changes, decentralization, etc.

In some cases it is difficult to understand the level of certainty in these qualitative observations. A first look at Figure 5a suggests that three different kinds of perturbations push the circuit activity into different dysfunctional cluster regions. However, the apparent spatial differences between these three groups of perturbations might be due to animal-level differences (i.e. each preparation produces multiple points in the low-D plot, so the number of effective statistical replicates is smaller than it appears at first glance). Similarly, in Figure 9, it is somewhat hard to understand how much the state occupancy plots would change if more animals were collected -- with the exception of proctolin, there are ~25 animals and 12 circuit activity clusters which may not be a favorable ratio. It would be useful if a principled method for computing "error bars" on these occupancy diagrams could be developed. Similar "error bars" on the state transition diagrams (e.g. Fig 6a) would also be useful.

Finally, one nagging concern that I have is that the ISIs and spike phase statistics aren't the ideal features one would use to classify pyloric circuit behaviors. Sub-threshold dynamics are incredibly important for this circuit (e.g. due to electrical coupling of many neurons). A deeper discussion about what is potentially lost by only having access to the spikes would be useful.

Overall, I think this work provides a useful starting point for large-scale quantitative analysis of CPG circuit behaviors, but there are many additional hurdles to be overcome.

Reviewer #1 (Public Review):

There has been a great deal of recent interest in the neural basis for offset responses given their hypothesised importance to perception. The possible behavioural relevance to cues like sound duration and gap duration has been taken as a self-evident truth in some work. I found this work attractive in actually testing the relevance of offset responses to duration perception in a mouse model in addition to examining the brain basis. The work is thorough and well executed. The work demonstrates offset responses that occurs for the first time in auditory cortex distinct from A1 where prevention of offsets by activating cells causes worsening of behavioural performance.

Reviewer #1 (Public Review):

In the study "Zika virus causes placental pyroptosis and associated adverse fetal outcomes by activating GSDME", the authors report that Zika infection induces GSDME mediated cell death. The significance of this finding is high, as the impact of Zika on cell death in the placenta is probably key to pathogenesis. The data implicating GSDME in vitro and in vivo is impressive. However, the pathways proposed that lead to GSDME cleavage and pyroptosis are not supported by the data. Much additional work is needed here. If this pathway is mapped as suggested, the impact on the field would be significant.

Reviewer #1 (Public Review):

This manuscript examines the behavioral and physiological responses of wild bonobos to the birth of a younger sibling. It contains some quite interesting findings that contribute to our understanding of the effects of a major life history transition in a primate species that is closely related to humans. There are many strengths of this paper, including a novel use of a fantastic longitudinal data set that incorporates both behavioral and physiological measures.

I think there are several things that can be done to help improve the clarity and utility of the paper for readers. First, the very human-centric framing doesn't feel like it is setting up the findings as well as it could be. It repeatedly emphasizes that a study like this has not been done in humans. That would be interesting to do, and this study certainly helps provide justification for doing one, but studies that haven't been done in humans aren't a very good justification for doing this study in bonobos.

The transition to siblinghood is not something that is unique to humans, and I think the framing would feel like a better fit for the topic if it was less concerned with humans and took a bigger-picture comparative approach. Becoming a sibling at or shortly after weaning is something that happens to the majority of individuals in species with slow life histories, and is a fairly significant life-history transition. Re-orienting towards that would help make the introduction feel less 'forced' than it does currently.

Second, there are a couple of non-trivial results (or really interpretations of results) that require more explanation. The authors contend that their data show evidence of a precipitous drop in cort at around seven months post-TTS. However, the paper does not address why this might be. I would assume that the most biologically plausible scenario would be that cort would slowly return to baseline over the course of several months, as older siblings adjust to their new reality. If that's not what we find, then what potential explanation do we have both for the massive drop itself, and the timing of that drop?

Somewhat relatedly, the paper connects the jump in cort to the observed decrease in neopterin around the birth of younger siblings. But, it does not address the fact that the return of cort to baseline levels does not correspond with an increase in neopterin. Instead, the data suggest that neopterin does not recover, at least not on the (non-trivial) timescales examined in this study. This is not currently talked about in the discussion, and it should be.

Finally, in the discussion, the authors propose that a potential reason for the observed increase in cort levels is increases in male aggression. This paragraph is currently not well-integrated into the rest of the paper. If this phenomenon (i.e., the cort increase) is caused by male aggression, then this has important implications for the applicability of these findings to other species. This is a fundamentally different thing than animals being psychologically stressed by the arrival of a new sibling, which seems to me to be the primary take-away that is emphasized throughout the rest of the manuscript. Related to an earlier point, it would be useful to know if and how this explanation comports with the observed cort drop 7 months post-birth.

Reviewer #1 (Public Review): 

The manuscript by Bellio and colleagues is based on the experimental model of T. cruzi infection in WT, MyD88-/- and IL-18-/- mice previously described by the same group in a 2017 eLife publication. The main message of the current study is that, in addition to IFN-g+ Th1 effectors, T. cruzi infection induces an even larger population of cytotoxic CD4+ T cells. 

The characterization of the cytotoxic CD4+ T cells is well documented. The data shown are convincing. However, since Burel et al. (2012) described the existence of a similar population in humans infected with P. falciparum (an intracellular pathogen), the authors should modify the statement (line 35-36) in the abstract. Similarly, the title "Cytotoxic CD4+ T cells... predominantly infiltrate Trypanosoma cruzi-infected hearts" is an overstatement. If cytotoxic CD4+ T cells outnumber 10:1 IFN-g-secreting population (in lymphoid tissue) their higher representation in hearts of infected mice is not a selective phenomenon but rather expected. 

My major concern is that the function of these cells remains undefined. Are they beneficial or detrimental for the host? It appears that the authors themselves could not make up their minds. The GzB+ CD4+ T cells protect but do not decrease the parasite load (Fig 6G). Are they terminally differentiated or "exhausted" effectors? GzB+ CD4+ T cells can be found in the hearts of chronically infected mice, but we do not know if they are specific for pathogen or self Ags. Do they express the markers of exhaustion on day 14 in the heart? 

The factors that control differentiation of cytotoxic CD4+ T cells are the same as for IFN-g- Th1 cells. MyD-88-/- and IL-18-/- mice significantly lack both populations and succumb to T. cruzi infection. In their 2017 eLife publication, this group reported that survival of infected MyD-88-/- and IL-18-/- mice can be rescued by adoptive transfer of purified total WT CD4+ T cells, which was attributed entirely to their ability to secrete IFN-g (at least in the case of MyD-88-/- recipients). In the current study, the authors only used infected IL-18-/- recipients and show that this time transfer of GzB+ CD4+ T cells is sufficient to confer the protection. When compared with the old data, the rescue of the infected IL-18-/- with only GzB+ CD4+ T cells looks weaker (2 surviving animals out of 10 pooled from 2 experiments), strongly suggesting that IFN-g Th1 cells do play a significant role. It is unclear when the parasite load in Fig G6 was evaluated. It would be good to show deltaCT values for individual mice. 

Because donor IFN-g-/- CD4+ T cells do express IFN-gR (Supp Fig 6-2), IFN-g produced by IL-18-/- host cells could enhance the activity and/or help expand cytotoxic CD4+ T cells among the IFN-g-/- CD4+ donor population. To directly test the protective role of cytotoxic CD4+ T cells in the absence of IFN-g, the authors should treat infected IL-18-/- mice that have received IFN-g-/- CD4+ T cells with anti-IFN-gamma Ab. 

The intracellular cytokine staining in this study appears to be suboptimal. Instead of stimulating with PMA/ionomycin in the presence of Golgi block, Roffe et al. (2012) stimulated lymphocytes with anti-CD3 prior to adding Brefeldin A, an important technical difference which may explain the rather low frequencies of IFN-g+ and IL-10+ cells in this study.

Reviewer #1 (Public Review):

The manuscript submitted by Juliane John et al. reported two crystal structures of the Mn-dependent ribonucleotide reductase R2b in complex with flavin-bound NrdI at different redox states. An advanced photoreduction-free crystallographic technique (SFX) was used for crystal diffraction to preserve the structural information reflecting the actual oxidation states of the flavin cofactor. By comparing the hydroquinone- and oxidized quinone-bound complex structures, previous synchrotron structures, and theoretical conformations, the authors showed unbending flavin conformations despite its oxidation state, a binding site relocation near the superoxide-shuttling channel, and protein structural rearrangement around the interaction surface. The results led them to propose that the complex formation restricts flavin movement, which changes its redox potential to promote the generation and migration of the superoxide from the flavin oxidation site in NrdI to the di-Mn radical generation site in R2b. The authors claim that the redox potential of flavin induces the structural reorganization of the complex. However, a major concern for this manuscript is that the obtained structures do not represent a consecutive biological process. More investigations are needed to attribute the structural reorganization to the change in the redox states of flavin.

Strengths:<br /> 1. The technique of SFX coupled with XEFL provides advantages in determining the crystal structures of the redox-active enzymatic systems, which is a perfect tool to study the target system. All previous structures on this system were obtained with the classic synchrotron X-ray sources on single crystals. The results presented here provide the first views of photoreduction-free structures reflecting the true oxidation states of the flavin cofactor, giving valuable insights into how the flavin cofactor could induce the structural changes and thus, deliver the reactive oxidative species to activate a distant di-Mn center. Such a detailed investigation would be impossible with conventional crystallographic methods.<br /> 2. It is a well-done structural study. The structural refinement was nicely conducted and finalized with thorough consideration. For example, the unknown density in close vicinity to the flavin-oxygen interaction site was carefully assessed with different possibilities and analyzed in the context of coordination and surrounding residues, leading to the hypothesis of how superoxide binds and is shuttled rewards R2b. In addition, the use of the isomorphous difference map wisely indicated the differences between the structures of two oxidation states, reducing the model-based bias and pinpointing the most significant structural changes.

Weaknesses:<br /> 1. Two structures obtained in this study correspond to R2b-NrdI bound with fully reduced and fully oxidized quinone molecules that differ by 2 electrons. The authors compared these structures and attributed the observed structural changes to different redox states of FMN. However, FMN reduction is a one-electron transfer process to generate a single superoxide molecule. Either hydroquinone (hq) is oxidized to semiquinone (sq), or sq is oxidized to the oxidized form (ox), which should be independent of each other. Therefore, without knowing an sq-bound structure, the structural comparison between the hq and ox states does not represent the natural reduction process. In other words, the results presented in this manuscript do not support the proposed mechanism in scheme 3 showing the reduction from hq to sq. The interpretations regarding oxygen reactivity and superoxide positioning could also be overclaiming.<br /> 2. A major conclusion is that the structural reorganization is induced by changes in FMN redox state, which is hard to understand considering the FMN conformation shows marginal differences between the oxidized and reduced states. A rationale demonstrating the connection between the redox state and local structural movement is lacking. The previously reported R2b-NrdI complex structures (Boal et al., Science, 2010) obtained using a synchrotron X-ray source also showed structural reorganization at the complex interface between the oxidized and reduced forms (a noticeable loop movement at the position corresponding to the 40s loop). If the reorganization is indeed redox-controlled, such structural differences should not occur in the synchrotron structures since they had already been reduced. Therefore, the structural reorganization may be irrelevant to or not much affected by the redox state of FMN.

Reviewer #1 (Public Review):

Ting Tang et al. present the results of a species x genotype diversity experiment within BEF China. The authors assess the relative impacts of species and genotype diversity on community-level primary productivity of the trees and the potential mediation of this effect via interactions of plants with soil fungi and herbivores. The results show that both species and genotype diversity influence productivity via changes in herbivory, soil fungal diversity, and other unknown mechanisms. Most of the species diversity effects could be directly related to functional diversity, while genotype diversity effects were not well represented by the way functional diversity was measured in this study.

The study is based on an impressive experiment that will certainly allow achieving major insights into the role of genotype and species diversity on ecosystem functioning. However, there are some significant shortcomings in the methods that limit this study. In particular, the incomplete assessment of functional traits, herbivory, and fungal diversity across the subplots used for this study reduces statistical power. Specific measurements of traits, herbivory and fungal diversity in each plot would substantially simplify the design and the analyses and likely also reduce the unexplained variance observed in the study. However, this is nothing that can be changed now and has the likely explanation of feasibility constraints.

The writing of the manuscript is generally good. However, given the somewhat diffuse results obtained for genetic diversity effects, they receive a lot of attention in the discussion, while species diversity effects are little mentioned. This could be better balanced and also referred back to the hypotheses. For example, I miss the discussion of the very clear hypothesis that genotype diversity effects are positive in species monocultures but neutral in species mixtures. How do your results fit with this hypothesis? My general impression is that the study is very well framed, but lacks to stick to this frame in the discussion. I am aware that this might be a challenge with the results obtained, but worth trying.

Given the complex results obtained, I also feel that the title and main message received in the abstract do not fully reflect the results. Genetic diversity effects on productivity, but also on herbivory and fungal diversity, are not general (e.g. Fig. 2) nor are all genetic diversity effects on productivity mediated by functional diversity and trophic feedback. I think the title and main message of the study should be articulated more precisely.

Reviewer #1 (Public Review):

The manuscript "Centrally expressed Cav3.2 T-type calcium channel is critical for the initiation and maintenance of neuropathic pain" identifies a subset of parvalbumin-expressing GABAergic neurons in the anterior pretectum (APT) that co-express Cav2.3 T-type calcium channels. The firing frequency and burst patterns are potentiated in these neurons following spared nerve injury (SNI) and the development of neuropathic pain. Deletion of the channels in these cells reduced both the development and maintenance of mechanical and cold allodynia. Studies show nice co-expression of the PV and GFP in the Cav2.3.2eGFP-flox KI mouse line.

Multi-unit recordings from PV-Cre X Ai32 mice show that PV neurons in the APT are fast-spiking and that the mean firing rate and frequency of spikes in bursts are potentiated in SNI animals. The graphs in Fig. 2, panel F show compiled data of 18-20 cells from 6-8 animals depending on the treatment. The statistical design for the in vivo experiments (and actually all of the studies) are not clearly stated with degrees of freedom. It is important to know if recordings from a single animal are considered independent observations, and if so, what the rationale for that is. This information should be included in the Quantification and Statistical Analysis section. In addition, it would be interesting to determine if T-type calcium channel blockers can reverse this behavior in these recordings.

In vitro electrophysiological studies show that the PV-expressing APT neurons exhibit fast-spiking to depolarization and single-cell RT-PCR shows that Cav3.2 is expressed in APT neurons that also express GABA. These cells show an after-hyperpolarization burst of APs that is reduced by blockers of Cav3.2 channels. There are no statistics displayed on panels C-E in Fig. 3, although they are reported in the text. Again, the test used and degrees of freedom, etc. should also be reported as it allows for evaluation of the experimental design. It is also noted in the Discussion (lines 185-186) that "Our in vitro data indicate that 92% of APT-PV+ neurons are able to discharge bursts of action potentials at high frequency underpinned by a large transient depolarization due to the activation of T channels." It would be more clear to refer to the rebound as the figure also shows the fast-spiking properties due to depolarization as well as the transient depolarization due to the rebound but only an effect of the Cav2.3 on the rebound.

Behavioral studies of mechanical and cold allodynia in male and female naïve and SNI-treated KI and KO mice were performed. These results show a clear contribution of the Cav3.2 channels in APT in both the development and maintenance of neuropathic pain. Again, the statistical design is not clearly defined and it is extremely difficult to resolve what comparisons are delineated in panels B-E of Fig. 4.

Reviewer #1 (Public Review):

This study focused on a bi-specific antibody, TG-1801, which binds to both CD19 and CD47. The overall objective of this study was to examine the anti-tumor efficacy of combining TG-1801 and U2-regimen (ublituximab and umbralisib) in B-cell non-Hodgkin Lymphoma as a triplet treatment, and to understand the underlying mechanisms of the synergism.

The authors demonstrated superior effects of the triplet therapy in inducing antibody-dependent cellular cytotoxicity (ADCC) and antibody-dependent cellular phagocytosis (ADCP) of CD19-expressing B-cell lymphoma cells. Using a Raji tumor mouse model, the triplet treatment achieved significant tumor inhibition. The transcriptional analysis identified an upregulation of GPR183 in B-cell lymphoma cells upon triplet treatment but not in TG-1801 or U2 treatment. Knockdown of GPR183 expression in Raji cells diminished ADCP and ADCC induced by TG-1801, U2 or the triplet treatment. The inhibition of cell migration and F-actin intensity by TG-1801, U2 or the triplet treatment were also reversed in the absence of GPR183.

The strengths include: appropriate models have been used for evaluating the efficacy of the combination treatment; the superior efficacy of triplet therapy has been demonstrated with both in vitro and in vivo experiments; GPR183 was identified as a critical mediator for the effects of the triplet therapy and its function was evaluated with loss-of-function models.

The weaknesses include: the rationale of how GPR183 was chosen from differentially expressed genes was not clearly interpreted; the role of GPR183 in mediating the effects of TG-1801/U2 combination therapy was inconclusive based on the data provided in the manuscript and was not adequately examined with appropriate models.

Overall, this interesting study supported a rational combination of TG-1801 and U2-regimen for B cell non-Hodgkin lymphoma treatment with preclinical models. However, mechanistically, the role of GPR183 in mediating the effects of the triplet treatment remained inconclusive.

Joint Public Review:

This manuscript focuses on the neural basis of the hidden causal structure between visual and proprioceptive signals in the primate premotor and parietal circuit during reaching tasks executed in a virtual reality environment, where information between the two modalities can be dissociated. In the visual-proprioceptive conflict condition, there was a proprioceptive drift in reaching due to the angle disparity between the nonvisible monkey arm (proprioceptive) and a virtual visual arm. The drift showed large values for small levels of disparity, suggesting integration between modalities, and small values for large disparities, suggesting modality segregation. This was captured by a BPI model that provides the posterior probability of a common hidden sensory source and integrates the effect of previous trials on updating the causal structure and the sensory representation. In the bimodal conflict task, neurons in premotor cortex showed stronger visual arm information, whereas parietal cortex showed stronger proprioceptive signals. Notably, single cell and population activity carries information about both the integration of bimodal information for small disparities and the segregation for large disparities, especially in premotor cortex. In addition, some information about the hidden causal structure of the previous trials is present in premotor cortex. Finally, the comparison in neural activity in the visual proprioceptive aligned task and the visual proprioceptive conflict condition revealed that parietal cortex shows smaller accuracy to represent arm location in the later condition, supporting the notion that this area is involved in updating sensory uncertainty. These results support the notion that premotor cortex encodes the causal hidden structure of bimodal integration/segregation, while parietal cortex is more focused on weighting the sensory input from both modalities. In general, the experiments are technically sound, and the conclusions are mostly well supported. However, a simpler framing of the paper could make the main message easier to grasp, the analysis of Bayesian models seems to lack major details, and the statistical reporting is below standard.

On the upside, this is one of the few studies that have investigated, at single-cell level, the neural representation of body ownership and sense of agency in monkeys, through a well-designed experimental paradigm. By using a Bayesian causal inference model, the author explored in a quantitative fashion these neural constructs, by inferring hidden variable such as the posterior probability of a common source of inputs and the relative neural representations. Through advanced data analysis, the findings offer an interesting view of neural dynamics associated to causal inference in frontal and parietal cortex. Distinct roles of premotor and parietal neurons are highlighted, particularly in the temporal domain of these processes. Based on the results, the authors suggest that premotor cortex is where causal inference is computed and then the outcome of this computation is addressed to parietal cortex.

On the downside, to be appreciated by a wide audience the manuscript would benefit from a significant re-writing, aimed at simplifying the way the overall aims are introduced, as well as the results and data analysis reported. The latter is mostly based on rigorous, but niche statistical tools, that beyond their inherent complexity, do not allow the reader to easily follow and appreciate the quality of the neural data. The experimental paradigm is well designed to dissociate visual and proprioceptive inputs during arm reaching. However, the data set do not include eye movement control, necessary when studying visuomotor behavior associated to neural activity of cortical areas influenced, although with different strength, by eye position and movement signals, such as premotor and parietal cortex. Importantly, the analysis of Bayesian models seems to lack major details, the statistical reporting is below standard (missing effect sizes, degrees of freedom, lack of individual data in figures), the study is features many unjustified parameter choices and key results seem to lack statistical support: not all statements about differences between parietal and premotor cortex seem supported by a direct statistical comparison. Further, while three monkeys contributed data, for only one does the study report data from both brain regions; this makes the claim of a difference between brain regions rather weak and this shortcoming needs to be clearly acknowledged. The actual underlying data (e.g. how single neuron responses are converted to tuning curves; how decoding accuracies vary across neurons) is not shown, which makes it difficult to interpret the robustness of the results. In particular, the units of analyses vary tremendously between Figures (experimental blocks, neurons, pseudo-epochs, etc). Finally, the associated literature, consisting in many studies, has been totally ignored.

Reviewer #1 (Public Review):

This work follows up on a previous study which found that R-type (CaV2.3) channels contribute to pacemaking and somatodendritic Ca2+ oscillations in substantia nigra dopaminergic (SN DA) neurons. That study showed that SN neurons from CaV2.3 knockout mice were protected from neurodegeneration in a neurotoxin-induced Parkinson's disease model. Here the authors address how CaV2.3 channels are able to contribute to SN DA neuron pacemaking given that what is currently known about their gating suggests they would be mostly inactivated at pacemaking potentials. They first confirm using the blocker SNX that CaV2.3 contributes to pacemaking and whole-cell currents in cultured DA neurons. They further show that recombinant CaV2.3 channels reconstituted in tsA201 cells with auxiliary b2a and b2e, but not b3 or b4, subunits displayed biophysical properties (right-shifted voltage-dependence of steady-state inactivation, decreased rate of inactivation) that permitted significant CaV2.3 activity during pacemaking potentials, inferred from SN DA neuron-derived action potential clamp experiments. They find b2 and b4 transcripts dominate in SN DA neurons and that b2a and b2e splice variants are present. In slice recordings, they find that pharmacologically isolated CaV2.3 currents in SN DA neurons display window currents over the range of voltages at which pacemaking occurs.

The results are quite interesting and potentially have important therapeutic implications. Nevertheless, in the current form there are several weaknesses that diminish the strength of the findings.

1. As the authors note, they do not provide direct evidence for the ultimate conclusion of the study that assembly with b2a and b2e subunits are necessary for CaV2.3 channels to contribute to pacemaking in SN DA neurons. The authors state siRNA knockdown experiments in SN DA neurons are technically challenging. Nevertheless, shRNA knockdown studies in SN neurons have been previously published. Such a study is critical to provide direct evidence for what would be a very important and impactful finding.

2. Relative contribution of CaV1.3 (L-type) and CaV2.3 channels to pacemaking in SN DA neurons. As the authors note, a phase III clinical trial for the L-type channel blocker, isradipine, showed no efficacy for neuroprotection, even though some mice studies suggested this might be efficacious. On the other hand, the authors' previous work with CaV2.3 knockout mice suggest inhibition of this channel would be more appropriate for a neuroprotective response. It would be useful to get a direct comparison of the impact of isradipine and SNX on pacemaking in SN DA neurons (Figs. 1 and 2). If their impacts on pacemaking (an Ca2+ oscillations) are similar it would suggest something beyond the pacemaking Ca2+ influx could be responsible for neuroprotection (e.g. changes in NCS-1 expression as previously suggested by the authors).

3. The slice recording data (Fig. 9) are confusing and raise concerns about adequacy of pharmacological isolation of CaV2.3 currents in this preparation. The accuracy of interpretation of the data in Fig. 9 rests critically on the idea that the cocktail of CaV channel blockers given successfully isolates CaV2.3 currents. Yet, the amplitudes of the exemplar currents shown for plus or minus the CaV channel blocker cocktail are almost the same. This cannot be due to CaV2.3 providing the dominant current in the slice preparation since addition of SNX only decreased Ca2+ current amplitude by 13% (Suppl Fig. 5). It is not clear to me why the steady-state activation and inactivation curves experiments were not conducted in the cultured neuron preparation (Figs. 1 and 2) where there seems to be better control of pharmacological block of different Cav channel isoforms.

4. While the transcript data show that b2a and b2e are present in SN DA neurons, numerically they would still represent only a minority of the beta subunits present (<25%). I don't think sufficient thought has been given to this in the discussion of the results. Unless there is some preferential association of CaV2.3 with b2a and/or b2e, there would be a mix of channels with the majority incapable of supporting pacemaking in SN DA neurons. Given this, one would not necessarily expect that the gating characteristics of CaV2.3 would be the same as what is obtained with reconstituted channels in tsA201 cells where all the channels are assembled with b2a or b2e (see point #5 below).

5. The V0.5,inact of putative CaV2.3 channels in SN DA neurons of -52.4 mV was said to be 'very similar' to the value of -40 mV that was observed in tsA201 cells. A difference of +12 mV in voltage-dependence gating of ion channels is substantial and should not be brushed off. A more nuanced interpretation would be that in SN DA neurons CaV2.3 likely associates with other beta subunits in addition to b2a and b2e and so one would not necessarily expect the V0.5,inact to be the same as what is observed in reconstituted channels in tsA201 cells.

Reviewer #1 (Public Review):

The manuscript by Hiller et al. uses a comparative genomics approach to uncover a previously unknown role of SERPINE3 in eye anatomy. The authors take advantage of available genomes for a variety of mammalian species and search the genomes for evidence of loss of functions or relaxed selection in genes shared in species with low visual acuity (e.g., species that live underground, are nocturnal or mainly rely on echolocation). They identify 26 genes of which many have known functions in eye development or function, but also a handful that have previously not been described to have a role in eye function. The authors then focus on one of these genes (serpine3) as a proof of principle and study expression and function of serpine3 in zebrafish. They find that the gene is expressed in the retina and that knocking out this gene leads to morphological alterations in the zebrafish eye. Finally, they identified SNPs nearby serpine3 in humans that are linked to eye disorders.

Taken together, the study shows that it is possible to take advantage of evolutionary divergence patterns to reveal novel insights into gene function.

What is exciting about this paper is that it uses evolutionary knowledge and comparative approaches to uncover a novel role for a gene that would not necessarily have been discovered without it. While not completely unique, this is rather rare in evolutionary studies which usually rely on knowledge from other systems to uncover candidate genes involved in evolution.

Reviewer #1 (Public Review):

In the article "Whole transcriptome-sequencing and network analysis of CD1c+ human dendritic cells identifies cytokine-secreting subsets linked to type I IFN-negative autoimmunity to the eye," Hiddingh, Pandit, Verhagen, et al., analyze peripheral antigen presenting cells from patients with active uveitis and control patients, and find several differentially expressed transcription factors and surface markers. In addition, they find a subset of antigen presenting cells that is decreased in frequency in patients with uveitis that in previous publications was shown to be increased in the eye of patients with active uveitis. The greatest strength of this paper is the ability to obtain such a large number of samples from active uveitis patients that are not currently on systemic therapy. While the validation experiments have methodologic flaws that decrease their usefulness, this study will still serve as a valuable resource in generating hypotheses about the pathogenesis of uveitis that can be tested in future projects.

Minor concerns:<br /> Consistency between experiments: In Figure 1F only ~15% of the CD1c+ cells are CD14+, while in Figure 4 D shows that 100% of CD36 CX3CR1 DP cells are CD14 positive (and half the CD36+CX3CR1- cells), and the DP account for 30% of CD1c DCs.

Since all CD36+CX3CR1+ cells are CD14+ (Figure 4D), how CX3CR1 ended up being differentially regulated in a similar way despite this population was excluded from 2nd bulk RNAseq data set should be commented on by the authors

Line 153: "...substantiates this gene set as a core transcriptional feature of human autoimmune uveitis." It would be difficult to argue that when only 137 of the 1236 DEGs from the first module are repeated in a validation data set that this is the core transcriptions set that defines the population in any uveitis. Further concerns include that the validation data set is not the same population, but rather a subset not containing CD14.

Line 220: Notch-dll experiments: with the experiments presented it is not possible to say that the changes are due to maintenance of CD1c+ DCs without further experiments outlining what NOTCH2 signaling changes throughout time. Is the population fully developed in the first 7 days of culture prior to adding NOTCH2 or ADAM10 inhibitors? Is there more apoptosis in this pathway? Less proliferation? It would be more accurate to say that there are fewer cDC2s after 14 days of culture without speculating the cause. In this experiment it is unclear why the gate of CD141/CD1c was chosen, as this appears to be in the middle of the population. In normal PBMCs CD141+ DCs would be CD1c negative; therefore why exclude the CD141hiCD1c+ and CD141loCD1c+ populations?

Line 256: The hypothesis that the loss of CD36+CX3CR1+ cells was due to migration to the eye doesn't make sense based on volume and number of cells. 0.1% of all PBMC is ~1x107 cells, and distributed throughout the eye would give about 1.3x106 cells/mL of eye volume. This would make the eye turbid which is not consistent with birdshot chorioretinopathy and would be rare in HLA-B27 anterior uveitis and intermediate uveitis.

Line 267: Would have liked to see the gating of CX3CR1/CD36 cells be more consistent (there are overlapping CX3CR1+ and CX3CR1- populations in 5A, but in Figure 4 quadrants were used to define the populations when evaluating the numbers in uveitis and healthy controls. The populations in Figure 5 are more separated by CD36.

Line 269, IN VITRO stimulation: The experimental paradigm is set up to find a difference between cells but does not to test any biologically relevant scenario. By sorting on a surface marker, then stimulating with the ligand for that receptor, the result better proves that CD36 is important in TLR2 signaling than does it give any information on how these dendritic cells might behave in uveitis.

Reviewer #1 (Public Review):

This article considers the common scenario wherein it is desired to construct a dose-response curve. Response is a continuously valued number between 0 and 1, e.g. % cell viability. A typical approach is to transform response using a logistic function, log(x/[1-x]), so that the transformed response spans the real number line and then applying linear regression models. However, when some responses are very close to 0 (or very close to 1), this typical approach will be susceptible to these highly influential values, and the fitted model will not estimate well parameters of usual interest, such as the slope or intercept of the regression or the IC50 or the back-transformed dose response curve.

The proposed approach is an adaptation of Beta regression. The underlying systematic component of the generalized linear model is the same as in the standard case described above but the random component is based upon the beta distribution. Further, rather than using maximum likelihood to fit the model, the authors propose to use 'robust minimum density power divergence estimators'. This method is called REAP, for Robust and Efficient Assessment of Potency. The authors present an online, interactive application where users can upload data and fit the authors' method.

Overall, the REAP methodology seems useful and achieves exactly what they are intended to achieve: robustly fitting dose-response curves subject to extreme measurement error. The mathematical arguments are sound and further supported by simulation. A potentially straightforward alternative solution to this problem would be to directly model the measurement error process by using a heavy-tailed Cauchy distribution to model the measurement error process, thereby downweighting the influence of extreme responses but not removing them altogether. This would seem to have a 'familiarity advantage' over the authors' method.

The current version of the interactive application, although relatively easy to use, seems a little buggy. For example, the 'width of CI' argument accepts any positive number, including those greater than 1, and it's not clear how to interpret the different choices that one can plug in for this parameter. Also, even if there is just one 'Agent' provided in the data, the numerical comparisons below the plot are still presented in triplicate.

Reviewer #1 (Public Review):

This is an interesting manuscript that has the potential to answer questions about a controversial topic in evolutionary biology - the evolutionary patterns and drivers of hand preferences in humans and nonhuman primates. To accomplish this, the authors generate new data and gather an impressive amount of published data across many anthropoid species, and test for the effects of ecology (terrestrial vs. arboreal), brain size, and tool use on handedness using phylogenetically informed statistical analyses. They find that humans represent an extreme among the species sampled, that direction of handedness was not correlated with any of the predictors tested, and that strength of handedness was higher among arboreal species.

Although phylogenetic modeling (which accounts for relatedness between species) is implemented in the primary analyses reported in this paper (e.g., testing the effects of ecology, brain size, and tool use on handedness), this is not the case for some other analyses (e.g., testing the effects of sex, age, and subgroup on handedness). This represents one potential area of improvement.

Overall, the manuscript is very well-written and the new data gathered is impressive. This work is critical for our understanding of the role of handedness in primate evolution.

Reviewer #1 (Public Review):

In this work, Cai et al identified WDR5 as an important and druggable chromatin regulator involved in metastatic progression of triple-negative breast cancer (TNBC) through an in vitro and in vivo genetic screening. The authors further studied the underlying mechanisms and found that WDR5 promotes TNBC cell growth by positively regulating the ribosome gene expression and therefore the translation efficiency in cancer cells. Interestingly, this regulation by WDR5 is independent of MLL/KMT2 complex in their breast cancer models. Pharmacological inhibition by WDR5 inhibitor or WDR5 degrader showed the decreased clonogenic ability, reminiscent of genetic knockdown of WDR5, in the breast cancer models. Finally, the authors provided a potential therapeutic strategy for the treatment of metastatic breast cancers by combinational WDR5 and mTOR inhibition.

Overall, this study is well designed, comprehensive and well done.

Reviewer #1 (Public Review):

In this manuscript, the authors have characterized the action of HOTAIR in enhancing tumor metastasis using a doxycycline-inducible transgenic mouse model. The authors have shown that the breast tumor cells are addicted to HOTAIR expression. In addition, the authors have shown that HOTAIR overexpression modulates both the cellular transcriptome and chromatin accessibility landscape for multiple metastasis-associated genes, and promoted epithelial to mesenchymal transition. The authors present numerous intriguing observations, and the findings are well supported by the data, Overall, this is an excellent manuscript that uncovers many interesting new aspects of HOTAIR in breast tumors. The strengths of this manuscript include the use of appropriate in vivo mouse cancer models, detailed analyses, and high translational potential.

Reviewer #1 (Public Review):

Meshcheryakov et al present a compelling story showing that upon binding to DNA, an NF-kB homodimer induces distinct conformations in DNA, regardless of the DNA sequence or their level of transcriptional activity. To understand how the protein achieves transcriptional selectivity for distinct sequences, authors use simulations, binding assays, and kinetic studies. They observe a correlation between kinetics and transcriptional activation, showing that binding to active DNA sequences is driven by entropy and occurs without a large deformation of the DNA. Conversely, binding to DNA sequences with lower transcriptional activity requires more significant alteration of DNA conformation and is largely enthalpic with slower kinetics. These studies uncover an important role of binding kinetics in DNA selectivity by transcription factors that should motivate many future studies to further explore and elucidate these mechanisms.

Reviewer #1 (Public Review):

The paper correctly identifies two biophysical properties that may impact an OHC contribution to cochlear amplification. These are the membrane RC time constant and prestin kinetics. The RC problem was identified by Santos-Sacchi 1989 (1) based on measures of OHC membrane capacitance, electromotility (eM) and published OHC resting and receptor potential data. At issue was a 20 dB disparity between threshold BM measures and eM when the resting potential (RP, ~ -70 mV)) is displaced from the voltage at maximal eM gain or peak NLC (Vh; ~ -40 mV). If RP were actually at Vh then the problem would not have been identified, assuming that prestin's voltage-responsiveness were frequency-independent, which was not in question at that time. Over the last two decades several groups have found prestin performance to be low pass. Isolated OHCs, macro-patch and OHCs in situ cochlear explants all show this low pass behavior. To date, no manipulations of load have pushed the voltage responsiveness to frequency-independent. This manuscript tries to avoid the kinetics issue and attempts to focus on the RC problem that has been dealt with extensively since 1989, including at that time a suggestion that the RC problem points to the dominance of the stereocilia bundle (2).

The authors suggest that kinetics of prestin is not addressed in the current manuscript, but this is not the case. In ignoring the paper from Santos-Sacchi and Tan 2018 (3), reliance on Frank et al.'s (4) data explicitly utilizes their kinetic results. OHC84 (so-called short cell, 51 um long) is essentially frequency-independent after microchamber voltage roll-off correction. The authors choose 1 nm/mV gain at 50 kHz to work with in their arguments. As it turns out, the corrected eM of OHC84 is wrong since it does not fix the reported 23 kHz microchamber voltage roll-off. While OHC65 is appropriately fixed, OHC84 is over compensated. Gain at 50 kHz should be about half the chosen gain. This is not the most problematic issue for their arguments, however.

In Santos-Sacchi and Tan 2018 (3) we show that low frequency (near DC) eM gain for OHCs averaging 55.3 um long is about 15 nm/mV. This indicates, as noted in that paper, that the resting potential of OHC84 was far shifted from Vh, accounting for its wide-band frequency response. If indeed, the authors still maintain that OHC eM is frequency-independent, ala Frank et al. (and in disregard to other publications where, to the contrary, eM gain would be far less at 50 kHz - see (5, 6)), then the eM gain at 50 kHz should be closer to 15 nm/mV; large enough, I think, to make their RC problem exercise overkill. That is, even in 1989 such a gain would not have suggested an RC problem. This is assuming that the normal resting potential is at Vh. Of course, at Vh membrane capacitance would be about twice that of linear capacitance (due to peak NLC) - the cell time constant does not discriminate against source of capacitance. All in all, isolated OHC biophysics that provides the voltage dependence and the kinetics of prestin cannot be ignored to deal with the RC problem in isolation. Doing so will give a false sense of how the cochlea works, and will encourage others to neglect, without rationale, published pertinent data, as with the Sasmal and Grosh 2019 (7) model where the OHC is treated as a frequency-independent PZE device.

Finally, to scorn the significance of component characteristics comprising the whole cochlea, e.g., based on isolated OHC biophysics or prestin's cryo-EM structure, as a fallacy of composition suffers itself from hasty generalization. Of course, knowing the biophysics of single OHCs informs on the system response. Otherwise, the prestin KO would have been an unfunded goal, never allowed to pass beyond a system modeler's review. Indeed, the authors would have none of the "carefully" chosen data to present their RC counter argument. Pertinent, published biophysical characteristics must be included in any critical discussion on OHC performance. For that matter, cochlear modelers must follow the same rule.

1. J. Santos-Sacchi, Asymmetry in voltage-dependent movements of isolated outer hair cells from the organ of Corti. J. Neurosci. 9, 2954-2962 (1989).<br /> 2. A. J. Hudspeth, How the ear's works work. Nature 341, 397-404 (1989).<br /> 3. J. Santos-Sacchi, W. Tan, The Frequency Response of Outer Hair Cell Voltage-Dependent Motility Is Limited by Kinetics of Prestin. J. Neurosci. 38, 5495-5506 (2018).<br /> 4. G. Frank, W. Hemmert, A. W. Gummer, Limiting dynamics of high-frequency electromechanical transduction of outer hair cells. Proc. Natl. Acad. Sci. U. S. A. 96, 4420-4425 (1999).<br /> 5. J. Santos-Sacchi, D. Navaratnam, W. J. T. Tan, State dependent effects on the frequency response of prestin's real and imaginary components of nonlinear capacitance. Sci. Rep. 11, 16149 (2021).<br /> 6. J. Santos-Sacchi, W. Tan, Complex nonlinear capacitance in outer hair cell macro-patches: effects of membrane tension. Sci. Rep. 10, 6222 (2020).<br /> 7. A. Sasmal, K. Grosh, Unified cochlear model for low- and high-frequency mammalian hearing. Proc Natl Acad Sci U S A 116, 13983-13988 (2019).

Reviewer #1 (Public Review):

The manuscript describes several optimizations of classic DNA reporter constructs to monitor closely the dynamics of Wnt/β-catenin signalling during development using transgenic avian lines. The generation of these tool, and in particular the construct 16xTCF-VNP, will be of great value to the scientific community as it can be used in electroporations and also to generate transgenic in different organisms. Moreover, the stable quail transgenic line are useful to explore Wnt signalling function in labs interested in avian species as developmental biology models and also in labs interested in evo/devo comparative analysis.

Reviewer #1 (Public Review):

In this manuscript, Abdellatef et al. describe the reconstitution of axonemal bending using polymerized microtubules (MTs), purified outer-arm dyneins, and synthesized DNA origami. Specifically, the authors purified axonemal dyneins from Chlamydomonas flagella and combined the purified motors with MTs polymerized from purified brain tubulin. Using electron microscopy, the authors demonstrate that patches of dynein motors of the same orientation at both MT ends (i.e., with their tails bound to the same MT) result in pairs of MTs of parallel alignment, while groups of dynein motors of opposite orientation at both MT ends (i.e., with the tails of the dynein motors of both groups bound to different MTs) result in pairs of MTs with anti-parallel alignment. The authors then show that the dynein motors can slide MTs apart following photolysis of caged ATP, and using optical tweezers, demonstrate active force generation of up to ~30 pN. Finally, the authors show that pairs of anti-parallel MTs exhibit bidirectional motion on the scale of ~50-100 nm when both MTs are cross-linked using DNA origami. The findings should be of interest for the cytoskeletal cell and biophysics communities.

Reviewer #1 (Public Review):

The authors attempt to optimize the FluoroSpot assay to allow for the assessment of cross-reactive antibodies targeting conserved epitopes shared by multi-allelic antigens and those specific to unique antigen variant at the B cells level. This is a critical aspect to consider when identifying targets of a broad range of cross-reactive antibody for vaccine development and the antigen VAR2CSA used in this work is one that will benefit from the method described in the manuscript.

Overall, this is a method manuscript with extensive detail of the assay validation process. The description of the assay performance steps using, first monoclonal antibodies and later hybridoma/immortalized B cells was important to understand conditions that can influence the antigen-antibody interactions in the assay. This multiplex approach can assess the cross-reactivity of antibody to up four allelic variants of an antigen with the possibility to explore the affinity of antibody to a particular variant using the RSV measurements. The validation of their assay with PBMC from malaria exposed donors both men and women (that naturally acquired high titer of antibodies to VAR2CSA during pregnancy) is a strength of this work as this is in the context of polyclonal antibodies with more heterogenous antibody binding specificities.

The ability of the assay to detect cross-reactive antibodies using all four tags appear highly variable even in the context of monoclonal antibody targeting the homologous antigen labelled with all 4 tags. Overall, it appears that the assessed antibody reactivity with TWIN tagged antigens was relatively low and this needs to be explained and discussed as the current multiplex method, as it is, might just be optimized for study of cross-reactive antibodies to 3 antigens. As acknowledged by the authors, the validation of this assay on PBMC from only 10 donors (7 women and 3 men) is a caveat to the conclusion and increasing this number of donors (the authors have previously excelled in B cells analyses of PfEMP1 proteins and would have PBMC readily available) will strengthen the validity of this assay.

Reviewer #1 (Public Review):

The authors have successfully engineered human NK cells expressing a chimeric antigen receptor (CAR) targeting CD73. CD73 is important enzyme for generating immunosuppressive adenosine in the tumor microenvironment, so it serves as a potential immunotherapeutic target to prevent production of adenosine and reverse the immunosuppression. Here the authors present data demonstrating the efficacy of their CAR-bearing NK cells in eliminating CD73-expressing tumors in vitro and in vivo.

Strengths of the manuscript are:<br /> 1. demonstration that the CAR can be efficiently expressed on the NK cell surface through lentiviral transduction<br /> 2. demonstration that the CAR-bearing NK cells can better kill CD73-expressing target cells and suppress their production of adenosine<br /> 3. data showing that the CAR-bearing NK cells can reduce the burden of CD73-expressing tumor cells in mice and that they seem to better infiltrate these tumors in vivo, as compared to NK cells lacking the CAR

Weaknesses include:<br /> 1. a need to better characterize whether or not the CAR is actually transducing activation signals into the NK cells when it engages with CD73 on tumor cells or if it is primarily functioning by inhibiting adenosine production to enhance natural cytotoxicity responses by the NK cells. This is a fundamental question that needs to be addressed to understand the basic function of the CAR-bearing NK cells.<br /> 2. the in vivo studies of anti-tumor responses by CAR-bearing NK cells should be bolstered by analysis in more mice than currently presented

Reviewer #1 (Public Review):

The authors test whether neurons in V1 show "multiplexing", which means that when two stimuli A and B are presented inside their receptive fields (RFs), the neuronal response fluctuates across trials between coding one of the two, leading to a bimodal spike count histogram. They find evidence for this "mixture" model response in a subset of V1 neurons. They next test whether the spike count noise correlations (Rsc) vary between pairs of neurons that prefer the same versus different stimuli, and show that Rsc is positive for neurons that prefer the same stimulus but negative for neurons that prefer different stimuli.

While this paper shows some intriguing results, I feel that there are a lot of open questions that need to be addressed before convincing evidence of multiplexing can be established. These points are discussed below:

1. The best spike count model shown in Figure 2C is confusing. It seems that the number of "conditions" is a small fraction of the total number of conditions (and neurons?) that were tested. Supplementary Figure 1 provides more details (for example, the "mixture" corresponds to only 14% of total cases), but it is still confusing (for example, what does WinProb>Min mean?). From what I understood, the total number of neurons recorded for the Adjacent case in V1 is 1604, out of which 935 are Poisson-like with substantially separated means. Each one has 2 conditions (for the two directions), leading to 1870 conditions (perhaps a few less in case both conditions were not available). I think the authors should show 5 bar plots - the first one showing the fraction for which none of the models won by 2/3 probability, and then the remaining 4 ones. That way it is clear how many of the total cases show the "multiplexing" effect. I also think that it would be good to only consider neurons/conditions for which at least some minimum number of trials are available (a cutoff of say ~15) since the whole point is about finding a bimodal distribution for which enough trials are needed.

2. More RF details need to be provided. What was the size of the V1 RFs? What was the eccentricity? Typically, the RF diameter in V1 at an eccentricity of ~3 degrees is no more than 1 degree. It is not enough to put 2 Gabors of size 1 degree each to fit inside the RF. How close were the Gabors? I am confused about the statement in the second paragraph of page 9 "typically only one of the two adjacent gratings was located within the RF" - I thought the whole point of multiplexing is that when both stimuli (A and B) are within the RF, the neuron nonetheless fires like A or B? The analysis should only be conducted for neurons for which both stimuli are inside the RF. When studying noise correlations, only pairs that have overlapping RFs such as both A and B and within the RFs of both neurons should be considered. The cortical magnification factor at ~3-degree eccentricity is 2-2.5mm/degree, so we expect the RF center to shift by at least 2 degrees from one end of the array to the other.

3. Eye data analysis: I am afraid this could be a big confound. Removing trials that had microsaccades is not enough. Typically, in these tasks the fixation window is 1.5-2 degrees, so that if the monkey fixates on one corner in some trials and another corner in other trials (without making any microsaccades in either), the stimuli may nonetheless fall inside or away from the RFs, leading to differences in responses. This needs to be ruled out. I do not find the argument presented on pages 18 or 23 completely convincing, since the eye positions could be different for a single stimulus versus when both stimuli are presented. It is important to show that the eye positions are similar in "AB" trials for which the responses are "A" like versus "B" like, and these, in turn, are similar to when "A" and "B" are presented alone.

4. Figures 5 and 6 show that the difference in noise correlations between the same preference and different preference neurons remains even for non-mixture type neurons. So, although the reason for the particular type of noise correlation was given for multiplexing neurons (Figure 3 and 4), it seems that the same pattern holds even for non-multiplexers. Although the absolute values are somewhat different across categories, one confound that still remains is that the noise correlations are typically dependent on signal correlation, but here the signal correlation is not computed (only responses to 2 stimuli are available). If there is any tuning data available for these recordings, it would be great to look at the noise correlations as a function of signal correlations for these different pairs. Another analysis of interest would be to check whether the difference in the noise correlation for simply "A"/"B" versus "AB" varies according to neuron pair category. Finally, since the authors mention in the Discussion that "correlations did not depend on whether the two units preferred the same stimulus or different", it would be nice to explicitly show that in figure 5C by showing the orange trace ("A" alone or "B" alone) for both same (green) and different (brown) pairs separately.

Reviewer #1 (Public Review):

(Muscle) acetylcholine receptors are hetero-pentamers incorporating two alpha and one beta, delta and epsilon subunit. This paper closely examines ancestral beta subunits constructed in previous phylogenetic work from this lab using careful experiments and analyses of single channel activity.

Unlike their previous work on these ancestral subunits, the authors find that the ancestral beta subunit forms pentamers that are active in the absence of the agonist acetylcholine. This shows that ancestral beta subunits, unlike their extant descendants, were probably capable of assembling into homomers and allows subsequent experiments showing that, like their extant hetero-pentameric cousins, these homomeric channels show pre-opening gating steps. Observing homomeric assembly of an ancestral subunit is exciting, as it offers experimental dissection of previous hypotheses on acetylcholine receptor - and the broader pLGIC family - evolution, from homomers into pentamers. It also shows, perhaps not surprisingly but still importantly, that channel gating mechanisms observed in today's receptors was around in those earlier receptors.

Reviewer #1 (Public Review):

DNA replication and DNA repair require the loading of proteins onto DNA that in turn recruit other proteins that either synthesize new DNA or repair gaps in DNA. One of these proteins is the Proliferating Cell Nuclear Antigen, an autoantigen in some autoimmune disorders, that is loaded onto DNA by Replication Factor C. The PCNA clamp is loaded by RFC onto ssDNA/dsDNA junctions with a 3'-recessed end during DNA replication, but how RFC/PCNA recognize double-stranded DNAs with a single strand nick or a short single-strand gap has not been known. The current paper nicely confirms that RFC has a second DNA binding site that is different from the well-known DNA binding site that recognizes ssDNA/dsDNA junctions with a 3'-recessed end. The new site binds to ssDNA/dsDNA junctions with a 5'-recessed end and the paper shows that RFC can locally unwind DNA at the ssDNA/dsDNA junctions. These structural studies reveal how RFC/PCNA can recognize DNA with a short gap or a nick for subsequent DNA repair.

Reviewer #1 (Public Review):

In this study Crawford et al., studied the protein composition of translating ribosomes in yeast under unstressed or stress conditions. To achieve this, the authors employed a combination of polysome profiling, which is a method that separates translating from non-translating ribosomes based on their sedimentation in sucrose gradients, and mass spectrometry. They identified aspartate aminotransferase, Aat2 as one of the proteins that is enriched in translating ribosomes in stressed cells. Crawford et al., went further to show that deletion of Aat2 impairs adaptation of yeast to osmotic stress and provided some evidence that Aat2 may play a role in integrated stress response. Finally, the authors show that the aminotransferase activity is not required for its function in translation and stress responses. Altogether, this study was found to be of high interest as it provides further potential insights in the molecular underpinnings of stress adaptation and further emphasizes potential unconventional roles of metabolic enzymes in regulation of translation. It was therefore thought that this study is likely to be of broad interest to the fields of biochemistry, molecular and cellular biology and beyond.

Strengths: This study is based on an elegant combination of biochemical and genetic approaches. Evidence implicating Aat2 in oxidative stress response was found to be strong. In addition, it was appreciated that authors demonstrate that a potential role of Aat2 in regulation of protein synthesis under stress is independent of its aminotransferase activity.

Weaknesses: The major weaknesses were thought to be related to the relative lack of the mechanistic evidence of how Aat2 is recruited to the ribosomes. In addition, factor(s) that transduce signals from stressors to entice Aat2:ribosome association remain(s) elusive.

Reviewer #1 (Public Review):

Ivica et al. provide both functional and structural characterization of a relatively unstudied glycine receptor agonist AMS. They show that at pH 5 where AMS does not readily degrade, that AMS is slightly less efficacious than the full agonist glycine but more efficacious than partial agonists beta-alanine and taurine. Consistent with AMS acting as a nearly full agonist, cryo-EM structures of AMS-bound glycine receptors adopted primarily an open pore conformation similar to glycine-bound channels, whereas AMS-bound channels were not observed in a closed pore conformation as previously observed for partial agonist-bound channels (Yu et al. 2021). This work lends support to the authors' prior conclusions of the actions of full vs. partial agonists at these receptors, but also provides valuable functional and structural data for a ligand that is structurally in between the full agonist glycine and the partial agonist taurine. The authors further show that the energetic effect of swapping between carboxylate and sulfonate charged groups on the ligand differs depending on the ligand's length. Together, these data will be of interest to both biophysical and pharmacological investigations of ligand-gated ion channels. My only concern is in the interpretation of some rather small structural differences.

Joint Public Review:

PSD95, localizes at excitatory synapses, where it plays a scaffolding role, linking different functionally-related synaptic proteins together. It is representative of the complexity of multi-domain proteins found in signal transduction. The flexibility of its domain-domain interactions makes its dynamic supertertiary structure difficult to characterize.

The authors have a decade-long history of applying smFRET to PSD95, and so from their portfolio of FRET-labeled mutants they specifically drew 12 double-cysteine versions to probe the supertertiary orientation of two domains of PSD95, PDZ3, and the interlinked SH3 and GuK domains, usually referred to as SH3-GuK. They worked with both full-length PSD95 and a truncated version containing only PDZ3 and the SH3-GuK domain (PSG). They and other investigators have shown that the PSG supramodule remains well-folded when the first two PDZ domains are removed. Motions of the PDZ3 domain in relation to the SH3-GUK domain were studied with a combination of techniques that probe protein dynamics in complementary ways. First, interactions between the PDZ3 domain and the SH3-GUK domain were probed by Multiparameter fluorescence measurements (MFM) in which single-molecular Fluorescent Resonance Energy Transfer (FRET) data were acquired by sub-ensemble Time-Correlated Single Photon Counting (seTCSPC) and used to determine a range of "FRET-distances." The authors then used Total Internal Reflectance Fluorescence microscopy (smTIRF) to compare fluorescence measurements from truncated PSG supramodules to those from full-length PSD-95. The FRET measurements were further analyzed by Photon Distribution Analysis (PDA) to extract two limiting energy states governing interdomain interactions. To resolve fast dynamics, they performed filtered Fluorescence Correlation Spectroscopy (fFCS). To fully map and refine the conformational energy landscape of the supramodule, they performed replica exchange Discrete Molecular Dynamics (DMD). Conformations predicted from the DMD data were verified by disulfide (DS) mapping after the introduction of pairs of cysteine mutations. Finally, the authors used the FRET distance restraints to simulate the accessible volume (AV) for dye pairs at each labeling site and map the conformational dynamics within the two limiting energy states.

For MFM, the authors used 12 variants of full length PSD-95 each of which contains a FRET pair between distinct defined sites in the PSG supramodule. They used seTCSPC to measure the dynamics of interactions between each pair. They plotted the FRET efficiency of each pair against the average donor fluorescence lifetime and found that all variants showed dynamic rather than static conformations. A simultaneous global analysis of all of the variants indicated that a two-state model is sufficient to fit all the data. Their analysis shows that the PSG module moves between two non-overlapping limiting states (A [46%] and B [54%]). Variants involving a FRET pair between PDZ3 and the rest of the module showed broad, irregular distributions indicating heterogeneity in their conformations. FRET variants spanning only the SH3-GUK domain showed less heterogeneity.

They used smTIRF to show that the truncated PSG module results in broader FRET distributions than when it is contained within full-length PSD95. They used donor-acceptor cross-correlation amplitudes to show a uniform increase in FRET transitions in the truncated compared to full-length variants. The authors refined the seTCSPC data from truncated and full-length variants using PDA. That analysis suggests that the slowest exchanges between conformations were slowed further by the truncation. The results from PDA agreed well with measurements made with smTIRF. A global fit of seTCSP for the truncated PSG module demonstrated two states (A and B) similar to the full-length protein, but slightly reduced the occupancy of state B (48%).

To resolve fast conformational dynamics, the authors performed fFCS. They then used global fitting to assign three principal decay times representing 1) fast local motions, 2) slower domain reorientations that alter interdomain interaction interfaces, and 3) the slowest large-scale translational transitions between the two energy basins (A and B). They concluded that the major contributions to dynamics came from the fast local motions, and from the slowest large-scale conformational transitions between states A and B.

The authors next performed DMD simulations to map the conformational energy landscape of the PSG supramodule. They found that PDZ3 primarily adopted a docked medium conformation (α) with a mean Rg of 27.6 Å and a more compact conformation (β) with a mean Rg of 23.4 Å. Further analysis revealed a 2D free energy profile with a broad low energy basin corresponding to conformation α with PDZ3 closer to SH3 and localized near the HOOK insertion. A second shallower energy basin shows PDZ3 in the β conformation localized closer to the GuK domain. The two basins are separated by a 2.0 kCal /mol energy barrier. The authors conclude that α and β-basins correspond to states A and B deduced from FRET analyses.

The authors performed disulfide mapping with discrete cysteine substitutions to test the predictions of close pairwise interactions between residues in the two basins. The α-basin variant pair showed slightly more disulfide formation than the β-variant pair but the rates of formation were similar. In truncated variants, the rate of disulfide formation increased by 30% for the α-basin variant but decreased six-fold for the β-basin variant. The disulfide cross-linking data is consistent with the conclusions reached from FRET analyses and DMD, including the finding that truncation of PSD-95 to remove PDZs 1 and 2 produces significant differences in the conformational kinetics of the PSG supramodule.

The authors next modeled the structures of the PSG supramodule in the two limiting states (A and B) using FRET distances as restraints. Rigid body docking and screening against DMD simulations were performed using the FRET Positioning and Screening (FPS) software. The best fit models for state A showed PDZ3 adopting positions near the HOOK insertion, and, less often, extended without interdomain contacts. The best fit models for state B were more tightly clustered with PDZ3 positioned near the nucleotide binding pocket of the GuK domain.

To independently corroborate these docking models, the authors calculated the accessible volume (AV) for all the structures from the DMD trajectories. The analyses shows that the interdomain interactions between PDZ3 and SH3 (the α-basin) are "fuzzier" or more varied, than the interactions between PDZ3 and the GuK domain (the β-basin).

One protein that interacts with the PDZ3 domain in vivo is neuroligin. Because some of the conformations of PDZ3 in the α-basin would block access to the PDZ-ligand binding site of PDZ3, the authors placed fluorescein at the N-terminus of a 10 residue C-terminal neuroligin peptide containing the PDZ ligand. They used the peptide to measure binding to PDZ3 alone, and to the PSG supramodule by fluorescence anisotropy. They found that binding of the neuroligin peptide to PDZ3 alone is strongly pH dependent being reduced at pH 7; but in the PSG supramodule binding is slightly stronger at pH 7. They identified stabilizing electrostatic interactions of histidines in the neuroligin peptide with acidic residues in PDZ3 that are predicted to occur in several of the α-basin conformations. They conclude that the supertertiary context of PDZ3 enables the recognition of this critical physiological ligand.

The overall research strategy and the transparent sharing of complicated structure/dynamics results should serve as a textbook example to the field. The addition of FRET-unbiased DMD and, later on, integration of DMD and FRET data in FRET-restrained modeling is a much-needed step to translate smFRET data to structural models and this paper paves the way to enable such investigations for other researchers. Finally, disulfide mapping is an excellent strategy to confirm predictions of the smFRET/DMD results. Nonetheless, there are some potential inconsistencies and issues that need to be clarified.

1. The structures of the PDZ domains of PSD95 have been determined and they are well-folded and stable. In addition, the PSG module has been shown to adopt a stable structure after expression and purification. The authors should cite papers, their own and those by Zeng et al. (e.g. J. Mol. Bio, 2018), to reassure readers that the protein is not destabilized by the cysteine mutations. The authors need to state how many purifications of the mutants have been done and how many replicates have been made for the FRET measurements. Did the FRET data change over time?<br /> 2. The authors have not explained how the approach taken in this paper compares to their previous simulated annealing approach of mapping PDZ3 using FRET data in McCann et al., 2012. That study resulted in a model in which PDZ3 binds to a completely different interface, which is not mentioned in this manuscript.<br /> 3. The biochemical disulfide (DS) mapping experiments provide a useful check of predictions of the FRET and DMD conclusions. However, in order to interpret these correctly, the authors need to show data from negative controls testing cysteine pairs that are predicted NOT to interact.<br /> 4. The SH3-GUK domain of PSD95 can undergo domain swap dimerization and the dimerization is promoted by binding of the synGAP PDZ-ligand to PDZ3. The authors should mention the existence of domain-swap dimerization (citing McGee [2001] and Zeng et al. [2018]) and indicate whether they tested that the FRET-labeled proteins are monodisperse. This is particularly important in light of the high variation in diffusion time for individual variants - 0.91-10.19 ms (see also #10 below). In particular, the P3-G4 FRET variant has a long diffusion time of 10.19; could it be undergoing domain swap dimerization?<br /> 5. On page 4, line 5 the authors state: "the number and occupancy of conformational states were set as global fitting parameters". This assumes that the protein is unbiased by the labeling and that the protein behaviour is independent of the purification batch. Have the authors verified this?<br /> 6. On line 6 the authors state: "Based on fitting statistics, we demonstrate that a two-state model with a small donor-only (or no FRET) population (Supplementary file 1C &D) is sufficient to fit all data.". From the average chi^2 this can be concluded, but for individual datasets sometimes a 1 state model or 3 state model seems more appropriate. The authors should explain why measuring more cys mutants justified using 'one unifying model'? How can the data contain donor-only contributions if pulsed-interleaved excitation (PIE) is used to select only molecules with active donor and acceptor fluorophores?<br /> 7. All variants are shown to be dynamic, but they are positioned differently on the dynamic FRET line (Fig. 1D and S3). Does the same kinetic model underly each variant? If the same state occupancies are implied, then why not the same kinetic constants, especially for distances probing the same two domains?<br /> 8. Could the data also be explained by "fuzziness" within domains, without interdomain dynamics? The authors should discuss this given the possibility of domain swap dimerization of the SH3-GuK domain.<br /> 9. Regarding supplemental File 2: The authors should justify that PDA is an appropriate method to quantify relaxation time of Fluorophores. Dynamics being so fast, how do the authors explain that when binned in 2 ms time bins, discrete subpopulations in the PDA histograms are still clearly observed (e.g. Figure 2B, Fig. 2 supp 3)? Why would the protein move through certain very discrete states and not others? Doesn't this imply that the model is oversimplifying the actual mechanism (even though the Chi^2 is alright)? It is strange that for some mutants (fig 2 supp 3B P1G3) PDA displayed discrete states, while for others (e.g. fig 2 supp 3A P2G6) PDA histograms were smooth, implying it cannot be a low-histogram-count artifact. Or can it?<br /> 10. Regarding supp file 3A and Table S9: The spread on tdiff, (the average diffusion time through the confocal volume) for individual variants is very broad - 0.91-10.19 ms. Considering that the authors use global fits for many different parameters, it's surprising that they didn't use it for this parameter which should unbiasedly be the same for all the protein mutants, at least if all are well-behaved (i.e. non-aggregating). The high variation in tdiff may be a warning that the model is not accurately accounting for all dynamics. For example, might the P3-G4 variant be undergoing domain swap dimerization?<br /> 11. In the results section the authors state: "Summarizing the dynamics observed for the PDZ3-GuK variants, fFCS depicts three relaxation times." This is an overstatement because the authors imposed these three broad relaxation times. The authors should describe how they made these assignments. Is this common practice? Regarding Supplemental File 2 versus Supplemental File 3A: In principle, the relaxation time implied from fFCS and that from PDA should align. However, the 'Average' of fFCS and the T_R of PDA do not align. Is it possible that the dynamics analysis from PDA should have been constrained in some way by the results from fFCS? It would be useful to add error estimations for PDA here.<br /> 12. Regarding the DS bond formation data, the authors state, "The α-basin variant showed slightly more DS formation than the beta-basin variant in full-length PSD-95 but the rates of DS formation were similar". It isn't clear what this means physically. It seems to suggest that there is static heterogeneity in the population, i.e. some proteins can and some proteins cannot form DS bonds. The presence of this effect may contradict the assumption that every state at some point interconverts to any other state, which underlies the FRET PDA analysis. The authors should discuss this possible inconsistency.<br /> 13. In the discussion of the DS experiments, the authors state, "We also observe significant kinetic differences when PSD-95 is truncated in agreement with FRET studies." This sentence is vague. The authors need to state more completely what they mean here. Exactly what is in agreement with the FRET studies?<br /> 14. The text in the section on "Structural Modeling with Experimental FRET Restraints" is often unclear. The authors appear to have equated States A and B, formerly used only in the seTCSPC analysis to the alpha and beta basins extracted from the DMD snapshots. The authors should discuss whether there might be other conformations in the DMD results that would be consistent with the FRET-derived distances from seTCSPC? It seems possible that there could be, given that in Fig 6 sup 1, large discrepancies exist between simulated distances and FRET-measured distances for some of the FRET pairs. The authors should discuss explanations for the discrepancies that do not compromise the actual model.<br /> 15. A weakness of the modeling approaches in this manuscript is that they are difficult to validate. Could the authors include a test of the modeling in which they show how small changes of the input FRET data would influence the final FRET-restrained model? Could they quantify their confidence in the final model, given all the limitations of the FRET data?

Reviewer #1 (Public Review):

This manuscript by de la Vega and colleagues describes Neuroscout, a powerful and easy-to-use online software platform for analyzing data from naturalistic fMRI studies using forward models of stimulus features. Overall, the paper is interesting, clearly written, and describes a tool that will no doubt be of great use to the neuroimaging community. I have just a few suggestions that, if addressed, I believe would strengthen the paper.

Major comments<br /> 1. How does Neuroscout handle collinearity among predictors for a given stimulus? Does it check for this and/or throw any warnings? In media stimuli that have been adopted for neuroimaging experiments, low-level audiovisual features are not infrequently correlated with mid-level features such as the presence of faces onscreen (see Grall & Finn, 2022 for an example involving the Human Connectome Project video clips). How to disentangle correlated features is a frequent concern among researchers working with naturalistic data.

2. On a related note, do the authors and/or software have opinions about whether it is more appropriate to run several regressions each with a single predictor of interest or to combine all predictors of interest into a single regression? (Or potentially a third, more sophisticated solution involving variance partitioning or another technique to [attempt to] isolate variance attributable to each unique predictor?) Does the answer to this depend on the degree of collinearity among the predictors? Some discussion of this would be helpful, as it is a frequent issue encountered when analyzing naturalistic data.

3. What the authors refer to as "high-level features" - i.e., visual categories such as buildings, faces, and tools - I would argue are better described as "mid-level features", reserving the term "high-level" for features that are present only in continuous, engaging, narrative or narrative-like stimuli. Examples: emotional tone or valence, suspense, schema for real-world situations, other operationalizations of a narrative arc, etc. After all, as the authors point out, one doesn't need naturalistic paradigms to study brain responses to visual categories or single-word properties. Much of the work that has been done so far with forward models of naturalistic stimuli has been largely confirmatory (e.g., places/scenes still activate PPA even during a rich film as opposed to a serial visual presentation paradigm). This is a good first step, but the promise of naturalistic paradigms is ultimately to go beyond these isolated features toward more holistic models of cognitive and affective processes in context. One challenge is that extracting true high-level features is not easily automated, although the ability to crowdsource human ratings using online data collection has made it feasible to create manual annotations. However, there are still technical challenges associated with collecting continuous-response measurement (CRM) data during a relatively long stimulus from a large number of individuals online. Does Neuroscout have any plans to develop support for collecting CRM data, perhaps through integration with Amazon MTurk and/or Prolific? Just a thought and I am sure there are a number of features under consideration for future development, but it would be fabulous if users could quickly and easily collect CRM data for high-level features on a stimulus that has been uploaded to Neuroscout (and share these data with other end users).

4. Can the authors talk a bit more about the choice to demean and rescale certain predictors, namely the word-level features for speech analysis? This makes sense as a default step, but I wonder if there are situations in which the authors would not recommend normalizing features prior to computing the GLM (e.g., if sign is meaningful, if the distribution of values is highly skewed if the units reflect absolute real-world measurements, etc). Does Neuroscout do any normalization automatically under the hood for features computed using the software itself and/or features that have been calculated offline and uploaded by the user?

Reviewer #1 (Public Review):

Hart et al set out to determine how cancer cells lacking succinate dehydrogenase are able to proliferate despite having dysfunctional mitochondria. They use cell-line based models to show that inhibition of mitochondrial complex I rescues succinate dehydrogenase (SDH) deficient cells and that cells adapt to loss of SDH by decreasing complex I levels. The authors propose that lack of complex I causes an increase in mitochondrial NADH and that increased mitochondrial NADH leads to a restoration of Asp levels. The study confirms and complements previous work addressing these relationships and the experiments are expertly performed. The authors do a nice job of using genetic tools, for example, to modulate mitochondrial NADH levels, and the metabolomics analysis is of a high quality. The conclusions of this paper are mostly supported by data, but some aspects of the analysis of Asp levels and the NAD+/NADH ratio need to be clarified. Extensive previous work has detailed a complex relationship between mitochondrial dysfunction, Asp levels and proliferation. In this version of the manuscript, the authors advance our understanding, but do not unambiguously demonstrate a molecular mechanism that fully explains how complex I inhibition rescues loss of SDH.

Reviewer #1 (Public Review):

The authors show that the unmitigated generation interval of the original variant of SARS-CoV-2 is longer than originally thought. They argue that in the absence of interventions that limit transmission late in the course of infection, the fraction of transmission events that occur before symptom onset would be considerably lower, and the fraction of transmission events occurring 10 days or more after infection of the index case would be substantially higher.

These findings improve our ability to accurately estimate the basic reproductive number (R0), to evaluate quarantine and isolation policies, and to model counterfactual intervention-free scenarios. Many applied analyses rely on accurate generation interval estimates. To head off confusion, it would be helpful if the authors could provide more comprehensive guidance about which applied analyses should be informed by the unmitigated generation interval, or the observed generation interval. (E.g. the unmitigated interval is useful for quarantine and isolation policies, but would we ever want to use the unmitigated interval to estimate R?).

The analysis estimates a longer generation interval after accounting for three main sources of bias or error that are common in other analyses:<br /> 1. Recently infected individuals are intrinsically overrepresented in data on a growing epidemic. Thus, shorter incubation periods and forward serial intervals are more likely to be observed, even in the absence of interventions. This analysis adjusts for these dynamical biases.<br /> 2. Interventions or behavioral changes can prevent transmission late in the course of infection. This can shorten the generation and serial intervals over the course of an epidemic. This analysis focuses specifically on transmission pairs observed very early, before the adoption of interventions.<br /> 3. The incubation period and generation interval should be correlated - infectors that progress relatively quickly to symptoms should also become infectious sooner (symptom onset occurs near the peak of viral titers). Most existing analyses assume these intervals are uncorrelated, but this analysis accounts for their correlation.

Overall, the conclusions seem reasonable and well-supported. The observation that the generation interval decreases over the course of an epidemic is also consistent with existing studies that show the serial interval has similarly decreased over time. But given the implications of the findings, I hope the authors can address a few questions about potential additional sources of bias:

1. It is somewhat reassuring that the generation interval decreases relatively smoothly as the cutoff date increases (Fig. S6), but it would be helpful if the authors address the potential impact of ascertainment biases. One of the main reasons that the authors estimate a shorter generation interval is that they define January 17th, early in the outbreak before interventions and behavioral changes had taken place, as the cutoff point for the infector's date of symptom onset. This cutoff eliminates biases from interventions, but it also severely limits the size of the transmission-pair dataset (Fig. S3), and focusing on this very early subset of cases may increase the influence of ascertainment bias. Prior to January 17th, should we expect observed transmission pairs to involve more severe cases on average? And is the unmitigated generation interval correlated with case severity?

2. The analysis assumes the incubation period follows a fixed distribution, whose parameterization comes from a meta-analysis of previously estimated incubation periods. But p.5 discusses the idea that observed incubation periods are affected by the same dynamical biases as forward serial intervals,

"For example, when the incidence of infection is increasing exponentially, individuals are more likely to have been infected recently. Therefore, a cohort of infectors that developed symptoms at the same time will have shorter incubation periods than their infectees on average, which will, in turn, affect the shape of the forward serial-interval distribution."

Has the incubation period been adjusted for these dynamical biases, or should it be?

3. It appears that correlation parameter estimates co-vary with estimates of the mean generation interval (Fig. S6; S13b). Are the authors confident that the correlation parameter is identifiable? How much would the median generation interval estimate in the main analysis change if the correlation parameter had been fixed to 0 (which isn't realistic) or to 0.5 (which might be plausible)?

Reviewer #1 (Public Review):

The authors screen large libraries of small proteins to identify three proteins of <50 aa that rescue the growth of an auxotrophic serB deletion Escherichia coli strain. They convincingly show that the growth rescue is due to the small proteins increasing expression of the his operon by reducing transcriptional attenuation. The authors argue that the small proteins function by directly binding the his RNA 5' UTR to alter RNA secondary structure.

The conclusion that the three small proteins reduce his operon attenuation is well supported by the data. A previous study suggested this mechanism for a somewhat larger, randomly selected protein, but the current study extends this prior work by firmly establishing that the proteins modulate attenuation. The suggestion that the small proteins function by directly binding the his RNA is less well supported by the data. The RNase T1 mapping data are not straightforward to interpret, and there is no assessment of protein-RNA interactions in vivo.

Major comments:

1. The RNase T1 probing data are not straightforward to interpret, and hence are insufficient to conclude that Hdp1 binding to the his 5' UTR is the mechanism by which it reduces attenuation. Specifically, G96 has reduced cleavage in the presence of Hdp1, inconsistent with the antiterminator conformation. The authors argue that G96 could be within the site of Hdp1 binding. This is certainly possible but would require additional experimental evidence to draw a confident conclusion. Also, the increased cleavage of bases around the start codon and Shine-Dalgarno sequence is inconsistent with a shift from the terminator to the antiterminator conformation. One confounding issue here is the lack of replicates and the lack of quantification. Additional probes could be tested, which would provide complementary structural information.

2. There are no experiments to test whether Hdp1 binds the his RNA in vivo. The in vitro data show that Hdp1 can bind the his RNA, but they do not show that this occurs in vivo, or that this is the mechanism by which Hdp1 regulates the expression of the his operon.

Reviewer #1 (Public Review):

In this manuscript, the authors proposed that SEMA3F inhibits PI3K activity in vascular endothelial and smooth muscle cells to provide an atheroprotective effect. The authors used a combination of cell culture and in vivo studies to provide evidence that loss of SEMA3F increases PI3K signaling. The authors performed detailed analyses to identify the involved mechanism through which SEMA3F inhibits PI3K activity. This work provides some novel directions for SEMA3F-based candidates for atheroprotection. However, there are a few limitations that need to be addressed:

1. Authors used global KOs of SEMA3F which does not allow to distinguish whether SEMA3F from endothelial or smooth muscle or macrophage is contributing to the atheroprotective effect. How about compensatory mechanism(s) activated in response to SEMA3F global deletion? Some discussion about it will enhance quality of work.

2. Authors used Wortmannin as a PI3K inhibition. Wortmannin is not a selective inhibitor and it also disrupts the actin cytoskeleton. The authors should use another PI3K inhibitor to confirm their findings.

3. It is unclear which class or subtype of PI3K is affected in response to SEMA3F signaling.

4. Actin blot in Figure 3D is not acceptable wand would need to be replaced. Molecular weight markers need to be included in all the representative western blots.

5. Authors did not perform any vascular functional measurements (with vessel arteriographs). The results were mostly collected from in-vitro cell culture studies along with limited in vivo measurements. This should be listed as a limitation.

6. A schematic with the proposed mechanism will enhance the readability of the work.

Reviewer #1 (Public Review):

Shaheen and Tse et al. investigate the mutational landscape of lymphatic malformations, find numerous PIK3CA mutations (as expected), and NRAS mutations (in what likely is a cohort of complex lymphatic anomalies), as well as a GOPC-ROS1 fusion. They present data on the treatment of one patient with an unresectable LM, show immunohistochemistry, and in vitro data on alpelisib's effect on LEC cultures.

Strengths:

- The authors present a sample size of 30, which is a good number of patients with these rather rare diseases.<br /> - The broad approach analyzing the mutational landscape led to the identification of a GOPC-ROS1 fusion, which I thought was very interesting, as this has not been described in the setting of vascular malformations so far.<br /> - The attempt to perform RNA-Seq on LM tissue is laudable. However, I wonder, if more discussion on the selected tissue (also keeping in mind the low variant allele frequency), the low number of patients analyzed with RNA-Seq (only one case), and the choice of control tissue would be warranted

Weaknesses:

- The presentation of the clinical data in table 1 is very short and patchy and seems incomplete, also some of the classifications don't appear to be correct E.g. PTEN hamartoma tumor syndrome is a genetically distinct entity, that does not harbor somatic PIK3CA mutations but rather germline PTEN mutations. There are 5 patients with CLOVES and 1 patient with KTS, these patients often have mixed (e.g. lymphatic-venous) malformations, are the analyzed samples truly pure LMs? There are some more instances where I wonder if the presented data allows the reader to understand the cases.<br /> - If the histology is described as kaposiform, these cases likely represent kaposiform lymphangiomatosis, which is a very different disease than common LMs. KLA belongs to the group of complex lymphatic anomalies and usually is caused by NRAS mutations, which would be in line with the presented data. Case 24 (conventional histology, NRAS mutation) could also be a generalized lymphatic anomaly. This distinction of common LMs and complex lymphatic anomalies (including GLA and KLA) should be made and should include what is known about the genetics of these diseases.<br /> Taken together with the first point, the presentation of the cases might benefit from a more structured description and classification.

- In the discussion and other parts of the manuscript, terms describing LMs and tumors are interchanged frequently. This mistake is also present in the study protocol (NCT03941782), in which "locally advanced or metastatic cancer" is listed as an inclusion criterion. Other examples include "tumor nuclei". Much of the cited literature also focuses on oncology rather than vascular malformations. And LMs are directly compared to "other low-grade pediatric tumors".<br /> Also, clonality is a concept not too often used in vascular malformations, as an aberrant development of vascular structures during embryogenesis is seen as the cause of vascular malformations, as opposed to clonal expansion in tumors (but this might warrant further investigation in the field).<br /> Thus, the manuscript mixes tumors and malformations, however, it should be stressed, that LMs are not tumors but vascular malformations.

- The explanation for the reduced EF doesn't quite make sense, as there should be little blood flow into the LM. This is different from the Venot paper in Nature, where the reduced EF was due to the presence of an AV malformation.

- The data on the (back at the start of treatment of the patient novel) alpelisib is presented as a rather new finding. However, clinical data on its use in PROS diseases have already been published starting in 2018 (the paper from Venot is also mentioned in the manuscript). At the moment, an international clinical trial on alpelisib in PROS disease is recruiting, which could be mentioned.<br /> - Treatment: The rather high dose of 350 mg/d is not further discussed. Also, a patient like this would usually first receive sirolimus, especially back when alpelisib was started in this patient since it was much more experimental at that time point. This should also be explained.

In summary: The manuscript shows data that is in line with the current state of knowledge in the field of vascular anomalies. The presented data, while not novel, support the conclusions of the manuscript. The manuscript harbors some inaccuracies in the nomenclature of vascular malformations, which should be corrected.

Reviewer #1 (Public Review):

This manuscript confirms and extends a recent study from this same group analyzing mosaicism in sperm and transmission of new mutations to relevant offspring. The current work extends this analysis to human blastocysts from in vitro fertilization for three subjects (a total of 55 blastocysts), demonstrating transmission of mosaic mutations at close to expected frequencies. The experiments represent a carefully performed genetic study by cutting edge genetic sequence analysis in a uniquely relevant, but very limited set of human samples. The weakness of this study is that the findings are largely expected and represent an incremental advance relative to the analysis of sperm mosaicism recently published by the same group (Yang et al, Cell, 184:4772, 2021). Nonetheless, these valuable data from primary human blastocysts are the first of their kind and of translational relevance for the field of clinical genetics and prenatal genetic testing, with the potential to contribute to strategies to reduce genetic disease risk in future offspring. The computational genomic analysis is cutting-edge and the methods protocol reported here could prove useful to other workers in the field.

Reviewer #1 (Public Review):

This study by Hurwitz et al. defines a functional relationship between the ISR and microtubule dynamics. This is mediated through the mRNA-specific translation of genes including ATF5 in the context of proteotoxic stress. They further show that this relationship is particularly important in the context of recovery from bortezomib treatment which in the WT setting leads to efficient clearance of protein aggregates. However, this process is significantly less efficient when the ISR is impaired through phosphorylation defective eIF2alpha. The authors use whole transcriptome ribosome profiling to identify mRNAs that are differentially translated upon treatment with bortezomib and uncover a subset of mRNAs which exhibit an increase in translation. This list of 24 mRNAs includes ATF5 which they functionally show is important for cell survival in the context of proteotoxic stress. Overall, this work is solid and provides new insights into SCC management proteotoxic stress. This work reveals a new arm of cellular regulation dynamics that is controlled by the ISR and helps grow our understanding of how the ISR enables survival in the context of distinct stress.

Reviewer #1 (Public Review):

The software is open source and runs in any of: a desktop computer, a computer cluster, or a cloud computing cluster. The approach minimizes costly image I/O by storing extracted image features and their correspondences to those of other images, both in-section and cross-section. Costly image data storage is minimized by avoiding image duplication in disk, with images being rendered on demand by loading images from disk and applying transformations on the fly.

ASAP software can operate concurrently with image acquisition, therefore fulfilling a most sought after feature: avoiding slowing down connectomics.

The basic steps are first computing the lens distortion correction, specific of each microscope. Second extracting scale invariant image features, which are used to estimate affine transformations for in-section and coarse cross-section alignment. Third estimating elastic section-wide 3D transformations. All operations run on scaled down versions of the images for best performance and also to match the effective feature size across multiple adjacent sections. The overall approach builds on prior work by Kaynig et al. (2010) Saalfeld et al. (2010, 2012) and Cardona et al. (2012) among others, with an implementation impressively capable of scaling to petabytes of images.

Reviewer #1 (Public Review):

This is a fine paper linking birth order to connectivity in a specific example in Drosophila. The stated key findings of the work are (1) the identification of sharp temporal cohort divisions for the lineages under investigation, (2) synapse formation between neurons of different lineages and temporal cohorts, and (3) the observation that output neurons in this instance are born prior to input neurons. The strengths of the manuscript lie in a thorough and solid characterization of all three aspects. The experiments and data are of a high level and the results highly informative for the 'feed-forward circuit' under investigation. I see the main weakness in overinterpretations of results that may rather represent statistically expected findings for a specific study, questioning generalizations and the attempt to formulate more general principles. While the conclusions regarding (1) leave little room for interpretation, the findings regarding (2) and (3) may require some clarifications, representing a weakness of the manuscript.

Reviewer #1 (Public Review):

In this article the authors use paired gene expression and chromatin accessibility data on isolated Sox9 positive progenitors to identify a role for Pi3K in lung epithelial differentiation and branching. The authors show some intriguing findings.

However, some additional experiments are necessary to confirm/validate their conclusions.<br /> Some issues with the current experiments make them hard to interpret.<br /> 1) While the experiments in Fig.6 show an increase in branching morphogenesis after treatment with different inhibitors, it is unclear whether this is because inhibition of Pi3K in the epithelium or mesenchyme.<br /> 2) Similarly it is difficult to assess whether the effect on Sox9+ EPCs is due to the inhibitor acting on the epithelium or mesenchyme.<br /> 3) In the abstract the authors mention that prior to E13.5, SOX9+ progenitors are multipotent, generating both airway and alveolar epithelium, but are selective alveolar progenitors later in development. To further investigate this the authors isolated Sox9 positive progenitors at 11.5 and 16.5. The authors then as expected find some genes being differentially expressed in the progenitors at these different time points. However, while these changes in expression likely reflect the narrowed differentiation potential of the Sox9+ EPCs at E16.5 it is unclear whether this really helps to explain how Sox9+EPCs at E11.5 differentiate into proximal epithelium.<br /> 4)qPCR in Fig.8 reflects the lack of airways but doesn't reflect their differentiation, it appears differentiation in club and ciliated cells still occurs but appears delayed. Differentiation of the bronchial epithelium occurs after Sox9+ EPCs have differentiated into Sox2+ airway cells.<br /> It is unclear if the differentiation of the Sox2+ airway epithelium is delayed or whether Pik3ca plays a role in the differentiation of these Sox2+ airway epithelial cells.

Reviewer #1 (Public Review):

On the basis of 13 endocast data including six new lungfish data, Clement and colleagues conducted the multivariate morphometric analyses using 17 neurocranial variables, and revealed quantitatively several transformational trends of brain in lungfish evolution. With its important anatomical details, this work will be of broad interest to many scientists. The inference in the manuscript is overall clear, and the conclusions are well supported by data. Re-organization of figures and changes to the figures and the text will improve the readability of the manuscript.

Reviewer #1 (Public Review):

Giant dsDNA viruses, with genomes in excess of 1Mb encoding more than one thousand genes, were only recently discovered and their study offers new opportunities to probe life's evolved mechanisms. Little is known how these "organisms" protect and organize their genomes. This fascinating study reveals a helical protein casing comprised of oxidoreductase-family proteins, which assemble 5- or 6-start helices genomic DNA lining the lumen of the helix. The remarkable evolutionary strategy for packaging the genome appears to be a convergent solution by comparison with distant thermotolerant viruses.

Reviewer #1 (Public Review):

In this study, the authors aimed to address the important question of the mechanism of deep brain stimulation (DBS) in treating Parkinson's disease, based on a mouse model that the authors established previously.

The strength of the study lies on 1) avoiding the interference of stimulation artefacts of using electrophysiological recording technique, and 2) examining effects on cell-type or projection-specific targets.

However, there are several critical problems in this study. First, the low temporal resolution and the averaged population signal (rather than from individual neurons) of the fibre photometry data prevents in-depth enough analysis of the effects of DBS on the target areas to draw useful conclusion. Thus, all interpretations were based on an average rise in GCaMP-reported calcium signals with pretty low temporal resolution. As a result, important readouts that were analysed in many previous studies such as the firing patterns (e.g. rhythmic) or synchrony among neurons were missed by this approach. Take one example. The conclusion that antidromic activation is excluded as a possible mechanism is based partly on the lack of good correlation of the averaged calcium signal with the behavioral improvement. However, such a lack of correlation is also evident in the averaged calcium signal and the improvement in movement behavior under 60Hz and 100 Hz stimulation (Figure 2). While a higher average in calcium signal is observed under 60Hz DBS than 100Hz, the improvement in motor behavior is lower than that induced by 100 Hz DBS. This highlights the severe limitation of the fibre photometry data in revealing the therapeutic mechanism of DBS.

Second, there is no clear elucidation of the pathological changes revealed by the fibre photometry in PD mice to illustrate what is normal and what is abnormal, and how the DBS rectifies the abnormal changes. For example, when we need to interpret the effect of the DBS on calcium activities in the subthalamic nucleus (STN), the substantia nigra pars reticulala (SNr) and the primary motor cortex (M1), what abnormal GCaMP signal did the authors find, compared with healthy control mice? Without such information, it is difficult to get a sense of what an increase in GCaMP signal in STN, SNr and M1 mean with respect to motor control, and therefore what it means with respect to the effect of DBS. With the specific context of a peak (actually a biphasic waveform) of the calcium activity in the PD anima, it is puzzling that a surge of STN is correlated with movement onset, while in principle it should result in movement termination. Therefore, it is critical to know if there is there such a correlation in healthy animals. If yes, this may not indicate a pathological change that needs to rectified by DBS. If no, how the pathological appearance of such change leads to parkinsonian motor symptoms (akinesia, bradykinesia etc) must be established.

Third, it is well-known that clinical DBS employed at least 120 Hz stimulation. In fact, the authors had also demonstrated in their previous report that the optimal stimulation frequency in the mouse model is around 180Hz. But the present study utilised clearly suboptimal frequencies (60 and 100Hz only) to address the mechanism. It is possible that different mechanisms or combinations of mechanism may take place under different stimulation frequencies. As such, any conclusion drawn from this study may not represent the whole picture.

Given the above consideration, I do not think that the authors have achieved the aim of their study, as the results cannot convincingly support their conclusions.

Reviewer #1 (Public Review):

Vocal learning has long been assumed to rely mainly on vocal feedback. In finches, birds aim to imitate a memory of a tutor song heard early in life. Here, the authors show that Bengalese finches can also learn from song-targeted cutaneous feedback. When cutaneous stimulation is made contingent on the way a bird sings a note, the bird changes that note to avoid the stimulation. This learning required dopamine-basal ganglia previously implicated in natural vocal learning. Thus vocal circuits can leverage distinct types of sensory feedback for learning.

The learning paradigm by design lacks ecological validity. Thus it is even more surprising that birds could learn.

Reviewer #1 (Public Review):

The authors succeed at generating a large amount of data using a high-throughput platform to measure bacterial growth, analyzing its complexity and deriving some simple rules to model the system. The limited complexity of the system under consideration (with 3 nutrients quantitatively determining all dynamic parameters for this bacterium) suggests that very simple analysis tools would be enough to tackle this large amount of data.

This study is a clear example of a clever combination of high-throughput data generation and machine learning.

Parametrization of growth curves (with lag times, growth rates, and growth saturation plateau as all-encompassing parameters) is simple, accurate and ultimately addressable. Indeed, using the large number of combinations of growth conditions (varied amino acids, metal ions, etc.) at different concentrations. It is very satisfying that a simple growth model and 3 parameters are enough to capture the entire dynamic complexity of these bacterial growth curves in vitro.

The authors argue that the 3 dynamic parameters (lag time, growth rate, and carrying capacity) are essentially bimodal across all conditions (Fig. 2B). A closer inspection of the parameter K actually reflects 4 separatable peaks (see also Fig 7).

Moreover, a simple PCA of the 3 dynamic parameters reveals only 4 separate clusters (while one could anticipate 2^3=8 clusters if the 3 parameters were truly bimodal and independent). The authors need to comment on the missing clusters e.g. what rules forbid some combinations of parameters (cf correlation between parameters as shown in Fig. 7). Additionally, the relevance of the Machine Learning (ML) framework to analyze the data read like over-complicated for a "simple" classification task: the authors need to explain better what insight was derived from the ML analysis compared to simpler/unsupervised PCA and such.

Overall, this study reads strong in its experimental implementation and insight. Additional analysis and easier interpretation will help the reader better assess the relevance of the findings.

Reviewer #1 (Public Review): 

This study examines whether the D2 receptor antagonist amisulpride and the mu-opioid receptor antagonist naltrexone bias model-based vs model-free behavior in a well-established two-step task of behavioral control. The authors find that amisulpride enhances model-based choices, which is further supported by computational modeling of the data, revealing an increase in the relative contribution of model-based control of behavior. Naltrexon on the other hand had no reliable effect on model-based behavior. 

Overall, this is a very nice study with many strengths, including the task and data analysis. A particular strength of the design is the combination of a between-subject drug administration protocol with two within-subject (baseline vs. drug) sessions. This reduces between-subject variability in baseline model-based vs model-free behavior and enhances the power to detect drug effects. 

The introduction could do a better job articulating the rationale for testing the effect of these two specific drugs. Currently, the rationale is that both transmitter systems targeted by these drugs are involved in drug addiction, which is characterized by an imbalance in model-based vs. habitual control of behavior. This appears somewhat indirect. 

Blood draws were used to determine serum levels for amisulpride and naltrexone but these data are not included as covariates in the analysis.

Reviewer #1 (Public Review):

This study focuses on changes in regional cerebral blood flow (rCBF) during infancy, in relation to the emergence of the default mode network (DMN), a fundamental system associated with cognitive processes that are directed towards the self. The authors used cutting-edge complementary MRI modalities allowing them to measure rCBF (with pseudo-continuous arterial spin labelling -pCASL- and phase-contrast -PC- MRI) and functional connectivity (with resting-state -rs- functional MRI) in 48 sedated infants aged between 0 and 24 months. They first showed that rCBF dynamics differ across functional networks, with high changes in cortical regions supporting the DMN. Their analyses further aimed to reveal a coupling between the increase in rCBF in these regions and the increased functional connectivity strength within this network, to suggest relationships between physiological mechanisms and the emergence of functional networks.

* Major strengths of the study:<br /> Relating physiological changes such as the increase in CBF across cortical regions and the development of functional connections supporting cognitive brain networks is a question that has been little asked until now because of the difficulty of acquiring such complementary data in infants. It is nevertheless highly relevant, especially in the context of the DMN development, which is known to be intense in early childhood. This study relies on reliable multimodal data, measured with robust and complementary methodologies, despite the challenge of studying such population. This allowed the authors to highlight spatiotemporal differences in rCBF during infancy, in particular across the DMN regions compared to the visual and sensorimotor networks. The provided analyses support the presented results.

* Major weaknesses of the methods and results:<br /> The actual description of the methods does not allow the reader to evaluate the precision of two important processing steps. First, rCBF measures are supposed to be restricted to the cortex, but given the pCASL image spatial resolution, partial volume effects with white matter probably exist, especially in younger infants. Furthermore, segmenting tissues on the basis of anatomical images (especially T1-weighted) is complicated in the first postnatal year. As rCBF measurements are very different between grey and white matter, the performed procedure might impact the measures at each age, or even lead to a systematic bias on age-dependent changes.<br /> Second, the methodology and accuracy of the brain registration across infants are little detailed whereas it is a challenging aspect given the intense brain growth and folding, the changing contrast in T1w images at these ages, and the importance of this step to perform reliable voxelwise comparison across ages.

* The authors achieved their aim in showing that the rCBF increase differs across brain regions (the DMN showing intense changes compared to the visual and sensorimotor networks). Nevertheless, an analysis of covariance (instead of an ANOVA) including the infants' age as covariate (in addition to the brain region) would have allowed them to evaluate the interaction between age and region (i.e. different slopes of age-related changes across regions) in a more rigorous manner.<br /> Regarding the evaluation of the coupling between physiological (rCBF) and functional connectivity measures, the results only partly support the authors' conclusion. Actually, both measures strongly depend on the infants' age, as the authors highlight in the first parts of the study. Thus, considering this common age dependency would be required to show that the physiological and connectivity measurements are specifically related and that there is indeed a coupling.

* As a whole, this work will have an important impact on the field, as this is a seminal study on the physiological bases of the emergence of functional networks during child development. In addition, both the data and described MRI methods are original and rare.

Reviewer #1 (Public Review):

Following previous publications showing that NR2F2 controls atrial identity in the mouse and human iPS cells, the authors address in the fish the role of the transcription factor Nr2f1a, which is specific to the atrial chamber. This had been initiated in a previous publication (Duong et al, 2018) and is extended in this manuscript. In mutant fish, the atrial chamber is smaller and mispatterned. Markers of the atrioventricular canal and of the pacemaker are expanded. Transcriptomic analyses and electrophysiological measures further support this observation. A putative enhancer of nkx2.5 is identified by ATAC-seq and shown to be repressed in nr2f1a mutants, suggesting that Nkx2.5, a known repressor of pacemaker identity, may be a mediator of Nr2f1a. Overexpression of nkx2.5 delays the appearance of pacemaker cells, and is proposed to partially rescue the absence of nr2f1a.<br /> Overall, this work provides novel insight into the mechanism of atrial chamber patterning in the fish and discusses the conservation of the role of nr2f1a. However, the claim that atrial cells switch their identity into ventricular and pacemaker cells is currently not demonstrated. Alternative hypotheses of mispatterning, cell number changes by proliferation, survival, or ingression are not ruled out by the data presented. The claim that "Nr2f1a maintains atrial nkx2.5 expression" or of a "progressive loss of Nkx2.5 within the ACs" needs to be further supported. The definition of "atrial cells (AC)" varies between figures.

Major comments:

- The definition of "AC" varies from figure to figure: amhc+ in Fig 1A, amhc+vmhc- in Fig.1S1A, amhc+fgf13a- in Fig. 2 and 5, morphological area in Fig. 3. Please clarify how the atrial chamber is delineated in mutants in Fig. 3 since the avc constriction is not obvious.

- The claim of a switch in cell identity or transdifferentiation is not demonstrated. This would require cell tracking or single-cell transcriptomics. I don't see how "AVC (..) [is] resolving to ventricular identity", since amhc seems to be maintained throughout the atrial chamber at all stages. The claim that "the number of vmhc+ only cardiomyocytes progressively increased" is not supported by Fig1S1. The expansion of pacemaker cells may result from cell ingression at the arterial pole. This hypothesis is in keeping with the expression of nr2f1a outside the heart tube in putative atrial progenitors (Duong, 2018). The phenotype upon nkx2.5 overexpression may also be interpreted along this line: ingression of pacemaker cells is delayed. The claim that "PC identity progressively expands throughout nr2f1a mutant atria" is not supported by the quantifications of a mean of 12 fgf13a+amhc+ cells at 96hpf (Fig. 2H), which is as many as fgf13a-amhc+ cells (Fig. 2G) and a quarter of the total amhc+ cells in Fig. 1J. The schema in Fig 6 does not reflect quantifications at 96hpf, which indicate the persistence of amhc+vmhc+ cells, amhc+ only, or amhc+fgf13a- in Fig 1S1 and 2G.<br /> "We did not observe effects on cell death or proliferation in the hearts of nr2f1a mutants": please provide the data, since proliferation was shown to be affected in mouse mutants (Wu, 2013).

- The claim that "Nr2f1a maintains atrial nkx2.5 expression" or of a "progressive loss of Nkx2.5 within the ACs" needs to be further supported by quantification of the number of nkx2.5 positive cells in nr2f1a mutants. It seems that some cells in Fig. 4 co-express nkx2.5 and pacemaker markers in the mutant, which questions the repressive role of Nkx2.5. Following the observation of an nkx2.5 enhancer active next to pacemaker cells in control heart but absent in nr2f1a mutants, shouldn't we expect a gap of nkx2.5 expression next to pacemaker cells in mutants? It is unclear why pacemaker cells express nr2f1a (Fig. 6S1) but not nkx2.5. This needs clarification.

Reviewer #1 (Public Review):

An interesting report by authoritative investigators using computational tools and large amounts of prospective samples from clinical trials to identify different signatures (eg, related to interferon signaling or to cytokine/chemokine production) associated to clinical outcomes in COVID-19. An additional value is the comparison investigation of these signatures also in the context of COVID-19 vaccination. This study adds to the understanding of the host immune response against COVID-19, as well as the potential of computational tools for the molecular taxonomy of immune responses. The next logical step is to apply this methodological approach to predict responses to treatment at the individual levels or to instruct enrolment in clinical trials or administration of targeted immunomodulatory agents.

Reviewer #1 (Public Review):

In this study, Xin Shen et al. aim to establish a link between prolonged β-Andrenergic receptor (β-AR) stimulation and the fragmentation of Calcium Release Units (CRUs) in healthy cardiomyocytes and demonstrate that phosphorylation of ryanodine receptor (RyR) by downstream effectors of β-AR (PKA and CaMKII) is the driver of this fragmentation. They then aim to measure the effects of prolonged β-AR stimulation on the measurable properties of calcium-induced calcium release (CICR) and deduce the consequences for CICR efficacy. Finally, the authors seek to infer the role of β-AR induced CRU fragmentation in heart failure by comparing the results obtained from healthy β-AR stimulated cardiomyocytes with results from failing cardiomyocytes.

This work is a logical progression from a previous study demonstrating RyR dispersion in failing cardiomyocytes (Kolstad et al. eLife 2018;7:e39427) and the synergy of super-resolved RyR imaging, functional Ca2+ imaging, and Ca2+ spark simulation is the paper's strength. This work also takes full advantage of the previously published extension of the super-resolved imaging of RyR clustering to three dimensions (Shen et al. J Physiol 597.2 (2019) pp 399-418) in order to obtain more accurate reconstructions of CRUs and to also enable the reconstruction of correlated t-tubule/RyR images. Correspondingly, the supporting spark simulations are now generated using spatial models that are a more realistic 3D representation of the range of dyadic CRU organisation. A potential weakness in their approach lies with the use of indirect immuno-labelling of receptors which will introduce larger linkage errors to the RyR localisation data, compounding the localisation error. That said the linkage error appears to have been reduced somewhat by the judicious selection of an Alexa-647 labelled Fab secondary. Plus intuitively, greater positional uncertainty is likely to result in a small systematic underestimation of CRU fragmentation that would not have a major impact on the relative results reported in this study or their interpretation.

Generally, the experiments in this paper are well thought out, with appropriate controls, and achieve the aims of the authors. Their 3D dSTORM data indicates that after 60 minutes of β-AR stimulation there is a significant disruption of RyR clustering in dyadic CRUs. Both PKA and CaMKII activity is convincingly implicated by control experiments that partially reverse the cluster dispersion when the activity of either effector is inhibited. Non-phosphorylatable or phosphomimetic RyR mutants are offered as direct evidence that the phosphorylation of RyR by CaMKII indeed drives the observed RyR dispersion, but it is notable that the same controls are not available for the site of RyR phosphorylation by PKA. The properties of Ca2+ sparks, transients, waves, and spark-mediated Ca2+ leak are thoroughly quantified before and after prolonged β-AR stimulation. The measured disruption to CICR is then shown to be reversible by inhibition of CaMKII and PKA. Simulations of sparks using a mathematical model are required to predict the effects of β-AR stimulation on spark fidelity and silent Ca2+ leak, in order to complete the picture. It is reassuring that the simulation results do not contradict experimental results for spark time course variables that are reported by both and the authors are careful to distinguish between observations made in vivo and in silico. 3D dSTORM and Ca2+ imaging results obtained from post-infarction cardiomyocytes were found to mimic important aspects of the results β-AR stimulation of healthy cells including their reversibility by CaMKII or PKA inhibition. The totality of this wealth of data is consistent with RyR disruption during heart failure being caused by the downstream effects of prolonged β-AR stimulation.

Interestingly, their results suggest that the downstream effects of prolonged β-AR stimulation have different functional consequences in healthy and failing cells. This has implications for why β-AR blockade can have a beneficial effect on failing cardiomyocytes and also if control of RyR dispersion is to be considered as a potential therapeutic target. Additionally, with this work as an example, the correlative 3D localisation microscopy-confocal reconstruction technique is likely to find applications in the study of other cellular processes.

Reviewer #1 (Public Review):

Bistable visual perception, given its switching visual perception for constant inputs, offers a unique window to study how our perception arises and changes via an interaction between bottom-up and top-down processes. Previous studies have mainly focused on perceptual contents, while how perception is sustained over time, i.e., perceptual stability, as well as its potential dissociated neural mechanism from perceptual content, remain unclear. Hardstone et al., used magnetoencephalography (MEG) recordings in combination with advanced approaches and revealed dissociated neural implementations for perceptual content and perceptual stability, that is, slow cortical potential (SCP) and alpha-beta neural oscillations, respectively.

This is a very interesting study addressing important questions in bistable perception and the findings would be generally interesting for broad fields, including perception, consciousness, attention, and working memory. The authors have carefully designed several conditions for comparisons, e.g., Ambiguous, Unambiguous, Discontinuous. The last condition is particularly interesting given its close link to perceptual memory, a less touched field. Moreover, the neural state-space analysis is an innovative approach to complement the widely used multivariate decoding method, by projecting MEG data to the subspace that covaries with various behavioral matrices. The experiment was well-motivated based on previous findings, and the paper was well written.

Meanwhile, although the multivariate content decoding results support the separation of SCP and alpha-beta oscillation, it seems to be a mixed case for perceptual stability, i.e. SCP still shows representations of perceptual duration as well as the trend of tracking perceptual memory for the Discontinuous condition. These seem to somewhat weaken the perception/stability dissociation conclusion.

Reviewer #1 (Public Review):

This fMRI study investigated how memories are updated after reinterpreting past events. Participants watched a movie and subsequently recalled individual scenes from that movie. Importantly, the movie ends with a twist that changes the interpretation of earlier scenes in the movie. One group of participants watched the movie with the twist at the end, one group did not get to see the twist, and a third group was already informed about this twist before watching the movie. Analyses compared the similarity of activity patterns to (encoded or recalled) events across participants within regions of the default mode network (DMN). The design allowed for multiple relevant comparisons, confirming the prediction that activity patterns in DMN regions reflect the (re)interpretation of the movie (during movie viewing and/or during recall).

The study is well-designed and executed. The inclusion of multiple analyses involving distinct comparisons strengthens the evidence for the role of the DMN in memory updating.

The following points may be relevant to consider:

1. The cross-participant pattern analysis method used here is not standard, with such analyses typically done within participants (or across participants, but after aligning representational spaces). Considering individual variability in functional organization, the method is likely only sensitive to coarse-scale patterns (e.g., anterior vs posterior parts of an ROI). This is not necessarily a weakness but is relevant when interpreting the results.

2. Unlike previous work, analyses are not testing for scene-specific information. Rather, each scene is treated separately to establish between-group differences, and results are averaged across scenes. This raises the question of whether the patterns reflect scene-specific information or generic group differences. For example, knowing the twist may increase overall engagement, both when viewing the movie (spoiled group) and when recalling it (spoiled group + twist group). The DMN may be particularly sensitive to such differences in overall engagement.

3. The study does not reveal what the DMN represents about the movie, such that its activity changes after knowing the twist. The Discussion briefly mentions that it may reflect the state of the observer, related to the belief about the identity of the doctor. This suggests a link to the theory of mind/mentalizing, but this is not made explicit. Alternatively, the DMN may be involved in the conflict (or switching) between the two interpretations.

4. The design has many naturalistic aspects, but it is also different from real life in that the critical twist involves a ghost. Furthermore, all results are based on one movie with a specific plot twist. It is thus not clear whether similar results would be obtained with other and more naturalistic plot twists.

5. Only 7 scenes (out of 18) were included in the analysis. It is not clear if/how the results depend on the selection of these 7 scenes.

Reviewer #1 (Public Review):

This manuscript by Chini et al., investigates the role of local inhibition in the maturation of neural activity within the developing prefrontal cortex. They employ a range of methods to tackle this problem, including in vivo recordings, optogenetic manipulations and computational modeling. Their findings provide evidence that support recent studies that claim GABA may be inhibitory within the early brain (i.e. GABA reversal is below spike threshold), even though the GABA reversal potential may be more positive relative to the adult. This idea is not new, but there has been ongoing debate within the field for some time. The evidence they provide nicely supports their interpretation that GABA inhibits firing. One main advance of the study is that they perform these experiments in the prefrontal cortex, whereas previous studies have focused on sensory regions whose developmental timeline may differ. They also compare across mice and humans, showing that, at least at the level of neural activity, findings from mice mirror what is happening in the developing human brain. This suggests that the mechanisms they describe are shared across species. Finally, they extend their analysis to a recently published mouse model of neurodevelopmental disorder, observing that the changes occurring in the healthy brain are not present in these mice. Overall, their data support their claims and are consistent across a range of different forms of analysis. This work may help us look for early biomarkers for neurodevelopmental disorders linked to abnormal circuit maturation and interneuron dysfunction.

Reviewer #1 (Public Review):

Most work on antibiotic resistance focuses on particular resistance genes often located on plasmids, but rarely how these genes interact with others located on the chromosome of the host organism. Considering variation in the host genome and its interaction with resistance plasmids can help predict which hosts are more likely to become resistant to a given antibiotic and explain why the same plasmid may not confer the same level of resistance to different strains.

The authors take a clever approach to finding such genetic interactions by designing an evolution experiment using E. coli carrying an MCR-1 plasmid containing resistance genes to colistin. They then select for increased resistance to colistin and sequence the genomes of the most resistant isolates. This allowed them to identify a particular gene lpxC that confers increased resistance to E. coli when combined with the MCR-1 plasmid (more than the sum of each mutation alone) and find that this is because of decreased membrane surface charge. They then investigate whether this mutation is relevant in wild E. coli isolates by analysing environmental samples from patients and other sources and find that indeed, this mutation is often found in carriers of the MCR-1 plasmid.

The study is very well-designed and presented in a concise and logical manner. The use of evolution experiments to identify the mutations and then engineer them to quantify the epistatic effects and understand the mechanism behind them is very elegant. The real-world relevance is then supported by looking for these mutations in environmental samples. Despite this simplicity and clarity, in some places, the writing could be improved. I particularly found that the second half of the paper was not as easy to follow as the first part and could benefit from some clarifications. The figures could also contain a bit more information to help the reader.

For example, the abstract starts by talking about standing genetic variation but it's not immediately clear what is meant by that. Standing genetic variation seems to suggest that the resistance gene itself is present in the initial population, rather than variation in other loci that might affect the selection of the resistance gene. This could be better formulated.

The figures could be improved by being more specific about the datasets: are mutations in Figure 2 in the WT or the MCR-1 positive lines? Are the SNPs in Fig. 4A in lpxC? Do all isolates in Fig. 4 have the MCR-1 plasmid?

Finally, the arguments being made about diversity in the different phylogroups were not very clear. This could be made more explicit at first mention, rather than later in the discussion section.

Reviewer #1 (Public Review):

The authors performed a detailed genetic characterization of influenza virus variants in vaccinated versus unvaccinated pigs. A major strength of the paper is that the challenge was done via a more or less natural route, by comingling seeder pigs infected with H1N1 and H3N2 virus with the vaccinated groups. Another strength is the relatively large size of the study (70 animals). On the other hand, even this large number of animals still does not allow a very robust assessment (statistically) of the impact of vaccination on reassortment generation at the level of individual pigs. Nevertheless, vaccination reduced the number of reassortant viruses detected upon plaque purification. Vaccination had limited effect on natural selection of other genetic variants (SNVs). This is an important conclusion, as it seems that vaccination provides an ability to reduce the genetic diversification of swine influenza viruses.

Reviewer #1 (Public Review):

Although a bunch of studies have been carried out to see whether calcium supplementation is a prerequisite for the promotion of bone health or prevention of bone diseases, this is the first trial to see its effect on the population whose age is reaching peak bone mass. Outcomes are clear and justified by sound methodology. Also, the message from this systematic review could directly influence the clinical decision on who might gain benefit from calcium supplementation.

Strengths of this study are:<br /> 1) This is the first systematic review by meta-analysis to focus on people at the age before achieving peak bone mass (PBM) and at the age around the PBM.

2) Detailed subgroup and sensitivity analyses drew consistent and clear results.

Limitations of this study are:<br /> 1) Substantial intertrial heterogeneity should be considered in terms of dose effect of calcium supplementation and differences between both sexes etc.

2) Rarity of RCTs focused on the 20-35-year age group.

Reviewer #1 (Public Review):

In this manuscript, the authors present a new technique for analysing low complexity regions (LCRs) in proteins- extended stretches of amino acids made up from a small number of distinct residue types. They validate their new approach against a single protein, compare this technique to existing methods, and go on to apply this to the proteomes of several model systems. In this work, they aim to show links between specific LCRs and biological function and subcellular location, and then study conservation in LCRs amongst higher species.

The new method presented is straightforward and clearly described, generating comparable results with existing techniques. The technique can be easily applied to new problems and the authors have made code available.

This paper is less successful in drawing links between their results and the importance biologically. The introduction does not clearly position this work in the context of previous literature, using relatively specialised technical terms without defining them, and leaving the reader unclear about how the results have advanced the field. In terms of their results, the authors further propose interesting links between LCRs and function. However, their analyses for these most exciting results rely heavily on UMAP visualisation and the use of tests with apparently small effect sizes. This is a weakness throughout the paper and reduces the support for strong conclusions.

Additionally, whilst the experimental work is interesting and concerns LCRs, it does not clearly fit into the rest of the body of work focused as it is on a single protein and the importance of its LCRs. It arguably serves as a validation of the method, but if that is the author's intention it needs to be made more clearly as it appears orthogonal to the overall drive of the paper.

Overall I think the ideas presented in the work are interesting, the method is sound, but the data does not clearly support the drawing of strong conclusions. The weakness in the conclusions and the poor description of the wider background lead me to question the impact of this work on the broader field.

Technical weaknesses

In the testing of the dotplot based method, the manuscript presents a FDR rate based on a comparison between real proteome data and a null proteome. This is a sensible approach, but their choice of a uniform random distribution would be expected to mislead. This is because if the distribution is non-uniform, stretches of the most frequent amino will occur more frequently than in the uniform distribution.

More generally I think the results presented suggest that the results dotplot generates are comparable to existing methods, not better and the text would be more accurate if this conclusion was clearer, in the absence of an additional set of data that could be used as a "ground truth".

The authors draw links between protein localisation/function and LCR content. This is done through the use of UMAP visualisation and wilcoxon rank sum tests on the amino acid frequency in different localisations. This is convincing in the case of ECM data, but the arguments are substantially less clear for other localisations/functions. The UMAP graphics show generally that the specific functions are sparsely spread. Moreover when considering the sample size (in the context of the whole proteome) the p-value threshold obscures what appear to be relatively small effect sizes.

Reviewer #1 (Public Review):

The authors sought to create a machine learning framework for analyzing video recordings of animal behavior, which is both efficient and runs in an unsupervised fashion. The authors construct Selfee from recent computational neural network codes. As the paper is methods-focused, the key metrics for success would be (1) whether Selfee performs similarly or more accurately than existing methods, and more importantly (2) whether Selfee uncovers new behavioral features or dynamics otherwise missed by those existing methods.

Strengths:<br /> * The authors put their work in context very well, discussing machine learning approaches to behavior extraction generally, and clearly stating the unique aspects of their own approach. The schematic framework of Selfee is nicely described.<br /> * The authors use their new methods on existing data sets, mostly in adult Drosophila but also in rodents, with the resulting outputs confirming and accurately classifying known behaviors, in agreement with manual annotation.<br /> * The analysis focuses on behavior video that depicts interactions between animals, typically more difficult than either individual animal video or video with noninteracting animals. This adds to the strength of the method.<br /> * Experiments with mutants and Kir-silenced lines were nicely designed, and highlighted Selfee's anomaly detection methods by finding a short-time-scale behavior unlikely to be noticed by manual human observation.<br /> * Similarly, experiments investigating Trh in flies were very thorough and detailed, and illustrate the effectiveness of the machine learning analysis when combined with follow up experiments to investigate Selfee's initial findings.

Weaknesses:<br /> * Although the basic schematics of Selfee are laid out, and the code itself is available, I feel that material in between these two levels of description is somewhat lacking. Details of what other previously published machine learning code makes up Selfee, and how those parts work would be helpful. Some of this is in the methods section, but an expanded version aimed at a more general readership would be helpful.<br /> * The paper highlights efficiency as an important aspect of machine learning analysis techniques in the introduction, but there is little follow up with this aspect.<br /> * In comparing Selfee to other approaches, the paper uses DeepLabCut, but perhaps running other recent methods for more comprehensive comparison would be helpful as well.<br /> * Using Selfee to investigate courtship behavior and other interactions was nicely demonstrated. Running it on simpler data (say, videos of individual animals walking around or exploring a confined space) might more broadly establish the method's usefulness.

Overall, the results of the paper seem to clearly achieve what was set out in the introduction, which was to use an unsupervised machine learning video analysis method to uncover new features of behavior. The experiments establishing the effectiveness seem very sound and reasonable.

For a reader who does not work directly with implementing machine learning, the paper is highly readable and interesting and should generate interest to a wider audience of researchers who would wish to try Selfee on their own data. The paper could have made it a bit more clear how an inexperienced user might deploy the Selfee software, whether with one of the model systems used here or a different one. But there is certainly something very appealing about an unsupervised method, which has the potential to be more accessible to a wider audience of researchers, allowing more people to take advantage of sophisticated behavior analysis.

Reviewer #1 (Public Review):

The authors present results of experiments and computer simulations of magnetotactic bacteria confined within sealed microfluidic devices. They isolate a single cell within circular compartments and track its motion over long periods of time, to study its statistical properties. Following previous work on microalgae, the present authors study the behavior of the radial distribution function of the bacterial cells for difference confining radii, and also in the absence/presence of a magnetic field. The inclusion of a magnetic field produces qualitatively different trajectories on account of the reorienting magnetic torque leading e..g to U-turn-like motion.

This work adds interesting results to the characterization of microbial motility at the single cell level for a class of cells with important potential applications: magnetotactic bacteria.

The conclusions of this manuscript are well supported by the evidence provided. Some weaknesses of the analysis is that there is no clear discussion of the interactions among cells when more than one are trapped within the same fluidic chamber. The authors could have separated contribution from isolated cells from the pairs or triplets of cells in the radial distribution function to measure the role of cell-cell interactions.

The methods used in both experimental and simulation analysis are adequate and useful to the community. Comparing, as they do, the results of this manuscript with the work on microalgae by Ostapenko et al., the present authors demonstrate that features of single-cell motion in confinement do not depend sensitively on the details of the propulsion mechanism. This aspect of this work will have considerable impact in the field, beyond the importance to magnetotactic cells.

Reviewer #1 (Public Review):

This work offers a simple explanation to a fundamental question in cell biology: what dictates the volume of a cell and of its nucleus, focusing on yeast cells. The central message is that all this can be explained by an osmotic equilibrium, using the classical Van't Hoff's Law. The novelty resides in an effort to provide actual numbers experimentally.

In this work, Lemière and colleagues combine physical modeling and quantitative measures to establish the basic principles that dictate the volume of a cell and of its nucleus. By doing so, they also explain an observation reported many times and in many different types of cells, of a proportionality between the volume of the cell and of its nucleus. The central message is that all this can be explained by an osmotic equilibrium, using the classical Van't Hoff's Law. This is because, in yeast cells, while the cell has a wall that can contribute to the equilibrium, the nucleus does not have a lamina and there is thus no elastic contribution in the force balance for the nucleus, as the authors show very nicely experimentally, using both cells and protoplasts and measuring the cell and nucleus volume for various external osmotic pressures (the Boyle Van't Hoff Law for a perfect gas, also sometimes called the Ponder relation) ­- this was performed before for mammalian cells (Finan et al.), as cited and commented in the discussion by the authors, showing that mammalian cells have no significant elastic wall (linear relation) while the nucleus has one (non linear relation). This is well explained by the authors in the discussion. It is one of the clearer experimental results of the article. Together, the data and model presented in this article offer a simple explanation to a fundamental question in cell biology. In this matter, the principles are indeed seemingly simple, but what really counts are the actual numbers. While this article sheds some light on this aspect, it does not totally solve the question. The experiments are very well done and quantified, but some approximations made in the modeling are questionable and should at least be discussed in more length. Overall, this article is extremely valuable in the context of the recent effort of the cell biology and biophysics communities to understand the fundamental question of what dictates the size of cells and organelles. I have a few concerns detailed below. Importantly, there are many very interesting points of the article that I am not discussing below, simply because I completely agree with them.

1) The main concern is about the assumption made by the authors that the small osmolytes do not count to establish the volume of the nucleus. It was shown that small osmolytes such as ions are a vast majority of the osmolytes in a cell (more than ten times more abundant than proteins for example, which represent about 10 mM, for a total of 500 mM of osmolytes). This means that just a small imbalance in the amount of these between the nucleus and cytoplasm might have a much larger effect than the number of proteins, which is the osmolyte that authors choose to consider for the nuclear volume.

The point of the authors to disregard small osmolytes is that they can freely diffuse between the cytoplasm and the nucleus through the nuclear pores. They thus consider that the nuclear volume is established thanks to the barrier function of the nuclear envelope, which would retain larger osmolytes inside the nucleus and that the rest is balanced. This reasoning is not correct: for example, the volume of charged polymers depends on the concentration of ions in the polymer while there is no membrane at all to retain them. This is because of an important principle that the authors do not include in their reasoning, which is electro-neutrality.

Because most large molecules in the cell are charged (proteins and also DNA for the nucleus), the number of counterions is large, and is probably much larger than the number of proteins. So it is hard to argue that this could be ignored in the number of osmotically active molecules in the nucleus. This is known as the Donnan equilibrium and the question is thus whether this is actually the principle which dictates the nuclear volume.

The question then becomes whether the number of counterions differs between the cytoplasm and the nucleus, and more precisely whether the difference is larger than the difference considered by the authors in the number of proteins.

How is it possible to estimate this number? One of the numbers found in the literature is the electric potential across the nuclear envelope (Mazanti Physiological Reviews 2001). The number is between 1 and 10 mV, with more cations in the nucleus than in the cytoplasm. This number could correspond to much more cations than the number of proteins, although the precise number is not so simple to compute and the precision of the measure matters a lot, since there is an exponential relation between the concentrations and the potential.

This point above is simply made to explain that the authors cannot rule out the contribution of small osmolytes to the nuclear volume and should at least leave this possibility open in the discussion of their article.

As a conclusion, I totally agree with equation 3 which defines the N/C ratio, but I think that the Ns considered might not be the number of large macromolecules which cannot pass the nuclear envelope, but rather the small ones. Whether it is the case or not and what is actually the important species to consider depends on the actual numbers and these numbers are not established in this article. It is likely out of the scope of the article to establish them, but the point should at least be discussed and left open for future studies.

2) The authors refer to the notion of colloidal pressure, discussed in the review by Mitchison et al. This term could be confusing and the authors should either explain it better or just not use it and call it perfect gas pressure or Van't Hoff pressure. Indeed, what is meant by colloidal pressure is simply the notion that all molecules could be considered as individual objects, independently of their size, and that it is then possible to apply the Van't Hoff Law just as it was a perfect gas, hence the notion of 'colloidal' pressure, which would be the osmotic pressure of all the individual molecules. The authors might want to discuss, or at least mention, that it is a bit surprising that all these crowded large macromolecules would behave like a perfect osmometer and that the Van't Hoff law applies to them. Alternatively, it could be simpler to consider that what actually counts for the volume is mostly small freely diffusing osmolytes, to which this law applies well, and which are much more numerous.

3) Very small point: on page 7 the authors refer to BVH's Law (Nobel, 1969). It is not clear what they mean. If they refer to the Nobel prize of Van't Hoff, it dates from 1901 (he died in 1911) and not 1969. I am not sure if there is something in one of the Nobel prizes delivered in 1969 which relates to this law. I checked but it does not seem to be the case, so it is probably a mistake in the date.

4) On page 11, bottom, the result of the maintenance of the N/C ratio in protoplast is presented as an additional result, while it is a simple consequence of the previous results: both the cell and nuclear volume change linearly with the external osmotic pressure, so it is obvious that their ratio does not change when the external pressure is changed. Another result, not commented by the authors, is that this should be true only in protoplasts, since in whole cells, the cell wall is affecting the response of the cell volume, but not the nucleus, so the ratio should change.

5) The results in Figure 5, with the inhibition of export from the nucleus, are presented as supporting the model. It is not really clear that they do. First the effect is very small, even if very clear. Again, the numbers matter here, so the interpretation of this result is not really direct and more calculation should be made to understand whether it can really be explained by a change of number of proteins. The result in panel F is even more problematic. The authors try to argue that the nucleus transiently gets denser, based on the diffusion of the GEMs and then adapts its density. It rather seems that it is overall quite constant in density, while it is the cell which has a decreasing density ­- maybe, as suggested by the authors, because there are less ribosomes in the cytoplasm, so protein production is reduced. This could have an indirect effect on the number of amino acids (which would then be less consumed). A recent article by Neurohr et al (Trends in cell biology, 2020) suggests that such an effect can lead to cell dilution, in yeast, because the number of amino acids increases. In this particular case, this increase would affect the nuclear volume rather than the cell volume because of the presence of the cell wall and the rather small change.

6) Page 16: it seems to me that the experiments presented in the chapter lines 360 to 376, on the ribosomal subunits, simply confirm that export is impaired, and they do not really contribute to confirm the hypothesis of the authors that it is the number of proteins in the nucleus which counts.

The next paragraph with the estimation of the number of proteins in the nucleus and cytoplasm and how they change relatively upon export inhibition also appears to mostly demonstrate that export has been inhibited.

The authors propose to use the number they find, 8%, to compare it to the change in the N/C ratio, which is of the same order. Given how small these numbers are, and the precision of such measures, it is very hard to believe that these 8% are really precise at a level which could allow such a comparison. The authors should really estimate the precision of their measures if they want to claim that. It is more likely that what they observe is a small but significant change in both cases; a small change means it is small compared to the total, so it is a fraction of it, and it is measurable, which means it is more than just a few percent, which is usually not possible to measure. So it means that it is in the order of 10%. This is the typical value of any small but measurable change given a method for the measure which can detect changes around 10%. In conclusion, these numbers might not prove anything.

It could also be that the numbers match not just by chance, but that the osmolyte which matters is, for this type of experiment, changing in proportion to the amount of proteins (which would be possible for counter ions for example). But determining all that requires precise calculations and additional measures. It is thus more a matter of discussion and should be left more open by the authors.

Reviewer #1 (Public Review):

Kosillo et al. used dopamine neuron-specific cKO mice to examine the contributions of mTORC1 (Raptor cKO) and mTORC2 (Rictor cKO) to dopamine neuron dendrite and axon morphology, neuronal electrophysiological properties and dopamine release. Overall, Raptor cKO mice have stronger deficits as compared to Rictor cKO, while double cKO had additional deficits. These results suggest that mTORC1 is more critical to dopamine neuron function, and that there is some functional redundancy between mTORC1 and mTORC2.

The data presented is generally of high quality, and the conclusions drawn are consistent with the data presented. I have the following concerns:

1. Conclusion point 3: "mTORC2 inhibition leads to distinct cellular changes not observed following mTORC1 suppression, suggesting some independent actions of the two mTOR complexes in DA neurons." Currently, data supporting this conclusion is weak. Specifically, WT SNc DA neurons do not have typical morphology (very different from all other neurons, including WT neurons in Fig 1o), making the observed increase in proximal dendrite morphology hard to interpret. Data presented in Figure 1o suggest no significant increase in total dendrite length. Are there changes in primary dendrite number? The electrophysiological results presented in Fig 5 are inconsistent with increased dendrite arborization. The authors need to either provide more evidence showing significant increase in dendrite morphology in Rictor cKO mice, or reinterpret their results.<br /> 2. The manuscript is repetitive in some places, and the discussion largely reiterates the results. Could the authors please discuss why mTORC1 signaling contributes more to dopamine neuron function, as compared to mTORC2, based on existing knowledge of gene function and expression. Another point of interest is how the different parameters they measure are related, i.e. which parameters may be more causal than others in terms of changes in dopamine neuron function.

Reviewer #1 (Public Review):

The authors aim to understand how condensin I contributes to chromosome structure. The approach they take is to add back recombinant condensin II complexes to Xenopus extract depleted for both condensin I and condensin II and visualise the ability of the extracts to condense exogenously added chromatin. To test the requirement for individual condensin subunits CAP-D3 and/or CAP-G2 , the authors added back recombinant complex lacking these subunits, or with truncated versions. This led to the surprising conclusion that although the CAP-G2 subunit is required for condensin association with chromosomes, CAP-D3 is not. Indeed, the absence of CAP-D3 enhances condensin association with chromosomes. Similarly, deletion of the C-terminal region of CAP-G2 also increases condensin association with chromosomes. The authors also recapitulate these findings in an extract-free assay, adding strength to their conclusions, although future work will be required to determine the importance of their findings in vivo. Overall, the experiments in this manuscript support the conclusions for the most part and provide interesting insight into condensin II function which will be of great interest to those in the field.

Reviewer #1 (Public Review):

This manuscript focuses on establishing the parametric relations between critical membrane and morphological properties of spinal alpha-motoneurons (MNs), using data from 40 experimental in vivo studies, most of them in the cat. Importantly, the authors digitalized the data from original papers, then created a global dataset for each property pair using normalized values, and lastly used a step-by step inference approach to create final datasets, which allowed to computed correlations between all pairs of parameters, even for those with no direct experimental data. In addition, a validation method was successfully performed using crossvalidation with 70% of data for training and 30% for testing. The authors also performed an extrapolation to the data in rats and mice, using the scaled relationships obtained from cat anatomical and physiological information. Finally, the relationships between MN and mU properties were determined using the same methodology. Overall, the analytical approach is technically sound, and the results are potentially useful for neurophysiologists and modelling scientists. Indeed, Table 6 is a great summary of the pair-wise correlations for all nine anatomofunctional MN parameters, which can be utilized by people from different scientific communities. However, a simpler framing of the paper could make the main message easier to grasp. The abstract and introduction should be briefer, using a language that considers a broad audience. The results section should be distilled to the main findings, decreasing the number of main tables, and sending most of the detailed computations to the supplementary material section. I also recommend highlighting the procedure used to extract data from published experiments and the normalization and validation scheme to compute final correlation between parameters. This approach can be used to crease datasets in a wide variety of disciplines.

Reviewer #1 (Public Review):

Kang et al. studied the role of cystathionine beta-synthase (CBS), an enzyme involved in homocysteine catabolism, in the senescent state stimulated by Akt. They report that Akt induces expression of CBS and other enzymes necessary to convert homocysteine into cysteine, and that blocking CBS enhances cell proliferation and reduces beta-galactosidase expression. Mechanistic studies reveal that Akt activates several markers of mitochondrial metabolism, including respiration, and that CBS silencing mitigates this change and reduces reactive oxygen species. Analysis of human gastric tumors reveals methylation of the CBS locus and reduced CBS expression relative to nonmalignant gastric mucosa. Finally, re-expressing CBS in gastric cancer cells reduces growth and Ki67 staining in xenografts. The authors conclude that CBS is a required component of the Akt-induced senescence pathway, and that reducing CBS expression is a mechanism by which some cancers suppress senescence and promote growth. Overall, the paper describes an interesting metabolic process of oncogene-induced senescence that appears selective for Akt. Few such mechanisms have been described, so a thorough exploration of CBS's role in senescence could be impactful. The authors succeed in showing that manipulating CBS expression in a limited number of models has substantial effects on senescence and growth. However, not all of the conclusions are supported by the data in the current version of the paper, the metabolic analysis of CBS's function in Akt-expressing cells is incompletely characterized, and some central aspects of the overall mechanism (particularly the relevance of CBS to mitochondrial respiration) are unexplained.

Specific comments:<br /> 1) CBS expression is induced upon Akt activation, but there needs to be better evidence that activity of the pathway has changed. The metabolomics results are not very convincing, as siCBS has no or minimal effects on some metabolite pools that should respond. An isotope tracing study would help here.

2) Furthermore, the AOAA experiments are hard to interpret. This drug is a promiscuous transaminase inhibitor, so its effects on cell confluency are not surprising, and it is unclear which particular aspect of metabolism is responsible for the effect. A genetic experiment silencing the relevant transaminase would be more informative.

3) The GC/MS data in Fig. 3L are misleading, as the range on the color scale goes from FDR of 0.0504 to 0.0498. Also, the authors claim that CBS regulates the malate-aspartate shuttle, but no mechanism is proposed and this is not intuitive.

4) CBS's role in modulating mitochondrial function is complicated, but its ability to sustain OxPhos and ROS seem to underlie its effects on AIS. The key unanswered question is how CBS promotes OxPhos in these models.

Reviewer #1 (Public Review):

This is a very interesting paper on the connectivity between the brain and the spinal cord, that touches upon, in an elegant and complementary way, several important aspects in the field: first, a methodological advance which is then applied for studying the organization of descending pathways from a fundamental perspective and then in the context of spinal cord injury. The associated web-based interface is also excellent and well-made.

The authors convincingly show the potency of nuclear fluorescent tagging of supra-spinal neurons (using new retrograde vectors they engineered) combined with iDISCO-based clearing and reconstruction tools derived from the Brainglobe initiative. The method is potent, apparently rapid, and allows quantitative measurement in virtually all brain areas on the same sample, including in regions located deeply (cerebellum, brainstem).

In a second part, authors investigate, using retrograde viruses, whether different supra-spinal brain areas control distinct spinal segments through abundant collaterals, or by specific subsets that differ by their projection targets. This matter is becoming the center of attention in many studies on descending tracts. The strength here is to consider all supra-spinal brain areas in a unique study, which is extremely informative. Authors conclude that in all brain areas contacting the spinal cord, cells are uniquely labeled either from the cervical or the lumbar cord. However this part cannot be interpreted meaningfully without the quantification of the double-labeled cells, presently missing for all regions except the cortex. Are there strictly no cells with collaterals to both segments in the whole brain, or are these dual-projecting neurons not detected by the method? Another weakness is the possibility that one virus being brighter may occult or reduce the expression of the dimmer one. Quantifications of such controls are presently missing.

Finally, in the third part, the authors apply their labeling and detecting pipeline in the context of contusive injuries. They reveal that some brain areas show significantly more sparing (or plasticity potentially?) than others, and that this sparing might correlate with the extent of the locomotor recovery. This is convincing and should help draw a better picture of the relevance of different descending tracts in locomotor recovery following SCI.

Another strength is the web-based interface. While a few bugs are still there (notably on the indexation), this is an extremely timely and useful initiative.

Reviewer #1 (Public Review):

Santella describes methods to align fluorescence microscopy and EM datasets in the C. elegans embryo. The major use of this will be to help analyze the initial development of the embryonic nervous system, including process outgrowth, etc, which so far has been described at the fluorescence but not EM level. The work will be of interest to the community of researchers involved in developmental connectomics and annotation of serial section EM datasets.

The study uses FM and EM datasets for iterative refinement of cell identities at the tissues and single-cell levels. Some new information is provided about early C. elegans nervous system development e.g. entry points of pioneer axons into the nerve ring, or cell contacts during sensilla development; however, the main focus is the methodology. This is overall technically sound and represents a significant investment of effort.

Reviewer #1 (Public Review):

In this manuscript, the authors measured orofacial and head movements of marmoset fetuses at multiple gestation stages. They show that these two types of movements become more independent over the course of development. They then compared the structure of the orofacial movements to the one of contact vocalization of the neonate and found that only orofacial movements matching contact calls are increasingly more prominent during gestation, compared to orofacial movements of the fetuses matching twitters or licks.

I think these are two interesting results per se, that were somehow expected based on previous literature suggesting that human fetuses exhibit movements consistent with postnatal crying. They indicate that motor movements become more structured through gestation and that some preparatory programs may set the stage for some post-birth vocalizations (contact calls and not twitters). Some analysis could be refined to demonstrate fully the result.

Major concerns:

- Frame-by-frame analyses were performed by experts who are aware of the study goals. Such experts are also likely to recognize the gestation stages based on the images themselves. I am concerned that the manual image processing could be influenced by the lack of blind labeling.<br /> - How do the Shannon entropy and the KL divergence behave when a state probability is null (say stages 3 to 5 in the latest stages of gestation)? I think it is undefined.<br /> - Stage 5 is defined with an extremely stringent criterion (both head and orofacial movements starting at less than 30ms interval). I think this is way too stringent, and the impact of making this criterion more compliant should be quantified.

Reviewer #1 (Public Review):

The authors present data identifying the role of the bacterial enhancer binding protein (bEBP) SypG in the regulation of the Qrr1 small RNA, which is known to be a key regulator of Vibrio fischeri bioluminescence production and squid colonization. Previously, only the bEBP LuxO was known to activate Qrr1 expression. LuxO and Qrr1 are conserved in the Vibrionaceae, and the authors show that SypG is conserved in ~half of the Vibrio family, suggesting that this Qrr1 regulatory OR gate controlled by LuxO or SypG may play important roles in physiology processes in other species.

Successful squid colonization by Vibrio fischeri is a complex process, known to be influenced by several factors, including the formation of and dispersal from cellular aggregates prior to entering squid pores, and inoculation of the light organ crypts, and biofilm formation within the crypts. Previously, it was shown that strains lacking qrr1 were at a deficit for crypt colonization in the presence of wild-type V. fischeri. Conversely, cells lacking binK, which encodes a hybrid histidine kinase, were at an advantage for crypt colonization in the presence of wild-type cells. However, the authors identified BinK as a negative regulator of Qrr1 expression in a transposon screen. The authors used genetic epistasis experiments and found that Qrr1 transcription can be activated by either phosphorylated LuxO at low cell densities (in the absence of quorum sensing signals) or by SypG, presumably by binding to the two upstream activation sequences in the promoter of qrr1 to activate transcription by the required alternative sigma factor sigma-54. The competition between these bEBPs has not been tested. The model proposed is an OR gate through which quorum sensing and aggregation signals control Qrr1. However, there are several untested aspects of this model. First, the role of phosphorylation in SypG activity, and the connection to BinK, are not addressed in this manuscript, which may confound the observed effects observed on qrr1 transcription. Further, the authors did not test whether SypG directly binds to the qrr1 promoter, nor did they assess the individual role of LuxO binding to the two LuxO binding sites in the absence of SypG. The study is lacking an in vivo assessment of SypG and LuxO binding/competition at the Qrr1 promoter based on the authors' model of the OR gate.

Major comments:

• What is known about the connection between BinK and SypG? BinK is a hybrid HK (intro states this). Does BinK phosphorylate/dephosphorylate SypG - directly or indirectly? I saw a published paper (Ludvik et al 2021) with a diagram suggesting BinK does inhibit SypG, but the connection is unclear. This diagram also suggested that SypG needs to be phosphorylated. Can the authors comment - does SypG need to be phosphorylated to be active? Because SypG has the same sequence as the LuxO linker (Fig. S2), then I presume that SypG would also need to be phosphorylated to be active (like LuxO)? The authors utilize a phosphomimic of LuxO to test function under constitutive activity (Fig. S3), but they do not use a phosphomimic of SypG (Fig 4). If the authors used a constitutive allele, would those assays reveal more about the competition between SypG and LuxO, in the presence of phosphorylated LuxO at low cell density? The authors should include a putative cartoon model for how BinK HK activity connects to SypG, based on what is already in the literature, to aid the reader.

• Line 246: Figure S3: nucleotide substitutions in both UAS regions showed loss of Pqrr1-gfp, but this could be due to binding/activation by SypG or LuxO. This should be tested in a sypG- strain to determine the sole effect of LuxO binding to these two UASs. In Figures 4G and 7, the luxO- sypG- Ptrc-sypG strain backgrounds allow the independent analysis of the two bEBPs. It is important to test which of these two sites is critical for LuxO-dependent activation of Pqrr1, given the conservation of the LuxO-Qrr1 region in other Vibrios (line 327, Fig. S5). Thus, the authors could also discuss whether these two proteins would compete at both sites. Further, the authors should comment that they have not shown biochemical evidence that SypG binds to the two UASs in the Qrr1 promoter. The regulation of this locus by SypG is only shown by genetic assays in this manuscript.

• Examination of the binding of LuxO and SypG (e.g., ChIP-seq) in combination with their transcriptional reporter under varying conditions (low cell density vs high cell density, with or without rscS* overexpression) would be extremely beneficial in testing the model proposed.

Reviewer #1 (Public Review):

The authors address the origin of the macrophage increase in sensory ganglia after peripheral nerve injury, showing that there is no major influx by blood-derived monocytes into ganglia after injury and that resident macrophages proliferate, which is dependent on CX3CR1 signaling.

- Interesting and relevant question, mainly addressed with adequate experimental approaches.

- Most conclusions are supported by the data, however, some important controls and experiments are missing.

- The authors should demarcate their results from the study of Iwai et al, 2021 which addresses similar questions.

Reviewer #1 (Public Review):

Sensory information from photoreceptors (PRs) is first conveyed to the lamina neuropil of the optic lobe, which consists of 5 neuron types - L1-5. Signals from the developing PRs - and the glia that ensheathes them - are intricately involved during the development of the lamina to ensure that retinotopy is accurately established. This includes multiple processes such as induction of the precursors in the lamina, their terminal divisions, and organisation into columns.

In this manuscript, the authors identify another series of non-cell-autonomous processes that ensure the survival and differentiation of the appropriate number of L5 neurons - one of the 5 neuron types in the lamina. They use a variety of genetic manipulations and screens to show that:<br /> PR-derived EGF is sensed by a population of glial cells (the outer chiasm glia) that then secrete signalling molecules such as Spi and a type IV collagen. These signals in turn induce differentiation and survival of the L5 lamina precursor via MAPK. Finally, the authors show that the differentiated L5s inhibit the survival of an adjacent L5 lamina precursor by secreting Argos that eventually inhibits MAPK activity in it.

The manuscript is well written and clearly presented.

Reviewer #1 (Public Review):

In this paper, the authors ask: what is the physiological role of the Aster-A protein? They report that Aster-A is recruited to plasma membrane (PM)-ER contact sites when cholesterol levels rise above a threshold, and that ABCA1 flops cholesterol from the inner to the outer leaflet. Both of these findings have been previously reported. Naito et al. and Ercan et al. characterized the PM-ER localization of all Aster/GRAMD1 proteins, showing their responsiveness to PM cholesterol levels. The authors also previously reported that ABCA1 flops cholesterol (Okamoto et al., 2020; Ogasawara et al., 2019). This study argues that ABCA1 cholesterol flopping antagonizes Aster-A cholesterol sensing. This finding would be significant if it was demonstrated to occur in the same cell in a physiologically relevant context (not just in the presence of SMase). The authors use over-expressed proteins to demonstrate that ABCA1 activity can antagonize Aster-A binding to the inner leaflet cholesterol, so it is unclear if this would happen in vivo. Despite some intriguing initial results, this paper faces some challenges since the advancement of the work here in relation to the previous work is not clearly defined, and there is inadequate evidence that these processes are related under endogenous protein expression levels in a physiologically relevant context.

The authors use Alexa-PFO-D4 to measure outer leaflet cholesterol in their study. The authors should provide their size exclusion chromatography column traces or a Coomassie stained gel that demonstrates protein quality and purity. The authors should also demonstrate with controls that Alexa-PFO-D4 is faithfully reading out outer leaflet cholesterol levels in their flow cytometry assays. For example, they should test binding of Alexa-PFO-D4 to membranes after cholesterol loading or extraction at various concentrations. This is especially important given the concerning findings that SMase treatment hardly changed probe binding (Figure 4). Previous studies found that SMase treatment significantly increases PFO* binding to outer leaflet cholesterol (Das et al., 2014). The fact that the authors see insignificant changes in outer leaflet cholesterol levels upon SMase addition suggests there may be an issue with their Alexa-PFO-D4 probe.

In order to measure inner leaflet cholesterol level, the authors use PFO-D4H, a variant of PFO-D4 that has increased sensitivity for cholesterol. Despite the increased sensitivity, this probe only binds to membranes whose cholesterol level is greater than ~20 mole % (Maekawa and Fairn, 2015). This is at odds with the authors' discussion that inner-leaflet membranes contain only about 3% cholesterol (Line 55 and Discussion). The inner vs. outer leaflet cholesterol composition is still openly debated in the field. The authors cite findings from Liu et al. who reported that the outer leaflet has 10x greater cholesterol levels. Other papers report a more symmetric cholesterol content (Steck and Lange, 2020) and Courtney et al. has directly challenged the Liu et al. paper. The authors should report this controversy in their discussion.

Reviewer #1 (Public Review):

In computational modeling studies of behavioral data using reinforcement learning models, it has been implicitly assumed that parameter estimates generalize across tasks (generalizability) and that each parameter reflects a single cognitive function (interpretability). In this study, the authors examined the validity of these assumptions through a detailed analysis of experimental data across multiple tasks and age groups. The results showed that some parameters generalize across tasks, while others do not, and that interpretability is not sufficient for some parameters, suggesting that the interpretation of parameters needs to take into account the context of the task. Some researchers may have doubted the validity of these assumptions, but to my knowledge, no study has explicitly examined their validity. Therefore, I believe this research will make an important contribution to researchers who use computational modeling. In order to clarify the significance of this research, I would like the authors to consider the following points.

1. Effects of model misspecification

In general, model parameter estimates are influenced by model misspecification. Specifically, if components of the true process are not included in the model, the estimates of other parameters may be biased. The authors mentioned a little about model misspecification in the Discussion section, but they do not mention the possibility that the results of this study itself may be affected by it. I think this point should be discussed carefully.

The authors stated that they used state-of-the-art RL models, but this does not necessarily mean that the models are correctly specified. For example, it is known that if there is history dependence in the choice itself and it is not modeled properly, the learning rates depending on valence of outcomes (alpha+, alpha-) are subject to biases (Katahira, 2018, J Math Pscyhol). In the authors' study, the effect of one previous choice was included in the model as choice persistence, p. However, it has been pointed out that not including the effect of a choice made more than two trials ago in the model can also cause bias (Katahira, 2018). The authors showed taht the learning rate for positive RPE, alpha+ was inconsistent across tasks. But since choice persistence was included only in Task B, it is possible that the bias of alpha+ was different between tasks due to individual differences in choice persistence, and thus did not generalize.

However, I do not believe that it is necessary to perform a new analysis using the model described above. As for extending the model, I don't think it is possible to include all combinations of possible components. As is often said, every model is wrong, and only to varying degrees. What I would like to encourage the authors to do is to discuss such issues and then consider their position on the use of the present model. Even if the estimation results of this model are affected by misspecification, it is a fact that such a model is used in practice, and I think it is worthwhile to discuss the nature of the parameter estimates.

2. Issue of reliability of parameter estimates

I think it is important to consider not only the bias in the parameter estimates, but also the issue of reliability, i.e., how stable the estimates will be when the same task is repeated with the same individual. For the task used in this study, has test-retest reliability been examined in previous studies? I think that parameters with low reliability will inevitably have low generalizability to other tasks. In this study, the use of three tasks seems to have addressed this issue without explicitly considering the reliability, but I would like the author to discuss this issue explicitly.

3. About PCA

In this paper, principal component analysis (PCA) is used to extract common components from the parameter estimates and behavioral features across tasks. When performing PCA, were each parameter estimate and behavioral feature standardized so that the variance would be 1? There was no mention about this. It seems that otherwise the principal components would be loaded toward the features with larger variance. In addition, Moutoussis et al. (Neuron, 2021, 109 (12), 2025-2040) conducted a similar analysis of behavioral parameters of various decision-making tasks, but they used factor analysis instead of PCA. Although the authors briefly mentioned factor analysis, it would be better if they also mentioned the reason why they used PCA instead of factor analysis, which can consider unique variances.

Reviewer #1 (Public Review):

Bailon-Zambrano and colleagues were trying to answer the general question: what contributes to phenotypic variation when a gene of strong effect is mutated?

The work has several major strengths for answering this interesting question. First, they decided to study mef2ca in zebrafish for which they had previously shown that mutants displayed highly variable facial phenotypes. To learn how phenotypic variation depends on phenotypic severity, they realized they had studied more alleles, and so induced two more alleles to have three different types of molecular lesions (start codon mutation, premature stop codon, and full coding gene deletion). Investigating these alleles showed that increasingly severe alleles had more variation among individuals in the population but not necessarily more variation between the left and right sides of the face within individuals.

Over several years, these investigators had spent considerable effort to select lines of fish that segregate the start-codon mutation and have either severe or weak effects on facial phenotypes. They wondered: what factors were selected out of the original genetic background that would increase or decrease phenotypic severity? They hypothesized that one or more of the five mef2 paralogs in zebrafish might help to ameliorate the phenotype in the low line or reciprocally intensify the phenotype in the high line. They studied expression of the mef2 paralogs in neural crest cells by single-cell transcriptomics. They found that paralogs were downregulated in the high-penetrance line with respect to an unselected line, a result expected if expression of the paralogs contributed to buffering phenotypic severity. This experiment has two weaknesses, first that the method only examined neural crest cells but we know that signals from the ectodermal and endodermal epithelia contribute to craniofacial morphologies by diffusible signals. If genes regulating craniofacial morphologies that act in epithelia had genetic variation that contributes to severity, those genes would not be investigated in these crest-only experiments. A minor problem (which is associated with the expense of the experiment) is that the scRNA-seq experiments compared only the high and unselected lines, not the low line. To address both problems, the investigators performed qPCR on RNAs extracted from whole heads of genetically mef2ca-wild types from the high and low line. In these qPCR experiments, however, they did not investigate the unselected line. Leaving out the low line in one approach and leaving out the unselected line in the other approach somewhat weakens the strength with which one can draw conclusions (e.g., the qPCR conclusion assumes that the unselected line would be intermediate between the two selected lines) but is unlikely to change the basic conclusions the authors drew. In addition, using whole heads in the qPCR experiments, while it has the advantage that it includes epithelia, does not distinguish between genes expressed only in the crest and genes expressed in other cell types, and these experiments did not test for any genes known to affect craniofacial development that are epithelium-specific.

Finally, in key experiments that are a major strength of the work and require significant effort, the researchers systematically made mutations in four of the five zebrafish mef2 paralogs (mef2aa, mef2b, mef2cb, and mef2d, all except mef2ab, which didn't become mutated despite significant effort) in the genetic background of the low-penetrance strain and studied them in single homozygotes, in double mutants, and in various heterozygous combinations. These important experiments showed that some paralogs provided significant buffering in the low-penetrance strain, the strain that up-regulated expression of these paralogs. It would be helpful in the discussion to mention that mef2ab couldn't be mutated and a phrase added about what that means for the general conclusions - in the opinion of this reviewer, the impact of this is not great but it should be acknowledged.

A strength of the experiments is that the workers quantified effects of various genotypes by focusing on the length of the symplectic, a convenient element for quantification both within single individuals and among fish in a population. It would be helpful to have a statement on the evidence that this measure is a good representative for other aspects of the phenotype.

Finally, the paper presents a model for understanding the results presented that does a good job of summarizing the data and, importantly, suggests ways to move the analysis deeper. Missing from the description of the model is a discussion about whether the genetic variation that was selected and ultimately upregulated mef2 paralogs is in regulatory elements of the mef2 paralogs themselves or whether it might be in trans-acting transcriptional regulators that simultaneously regulate all mef2 paralogs due to the authors' hypothesized 'cryptic vestigial' functions.

This work is likely to have a significant impact on the fields of developmental biology, the interpretation of human mutational variation (in for example the concept of phenotypic expansion), and the way people think about the evolution of new morphologies over time. A brief comparison of the authors' results and interpretations to those of C.H. Waddington's concept of genetic assimilation would provide improved historical context and broaden the potential impact of the work.

Reviewer #1 (Public Review):

Using transcriptome data reporting responses to biotic stress Rhodes et al identified a new family of small peptides, called CTNIPs, which elicit cytoplasmic Ca2+-influx and MAP-kinase phosphorylation in a bak1 dependent manner. Based on the requirement of BAK1, they searched for BAK1-interactors in the presence of CTNIP using a combination of BAK1-pulldown with proteomics and identified the orphan receptor-like kinase HSL3. They use a combination of biochemical binding assays, protein modelling and genetics (based on root growth responses to the peptides) to confirm that HSL3 is indeed the receptor of CTNIPs. Thus, the study provides a novel small peptide ligand-receptor pair, with a likely function in the regulation of directional root growth. The work is thoroughly conducted and includes several orthogonal approaches to show that HSL3 is the receptor for CTNIP peptides. The manuscript is well and clearly written.

Reviewer #1 (Public Review):

In the present study, the authors use mouse cancer models to study the role of Piezo1 on DC-mediated priming of CD4+ T cells. They show that Piezo1 knockout results in faster tumor progression and accumulation of more regulatory T cells, and that Smad3 and STAT4 are involved in DC-mediated differentiation of Th1 and Treg. Overall this represents a mechanistic advance in our understanding of DC biology as it relates to cancer.

Reviewer #1 (Public Review):

This paper describes longitudinal MRI measurements of "grey matter volume" (GMV) and "white matter volume" (WMV) in the brains of mice that were trained in a well-established one-pawed reaching/grasping paradigm for fine-motor skill learning. GMV/WMV ratio is presumed to reflect the extent to which axons in the region of interest are ensheathed by water-poor myelin membrane ("myelinated"). The conclusion is that WMV increases during learning in several task-related brain regions such as the primary motor cortex and somatosensory cortex, as well as a number of regions that are not so obviously task-related. Parallel decreases in GMV were observed. No change in overall cortical volume was detected so the conclusion is that some intra-cortical axons become myelinated in response to motor learning - supporting the idea of "adaptive myelination" proposed by others. Supporting histochemical evidence is provided (quantitative myelin immunolabelling). The MRI changes observed did not occur in a simple linear or cumulative fashion during learning, but rather increased in a non-linear asymptotic way, or even peaked and decreased again during training ("quadratic"). This is an interesting and useful study that takes us a little closer to understanding what is going on in the brain during learning and memory formation and continues the development of MRI as a useful non-invasive tool for studying the contribution of myelin to these processes.

Specific points:<br /> 1. "Grey matter" and "white matter" are normally used to describe spatially distinct brain regions that are sparsely myelinated (grey) or heavily myelinated (white), for example, the cerebral cortex (grey) and underlying subcortical axon tracts (white). However, most or all regions are described here as white matter within the classical grey matter - within the motor cortex, for example. Classical white matter regions such as corpus callosum do not get a mention. Presumably, the authors' use of the terms grey and white matter refer to specific MRI signals that are designed to pick up relatively water-rich or water-poor domains that are presumed to reflect the abundance of myelinated versus unmyelinated fibers, not necessarily the classic anatomical grey or white matter. However, this is confusing. Is it possible to change the terminology from grey and white matter to myelin-rich and myelin-poor, water-poor and water-rich, or something similar? At the very least it requires a better explanation.

2. Several previous studies of motor learning in rodents, both MRI- and histology-based, have identified structural alterations and/or changes to oligodendrocytes and myelin in the corpus callosum underlying the motor cortex. In general, those white matter alterations were proportionally greater than those detected within the cortex itself. However, the present study apparently did not find significant MRI signal changes in sub-cortical white matter, which is surprising. Was this because the MRI sequences were not optimized for classical "white matter", or because the white matter was specifically excluded from the analysis (masked out)? If the latter, why was sub-cortical white matter excluded from the analysis? This needs discussion and explanation.

3. The quantitative MBP immunolabelling is a crucial piece of supporting evidence for the suggestion that MRI signal changes reflect adaptive myelination. What was the baseline against which immunoreactivity was measured? What did the fluorescence labelling look like at higher magnification - can individual myelin sheaths be distinguished, for example, and could these sheaths be counted, to complement and reinforce densitometry? Higher-mag images should be included in a revision.

Reviewer #1 (Public Review):

This manuscript explores the genetic makeup of the C. elegans pharyngeal cuticle. By taking advantage of multiple gene expression datasets, the authors extract a high confidence set of genes that are expressed by pharyngeal cells as they go through the molting process. The majority of these genes encode proteins that are predicted to be secreted, consistent with them becoming part of the cuticle. Among these secreted proteins, the authors identify a series of intrinsically disordered proteins that, together with a previously described family of secreted IDPs, may form a kind of aggregate that promotes flexibility of the pharyngeal cuticle.

Overall, the analyses are accurately described, including possible shortcomings that are carefully taken into account in the interpretations provided. The manuscript is well written, and the implications of the findings are insightfully discussed.

Reviewer #1 (Public Review):

Clathrin-mediated endocytosis has been extensively studied by many labs, resulting in a highly complex picture of a network of interacting proteins that need to be recruited to the right place at the right time. Some of these proteins are thought to act as pioneers, initiating the recruitment of the rest of the coat; others as curvature generators or sensors; while the final pinching off of the vesicle requires the GTPase dynamin. Much less is known about CCV formation from intracellular membranes, specifically the trans-Golgi network and endosomes, but it is generally assumed that the situation is very similar.

In the present study, the authors turn this assumption on its head by showing that in fact, it is possible to form a CCV from essentially any intracellular membrane by simply targeting a protein with a clathrin-binding domain to that membrane; the clathrin in the cell will do the rest. They succeeded in reconstituting CCV formation from mitochondria, the ER, the Golgi apparatus, and lysosomes. However, they mainly focus on mitochondria, as this result was particularly surprising, given that mitochondria aren't even part of the endomembrane system. The authors employed multiple complementary approaches. For instance, they used four methods to confirm that the "MitoPits" that form from mitochondria are coated with clathrin: immunofluorescence, electron microscopy, clathrin knockdown, and several different constructs with clathrin-binding sites, together with control constructs with point mutations in the clathrin-binding site. Live cell imaging was used to show that not only buds but actual vesicles were being formed from mitochondria, even in the absence of a "pinchase" like dynamin. Some of the data are presented in the form of "SuperPlots", showing each actual point from biological replicates rather than bar graphs with error bars. Thus, the authors' conclusion, that the other proteins implicated in CCV formation are modulators rather than mediators, is well justified.

Reviewer #1 (Public Review):

This paper provides new insights into how polymerization into two different structures modulates the activity of the enzyme PRPS. The molecular mechanisms proposed are supported by the data, and likely to be of general interest.

Joint Public Review:

The present manuscript compares the connectomes of a large range of mammal species using diffusion MRI data. The manuscript reports two main findings: (1) connectomes of more related species are generally more similar, as assessed using Laplacian eigenspectra, than of unrelated species; (2) differences between species' connectomes are generally driven by local regional connectivity profiles, whereas global features are generally preserved.

The first finding is comforting, but in a way not extremely surprising. It would be extremely surprising if more related species do not show more similarity in their connectome. Indeed, this is the reason many phylogenetic analyses use statistical techniques that take the relatedness of species explicitly into account. I find the statement that connectome organization recapitulates traditional taxonomies a bit over the top, as this suggests that a phylogenetic tree constructed based on connectomes would be similar to a tree based on other measures, such as morphology or genetics. This will probably be the case, but is not what the authors have tested here.

The second result is in my opinion the key result of the paper. The main novelty of the paper is that -finally, for the field-bridges approaches taken by some researchers in searching for differences across species (these are usually researchers interested in anatomy) and researchers searching for conserved principles across species (usually researchers approaching connectivity from a network or graph theory perspective). By showing what aspects of a connectome are generally conserved and which are changed, this paper starts unifying the two views and this is an important contribution.

It would, however, have been nice if the authors had explored this notion a bit further. Now, they just state that taking certain features into account means the connectomes look more different, but they do not zoom into the specific brains to see what this means at a biological level. Some of the authors have published, for instance, on the unique connectivity profiles of parts of the human brain and it would have been nice to show that these fall under the local regional connectivity profile aspects of the connectomes. This is a missed opportunity to even further unify the different research traditions.

The manuscript suggests that white matter connectivity in mammals is more similar between species within one taxonomic group than across different groups, proposing that the brain's connectome reflects phylogenetic relationships. The manuscript further details which features of the network organisation are associated with larger differences across groups and hence may drive speciation; and which features seem to be a common principle across mammals.

The authors present evidence based on the analysis of diffusion-weighted brain imaging data across 124 species, 111 of which were included in the comparison. The dataset is a great resource to address their research question.

The paper is clear and the evidence compelling. The manuscript adds valuable insights into the connectome architecture across species, potentially opening a new perspective on the link between genetics and behaviour. I would like to point out the great open science practice of the authors - code is available with a great ReadMe to guide potential users, connectivity matrices are available, and all software packages used in the analyses have been cited.

The figures are clear and complement the manuscript.

Technical Comments:

- Spectral approach / Interpretation<br /> It would be good to have more insight into the meaning of the spectral distance results. My understanding is this: the eigenvalues of the normalised Laplacian obviously have a mean of 1 (because their sum equals the trace of the Laplacian, which is equal to N [number of nodes]). Therefore, the distances between the spectra essentially amounts to comparing higher moments, and in particular the variance (as the histograms look quite Gaussian, I am guessing the distances are dominated by differences in the variance). But what does it mean that bats have a higher variance in these Eigenvalues than primates? I know that the authors try to give *some* insight, e.g. that when the distribution is peaky around 1, it means there are more stereotypical local patterns of connectivity. I understand that. But what are these patterns?

- Effect Size / Null Distribution<br /> I like the idea and the ambition of this paper. My main concern is that the differences are very small. Pretty much all the measures (laplacian eigenspectra and network-theoretic measures) are very similar between animals. This can be interpreted in two ways. (1) it may mean that the brain organisation is preserved, which is the interpretation of the authors. But it could also mean that (2) the metrics are not very informative. How do we know if we are in situation (1) or (2)? There is no comparison to a good null model (except in Fig4 but I don't think a random network is a good null). One possible null is two random networks connected to each other with a few random connections (to mimic left-right brains)?

* The authors use cosine similarity to compare the eigenspectra distributions. I think this does them a disservice. cosine similarity normalises the distributions quadratically instead of linearly. But the main thing that is changing is the variance. So normalising quadratically diminishes the dissimilarities between distributions. I have looked at their data (thanks for sharing!) and using multidimensional scaling with Euclidean looks much better than with cosine distance. I would suggest using euclidean.

* The authors use a bootstrapping method to calculate an average distance which they claim is useful because they don't have the same number of animals in each category. I don't think this bootstrapping is useful at all. If anything, it just adds noise. Averaging 10,000 samples with replacement does not change the outcome compared to simply averaging the matrices without the sampling. To test this: vary n and it should converge to the average of the original non-sampled data. (I've tried it!)

* The authors should clarify whether they are using the weighted or binarised connectivity matrices in the spectral approach (and also what threshold). I suspect that they are using binarised matrices, which probably explains why the spectral results fit better with the graph topology results when the latter uses binarised matrices.

- Parcellation.<br /> One main issue is the way in which the connectomes are divided up into 200 regions each, independent of the brain size. This to me seems a confound. I know it's rather standard practise in the field, but I have yet to see a validation that this does not influence the results. Given the enormity of the dataset here I would ask the authors to run their analyses in a way that the number of regions is a function of the size of the brain-this is a much more realistic assumption, as we know that a shrew size brain has about 20 cortical areas, whereas the human has about 180 according to Glasser et al.

Reviewer #1 (Public Review):

I'd first like to congratulate the authors for their impressive work, nicely building on their earlier work, and now representing an automated package which will enable the research community to use their approach widely. It is exciting to see how their approach opens new perspectives to investigate hippocampal organization and I am personally looking forward to all the work that will follow from this.

One important strength is that their overall approach allows for analyses that have not been possible before in this way. Their topological alignment allows to visualize data from other domains on the folded or unfolded surfaces and enables analyses regarding thickness and curvature. While this was possible using their own earlier work already before, they added one more piece so that the full pipeline can be applied automatically from beginning to end. They had the end user in mind during their development and thus made the pipeline available as BIDS App, provide their pipeline in containers and added a quality assessment step that allows others to flag suspicious results.

Another strength is that once hippocampal anatomy has been unfolded, their approach allows to label hippocampal subregions based on their own earlier work using the Big Brain 3D histology. While there are already approaches for automated subfield segmentation, one big advantage of their approach is the automated labeling of the hippocampal tail. This is very complicated and difficult to do even with manual segmentation due to the complex appearing anatomy because of the bending of the posterior hippocampus.

Yet another strength is their careful validation approach regarding their pipeline. They compare their own approach against other popular segmentation tools in the field, ASHS and Freesurfer. They find that their approach is more similar to other histology work, in particular in the hippocampal head, and report that their approach compares very well to their own manual segmentations. Further, they explore how well their approach generalizes to an aging sample as well as to a dataset with different and non-isotropic resolution.

However, one potential issue relates to the robustness of their approach regarding populations with different hippocampi due to age-related changes or disease. The authors have shown in this work, that their approach can produce solutions in an ageing dataset and a dataset with different resolution that apparently seemed to be correct. However, for example, how would a subject with pronounced thinning or overall volumetric changes in CA1 look like. This makes me wonder whether their approach would be sensitive, for example, to specific changes in one subfield but not others.

This relates also more broadly to the applied validation measures. For example, the authors state that their approach seems more favorable compared to other approaches because they see continuity of subfields along the long axis of the hippocampus. While I agree that based on the anatomy, subfields should be continuous, image quality does not always allow for segmentation at every voxel even when done manually. I'm wondering in general whether it helps to do so anyways by enforcing subfield labels based on a strong prior (subfield labels defined on Big Brain 3D histology), or whether it would be advantageous to rather not label a subfield in these cases.

Taken together my point is whether the approach presented here has the risk that it is too dependent on the prior and imposes the same subfield label information on every subject which would produce correct-looking results but would not necessarily be valid. While I appreciate the authors analysis of a dataset from an aged population as well as one with a different resolution, I do not think that these are enough yet to show validity in this respect.

Reviewer #1 (Public Review):

The aim of the research is to show the role of the transcription factor NCOR1-in the differentiation and function of regulatory T cells (Tregs). The study showed that NCOR-1 deficiency increased the fraction of effector Tregs, and enhanced MYC expression in Tregs and that disruption of interaction of LXR with NCOR1 led to an increased MYC expression in in vitro generated Tregs. NCOR1-mediated transcriptional regulation could be potentially important for T cell development and function; however, the results are insufficient for fully supporting their claim that NCOR1 controls Treg differentiation and function, in particular, the study lacks a basic analysis of the role of NCOR1 in Tregs.

Reviewer #1 (Public Review):

There are various ways in which homothallism (self-fertility) has arisen in the fungal kingdom from supposed heterothallic (obligate outbreeding) ancestors. Understanding the genetic basis of homothallism is important from both a fundamental basis, as it provides intriguing evolutionary insights, and also from a practical viewpoint as it impacts on variation and sporulation of a species - of particular importance for pathogenic and species of economic importance. In the present study, the authors describe an investigation of the genetic basis of homothallism in Cryptococcus depauperatus, a fungus closely related to Cryptococcus species causing serious human lung disease. The authors use a combination of genome analysis and experimental gene expression and manipulation work to show that C. depauperatus has a novel form of homothallism never reported before from fungi. This involves loss of the homeodomain genes which normally control mating in basidiomycete fungi, and instead signalling by a cognate pheromone and pheromone receptor and pathway seems sufficient to achieve self-fertility and induction of the sexual cycle. This is a very interesting and significant finding, adding to knowledge in the fungal kingdom and beyond as to the evolution of sexual breeding systems in nature. Overall my conclusion is that the authors' claims and conclusions are justified by their data and the work presented has a large number of strengths, although there are some minor weaknesses and need to qualify one assertion as follows.

Strengths<br /> (1) The work has been conducted to a very high and thorough standard and is very well written and illustrated throughout. The authors base their findings on a combination of genome analysis and experimental gene expression and manipulation work, together with additional work (e.g. microscopy and CHEF gel studies) where required. Results arising are then all subject to suitable statistical analysis.<br /> (2) Regarding the genomics part of the work particular credit is given for aspects such as the very high standard of bioinformatic analysis (e.g. use of both nanopore and Illumina sequencing methodologies and care taken in contig assembly) and presentation of data in figures; the thorough phylogenetic analysis involving over 4,000 protein-encoding genes in a concatenated study to show species relationships; careful checking of a range of mating genes to show mixed evolutionary origins of the de novo mating locus and other regions of the genome (e.g. via analysis of MYO2, STE12, STE11, STE20 origins from an a- or alpha-ancestor).<br /> (3) Regarding the experimental part of the work particular credit is given for aspects such as the de novo development of a gene transformation and selection system in C. depauperatus (based on Agrobacterium-mediated transformation); the use of a heterologous C. neoformans system to confirm the bioactivity of the putative MAT-2 alpha-type pheromone from C. depauperatus; very clever use of recessive drug resistance markers to select putative recombinant progeny and then use UV-induced markers to show recombination in intra-strain pairings; and analysis of expression of putative 'sex' genes during the sporulation cycle.<br /> (4) Novelty of the findings. Previous examples from the fungal kingdom have shown the evolution of homothallism by mechanisms such as the incorporation of complementary mating-type (MAT) genes into the same genome, mating-type switching, and unisexual mating. This is the very first study to describe a situation where the homeodomain genes that normally control sexual development in basidiomycete fungi have been lost, and sexual development is instead achieved by activation of a cognate pheromone and pheromone receptor system. To add further to the novelty is the fact that only one complementary pheromone precursor (a MAT-2 alpha-type) and pheromone receptor (a STE3 a-type) pair of genes were found, whereas normally in basidiomycete fungi and beyond a set of two complementary pheromone precursor and pheromone receptor genes are normally found in the same genome (i.e. an additional MAT-1 a-type pheromone precursor and STE2 alpha-type receptor gene).<br /> (5) The work contains an appropriate balance of reporting and yet also some speculation, such as the model at the end suggesting possible evolutionary routes.<br /> (6) The work is very well referenced throughout. The work also has a very extensive set of supporting data included as supplementary files to support the assertions made.

Weaknesses<br /> (1) The work only involves analysis of two isolates of C. depauperatus, whereas analysis of a wider range of isolates might have revealed additional insights. But to be fair to the authors, the species C. depauperatus has only been reported very rarely and the two isolates examined appear to be the only publicly available accessible isolates. The authors also concede themselves that additional isolates would ideally be examined in the future to see if the proposed models stand.<br /> (2) The authors provide evidence for meiotic recombination based on a very low number of markers - just three UV-induced markers and two drug resistance markers. And recombination is only shown conclusively in a very limited number of progeny (as shown in Figure 8C). Based on this very limited dataset they then produce some centimorgan mapping data and compare this rate to kb/cM data from other Cryptococcus species. However, this is at best preliminary, pilot data and should be cautioned as such, and ideally, many more markers would be used. Though to be fair to the authors they only had one intra-strain 'cross' to work with so were very limited in the markers available, and even within this limited dataset, there was good evidence for some meiotic recombination.<br /> (3) Some minor errors and clarifications are required at various points in the manuscript.

Reviewer #1 (Public Review):

In this paper, Bai et al. investigate in experiments and simulations how cohesion is maintained in chemotactic travelling waves of bacteria. These waves emerge from the bacterial population consuming an attractant, thus carving a gradient which they follow chemotactically. This paper builds up on previous work of some of the authors (Fu et al, Nat Commun 2018), which found that in these waves bacteria with varying degree of chemotactic sensitivity organize spatially in the band, which allows for its cohesiveness despite varying phenotypes. The authors investigate here an additional element for the cohesiveness of the wave: because the sharpness of the gradient increases from the front to the back of the wave, 'late' cells catch up via a stronger chemotactic response, and front cells slow down via a weaker one. This had been already postulated in earlier work on the phenomenon (Saragosti et al. PNAS 2011), but here the authors investigate how this applies to cells with varying chemotactic sensitivity. They also performed agent-based simulations of the cells behavior in the gradient and developed a model of the motion in the gradient. The latter maps the spatial dependence of the gradient steepness onto an effective travelling potential which keeps the cells together in a group as the gradient and the wave propagate. Importantly, the effective potential is predicted to be tighter for cells with higher chemotactic sensitivity, in agreement with the cell behavior they observe in experiments where the chemotactic sensitivity is artificially modulated. This suggests that weakly chemotactic cells are more weakly bound to the group and have a higher chance of being left behind. This last part is interesting in the context of range extension in semi-solid agar, where bacteria are known to be spatially organized and selected according to their chemotactic motility (Ni et al, Cell reports 2017, Liu et al Nature 2019)

This paper builds its strengths on the extensive experimental characterization of the system and a variety of modeling approaches and makes a fairly convincing case for the way of understanding the mechanism of cohesion maintenance they propose.<br /> From a methodological perspective, only a few points need to be addressed:

Control experiments need to quantify the cell-to-cell variability of the induction level of Tar by tetracycline.

Chemical attraction to cues released by other cells is a well-documented way to create cohesive large scale structures in E. coli (Budrene & Berg Nature 1995, Park et al PNAS 2003, Jani et al Microbiology 2017, Laganenka et al Nat commun 2016). The cohesion of the wave have never been analyzed in this optic, despite being a possible alternative explanation to the gradient shape. Since the authors main claim is about the wave cohesion, they should provide evidence that such an explanation can be ruled out or considered secondary.

Possible effects of physical interactions between cells on the chemotactic response are not accounted for. The consequences should be better discussed, because they are known to influence chemotactic motility at the densities encountered in the present experiments (Colin et al Nat commun 2019).

Additionally, the paper could better emphasize the new results and separate them from the confirmations of previous results.

Reviewer #1 (Public Review):

Hydrogen sulfide (H2S) is best known for it smell of rotten eggs and its high toxicity due to interference with oxygen transport. Recent studies suggest, however, that H2S is involved in various physiological and pathological processes and might exert beneficial therapeutic effects at very low doses. Thus, H2S-releasing compounds are considered for the treatment of e.g. cardiovascular diseases. In the present study, Pal et al. report that H2S also plays an important role in reactivation of HIV from latency. The first show that HIV reactivation reduces the levels of endogenous H2S in cell lines. They further show that suppression of the H2S producing enzyme CTH enhances HIV reactivation and modulates the expression of cellular genes associated with apoptosis and mitochondrial function. The authors also provide evidence that endogenous production of H2S or release from small molecule H2S donors allows U1 and Jurkat cells to maintain normal redox homeostasis and mitochondrial bioenergetics and suggest that this promotes HIV-1 latency. Finally, they provide evidence that the H2S donor GYY4137 modulates Nrf2, NF-kB, and YY1 pathways and suppresses HIV reactivation in latently infected T cells from HIV-infected individuals. The study addresses an important topic from a different angle. It is comprehensive and provides insights into the mechanisms underlying the effect of H2S on HIV latency. In addition, the impact of H2S donors on reactivation of HIV is of significant interest. Limitations of the study are that potential risks and problems associated with H2S as therapeutic agent are not addressed. H2S is highly toxic; thus, dosage will be a major challenge. Treatment would not only affect cells harboring latent HIV but all cells and the H2S-mediated mechanisms proposed to suppress HIV reactivation, such as inhibition of NF-kB and altered metabolism, would be expected to cause side-effects. Finally, the authors propose to combine H2S donors with ART to suppress HIV reactivation. However, effective ART usually prevents viral replication with high efficiency. Thus, rebound after treatment interruption (and not under ART) is the problem. In the end, a functional cure only makes sense if it doesn't need to be combined with ART and does not require daily treatment.

Reviewer #1 (Public Review):

In this paper the authors provide new information about the relative importance of the type I and type III interferon-driven gene expression and anti-viral responses, particularly focused on the role of the Intestinal microbiota to maintain background levels of type III (interferon lambda) signaling. They show with interferon lambda administration as a positive control, and antibiotic-mediated microbiota biomass depletion that low background levels of type III interferon-driven gene expression are mediated by the microbiota. Heterozygous mouse strain combinations for epithelial specific either type I or type III interferon receptor deficiency shows that the effect is type III mediated. In-situ hypbridisation shows that type III-driven gene expression is highly discontinuous in the epithelial layer and mainly at the villous tips. Rotavirus infection shows slightly accelerated kinetics in the absence of the type III receptor-signaling. Since intestinal type I and type III interferon responses are well described to occur, this provides a distinction between the two signaling pathways the consequent antiviral responses and the role of the microbiota in maintaining a basal level of type III signaling.

Reviewer #1 (Public Review):

In this manuscript, the authors present a model to relate FLIM measurements to mitochondrial metabolic fluxes. Using mouse oocytes, which have little NADPH, the authors develop a coarse-grained model to infer mitochondrial NADH oxidation by exploiting NAD(P)H FLIM. Using this approach, the authors uncover regional variation in mitochondrial fluxes in mouse oocytes. The modeled mitochondrial flux shows a strong negative correlation with mitochondrial membrane potential and no correlation with mitochondrial content. While this is not the first paper to use NAD(P)H FLIM to show subcellular metabolic variability, this manuscript does present a model to connect NAD(P)H FLIM to mitochondrial redox cycles. Therefore, the major utility of the model lies in its ability to provide subcellular information about mitochondrial NAD(P)H oxidation. The authors provide a comprehensive and accessible discussion of the assumptions, caveats, and conclusions enabled by their modeling. At present, however, it is not clear to this reviewer how generalizable this method will prove beyond mouse oocytes. This concern stems from the potential difficulty in establishing key parameters of the model in other cell types in which assumptions safely made in mouse oocytes may not be appropriate.