Reviewer #3 (Public Review):

In this manuscript Lituma and colleagues investigate a potential role for presynaptic NMDARs at hippocampal mossy fiber (MF) synapses in regulating synaptic transmission. The combined use of electron microscopy, electrophysiology, optogenetics, calcium imaging, and genetic manipulations expertly employed by the authors yields high quality compelling evidence that presynaptic NMDARs can participate in activity dependent short term facilitation of release onto postsynaptic CA3 pyramid and mossy cell targets but not onto inhibitory interneurons. Moreover, presynaptic NMDAR activation is demonstrated to be particularly effective in promoting BDNF release from MF boutons. The investigation is well designed with a clear hypothesis, appropriate methodological considerations, and logical flow yielding results that fully support he authors conclusions. The manuscript fills an important gap in our understanding of MF regulation by unambiguously confirming a functional role for presynaptic NMDARs that were first described anatomically at MF terminals nearly 30 years ago. Combined with a handful of other studies describing presynaptic NMDARs at various central synapses this study expands the role of NMDARs as critical players in synaptic plasticity on both sides of the cleft.

After examining one condition which renders an action involuntary (forced actions), Ar. moves on to examine the other condition: ignorance. His main claims in this section are thus: most actions performed in ignorance are involuntary, but can be counter-voluntary to the extent that the agent regrets being the cause of those wrongdoing; an action performed in ignorance is either involuntary or counter-voluntary, whereas an action performed because of general ignorance on the part of the agent is voluntary.

1110b17-1111a21 What comes about by force...and involve regret.

DOI: 10.1016/j.immuni.2020.10.007

Resource: (ZFIN Cat# ZDB-ALT-131018-3,RRID:ZFIN_ZDB-ALT-131018-3)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-131018-3

What is this?

Reviewer #3 (Public Review):

The current experiment tests whether the attentional blink is affected by higher-order regularity based on rhythmic organization of contextual features (pitch, color, or motion). The results show that this is indeed the case: the AB effect is smaller when two targets appeared in two adjacent cycles (between-cycle condition) than within the same cycle defined by the background sounds. Experiment 2 shows that this also holds for temporal regularities in the visual domain and Experiment 3 for motion. Additional EEG analysis indicated that the findings obtained can be explained by cortical entrainment to the higher-order contextual structure. Critically feature-based structure of contextual rhythms at 2.5 Hz was correlated with the strength of the attentional modulation effect.

This is an intriguing and exciting finding. It is a clever and innovative approach to reduce the attention blink by presenting a rhythmic higher-order regularity. It is convincing that this pulling out of the AB is driven by cortical entrainment. Overall, the paper is clear, well written and provides adequate control conditions. There is a lot to like about this paper. Yet, there are particular concerns that need to be addressed. Below I outline these concerns:

1) The most pressing concern is the behavioral data. We have to ensure that we are dealing here with a attentional blink. The way the data is presented is not the typical way this is done. Typically in AB designs one see the T2 performance when T1 is ignored relative to when T1 has to be detected. This data is not provided. I am not sure whether this data is collected but if so the reader should see this.

2) Also, there is only one lag tested. The ensure that we are dealing here with a true AB I would like to see that more than one lag is tested. In the ideal situation a full AB curve should be presented that includes several lags. This should be done for at least for one of the experiments. It would be informative as we can see how cortical entrainment affects the whole AB curve.

3) Also, there is no data regarding T1 performance. It is important to show that this the better performance for T2 is not due to worse performance in detecting T1. So also please provide this data.

4) The authors identify the oscillatory characteristics of EEG signals in response to stimulus rhythms, by examined the FFT spectral peaks by subtracting the mean power of two nearest neighboring frequencies from the power at the stimulus frequency. I am not familiar with this procedure and would like to see some justification for using this technique

Reviewer #3 (Public Review):

Previously published work had shown that small clusters of N protein can be observed at the newly forming interface between pIIa and pIIb and that these clusters are the source of nuclear N intracellular domain (NICD) following N activation by its ligand Delta (Dl). In this manuscript the authors have used live imaging of fluorescently tagged proteins to study the contribution of several regulators of apical-basal cell polarity and of N signaling to the formation of these N clusters. Their analysis revealed differences in the localization of polarity and junction markers between the SOP daughters and surrounding epithelial cells, confirming that the pIIa/pIIb interface has special features different from epidermal epithelial cells. They found that the polarity regulator Bazooka (Baz), the homolog of mammalian Par3, colocalizes with N in the clusters and is required for efficient cluster formation. Two other polarity regulators, Crumbs (Crb) and atypical protein kinase C (aPKC) do not colocalize with N and Baz in the clusters but are found in intracellular vesicles in pIIa and pIIb, in contrast to the surrounding epithelial cells where they localize at the apical plasma membrane. Two regulators of N signaling, Neuralized (Neur) and Sanpodo (Spdo) also localize in the clusters together with N and Baz, whereas the N ligand Dl and the negative regulator of N signaling, Numb (Nb) are not detectable in the clusters.

To test the functional contribution of the proteins mentioned above to formation of the clusters at the pIIa/pIIb interface, the function of the corresponding genes was eliminated by mutation, RNA interference, or ubiquitin-mediated degradation (degradFP). The authors found that Baz and Spdo are required for efficient cluster formation, while Neur, Dl and Nb are negative regulators of cluster formation. In the absence of these three proteins, N clusters are more frequent and show brighter fluorescence signals.

The data also confirm the intimate connection between N signaling and cell polarity. Nonethless, the data do not reveal a novel mechanism but rather describe a cell-biological phenomenon in greater detail than before. Thus, the data are certainly of great interest to specialists in the field, but it is less clear whether they will have a greater impact in the general cell biology or signaling community.

Reviewer #3 (Public Review):

Summary of goals:

In the moth Helicoverpa armigera the authors examined whether projection neurons from different antennal lobe tracts encoding sex-pheromone components with different valence occupy distinct projection areas in the protocerebrum of the midbrain.

Strengths and weaknesses of methods and results:

Methods chosen are adequate and state of the art. In vivo calcium imaging allowed for more easy imaging of a population of neurons, in search for statistically significant responses to pheromone components of different concentrations, quality, and valence. The main, general drawbacks of calcium imaging is the lower temporal resolution that does not allow for detection of single action potentials at the scale of few ms and the inability of fine spatial resolution of projection patterns of single neurons. This was compensated for by excellent intracellular recordings of single antennal lobe projection neurons, stainings of single cells, and embedding in the 3D standardized H. armigera brain. The data a very carefully analyzed with adequate analysis software and adequate statistical analysis and the most relevant results are shown in very good Figures. I also very much appreciate all of the supplementary figures. I do not see any relevant weakness in the methods and the respective results. However, as outlined in detail in the reply to the authors, the wording of the manuscript can be improved, to make it clearer and understandable without the need to read previous publications.

Everybody working with odors knows about the difficulty to precisely control and measure the exact molar concentration of odorants applied. But since the authors showed in previous publications that they take great care to control odor stimuli they should include also in the Material and Methods of this publication more details about concentration of the respective odor stimuli or mixtures employed.

Did they achieve their aims? Do data support conclusions?

Yes, the data support their conclusions as clearly shown in their excellent recordings, their excellent combination of physiological and morphological analysis, as well as their thorough statistical analysis.

Discussion of the likely impact of the work on the field, utility of methods:

This is an excellent, synergistic collaboration of different international experts in insect olfaction. It is still under-estimated how important the combination of single cell analysis in intracellular recordings with neural network analysis via calcium imaging is. Schemes of frequency encoding versus temporal encoding can only be deciphered with a clever combination of these techniques. This manuscript adds important insights into information processing of olfactory stimuli of antagonistic valence. It starts to become clear that in different sensory systems valence of aversive versus attractive sensory stimuli is processed in parallel pathways. Most likely antagonistic pathways connected to different neuronal units in premotor areas of the midbrain, connecting to parallel de- and ascending pathways of central pattern generators in the thorax. In addition, the current work provides relevant new information about processing of pheromone information in the different antennal lobe tracts in another important species. Thus, we may be one step closer to the future manipulation of sexual reproduction of specific insect pests.

Context for others for interpretations:

Sympatric heliothine moths use the same sex-pheromone components but at different concentration ratios, allowing for distinction of species that do not inter-mate. Thus, understanding how pheromone components at defined concentrations with opposite valence are processed in the brain to guide aversive or attractive behavioral interactions is relevant not only for determining principles of higher-order olfactory processing, but also to understand evolution of new species.

Reviewer 3 (Public Review):

In this work, the authors study the role of Adgrg6 in spine alignment. Using a battery of tissue-specific Cre deleter lines, they show that Adgrg6 activity in intervertebral disc (IVD), ligament and tendon cells is necessary to prevent spine misalignment. The finding that the phenotype appears around postnatal day 20 associates it with the human disorder adolescent idiopathic scoliosis. The authors show reduction in the phosphorylation of CREB in cartilage cells from Adgrg6 KO spine, suggesting a molecular entry point into this regulatory mechanism. Additionally, loss-of-function of Adgrg6 leads to a postnatal phenotype, indicating that its activity is important already in the embryo. Finally, they show involvement of Adgrg6 in regulating mechanical properties of tendons.

It would have been interesting to see if there is early indication for abnormality before the onset of scoliosis by showing histology and gene expression in cartilage, tendon and IVD of P10 mice.

Overall, it is an interesting and important paper, but it would have benefited from better organisation of the Results section.

Reviewer #3 (Public Review):

The authors have have convincingly demonstrated that modifying residues in the KDEL signal peptide and/or receptor can have a dramatic effect on retrograde targeting and their binding affinities. At a simplistic level, the structure of the electrostatic surface of the KDEL is very positively charged and then transitions into a negatively charged surface. The transition point from negative to positive charge is exactly at the level of the -4 position. As such, a bunch of arginine residues in the receptor progressively engage the signal until its locked in place by salt-bridges (E-117/D50) to the "K/H/R"-residue. In addition, pi-cation interactions between a tryptophan residue and the "K/H/R"- residue in the -4 position are important. To validate and quantify interactions, crystal structures have been solved with the HDEL and RDEL peptides and computational studies have analysed the pi-cation interactions. Moreover, differences are discussed between the tighter (HDEL) vs. weaker (KDEL) binding peptides in context with the differences in pH between the Golgi and ER.

Strengths:

The authors build upon their previous crystal structure of the KDEL receptor and the Newstead and Barr groups team up to provide a strong scientific approach combining structure-function analysis with trafficking and cell biology to yield important molecular insights into the recognition of ER-retention signals by the KDEL receptor. This paper is technically strong and well-written in most parts. They are able to build a connection between the variation of ER-retention signals "K/H-DEL" binding affinities with their pH dependancies and the natural abundance of ER proteins.

Weaknesses:

The authors have not made detailed pH dependent profiles for the "R"-DEL retention signal. This is an important comparative control, because unlike lysine and histidine, arginine has a very high pKa and will therefore always remain protonated. The authors refer to the "acidic" Golgi versus the neutral pH of the ER. However, it would be more correct to refer to the mildy acidic Golgi vs neutral pH of the ER and give the pH values of 7.4 for ER and pH 6.2 for the Golgi lumen. This sets up the scientific question to be more nuanced, as its only a pH difference of around 0.5 to 1.0 pH units. The authors have not included the computational estimates for the pKa values of the "K/R/H"-DEL residues nor the comparative pH dependence of KDEL and RDEL binding affinities, which is needed to properly asses the influence of differences in pH between Golgi and ER organelles and functional significance of the RDEL variant in particular.

DOI: 10.1016/j.devcel.2020.07.015

Resource: (ZFIN Cat# ZDB-ALT-120117-3,RRID:ZFIN_ZDB-ALT-120117-3)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-120117-3

What is this?

DOI: 10.1016/j.devcel.2020.07.015

Resource: (ZFIN Cat# ZDB-ALT-160122-3,RRID:ZFIN_ZDB-ALT-160122-3)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-160122-3

What is this?

DOI: 10.1016/j.cub.2020.06.086

Resource: (ZFIN Cat# ZDB-ALT-140811-3,RRID:ZFIN_ZDB-ALT-140811-3)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-140811-3

What is this?

Reviewer #3 (Public Review):

It is generally thought that the cerebellum is primarily involved in the short-timescale control of movements, while motor cortex is involved in motor planning. The present paper follows classic studies in primates and a recent study in mouse that investigated the role of cortico-cerebellar loops in motor control. To date, studies in both species applied perturbations to the cerebellum to then study changes in cortical activity. For example, it has been long known that cooling deep cerebellar nucleus produces changes in the responses of motor cortex neurons in primate (e.g., Meyer-Lohmann et al., 1975). Further, Gao and colleagues' recent paper (Nature 2018) used optogenetics to perturb responses in the deep cerebellar nucleus before licking movements. The authors of this 2018 nature paper conclude that persistent neural dynamics are maintained during voluntary movements by connectivity in within this cortico-cerebellar loop.

The experiments are well performed, and the results are logically organized and presented. However, a main concern is that the authors have not well justified that these experiments prove a conceptual advance. The conclusions appear to be largely consistent with those of prior work, both regarding changes in the responses of motor cortex neurons, and resultant (subtle) changes in behavior (i.e., altered arm kinematics). The impact of the paper would be improved if the authors adapted a more precise style of reporting the novelty of their results throughout.

Major concerns:

1) The experiments are well performed, and the results are logically organized and presented. However, a main concern is that the authors have not well justified that these experiments prove a conceptual advance. As noted above, prior studies have probed the role of cortico-cerebellar loops by applying perturbations to cerebellar activity (cerebellar cortex and/or deep cerebellar nuclei) and quantifying changes in cortical activity prior to and during movement. The main novelty of the present study is that the authors perturbed the loop at a different locus, namely in the pontine nuclei (PN) which send projections to the cerebellum rather than directly to the cerebellum. The rationale for why this specific perturbation provides a conceptual advance to the field was not adequately motivated.

The authors do clearly review prior literature showing that perturbation of cortico-cerebellar projections impacts the rest of the loop and behavior, they also well explain the application of their exciting new tool to specifically target PN neurons with their optogenetic stimulation. Yet, the authors do not motivate why it is important to specifically perturb the pontine nuclei (PN) to gain new insights into the role of "cortico-cerebellar loops" nor do they provide any reason to expect a difference in changes in loop dynamics for perturbations applied versus to the DCN. Indeed, the conclusions appear to be largely consistent with those of prior work, both regarding changes in the responses of motor cortex neurons, and resultant (subtle) changes in behavior (i.e., altered arm kinematics). Generally, these results are similar to those previously reported in primate DCN cooling experiments characterizing changes in hand movement in in a voluntary tracking task (e.g., Brooks et al., 1973; Conrad and Brooks 1974).

2) The description of the connectivity of the loop illustrated in Figure 1 is straightforward. Motor cortex recipient PN neurons project to PN neurons, which then project directly to the cerebellar cortex and deep cerebellar nuclei, etc. Thus, the effect of any perturbation to PN neurons should be realized rapidly within neurons in the cerebellar cortex and deep cerebellar nuclei if they are part of this direct loop. However, onset latencies for the effect of the perturbations are not documented for these experiments (Figs 3&6 in the test/reaching conditions, and associated text). Similarly, latencies are not reported for the onset of changes in motor cortex neuron responses to PN perturbations in either condition (Figs 4&7 in the test/reaching conditions, and associated text). The only reference I could find to latencies specified the that required to reach the peak firing rate - not latency of the change. Specifically: "these were stereotypical, mostly consisting of transient excitation (Fig. 4B, left; median time of firing rate peak 120 ms)" - 120ms seems very long for the loop in Fig 1. It would be useful to know the latency between optogenetic stimulation in PN and changes in PN firing rate. And then the question is at what latency are the neurons in subsequent nodes altered? Quantification of latencies of the effects that are observes in the different nodes of the cortico-cerebellar loops would strengthen the authors' conclusion that they are actually studying the direct loop in Figure 1 which would then make the study's conclusions more compelling.

3) Overall, there was often a sharp incongruity between the complexity of many of the findings described in results and accompanying figures and the short summary conclusion provided for the Results. Here is one of many examples (bottom of page 5), where the authors conclude "These results demonstrate that the cortico-cerebellar loop does not drive reaching, but fine-tunes the behavior to enable precise and accurate movement." Yet, what the results above describe is considerable heterogeneity and variability across animals and cases. These conclusion should be more aligned with/ justified by the author's description of their actual results.

4) A related issue is the disconnection between description and summary, in the description of Figure 6- 8. The emphasis on correlation, yet the authors' main point here seems to be that there are changes in the activity in cortex and DCN induced by the PN stimulation during movement explain the changes in hand trajectory. For example, Figure 6D and its implications are not effectively described in the text.

5) Finally, the authors conclude that changes in the activity in cortex and DCN induced by the PN stimulation during movement explain the subtle deviations in hand trajectory and conclude that the cortico-cerebellar loop is responsible for fine-tuning movement parameters (bottom pf page 5 and top of page 8). However, i) the statement that this pathway fine-tunes motion is not justified by the analysis, and ii) the novelty is not made clear relative to prior work that has investigated cortico-cerebellar loop (beyond the experimental difference in perturbation site).

Overall, the text that follows in the discussion presented the findings in a far more clear and compelling way than much of the text in the Abstract, Introduction and Results "perturbing cortico-cerebellar communication did not block movement execution: animals were typically able to generate the basic motor pattern during optogenetic stimulation of the PN, and neural activity in cortex and cerebellum largely recapitulated the firing patterns observed during normal movement. Instead, PN perturbation altered arm kinematics, decreasing the precision and accuracy of the reach, and perturbation-induced shifts in neural activity explained these behavioral effects." The paper would be improved if the authors adapted this more precise style of reporting throughout.

Reviewer #3 (Public Review):

In this article, Odermatt and colleagues report a detailed and quantitative description of the growth in dry mass and volume of single fission yeast cells. They first propose a new method for dry mass measurement and calibrate it. They then use this method to show that, while growth in dry mass shows a rather steady exponential trend, growth in volume changes with the cell cycle stage, depending on the rate of cell tip elongation, which results in changes in cell density. They then use various methods to arrest cells at various stages in the cell cycle, demonstrating that, for each cell cycle stage, the change in density is due to a specific growth rate which does not necessarily matches the growth in dry mass. All these experiments are very convincing. They close the article with two observations which are a bit harder to understand: first they show that there is an internal gradient of density, which corresponds to a difference in the rate of growth of both tips of the cell, and which is maintained on long timescales; second, they show that the differences in densities between the two ends of the cell lead, after the formation of the septum, to a difference in pressure which can be indirectly visualized from the bending of the septum - the denser side having a higher pressure, the septum is bent towards the lower density side.

Overall, the article provides one of the very few available quantitative description of growth from single cell measurements, and shows for the first time that density variations can arise from an uncoupling between volume growth and dry mass growth, which does not seem to be compensated for. I think that this particular finding is significant enough to make this article broadly interesting for the cell biology community, as it concerns a very fundamental aspect of cell physiology.

Reviewer #3 (Public Review):

Alghoul et. al are attempting to decipher the molecular mechanisms of Hox a3 and a11 TIE elements to inhibit cap-dependent translation. It is known that both TIEs possess an upstream Open Reading Frame (uORF) that is critical. Here they seek to understand the exact molecular mechanism used for inhibition. They were able to show that both a3 and a11 are regulated by different mechanisms to ensure the inhibition of ca-dependent translation. They found that the translation inhibitory mechanism mediated by TIE a3 requires the presence of the translation initiation factor eIF2D. However, the mechanism mediated by TIE a11 contains three elements that enable a highly efficient inhibition of cap-dependent translation, these are: an upstream start codon (uAUG), followed by a stop codon, and a long stable hairpin. These findings show that these TIE elements of Hox mRNAs enable regulatory control between canonical translation and non-canonical translation Internal Ribosome Entry Site (IRES) translation.

The authors use a vast amount of different sophisticated techniques to prove the molecular mechanism of inhibition conferred by the TIE elements. They start by cloning the regions upstream of the beta-globin 5'UTR in a RLuc vector and sequentially deleting regions to identify the region that confers inhibition. By using chemical modification probing, they confirm RNA secondary structure and identify regions of interest that might be responsible for inhibition. Then they focus of each element separately. In TIE a3, they identified an uORF that requires eIF2D for this process. They used MS analysis to identify the binding partners of the two elements and they further confirmed by silencing eIF2D that the inhibition doesn't occur in its absence. They further corroborated this finding by mutating an A-rich sequence found upstream of the uAUG that determines specificity of eIF2D binding. In the a11 case, they use toe-printing and mutagenesis to determine that a 'start-stop' sequence is located upstream of a highly stable stem loop structure which stalls the 80S ribosome and thereby inhibits cap-dependent translation of Hox a11 main ORF.

Reviewer #3 (Public Review):

Samineni et al. provide a beautiful insight into the mouse circuitries of itching in the Central Amygdala, a region of the brain that has apart from its role in pain, received ample attention for its role in feeding and freezing/escape to threat behavior. The manuscript provides an impressive amount experimental evidence, combining activity dependent gene expression with expression of genetically encoded calcium indicators, fluorescent proteins, optogenetic and chemogenetic tools, fiberoptometry and behavioral readouts. With these they identify a subpopulation of GABAergic neurons in the central amygdala that are activated by neck-applied chloroquine-induced itch (as witnessed by the presence of specific scratching in the neck). They show how their specific optogenetic reactivation (in the absence of chloroquine) induces 1). (non-directed all over the body) scratching 2). Real-time place aversion, and reduced spending in open arm of elevated zero maze. And they show how specific chemogenetic inhibition in the presence of chloroquine reduces scratching and real-time place aversion . They then go further to show by fluorescence axonal projections of these neurons in the vPAG and how optogenetic activation of these projections in the vPAG also induces (non-directed) scratching behavior. Finally they identify the genetic blueprint of these neurons with FACS. The experiments all well performed and provide convincing evidence for the implication of neurons in the CeA in sensitivity to itch and activity of scratching. It stands out for a rich combination of diverse state of the art technical approaches that are appropriate applied to answer the questions at hand.

In its completeness, the manuscript raises an important number of open questions in the field, and I would like to encourage the authors to identify these more clearly in their discussion, as they could set out a pathway along which this field may develop further.

Reviewer #3 (Public Review):

This study is quite directly a follow-up study of the recent work of Corbett-Detig et al (2015) and the commentary by Coop (2016) which aimed to understand the relation between population size and diversity, and the degree to which the shape of the relation could be explained by the action of linked selection. The analysis here scales up the sample size for a large-scale focus on comparative analyses of animals, and introduces the application of phylogenetic correction to control for relatedness.

As the most comprehensive analysis of its type to date, and with the addition of phylogenetic correction, this work's strength primarily lies in confirming the conclusions laid out in the commentary by Coop, notably that linked selection is unable to fully explain the narrowness of the diversity across species with orders of magnitude variation in population sizes. Through an explicit model-fitting of the effects of linked selection, the main conclusions are essentially that Lewontin's Paradox remains unexplained. The Introduction and discussion provide a very nice accounting of the range of possible explanations. I also appreciated the connection of the population size inferences to IUCN status.

I wasn't so convinced that the assessment of phylogenetic inertia (Lambda>0) really provides a way to assess Lynch's argument that coalescent times are too short to have a phylogenetic effect. For reasons outlined by the author in the discussion, it could well be that any phylogenetic inertia signal is due to inertia of life history traits correlated with effective population size rather than with diversity itself. The discussion raises this important point, but I think leaves us with the difficulty of really assessing how important that phylogenetic correction really is: if diversity has no direct phylogenetic non-independence, I am a bit unsure how much we have learned through this analysis alone (i.e. what is lambda telling us), without an explicit assessment of how often divergence times may actually truly be on the same order as coalescent times.

That said, I think it's a very open question whether diversity actually has phylogenetic independence because of short split times relative to effective population sizes. The author mentions the possible effect of large Ne on causing this to be violated; but I also wondered whether many of the small Nc species are still retaining a fair bit of ancestral polymorphism, further homogenizing diversity levels.

Overall a number of possible explanations (such as the effect of variable selected site densities, and variable recombination) were raised, and rather quickly rejected as 'unlikely to explain the qualitative patterns'. In a number of cases these statements were fairly brief, and I wondered whether in aggregate how likely a combination of these COULD explain the patterns. Looking at Figure 5B, it seems like the major effect of phylogeny (or correlated life history) is also apparent for the discrepancy between observed and predicted diversity- Chordates seem to have the largest discrepancy. With that in mind, I do wonder whether some feature of genome structure in Cordates, including a combination of the effects discussed in the paper that could account for the discrepancy (e.g. the effects of variable recombination rates/genome size and functional densities, variation in mutation rates, etc.) could collectively account for the paradox, even though individually the author rules them out as being able to explain the 'qualitative pattern'. Could the genome structure of chordates lead to a major difference in linked selection that's unaccounted for here?

Mei et al (2018) (American Journal of Botany, Volume 105, Issue 1, p1-124) argued that species with larger genomes have greater 'functional space', implying a greater deleterious mutation rate in species with larger genomes. This could potentially be a factor driving those Chordates with intermediate Nc values furthest below the predicted line?

Reviewer #3 (Public Review):

In the paper by Victorino et al., the authors describe the role for transcription factor HIF1a in NK cells during MCMV infection. They clearly demonstrate that HIF1a-deficiency results in impaired viral control, with a major effect visible in the impacted expansion of MCMV-specific NK cells. The paper brings novelty to the field as the role of HIF1a has not been addressed in NK cells in the course of viral infection.

The conclusions of the paper are mostly well supported by the data however there are still some aspects of the study that need clarification and extension.

i) It remains unclear what induces HIF1a expression during MCMV infection.

ii) The authors could speculate on the mechanisms of how HIF1a promotes repression of Bim during MCMV infection?

iii) The lack of expression of HIF1a glycolytic genes in HIF1a-deficient NK cells may not be surprising but it is very clear and convincing and supports the idea that HIF1a promotes survival of cells by promoting glycolysis. However, the study would benefit with a formal proof of this metabolic adaptation in the context of MCMV infection.

Reviewer #3 (Public Review):

Rosenberg and colleagues provide a thorough and comprehensive paper on an aging-associated biological phenomenon: greying. They are the first to show that pigment production can be switched of and on within one growth phase of the human hair follicle and that meaningful omics can be performed on pigmented versus non-pigmented hair and sections of hair. In addition, they provide evidence that greying episodes can be linked to stressfull periods in life. Overall, the authors describe a new method for the assessment of effects of stress and life style factors as well as biological strains on aging that can be used as outcomes in real world studies as well as randomized controlled therapeutic studies.

Reviewer #3 (Public Review):

The manuscript by Tarashansky et al., builds on this group's recently developed self-assembling manifold algorithm to develop methods for aligning cells of the same type across distantly related species using single cell gene expression data. The new method, SAMap, considers homologous genes in a novel way that takes into account paralog substitutions through gene expression correlations and the method further considers cell neighborhood relationships within and between species. Together, and through iterative analysis, these innovations maximally utilize the single cell data compared with only considering 1:1 orthologous genes and direct transcriptional correlations of cell types. Importantly (based on assumptions about cell type evolution), this method can identify homologous cell types based on shared neighbors, even if gene expression has diverged. The authors first apply SAMap to identify homologous cell types between developing zebrafish and xenopus at the whole organism level. SAMap captures nearly all homologous cell types, even with 1:1 orthologs using the mutual nearest neighbors approach whereas other top-in-field methods do poorly at this large evolutionary distance. SAMap also identifies 565 examples of candidate paralog substitution based on closer expression correlation of paralogs than orthrologs. The authors further extend these comparisons to flatworms and trematodes, and then to further include sponge, Hydra, and mouse. One fascinating result is that Spongilla choanocytes and apopylar cells show homology to the neuronal family, supporting recent predictions.

Overall, I find this approach extremely powerful and likely to be widely used in the study of cell type evolution and separately in the study of gene neofunctionalization. The validation among known homologs in distant vertebrates and benchmarking is convincing. My only major comment is that the authors could try a "leave one cluster out" analysis in the zebrafish xenopus comparison to ensure that the method does not overfit when a homologous cell type is absent.

Minor comments:

I am confused about how the homologous zebrafish and xenopus secretory cells with different developmental origins fit into the evolutionary cell type model. Could the foxa1 grhl cells that differ in their germ layer cells represent homology via horizontal transmission of a shared secretory gene network and convergent function rather than identity by descent and hierarchical diversification of a shared developmental gene regulatory network?

Are there any differences in the properties of genes that are deeply conserved in metazoan cell types (e.g., Fox, Csrp families in contractile cells) vs. genes that are more lineage restricted (e.g., mef2) - for example are the more conserved genes more central in regulatory networks within a species and thus more constrained?

Why did heart, germline, and olfactory placode cells not cluster in the xenopus atlas - these seem like conserved populations, or was this due to sampling / staging?

Reviewer #3 (Public Review):

In this study, Ide et al present a comprehensive analysis of single cell transcriptomic changes in the kidney in response to mild and recoverable injury compared to severe and persistent injury after renal ischemia reperfusion in an effort to identify cellular pathways that promote maladaptive repair. The analysis of their transcriptomic data identify pathways that persist in severe injury, confirming findings identified by other groups including induction of cell populations involved in the repair response after injury (SOX9-expressing cells), cellular interactions (tubular-derived chemokines/cytokines to monocyte/macrophage receptors), and cellular pathways (Gpx4-glutathione) that are important to prevent ferroptosis, a major pathway known to drive cell death in renal ischemia-reperfusion injury. Global deletion Gpx4 has been shown by others to drive ferroptotic cell death in renal ischemia-reperfusion. Ide et al use a genetic model of Sox9-specific deletion of Gpx4 to show that deletion of Gpx4 specifically in Sox9-expressing cells is sufficient to enhance ferroptotic cell death and maladaptive repair.

Strengths:

The strengths of this manuscript include the generation of a large dataset of scRNA-Seq in mild versus severe ischemic kidney injury at several time points as well as validation with qPCR and immunostaining of a subset of pathways and cellular injury markers identified in their scRNA-Seq analyses. Cellular differentiation and developmental pathways activated in the course of injury and repair in the adult kidney were also analyzed in relation to publicly available neonatal kidney scRNA-Seq data. Additional correlation of identified pathways and cellular injury markers were further supported by analyses of previously published scRNA-Seq data from human kidney biopsy samples from normal controls and non-rejection acute kidney injury allografts.

Weaknesses:

Data presented in the manuscript confirms previously published known findings and does not present a novel observation or pathway. Additional discussion should be included to acknowledge and explain the assumptions and limitations of the scRNA-Seq analyses, particularly the RNA velocity and pseudo time trajectory analyses, which are mathematical models based on assumptions of gene expression similarity to impute cellular transition states.

The conclusions of this paper are mostly well supported by data, but some aspects of the immunostaining need to be clarified. In particular the variability of VCAM1 staining across figures especially in the control contralateral kidney and the difference between SOX9 immunostaining as compared to the Sox9-TdTomato reporter.

Reviewer #3 (Public Review):

Signal peptidase is an essential enzyme involved in protein transport and secretion of all organisms. This contribution analyses the function of the non-essential subunit Spc1 of the yeast signal peptidase. The result suggests that Spc1 binds and recognizes hydrophobic membrane anchor sequences that should not be cleaved by the signal peptidase. Numerous variations of the signal peptides from CPY and Sps2 were tested for their cleavage and glycosylation in the presence or absence of Spc1. The authors conclude that the hydrophobicity and length are the main determinants to allow the cleavage.

The provided data are technically perfect and in a good, logical order. However, in contrast to their claim, no real internal membrane anchor sequence was tested. It also remains unclear whether the Sps2 protein has to remain in the ER as an uncleaved protein.

Reviewer #3 (Public Review):

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

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

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

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

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

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

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

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

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

Reviewer #3 (Public Review):

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

Major strengths:

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

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

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

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

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

Major weaknesses:

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

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

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

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

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

Did the authors achieve their aims?

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

Reviewer #3 (Public Review):

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

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

Reviewer #3 (Public Review):

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

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

Reviewer #3 (Public Review):

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

This one told and had a chronology that didn't feel like a list like in others. I thought "Day ???" was funny, too.

This one was also dramatic and made an interesting comparison between watching a dystopian movie and living in a dystopia.

Reviewer #3 (Public Review):

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

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

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

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

Reviewer #3 (Public Review):

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

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

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

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

Reviewer #3 (Public Review):

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

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

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

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

DOI: 10.1016/j.devcel.2021.03.027

Resource: (ZFIN Cat# ZDB-ALT-140623-3,RRID:ZFIN_ZDB-ALT-140623-3)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-140623-3

What is this?

Reviewer #3 (Public Review):

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

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

Reviewer #3 (Public Review):

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

Reviewer #3 (Public Review):

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

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

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

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

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

Reviewer #3 (Public Review):

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

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

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

Reviewer #3 (Public Review):

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

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

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

Suggestions:

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

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

Reviewer #3 (Public Review):

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

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

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

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

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

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

Reviewer #3 (Public Review):

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

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

Major issues:

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

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

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

Reviewer #3 (Public Review):

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

Strengths:

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

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

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

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

Weaknesses:

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

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

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

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

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

Reviewer #3 (Public Review):

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

Reviewer #3 (Public Review):

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

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

Reviewer #3 (Public Review):

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

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

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

Reviewer #3 (Public Review):

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

Strengths:

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

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

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

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

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

Weaknesses:

There are not many weaknesses in this work.

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

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

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

Reviewer #3 (Public Review):

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

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

Reviewer #3 (Public Review):

Summary

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

Strength & weaknesses

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

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

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

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

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

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

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

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

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

Reviewer #3 (Public Review):

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

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

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

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

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

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

Reviewer #3 (Public Review):

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

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

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

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

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

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

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

Reviewer #3 (Public Review):

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

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

Reviewer #3 (Public Review):

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

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

Reviewer #3 (Public Review):

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

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

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

Reviewer #3 (Public Review):

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

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

Reviewer #3 (Public Review):

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

Reviewer #3:

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

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

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

Reviewer #3 (Public Review):

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

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

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

Reviewer #3 (Public Review):

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

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

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

Reviewer #3 (Public Review):

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

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

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

Essential issues:

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

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

Reviewer #3 (Public Review):

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

Reviewer #3 (Public Review):

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

Reviewer #3 (Public Review):

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

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

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

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

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

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

Reviewer #3 (Public Review):

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

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

Reviewer #3 (Public Review):

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

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

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

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

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

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

Reviewer #3 (Public Review):

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

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

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

Reviewer #3 (Public Review):

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

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

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

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

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

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

Reviewer #3 (Public Review):

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

In my assessment, key strengths of this work are:

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

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

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

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

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

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

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

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

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

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

Regarding the ampicillin data, the odd behaviors are:

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

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

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

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

Reviewer #3 (Public Review):

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

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

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

Reviewer #3 (Public Review):

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

Strengths:

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

Opportunities for Improvement:

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

Reviewer #3 (Public Review):

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

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

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

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

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

I offer a few observations.

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

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

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

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

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

la non réponse n'est donc pas acceptable https://www.legifrance.gouv.fr/codes/article_lc/LEGIARTI000031367338/

Reviewer #3 (Public Review):

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

Minor concerns:

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

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

Reviewer #3 (Public Review):

In this manuscript, the authors use high-resolution live imaging to investigate how progenitor cells travel through an embryo to a distant site for differentiation and organ formation. The test case is the movement of dorsal forerunner cells (DFCs) in the zebrafish embryo, which give rise to a transient organ called Kupffer's vesicle that functions to establish the left-right body axis. DFCs are derived from enveloping layer (EVL) cells ~5 hours post-fertilization (hpf) and then move towards the vegetal pole of the embryo. They ultimately end up in the tailbud where they differentiate into epithelial cells to form Kupffer's vesicle between 10-11 hpf. Live imaging convincingly shows that EVL cells undergo apical constriction and delaminate from the EVL layer to form DFCs. Some DFCs remain connected to the EVL via ZO-1 enriched tight junction-like apical attachments. The authors propose that spreading of the EVL layer 'drags' the underlying DFCs towards the vegetal pole via these apical attachments. Supporting this model, EVL and DFCs co-migrate with the same speed and directionality, and perturbation of an actomyosin ring network in the yolk syncytial layer (YSL) disrupts movement of both EVL and DFCs. Between 8-9 hpf DFCs detach and are uncoupled from the EVL. The authors show that E-cadherin is necessary for DFC-DFC adhesion, and additional imaging experiments show that DFCs can extend long protrusions that 'capture' detached DFCs to facilitate clustering. Taken together, these data suggest an interesting drag mechanism for guiding progenitor cell movements, however the results presented do not fully demonstrate this mechanism, and alternative mechanisms were not thoroughly tested.

Reviewer #3 (Public Review):

The authors study the leaf transcriptomes of males and females in 10 species of Leucadendron and infer genes expressed significantly differently between males and females (sex-biased genes, hereafter SBGs). Most SBGs in Leucadendron leaves evolved recently, suggesting that SBGs turnover (evolution and reversion) is very high because the genus is ancestrally dioecious since >10My. Using species in which the genes orthologous to SBGs are not sex-biased, the authors show that SBGs have high rates of expression evolution already before becoming SBGs. This suggests that most SBGs evolved under drift and the majority of SBGE (sex-biased gene expression) is evolving neutrally. This is confirmed by the estimated small proportion of SBGs evolving under adaptation (about 20% of SBGs have 5 fold higher expression divergence compared to polymorphism divergence, a mark of relatively recent adaptation). Also, SBGs are more tissue specific (less pleiotropic). Finally, the percentage of SBG is not correlated to the intensity of morphological dimorphism. All these findings go against the classical view that SBGE is driven by sex-specific selection for sexual dimorphism.

The analyses are very cautious with well designed controls and randomizations.

The results support well the conclusions.

This study puts forward the role of drift in sex-biased gene expression, offering a new interpretation of this common evolutionary phenomenon.

Reviewer #3 (Public Review):

This analysis focuses on funding success for a set of NIH R01-mechanism grant applications submitted between 2011 and 2015, with a focus only on those which had white and Black Principal Investigators (PIs). It is presented as a follow-up to the previously published paper from Hoppe and colleagues in 2019, uses the same population of applications and relies on the same analysis of application text to cluster these applications by topic. The authors set out to determine how success rates associated with the application's proposed topic may be determined by the success rates associated with the Institute or Center within the NIH to which the application had been assigned for potential funding. This is a critical and important investigation that is of high potential impact. The scholarship of the Introduction and Discussion, however, fails to convey this to the reader. There are many recent publications in the academic literature that address why a disparity of funding to AA/B investigators, and a disparity of funding of topics that are of interest to AA/B investigators, are such critical matters for the NIH to identify and redress. Similarly, the Discussion and Conclusions sections do not suggest any specific actions that may be recommended by these findings, which is an unfortunate oversight that limits the likely impact of this work.

The significance of this work is limited by a number of methodological choices that are unexplained or have not been justified and therefore appear to be somewhat arbitrary. While it can be necessary to draw category lines in an investigation of this type, it is necessary to provide some indication of what would happen to the support for the central conclusions if other choices had been made. This includes the exclusion of multi-PI applications if the Black PI was not the contact PI, the definition of AA/B-preferred ICs as the top quartile (particularly given the distribution of success rates within this quartile), the definition of AA/B-preferred topics as the 15 word clusters that accounted for only half of the AA/B applications, and the ensuing inclusion of only 27% of the AA/B applications. Arbitrary choices to use only a subset of the data raise questions about what the conclusions would be if the entire dataset of grants assigned across all of the ICs, and on all of the topics, was used.

A fundamental limitation to this manuscript is that the authors are relying on an indirect logic of analysis instead of simply reporting the success rates for applications with AA/B and white PIs within each IC. The primary outcome deployed in support of the central conclusion is a reduction of the regression coefficient for the contribution of PI race to award success and an elimination of statistically significant contribution of research topic preferred by AA/B applicants to the award success once IC success was partialed out. The former analysis is interpreted in imprecise terms instead of simply reporting what magnitude of effect on the white/Black success rate gap is being described. And the latter analysis appears to show a continued significant effect of PI race on award success even when the IC success rate is included. The much more intuitive question of whether award rates for white and AA/B applicants differ within each IC has not been addressed with direct data but the probit model outcome suggests it is still significantly different. This gives the impression that the authors have conducted an unnecessarily complex analysis and thereby missed the forest for the trees- i.e. even when accounting for IC award rates there is still a significant influence of PI race.

The manuscript is further limited by atheism omission of any discussion of how and why a given grant is assigned to a particular IC (this is exacerbated by incorrect phrasing suggesting the applicant "submits an application to" a specific IC) and any discussion of the amount of the NIH budget that is assigned to a given IC and how that impacts the success rate. This is, at the least, necessary explanatory context for the investigation.

Reviewer #3 (Public Review):

In this study, the authors use a combination of fluorescent and electron microscopy to visualize the trafficking of HIV-1 viral particles during infection. The goal was to determine the components of nuclear HIV-1 virions during infection, and specifically to determine the degree to which reverse transcribed DNA in the nucleus associates with capsid protein and other fluorescent markers of infectious virions, such as fluorescently labeled integrase, which is increasingly used by many labs to track HIV-1 particles in the nucleus. The strengths of the manuscript lie in the imaging approaches used to quantitatively measure colocalization between viral DNA, CA and IN during infection, which are rigorous and well executed. Tomograms of high pressure frozen/plastic substituted samples showing apparently intact capsid cores at and within the nucleus are the most significant outcomes of the study and represent a significant technical achievement. These images provide some of the most compelling evidence to date that cores an enter the nucleus intact, despite previous studies suggesting that capsid disassembly occurs in the cytoplasm or at the nuclear pore.

The weaknesses of the manuscript lie in the use of HeLa based target cells in all but one of the figures. Although the results in primary cells are generally consistent with the results observed in HeLa cells, differences between HeLa and primary cells have been noted in other studies, and the manuscript would have been significantly improved with more extensive use of primary cells throughout. Additionally, the numerous other recent recent studies that have demonstrated that reverse transcription and uncoating complete in the nucleus, including works from the Pathak, Diaz-Griffero, Di Nunzio, Dash and Campbell labs, among others, reduce the potential impact of these studies. The Di Nunzio lab, in particular, has recently published a nearly identical system for labeling the reverse transcribed HIV-1 genome during infection. However, differences in approach prevented that study from being able to conclude that intact capsids exist in the nucleus (or made such conclusions open to alternative interpretations). In contrast, the CLEM-ET studies in this manuscript unambiguously show intact cores in the nucleus, and this is an advance for the field, in addition to being a substantial technical achievement. Nevertheless, the prior studies in this area, published last year, do impact the novelty of the observations made in this manuscript.

In the aggregate, this manuscript adds to a growing body of work suggesting that models of HIV-1 infection that have dominated the field for years should be reconsidered. As recently as 2 years ago, the idea that even a small amount of CA protein remained associated with the viral replication complex in the nucleus was somewhat heretical. While old models die hard, and some in the field are likely to debate how "intact" the capsid cores observed in this manuscript actually are, the idea that intact or nearly intact cores can enter the nucleus is increasingly difficult to deny in light of data provided here. This raises questions regarding how the HIV-1 core, which exceeds the generally accepted size limitation of nuclear pore complexes by 50%, based on the width of a capsid core, can enter the nuclear environment in an intact or nearly intact state (an issue that is addressed in the recent (and cited) Zila et al. bioRxiv paper, but not here).

Reviewer #3 (Public Review):

This manuscript by Koiwai et al. described the single-cell RNA-seq analysis of shrimp hemocytes and was submitted as a Resource Paper in eLife. In this study, they identified 9 cell types in shrimp hemocytes based on their transcriptional profiles and identified markers for each subpopulation. They predicted different immune roles among these subpopulations from differentially expressed immune-related genes. They also identified cell growth factors that might play important roles in hemocyte differentiation. This study helps to understand the immune system of shrimp and maybe useful for improving the control of the pathogen infections. The analysis of the data and interpretation is overall good but there are also some concerns:

1) The number of UMI and genes detected per cell after mapping to the in-house reference genome does not appear to be presented, and the similarities or differences between the three replicated samples are not discussed, as well as the low number of genes detected per cell (~300 in this study) .

2) The correlation between the morphology and the expression of marker genes demonstrated in Figure 6 is questionable. Cells of the same size could express totally different genes. On the other hand, cells that are different in size can express nearly identical genes. The evidence presented in this manuscript is not enough to support a correlation between cell size and gene expression. Therefore, the author would either need to provide more evidence to support this correlation, or not make such correlation.

3) There are many spindle-shaped cells in Figure 6B, but none of them appeared in Figure 6C and D after sorting, and the reason for this is unclear.

4) The hemocyte differentiation model in Figure 7 is not supported by any experimental data.

Reviewer #2 (Public Review):

In this manuscript, Lamers et al wanted to characterise the previously reported adaptation of SARS-CoV-2 to non-human (Vero) cells. Vero cells are commonly used by laboratories to grow experimental stocks of some viruses as these cells permit high titres of many viruses, they lack the ability to produce type I interferons (cytokines which could interfere with downstream assays), and their non-human nature means soluble factors in virus stocks are less likely to impact experiments in human cells. However, a number of reports have recently been published describing that growth of SARS-CoV-2 in Vero cells leads to loss of the SARS-CoV-2 Spike protein multibasic cleavage site (MBCS). This apparent adaptation to the Vero cell-line leads to a virus compromised in its ability to enter, and therefore replicate in, human cells, meaning that experimental results obtained in human cells using the Vero-adapted SARS-CoV-2 may not fully reflect the situation occurring with authentic SARS-CoV-2. It is therefore important for the research community to understand SARS-CoV-2 adaptation to laboratory cell-lines/conditions and to have propagation methods that are suitable for maintaining the authenticity of clinical virus isolates.

The major finding of Lamers et al in this manuscript is that human cell-lines (e.g. Calu-3) and primary human organoid systems can be used to propagate clinical isolates of SARS-CoV-2 to high titres without the acquisition of 'laboratory adaptations'. To get to this finding, the authors carefully study the adaptation of a representative SARS-CoV-2 isolate in Vero cells, monitoring plaque size phenotypes and performing whole-genome deep sequencing to identify adaptive variants that appear in the viral Spike gene. These variants (including newly-described substitutions as well as deletions around the MBCS) are validated for their impact on viral infectivity in human and Vero cells using pseudovirus assays, fusion assays, and western blot assays, and their role in affecting the entry route of SARS-CoV-2 is dissected using pathway-specific inhibitors (such as camostat and E64D) and cell-lines with/without TMPRSS2 (an important protease for Spike cleavage). Importantly, using these assays and tools, the authors can make solid and well-reasoned arguments as to why SARS-CoV-2 adapts to Vero cells, and thus why certain culture conditions and cell substrates lead to a loss of SARS-CoV-2 genetic stability. Using similar tools, this also allows the authors to carefully study whether any adaptations occur when SARS-CoV-2 stocks are passaged in human cell substrates (such as Calu-3 or primary human organoids), and study culture conditions in Veros (such as expression of TMPRSS2) that prevent changes in SARS-CoV-2.

The data in this manuscript are thorough and well-presented. Importantly, the conclusions are strongly supported by the data, particularly the overall take-home message that human cell substrates can be used to efficiently propagate SARS-CoV-2 isolates without introducing cell culture adaptations. However, beyond this simple message, the manuscript also provides new mechanistic insights into the reasons for such viral adaptations in the Vero cell system, and identifies previously undescribed adaptations in the MBCS region that will be valuable for other researchers to take note of. The authors also describe a methodological workflow to produce SARS-CoV-2 in human cells that highlights a buffer-exchange step to remove potentially interfering human cytokines/debris, and which will be useful for other researchers.

Overall, the manuscript makes a clear and important contribution to the SARS-CoV-2 field and will be of interest to active researchers who are studying this virus experimentally.

Reviewer #3 (Public Review):

In some species, supporting cells (SCs) of the cochlea can replace hair cells and thus restore hearing. In the mouse, neonatal SCs can also produce hair cells; however, this property is lost during early postnatal life. This study sought to test whether forced expression of two transcription factors normally associated with OHC development, Atoh1 and Ifzh2, can induce adult mammalian supporting cells to take OHC-like properties. Using Cre-dependent expression in mice, the authors showed that co-expression of Atoh1 and Izfh2 could induce a small number of adult SCs to express the OHC-specific gene, Prestin. This conversion was significantly enhanced when existing OHCs were ablated, in this case using a Prestin-DTR mouse model generated by the authors. A detailed phenotypic analysis combined with single cell RNA-sequencing (scRNA-seq) supports the idea that Atoh1/Izfh2 can partially convert adult SCs into OHC-like cells. However, the conversion is not complete, with immature bundles and a gene signature that resembles P1 OHCs (and sometimes E16 OHCs) more than P7/P30 OHCs or P60 SCs. Accordingly, the new OHCs are not sufficient to restore hearing in the Prestin-DTR mouse model. Together, these data encourage optimism that adult SCs can be steered along the OHC path, though clearly more manipulations will be needed to produce mature, functional OHCs.

The main weakness of the study is the scRNA-seq analysis, which depends on very small sample sizes. Suggestions to improve upon the analysis are listed under Specific Recommendations.

Reviewer #3 (Public Review):

Pettmann et al. aimed at significantly improving the accuracy of SPR-based measurements of low affinity TCR-pMHC interactions by including a 100% binding control (injecting of a conformation-specific HLA-antibody) in the surface plasmon resonance protocol. Interpolating with the information of saturated pMHC binding on the chip The authors arrive at KDs for low affinity binders that are significantly higher than the previously reported constants. If correct, this has considerable ramifications for the interpretations of the results obtained from functional assays measuring the T cell response towards pMHCs featured in a titrated fashion. Unlike what was put forward by earlier reports, the authors conclude that the discriminatory power of TCRs is far from perfect, as T cells still respond to low affinity pMHC-ligands without a sharp affinity threshold. This is also because they managed to detect T cells responding to even ultra-low affinity ligands if provided in sufficient numbers.

The body of work convinces in several regards:

(i) It is exceedingly well thought out and introduces a quality of analytical strength that is absent in most of the literature published thus far on this topic.

(ii) At the same time theoretical arguments are bolstered by a large body of experimental "wet" work, which combines a synthetic approach with cellular immunology and which appears overall well executed.

(iii) The data lead to hypotheses in the field of T cell antigen recognition in general and in the theatre of autoimmunity, cancer and infectious diseases.

There are a few aspects that may limit the impact of the study. I have listed them below:

(i) The study does not provide kinetic data for the low affinity ligand-TCR binding but rather argues from the position of affinities as determined via Bmax. This limits somewhat the robustness of the statements made with regard to kinetic proofreading.

(ii) Thresholds for readouts were arbitrarily chosen (e.g. 15% activation). It appears such choices were based on system behavior (with the largest differences observed among the groups) but may have implications for the drawn conclusions.

In summary, the work presented contributes to demystifying the link between TCR-engagement and (membrane proximal) signaling. It also provides a fresh perspective on the potential of TCR-cossreactivity.

Reviewer #3 (Public Review):

Sun et al have assembled, modified, and applied a series of existing gene editing tools to tissue-derived human fetal lung organoids in a workflow they have termed "Organoid Easytag". Using approaches that have previously been applied in iPSCs and other cell models in some cases including organoids, the authors demonstrate: 1) endogenous loci can be targeted with fluorochromes to generate reporter lines; 2) the same approach can be applied to genes not expressed at baseline in combination with an excisable, constitutively active promoter to simplify identification of targeted clones; 3) that a gene of interest could be knocked-out by replacing the coding sequence with a fluorescent reporter; 4) that knockdown or overexpression can be achieved via inducible CRISPR interference (CRISPRi) or activation (CRISPRa). In the case of CRISPRi, the authors alter existing technology to lessen unwanted leaky expression of dCas9-KRAB. While these tools have previously been applied in other models, their assembly and demonstrated application to tissue-derived organoids here could facilitate their use in tissue-derived organoids by other groups.

Limitations of the study include:

1) is demonstrated application of these technologies to a limited set of gene targets;

2) a lack of detail demonstrating the efficiency and/or kinetics of the approaches demonstrated.

While access to human fetal lung organoids is likely not available to many or most researchers, it is probable that the principles applied here could carry over to other organoid models.

Reviewer #3 (Public Review):

Moncla et al. investigated the transmission of mumps virus in Washington, USA during an outbreak in 2016-2017. They sequenced viral genomes from infected individuals in Washington and elsewhere within the United States and used phylogenetic approaches to understand the origins and patterns of spread exhibited by the virus during the outbreak. They observe a large fraction of cases in individuals who are part of the Marshallese community, and identify a link to a similar outbreak in the Marshallese community in Arkansas. They develop a method for determining the role of the Marshallese community in the Washington outbreak that is robust to sampling bias and size. This method is well thought-out and presented and demonstrates that the outbreak in Washington state was sustained by transmission within this particular community. This paper provides a thoughtful approach to dealing with sampling issues that are often overlooked in phylogenetic studies. By consulting with a public health professional from within the affected (Marshallese) community, the authors are able to contextualize their results and demonstrate the underlying issues that may have contributed to mumps spread within the state.

Working with public health advocates from affected communities is exceptionally important for long term public health impact, and this paper sets an example that should be followed by others in the pathogen genomics field. The methodology used to determine mumps transmission patterns in Washington is sound and the conclusions are well explained. However, some additional context on the issues and potential pitfalls of source-sink analyses based on phylogenetic inference would help improve this already solid paper. Specifically:

1) The authors seem to assume a somewhat random sample throughout Washington state. They state that given a low sampling proportion they do not expect to have captured infection pairs, which seems reasonable. However, they then go onto assume that their sample is primarily comprised of samples from long, successful transmission chains. This is a reasonable assumption if there is no major difference in accessibility of samples from long transmission chains and shorter ones (for example, decreased access to healthcare). Could this impact the assumption of sampling primarily from long transmission chains? It seems from the data collected in this outbreak that this was not the case for mumps in Washington but addressing this assumption clearly (and potential ways to interrogate it) could make their methodology more applicable to other pathogen studies.

2) There are many examples of phylogenetic analyses that have led to conclusions about pathogen sources and sinks that were later shown to be wrong because of oversampling or other sampling biases. The authors address unequal sampling between clades, but additional contextualization of the problem and how this approach is different may help strengthen the methodology presented in the paper.

3) The authors present compelling evidence that the mumps outbreak in Washington state was sustained by the Marshallese community, and state that mumps did not transmit efficiently among the general Washington populace. That said, there were several other mumps outbreaks in the United States in the same 2016-2017 time period. Was there something different about Washington state that prevented mumps transmission outside of the Marshallese community? Were there no other close-knit communities (universities, prisons, other cultural communities, etc.) affected? It just seems surprising that the Marshallese community was the only community sustaining transmission at a time where many different types of communities were affected across the United States.

Reviewer #3 (Public Review):

The authors investigated pupillary response looking at the changes corresponding to perceptual events (spontaneous or physical changes) and contrasting them with requirements of over reporting (changes were reported or ignored). They demonstrate that the former is associated with a rapid constriction and re-dilation, whereas the latter shows an opposite effect with dilation being followed by re-constriction. The particular strength of the work is in no-report conditions using on OKN-based inference about timing of perceptual events that allowed for this dissociation to be observed, whereas manual report conditions allowed for a direct comparison with prior work. The analysis and control experiments are very thorough showing that reported results are unlikely to be explained other factors such as saccades or blinks.

The study makes a significant contribution but proposing a no-report paradigm for identifying perceptual events that should work for any multistable display. The fairly rapid pupil constriction event could provide an easy to detect and temporally reliable marker of perceptual switches, expanding ways the multistability data is collected. The same approach could also be useful for no-report studies of visual awareness in general.

The ability to decompose pupillary response into two components - perception and over manual response - will also be useful for studying neural correlates of spontaneous perceptual switches, as it could help to better understand switch-time activity in various frontal and parietal regions. Here, also some regions are associated with active response, whereas other with perception, distinction that could be potentially better understood based on the idea that only the former involves noradrenaline-affected processing. My main worry methodologically is the under and overestimation of mean switch rate via OKN (figure 1C). OKN estimates are all within .4-.8 range, whereas for self-report rates differ from 0.2 to over 1. Further analysis would be helpful. I think it would be helpful if the authors elaborated on what kind of switches went unreported (or, conversely, what kind of events led to false alarms): switches before very short dominance phases (could be to fast to report via key presses), to return transitions, etc.

Reviewer #3 (Public Review):

Opsin proteins are ancient light-sensitive molecules found in photoreceptor cells throughout the animal kingdom. Recent discoveries including those made in the current paper have revealed that besides r-opsins, some classes of photoreceptor cell also express genes that are found in mechanosensory cells, and that r-opsins have both light-dependent and light-independent effects on mechanical force transduction or motion. A question remains as to whether or not: 1) a protosensory cell of animals existed which contained both photoreceptor and mechanoreceptor-like features and, 2) whether the original function of opsin included light-dependent mechanosensory features? The authors consider three competing hypotheses for the cellular evolution of photoreceptor and mechanosensory function. Two of the hypotheses envision either photo- or mechanosensory function for opsins evolving first, the third imagines them evolving simultaneously. The authors note that the majority of what we know about rhabdomeric opsins comes from studying the eye photoreceptors of the fruit fly, Drosophila melanogaster. But might this kind of photoreceptor have functions that are derived compared to the ancestral photoreceptor cell? To investigate this question, the authors turn to the non-model system, Platynereis dumerilii, which has both head and non-head photoreceptors. Here the authors use 1) a fluorescent cell sorting method to perform RNA profiling of eye and trunk photoreceptor cells of a mutant marine worm and find evidence of co-expression of photo- and mechanosensory genes in photoreceptor cells. They also compare the genes that are expressed in Platyneris photoreceptors with genes expressed in Drosophila JO (hearing organ in flies), Zebrafish lateral lines and mouse IEH (inner ear hair) cells, and again they find some commonly-expressed genes. 2) The authors use cell culture to express the opsin, demonstrate that it interacts with G-alphaq, and that it's peak sensitivity is in the blue range. 3) They use in situ hybridization to validate the RNA-seq and detect select enriched transcripts in the photoreceptor tissues. 4) They use a new method, which should be widely useful to other researchers, to detect undulation behavior of the opsin mutant vs. wildtype worms and show that the mutant worm behavior is perturbed in altered light cycles. Taken together, the authors suggest that an ancient light-dependent function of opsin was linked to mechanosensation and that light-independent mechanosensory functions of opsins evolved secondarily. The interpretation is somewhat reasonable given the available data but does not yet entirely rule out other possibilities (see below).

This paper is a tour-de-force and a really impressive collection of experiments which examines the function of r-opsin in Platyneris. There's lots of innovation here from the use of fluorescent cell sorting and cell-specific RNA-Seq on a non-model system to the deep-learning based approach to examining behavior. Overall, the authors' interpretation of their data seems reasonable however I do believe a even stronger case could be made that what we are talking about is shared ancestry vs. recent recruitment if the authors made phylogenetic trees of the numerous TRE genes that are enriched between Drosophila JO and mouse IEH cells. If a significant number of these genes were true orthologs vs. paralogs across all three species then this would provide stronger evidence of an ancient light-dependent mechanosensory function for r-opsin. GO enrichment terms, while intriguing and suggestive, don't go far enough into the weeds. Also, I think the estimate of there being only 12 genes involved in making a photoreceptor cell able to detect light is probably an underestimate, as this ignores, for example, the understudied molecular machinery required for chromophore metabolism and transport. At the very least, the work should help inspire vigorous debate between vision and auditory neuroscience communities (which do not usually converse with one another) to more carefully consider the ways in which their systems overlap and why.

Reviewer #3:

The authors hypothesized lower GABA levels in older adults would influence cortico-cortical phase relationships more than cortico-muscular phase relationships during performance of a bimanual motor task. To this end, they evaluated the mediating role of endogenous bilateral sensorimotor cortex GABA content in relation to behavioral performance and patterns of interhemispheric and cortico-muscular electrophysiological phase coherence during a bimanual motor control task. The central finding was that the mediating influence of right M1 GABA on the relationship between cortico-cortical electrophysiology and behavior diverged between the younger and older groups, with lower endogenous GABA concentrations potentially benefitting bimanual motor performance in young adults and hindering performance in older adults. The result was specific to right M1 GABA, raising questions about hemispheric asymmetry, and behavioral performance differed substantially between groups, possibly influencing the sensitivity of the analyses of the electrophysiological phase relationships. Moreover, several earlier studies suggest endogenous M1 GABA content relates to cortico-muscular excitability measurements, other than phase synchrony, and it is unclear what distinguishes phase synchrony from these other measurements. The behavioral, MRS, and electrophysiological methods employed are fairly well-established and are combined in a novel manner. The Bayesian moderated mediation analysis represents a new approach to evaluating relationships between these measures under the moderating influence of age. The central questions concerning the roles of cortical endogenous GABA in bimanual control, and in age-related changes in motor control more generally, are important for determining the neural computations underlying flexible and precise behavior.

1) The total number of finger taps within the 2000 ms transition epoch likely differed between groups and could influence the ISPC measures. It would be helpful to rule out this possibility by examining relationships between ISPC measures and the total number of taps.

2) The differences between right and left M1 are somewhat surprising and merit further attention, particularly given the cortico-cortical ISPC results. The interpretation provided in the discussion (lines 607-618) is not particularly satisfying since this asymmetry is a critical feature of a key result. Can the authors leverage their own data to provide further insight into why RM1 GABA+ may be more likely to exhibit a relationship than LM1 GABA+? Would analyzing the behavioral data separately for the left and right hands provide further insight? Does the non-dominant hand lag behind the dominant hand, and/or is it more susceptible to errors?

3) There were some general issues concerning the GABA+ data:

a. Figure 2a suggests an interaction in the pattern of variance in the GABA+ data between the Young and Older groups for the LM1 and RM1 voxels. Is this interaction in variance significant, and if so, what might this mean for the M1 GABA+ results? Specifically, Young show greater variance for LM1, and Older show greater variance for RM1. Also, Young appear to show considerably lower variance for RM1 than LM1. However, the data in Figure 2 supplement 2 suggest that variance in the Young is similar between LM1 and RM1. Do these numbers accurately reflect the data depicted in Figure 2a?

b. It would be helpful to show the difference spectra in Figure 2 supplement 1b with separate plots for Young and Older.

c. Figure 2, supplement 1a: Was the LM1 voxel more dorsal and medial than the RM1 voxel?

4) The authors interpret the decrease in failure and increase in error rate across the task in the Older group as an indication of a loss of precision over time. Alternatively, might this pattern also arise because these participants are becoming faster at correcting their errors (i.e. within 2000 ms), avoiding trials from being categorized as a failure? More generally speaking, it would be helpful if the authors provided additional information about the cumulative error rate trials and what behavior looked like on these trials.

5) The authors should provide further justification for the assignment of age as the moderator and GABA+ as the mediator in their statistical model. Conceptually, it seems these factors could be reversed.

6) Several studies have established relationships between transcranial magnetic stimulation measures of cortico-muscular excitability and endogenous GABA+ content in the dominant M1. The manuscript would benefit from further discussion of the relationship of the phase connectivity measurements used here in comparison to these other previous studies.

7) It is not clear that data or analysis code are available.

Reviewer #3 (Public Review):

This study provides a concept of circuit organization of a pathway from the brainstem to the primary somatosensory (S1) and motor (M1) cortices through the thalamus to control the hand/forelimb movements. Previous studies reveal detailed circuit organization of ascending somatosensory pathways in the whisker system. In contrast, much less is known about circuit organization of another ascending pathway controlling the hand/forelimb movements, although it is known that there are some similarities and differences between two different somatosensory systems.

This paper revealed detailed circuit organization of the ascending pathways including the lemnisco-cortical and corticocortical pathways to control the hand/forelimb movements. The strength of this study is to use a variety of sophisticated techniques, such as optogenetics, trans-synaptic viruses, both anterograde and retrograde viruses, mouse genetics, and electrophysiology, to map the neural circuits in details. The circuit was revealed by electrophysiology together with optogenetics, which is very convincing. In addition, the detailed circuit organization revealed by this study will provide an important information for future behavioral studies. The weakness is the limitation of trans-synaptic viruses. For example, pseudorabies viruses move between multiple neurons, so to interpret the results may be complicated. Although behavioral analyses have not been performed in this study, it is beyond the scope of this study and future study will follow up the behavioral analyses.

Reviewer #3 (Public Review):

In this manuscript, Böhm et. al. aim to understand how precise kinetochore assembly is tied to cell cycle progression in budding yeast. In this work, the authors identify CDK phosphorylation sites concentrated in the N-terminus of Ame1, a protein of the COMA complex, and set out to characterize the role these phosphorylation sites may play protein function at the kinetochore. Although phospho-null Ame1 does not affect cell viability, expressing an Ame1 mutant that lacks the phosphorylated domain results in cell death. Interestingly, overexpression of the phospho-null Ame1 mutant accumulates to a higher level than the wild type protein leading the authors to hypothesize that these phosphorylation sites function as phosphodegrons in the Ame1 protein. Through molecular modeling and genetic analysis, the authors determine that Ame1 is a substrate of the SCF E3 ubiquitin ligase and is likely recognized by the Cdc4 F box protein. The authors go on to convincingly show that phosphorylation of what is referred to as the "CDC4 phosphodegron domain" is phosphorylated in a step-wise manner that is cell cycle dependent and that the phosphorylated Ame1 protein specifically is degraded in mitosis. In addition to Ame1 phosphorylation, the authors show that Ame1 degradation depends on whether Ame1 is bound to the Mtw1c (binding prevents degradation), which only happens at a fully assembled kinetochore. Based on these observations, the authors propose a model in which the phosphodegron motif functions to degrade any molecules of the COMA complex that are not incorporated into the kinetochore and in this way prevents kinetochore assembly at ectopic regions of the chromosome.

Reviewer #3 (Public Review):

Computational models, provide a way to understand emergent network function, and at their best provide a canvas for experimentalists to probe hypotheses regarding function. In this manuscript, Bui and colleagues provide a set of iterative models to describe the locomotor development of larval zebrafish at key developmental stages. These include coil, double coil, and swimming behavior that leads to 'beat-and-glide' behavior. During development, the model steadily moves from gap junction mediated connectivity to more complex synaptic-based network models. In my opinion, this is a very interesting foundation that can be used as a catalyst for future research for experimentalists or to develop more involved models. Like any model it is possible to be critical of the assumptions made. But I expect that it will not be static and be revised over the years. It is important to realize that these sets of models are unique in that they strive to provide models for motor control of a single species across development. The zebrafish is an excellent example since genetic models are widely used, development is swift, and there is active research to understand the physiology of locomotion.

Strengths and weaknesses:

The key strength of this manuscript is the detailing of a set of related models detailing the motor output of the larval zebrafish across key stages of development. The models should form a basis for future research. It also a first of its kind - I don't know of similar models focusing on development of locomotor function. The main weakness is the reliance on assumptions of model connectivity. But I suggest that if the model is treated as a basis for the community to refine and validate it will be incredibly useful.

Reviewer #3 (Public Review):

In this revised manuscript (Oon and Prehoda), the authors performed additional live-imaging experiments and recorded aPKC and actin dynamics simultaneously in larval neuroblasts. They also provide evidence that aPKC polarization is lost upon F-actin disruption by Latrunculin A treatment. These are great improvements. The pulsatile dynamics of actin and myosin II showed in the manuscript are compelling. Images presented in this manuscript are of high-quality and impressive.

However, the pulsatile apical myosin network in delaminating neuroblasts in Drosophila embryos was reported previously (An Y. et al., Development, 2017). This important and relevant paper should be cited in the introduction of the current manuscript. Therefore, the finding on the pulsatile actomyosin in larval brain neuroblasts reported in this manuscript is not a total novel discovery. Another major concern is that Lat-A did not specifically disrupt actomyosin pulsatile movements, as it generally disrupts the F-actin network. So these experiments only strengthened the link between the F-actin network and Par polarity (which was already demonstrated in Kono et al., 2019; Oon 22 and Prehoda, 2019). Low doses of Cytochalasin D are known to disrupt myosin pulses still allowing the assembly of the actomyosin network (Mason et al., Nature Cell Biology 2014). The author should treat neuroblasts with low doses of CytoD to only disrupt actomyosin pulses, not the entire F-actin network, and examine the effect on Par polarity. It is also worthwhile to knockdown sqh to disrupt apical pulsatile actin dynamics. Besides, most of the concerns previously raised by the reviewer were not addressed in the revised manuscript.

Reviewer #3 (Public Review):

In this study, Tang and colleague report that the multikinase inhibitor YKL-05-099 increases bone formation and decreases bone resorption in hypogonadal female mice with mechanisms that are likely to involve inhibition of SIKs and CSFR1, respectively. The authors also report that postnatal mice with inducible, global deletion of SIK2 and SIK3 show an increase of bone mass that is associated to both an augmentation of bone formation and bone resorption.

The paper provides novel and interesting information with potentially highly relevant translational implications. The quality of the data is outstanding and most of the authors' conclusions are supported by the data as shown.

Reviewer #3 (Public Review):

The proposed model is a variation on existing probabilistic fitness landscapes with a number of novel ingredients that are crucial for explaining the observed patterns. The model successfully accounts for the experimental results and makes new predictions, some of which are confirmed by the analysis of existing data. It also provides a coherent picture of the dynamics of adaptation that matches experimental observations. Overall, this is a conceptually deep and potentially highly influential study.

I see only one major issue that requires clarification. This concerns the distinction between the directed mutation scheme (leading to Eqs.(3,4) in the main text) and the symmetric version (Eqs.(5,6)).

Reviewer #3 (Public Review):

Yang et al. build on earlier studies from the Zheng lab and show in tissues that (i) the Hedgehog (Hh) co-receptor Ihog mediates homophilic interactions that enable cytoneme bundling and (ii) that Ihog-Hh interactions are stronger than and displace Ihog-Ihog interactions during signaling, consistent with biochemical and cell-based studies of relative affinities of Ihog for itself or Hh. These studies are bolstered by modeling and experiments showing co-localization of Dally and Dlp, which presumably supply the heparan sulfate chains needed to promotes homo- and hetero-philic interactions involving Ihog. I found the studies convincing, interesting, and an important extension of biochemical/cellular work on Ihog to tissue behavior.

AssayResult: 0.01 µM: 70; 0.08 µM: 50; 0.8 µM: 40; 8 µM: 15

AssayResultAssertion: Abnormal

Approximation: Exact Olaparib concentrations and assay result values not reported; values estimated from Figures 4a and 4c.

AssayResult: 0.01 µM: 50; 0.08 µM: 35; 0.8 µM: 25; 8 µM: 10

AssayResultAssertion: Abnormal

Approximation: Exact Olaparib concentrations and assay result values not reported; values estimated from Figures 4a and 4c.

AssayResult: 0.01 µM: 60; 0.08 µM: 55; 0.8 µM: 40; 8 µM: 15

AssayResultAssertion: Abnormal

Approximation: Exact Olaparib concentrations and assay result values not reported; values estimated from Figures 4a and 4c.

Comment: This variant was used as an abnormal control in other assays in this publication, but it was not specifically designated as a control in this assay.

AssayResult: 0.01 µM: 65; 0.08 µM: 50; 0.8 µM: 30; 8 µM: 20

AssayResultAssertion: Abnormal

Approximation: Exact Olaparib concentrations and assay result values not reported; values estimated from Figures 4a and 4c.

AssayResult: 0.01 µM: 50; 0.08 µM: 40; 0.8 µM: 20; 8 µM: 15

AssayResultAssertion: Abnormal

Approximation: Exact Olaparib concentrations and assay result values not reported; values estimated from Figures 4a and 4c.

AssayResult: 0.01 µM: 90; 0.08 µM: 80; 0.8 µM: 75; 8 µM: 35

AssayResultAssertion: Normal

ControlType: Normal; wild type PALB2 cDNA

Approximation: Exact Olaparib concentrations and assay result values not reported; values estimated from Figures 4a and 4c.

AssayGeneralClass: BAO:0003009 cell viability assay

AssayMaterialUsed: CLO:0036938 tumor-derived cell line

AssayDescription: Transient expression of wild type and variant mCherry-tagged PALB2 cDNA constructs in Trp53 and Palb2-null mouse cell line; exposure to increasing concentrations of PARP inhibitor Olaparib for 5 days inhibits end-joining mediated by PARP and sensitizes cells to DNA damage; cell survival is determined by measuring fluorescence intensity after staining with a cell viability reagent.

AssayReadOutDescription: Percent cell survival after treatment with Olaparib

AssayRange: %

AssayNormalRange: Olaparib resistance levels comparable to that of cells expressing wild type PALB2; no numeric threshold given

AssayAbnormalRange: Not reported

AssayIndeterminateRange: Not reported

ValidationControlPathogenic: 0

ValidationControlBenign: 0

Replication: Not reported

StatisticalAnalysisDescription: Not reported

AssayResult: 5

AssayResultAssertion: Normal

StandardErrorMean: 0.58

HGVS: NM_024675.3:c.101G>A p.(Arg34His)

AssayGeneralClass: BAO:0003061 reporter protein

AssayMaterialUsed: CLO:0009454 U-2 OS cell

AssayDescription: U2OS cells were treated with PALB2 siRNA and synchronized to G1/S phase by double thymidine block. Cells were then co-transfected with peYFP-PALB2 expressing PALB2 variants (or empty vector), pCR2.1-mRuby2LMNAdonor, and pX330-LMNAgRNA, which generates mRuby2-Lamin A/C fusion if HDR is successful.

AssayReadOutDescription: Mean relative percentages of mRuby2-positive cells over the YFP-positive population relative to the wild type condition.

AssayRange: %

AssayNormalRange: Not reported

AssayAbnormalRange: <40%

AssayIndeterminateRange: 41%-77%

ValidationControlPathogenic: 1

ValidationControlBenign: 3

Replication: Three independent experiments, each with n > 300 YFP-positive cells per condition

StatisticalAnalysisDescription: One-way ANOVA followed by Dunnett's post hoc analysis

AssayResult: 5

AssayResultAssertion: Abnormal

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

ControlType: Abnormal; empty vector

AssayResult: 100

AssayResultAssertion: Normal

ControlType: Normal; wild type PALB2 cDNA

AssayResult: 34

AssayResultAssertion: Abnormal

PValue: < 0.0001

AssayResult: 68

AssayResultAssertion: Indeterminate

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 90

AssayResultAssertion: Normal

PValue: Not reported

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 46

AssayResultAssertion: Indeterminate

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 23.6

AssayResultAssertion: Abnormal

PValue: < 0.0001

AssayResult: 82

AssayResultAssertion: Normal

PValue: Not reported

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 53

AssayResultAssertion: Indeterminate

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 41

AssayResultAssertion: Indeterminate

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 95

AssayResultAssertion: Normal

PValue: Not reported

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 90

AssayResultAssertion: Normal

PValue: Not reported

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 83

AssayResultAssertion: Not reported

PValue: Not reported

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 77

AssayResultAssertion: Indeterminate

PValue: < 0.01

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 81

AssayResultAssertion: Not reported

PValue: Not reported

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 38

AssayResultAssertion: Abnormal

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 5

AssayResultAssertion: Abnormal

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 36

AssayResultAssertion: Abnormal

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 85

AssayResultAssertion: Not reported

PValue: Not reported

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 58

AssayResultAssertion: Indeterminate

PValue: < 0.0001

Approximation: Exact assay result value not reported; value estimated from Figure 6C.

AssayResult: 86.74

AssayResultAssertion: Not reported

PValue: 0.1836

Comment: Exact values reported in Table S3.

HGVS: NM_024675.3:c.11C>T p.(Pro4Leu)

AssayGeneralClass: BAO:0002805 cell proliferation assay

AssayMaterialUsed: CLO:0037317 mouse embryonic stem cell line

AssayDescription: Stable expression of wild type and variant PALB2 cDNA constructs in Trp53 and Palb2-null mouse cell line containing DR-GFP reporter; exposure to cisplatin for 48 h induces interstrand-crosslink DNA damage; cell survival is measured by FACS 24 h after cisplatin washout

AssayReadOutDescription: Relative resistance to cisplatin represented as cell survival relative to wild type, which was set to 100%

AssayRange: %

AssayNormalRange: Cisplatin resistance levels comparable to that of cells expressing wild type PALB2; no numeric threshold given

AssayAbnormalRange: Cisplatin resistance levels comparable to that of cells expressing empty vector; no numeric threshold given

AssayIndeterminateRange: Not reported

ValidationControlPathogenic: 2

ValidationControlBenign: 2

Replication: 2 independent experiments

StatisticalAnalysisDescription: Not reported

AssayResult: 128.59