Reviewer #2 (Public Review):

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

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

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

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

Minor points:

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

Wenn die Temperaturen bis 2100 um 4 Grad steigen, werden ca. 40% des antarktischen Schelfeises mit hoher Wahrscheinlichkeit kollabieren.

Reviewer #2 (Public Review):

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

Strengths:

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

Weaknesses:

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

They have generated SLOS hiPSCs and studied those too.

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

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

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

DOI: 10.7554/eLife.59687

Resource: (ZFIN Cat# ZDB-ALT-050728-2,RRID:ZFIN_ZDB-ALT-050728-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-050728-2

What is this?

Reviewer #2 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

Strengths:

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

Weakness:

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

Reviewer #2:

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

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

Reviewer #2 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

Strengths:

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

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

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

Weaknesses/Criticisms:

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

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

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

Reviewer #2 (Public Review):

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

Strengths:

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

Weaknesses:

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

Reviewer #2 (Public Review):

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

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

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

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

Reviewer #2 (Public Review):

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

Main strengths:

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

Weaknesses:

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

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

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

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

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

Major comments:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

Summary:

Frey et al develop an automated decoding method, based on convolutional neural networks, for wideband neural activity recordings. This allows the entire neural signal (across all frequency bands) to be used as decoding inputs, as opposed to spike sorting or using specific LFP frequency bands. They show improved decoding accuracy relative to standard Bayesian decoder, and then demonstrate how their method can find the frequency bands that are important for decoding a given variable. This can help researchers to determine what aspects of the neural signal relate to given variables.

Impact:

I think this is a tool that has the potential to be widely useful for neuroscientists as part of their data analysis pipelines. The authors have publicly available code on github and Colab notebooks that make it easy to get started using their method.

Relation to other methods:

This paper takes the following 3 methods used in machine learning and signal processing, and combines them in a very useful way. 1) Frequency-based representations based on spectrograms or wavelet decompositions (e.g. Golshan et al, Journal of Neuroscience Methods, 2020; Vilamala et al, 2017 IEEE international workshop on on machine learning for signal processing). This is used for preprocessing the neural data; 2) Convolutional neural networks (many examples in Livezey and Glaser, Briefings in Bioinformatics, 2020). This is used to predict the decoding output; 3) Permutation feature importance, aka a shuffle analysis (https://scikit-learn.org/stable/modules/permutation_importance.htmlhttps://compstat-lmu.github.io/iml_methods_limitations/pfi.html). This is used to determine which input features are important. I think the authors could slightly improve their discussion/referencing of the connection to the related literature.

Overall, I think this paper is a very useful contribution, but I do have a few concerns, as described below.

Concerns:

1) The interpretability of the method is not validated in simulations. To trust that this method uncovers the true frequency bands that matter for decoding a variable, I feel it's important to show the method discovers the truth when it is actually known (unlike in neural data). As a simple suggestion, you could take an actual wavelet decomposition, and create a simple linear mapping from a couple of the frequency bands to an imaginary variable; then, see whether your method determines these frequencies are the important ones. Even if the model does not recover the ground truth frequency bands perfectly (e.g. if it says correlated frequency bands matter, which is often a limitation of permutation feature importance), this would be very valuable for readers to be aware of.

2) It's unclear how much data is needed to accurately recover the frequency bands that matter for decoding, which may be an important consideration for someone wanting to use your method. This could be tested in simulations as described above, and by subsampling from your CA1 recordings to see how the relative influence plots change.

3)

a) It is not clear why your method leads to an increase in decoding accuracy (Fig. 1)? Is this simply because of the preprocessing you are using (using the Wavelet coefficients as inputs), or because of your convolutional neural network. Having a control where you provide the wavelet coefficients as inputs into a feedforward neural network would be useful, and a more meaningful comparison than the SVM. Side note - please provide more information on the SVM you are using for comparison (what is the kernel function, are you using regularization?).

b) Relatedly, because the reason for the increase in decoding accuracy is not clear, I don't think you can make the claim that "The high accuracy and efficiency of the model suggest that our model utilizes additional information contained in the LFP as well as from sub-threshold spikes and those that were not successfully clustered." (line 122). Based on the shown evidence, it seems to me that all of the benefits vs. the Bayesian decoder could just be due to the nonlinearities of the convolutional neural network.

Reviewer #2 (Public Review):

This work analyses the movement of the dorsal forerunner cells (DFCs) and its interaction with the extra-embryonic enveloping layer (EVL). By doing high-resolution time lapse microscopy the authors characterize the movement of the DFCc showing that they delaminate from the epithelium by apical constriction but they remain attached to the superficial EVL. By doing laser ablations they show that the movement of the DFCc depends on the attachment and vegetal displacement of the EVL. However, they show that with some frequencies some DFCc are detached from the rest of the cluster, leading to some random movement or even being left behind and differentiating into other cell types. Importantly, they investigate an additional mechanism to explain the movement of the DFCc detached cells. They show that single cells generate protrusions that connect them with the DFCc cluster forming an E-cadherin junction. This paper makes an important contribution by adding some new mode of migrations during development. Most of the conclusion are supported by the experiments.

Reviewer #2 (Public Review):

Scharmann et al. present a study of sex-biased gene expression as a function of sexual dimorphism in leaf tissue in the genus Leucadendron. Comparative studies of sex-biased expression across clades are still relatively rare, and this analysis tests some core findings of a recent paper (Harrison et al. 2015). Overall, I like the analysis and think it could be a valuable addition to the literature on sex-biased genes. This is particularly true given the difficulty of cross-species expression comparisons and the paucity of them in plants.

However, there are some critical differences between the Harrison paper and the one here, and I think it would be helpful if the authors present them early in the text. Specifically, Harrison et al. (2015) was primarily focused on gonad tissue, which in animals is the site of the vast majority of sex-biased genes. In contrast, the authors here focus on vegetative (leaf) tissue, which is analogous to animal somatic tissue. None of the patterns that Harrison et al. (2015) observed and report from the gonad were evidence in the somatic tissue they assessed. Also, by looking at gonadal tissue, Harrison et al. (2015) focused on the tissue that produces gametes, which are thought to be subject to some of the strongest sexual selection pressures. The fairest comparison would be flower tissue in plants, so I am unsure how much of the Harrison results would be expected to hold up in leaf samples. This doesn't mean the authors should do the analyses they present, just that they should be a little more upfront about what they might reasonably expect to find.

There is also a conflation at times in the paper between sexual dimorphism, which the authors can quantify in their leaf samples, and sexual selection. I explain this in more detail below, but to summarize here, I think the expectations for the relationship between sex-biased gene expression and sexual selection versus sexual dimorphism are somewhat distinct.

Finally, I am a little concerned that the low numbers of sex-biased genes, expected from leaf tissue, offer limited power for some of the tests the authors want to do. Harrison et al. (2015) had hundreds of sex-biased genes from the gonad, and this power made it possible to detect subtle patterns. The authors have a few dozen sex-biased genes, and this makes it difficult to know whether their negative results are the result of low statistical power. That they find clear associations between pre-sex-biased genes and rates of evolution is quite impressive given this low power.

Reviewer #2 (Public Review):

The paper by Lauer et al provides further insight into the factors that might determine why RO1 applications from AAB (African American Black) principal investigators appear to fare worse than their white counterparts. Their work is derived from an earlier analysis published by Hoppe et al that found 3 factors determined funding success among AAB PIs. These included decision to discuss at study section, impact score, and topic choice. The latter, topic choice (community and population studies) appeared to represent more than 20% of the variability in funding gaps. This raised the question of whether there was reviewer bias at study sections. In the Lauer paper, after controlling for several of these variables, the authors found that the topic choice of AABs (ie. preferred topics) were indeed important in respect to funding, but they uncovered the fact that the topic choices occurred more frequent in ICs that had lower funding rates. Thus the authors conclude that the disparity between AAB and white investigator RO1s is very dependent on topic choice which ultimately ends up in larger ICs with lower funding percentiles.

Overall the paper is relatively straightforward and could be important as It provides some additional data to consider. It is in fact basically a re-analysis of the Hoppe paper, but that is reasonable since that paper left many unanswered questions. Its implications however are less clear, and these raise additional questions of importance to the extramural scientific community as well as IC leadership.

Overall the reader is left with the unsettling question: Can we just wish away these disparities based on IC funding rates? (Figure 1).

1) Why would topic choice of community engagement or population studies fare worse at an Institute such as AI rather than at GM if both have the relatively same proportion of preferred topics, and both have relatively high budgets compared to other institutes. Is there one or more ICs that drive the correlations between IC funding and preferred topics or PIs?

2) Since only 2% of all PIs are AAB does that represents another issue of low frequency relative to the larger cohort?

3) It would be valuable to know if community engagement or population studies in total do worse than mechanistic studies. The authors do admit that preferred topics of AABs in general fare worse(Figure 2, Panel B).

4) Another concern is that the data are up to 2015; it has now been five years and things have changed dramatically at NIH and in society. There are now many more multiple PI applications including AABs that may not be the contact PI yet are likely to be in a preferred topic area.

5) There is nothing in the discussion about potential resolutions to this very timely issue; In other words we now know that the disparity in funding is such that AAB RO1s do worse than white PIs because they are selecting topics that end up at institutes with lower funding rates. Should the institutes devote a set aside for these topic choices to balance the portfolio of the IC and equal the playing field for AABs? Are there other alternative approaches?

Reviewer #2 (Public Review):

Thank you for the opportunity to review the short report "Regional sequencing collaboration reveals persistence of the T12 Vibrio cholerae O1 lineage in West Africa" by Ekeng and colleagues. The authors report an analysis of 46 new Vibrio cholerae genomes in context of 1280 published genomes. The goal of their analysis was to establish a recent snapshot of VC population genomics in West Africa and assess the occurrence importations of new lineages. From their analysis, they infer that the recent cases were endemic.

Overall, this report presents findings from a region with little genomic surveillance, and as such these data are valuable for the understanding of endemic cholera in the region. The authors' analysis is technically sound, and the figures are well constructed. However, the depth of the analysis is relatively shallow, even for a short report, and the conclusions drawn from the data appear more subjective then based on the analysis at hand. These weaknesses could be addressed by a more in-depth analysis and clarification of the points below. Last, I did appreciate that this study was conducted in the context of a regional training. This could be an effective model for future analyses of regional importance. I feel like that wasn't the main focus of the report. If they were to shift their focus, I would want to know:

1) Where did the isolates come from (e.g., cholera treatment centers, hospitals, or broader active surveillance)?

2) Do they conduct environmental sampling and could this be part of future efforts?

3) Who attended the training? Were they members from regional ministry of health labs, academic institutes etc?

4) Were the attendees laboratorians, bioinformaticians, clinicians etc?

5) Was there an effort to analyze the data, particularly the bioinformatics portion, locally or did the rely 100% on the collaborators at JHU? If the latter, then I don't think this is a good sustainable model for ongoing genomic epidemiology. If the prior, then were local or regional computing resources used? 6) Are they continuing to sequence isolates after the training?

Reviewer #2 (Public Review):

Halliday et al. sampled plant communities and foliar fungal diseases along an elevation gradient in Swiss Alps, to test the potential relationship between environment, plant communities and diseases in the context of climate change. The authors confirmed that elevation can affect diseases by both abiotic and biotic factors, and, host community pace-of-life was the main driver for diseases along elevation. The topic is important and new, the study is well-designed, and the analysis is reasonable.

Reviewer #2 (Public Review):

Despite the fact that reverse transcription was discovered 50 years ago, there are still some black boxes regarding RT spatiotemporal activity. Recent studies elsewhere and here indicate that RT can occur in the nucleus, revising the "dogma" that RT occurs exclusively in the cytoplasm of infected cells. However, it is still debated whether this concept can be extended to all HIV target cells and which RT processes can start and finish in the nucleus. The authors also performed several experiments designed to show that uncoating (loss of capsid) occurs in the nucleus. The authors deserve credit for developing and applying complicated imaging technologies. However, live imaging data comes from pseudo-viruses, which have low infectivity, so high amounts of virus have been used to obtain some of the results. This is a limitation, and I have some reservations about the conclusions and the generalization of the results, and also about the lack of statistics for the CLEM-ET studies, probably owing to the complexity of the technique (detailed below). In addition, despite using state-of-the-art CLEM-ET, it is possible to visualize only structures with strong fluorescence and recognizable structures. I therefore wonder how can the authors can conclude that only the forms that still have a conical or partial conical shape are the most important to follow? It is possible that more flexible CA structures can access the nucleus and that the authors neglect them owing to limitations of the technology. Immuno-gold CA labeling could solve this issue, and the authors have the technologies required to perform these experiments.

Reviewer #2 (Public Review):

This manuscript builds upon some important thought-leading work within the Ras field that the authors have published in recent years. They have previously demonstrated how changing the protein expression levels of KRAS can modulate the number of Ras-driven tumours that are observed and posited that this suggests an optimal level of Ras signalling that is neither too stressing nor too insufficient to promote tumourigenesis.

In this manuscript they use urethane to induce lung tumours in mouse models that have either normal or high levels of KRAS expression (also higher oncogenic stress). They are also able to modulate the associated oncogenic stress levels by the presence (higher stress) or deletion (lower stress) of p53. Urethane normally generates Q61 KRAS mutations, biochemical analysis by other groups has previously shown that these mutations are more active than G12 mutations. Following urethane induction, they observe an improved competence to support tumorigenesis in the high KRAS model when p53 is removed. They also observe a shift towards G12 mutants under genetic conditions where oncogenic stress is higher (higher KRAS expression, presence of p53). ie. stress compensators (p53 loss or weaker activating mutation) permit promotion of tumourigenesis in the high KRAS model. The converse was also observed. Loss of p53 (lower stress) resulted in higher mRNA levels of G12 mutants - suggesting that the weaker mutant increases protein expression/cancer signalling to occupy the new oncogenic stress headroom that has been created. Some support for the hypothesis that these effects are mediated by differences in Ras signalling amplitude between the different mutants was provided by analysing the expression of three key Ras gene targets. As predicted, higher expression (signalling output) was seen in Q61 vs Q12 mutants and when p53 was deleted.

Strengths:

The mouse model conditions provide a suitable range of options to allow the hypothesis to be tested. The data are all internally consistent and broadly support the general conclusions.

Weaknesses:

The mRNA data are interpreted as evidence for changes in protein expression and Ras signalling activity - there is no formal evidence that this is the case.

The similarity in G12/13 mutations between the KRAS normal and high KRAS mice in Figure 2C is unexpected. The authors focussed on the potential for higher G12/13 mutant expression in the KRAS normal mice to explain this. It is also intriguing how there wasn't a more complete switch to Q61 in the high KRAS tumours when p53 was deleted. Whilst the Ras signalling dosing/oncogenic stress nexus are a reasonable explanation, the model/methods are a snapshot in time and don't have the resolution to fully understand the detail of what is going on here.

This study represents a solid contribution supporting an important model and will stimulate future work to understand Ras variant cancer contributions.

Reviewer #2 (Public Review):

In this manuscript Koiwai et al. used single cell RNA sequencing of hemocytes from the shrimp Marsupenaeus japonicus. Due to lack of complete genome information for this species, they first did a de novo assembly of transcript data from shrimp hemocytes, and then used this as reference to map the scRNA results. Based on expression of the 3000 most variable genes, and a subsequent cluster analysis, nine different subpopulations of hemocytes were identified, named as Hem1-Hem9. They used the Seurat marker tool to find in total 40 cluster specific marker transcripts for all cluster except for Hem6. Based upon the predicted markers the authors suggested Hem1 and Hem2 to be immature hemocytes. In order to determine differentiation lineages they then used known cell-cycle markers from Drosophila melanogaster and could confirm Hem1 as hemocyte precursors. While genes involved in the cell cycle could be used to identify hemocyte precursors, the authors concluded that immune related genes from the fly was not possible to use to determine functions or different lineages of hemocytes in the shrimp. This is an important (and known) fact, since it is often taught that the fruit fly can be used as a general model organism for invertebrate immunologists which obviously is not the case. Even among arthropods, animals are different. The authors suggest four lineages based upon a pseudo temporal analysis using the Drosophila cell-cycle genes and other proliferation-related genes. Further, they used growth factor genes and immune related genes and could nicely map these into different clusters and thereby in a way validating the nine subpopulations. This paper will provide a good framework to detect and analyze immune responses in shrimp and other crustaceans in a more detailed way.

Strengths:

The determination of nine classes of hemocytes will enable much more detailed studies in the future about immune responses, which so far have been performed using expression analysis in mixed cell populations. This paper will give scientists a tool to understand differential cell response upon an injury or pathogen infection. The subdivision into nine hemocyte populations is carefully done using several sets of markers and the conclusions are on the whole well supported by the data.

Weaknesses:

One obvious drawback of the paper is first the low number of UMIs. A total number of 2704 cells gave a median UMI as low as 718 which is very low. Especially shrimp no. 2 has an average far below 500 and should perhaps be omitted. Therefore, one question is about cell viability prior to the drop-seq analysis. The fact of this low number of UMIs should be discussed more thoroughly.

Details about how quality control (QC) was performed would be needed, for example the cutoff values for number of UMI per cell, and also one important information showing the quality is the proportion of mitochondrial genes. The clustering into nine subpopulations seems solid, however the determination of lineages based upon the pseudo time analysis with cell-cycle related genes is not that strong. The authors identify four lineages, all starting from hem1 via hem2-Hem3- Hem4 and then one to Hem5, another through part of Hem 6 to Hem 7, next through part of Hem 6 to Hem 8 and finally through part of Hem 6 to Hem 9. Referring to Figure 3 - supplement 3, it seems as if Hem6 could be subdivided into two clusters, one visible in B and C, while another part of Hem & is added in D. Also, the data in figure 3 - supplement 1 showing expression of cell cycle markers do not convincingly show the lineages. Cluster Hem 3 and 4 seems to express much fewer and lower amount of these markers compared to cluster Hem6 - Hem9.

It is also clear (from figure 5 - supplement 1) that there are more than one TGase gene and the authors would need to discuss that fact related to differentiation.

While the part to determine subpopulations is very strong, the part about FACS analysis and qRT-PCR is weaker than the other sections, and doesn't add so much information. Validation of marker genes and the relationship between clusters and morphology shown in figure 6 is not totally convincing. It seems clear that both R1 and R2 contains a mixture of different cell types even if TGase expression is a bit higher in R1. A better way to confirm the results could be to do in situ hybridization (or antibody staining) and show the cell morphology of some selected marker proteins in a mixed hemocyte population. FACS sorting is very crude and does not really separate the shrimp hemocytes in clear groups based on granularity and size. This may be because the size of hemocytes without granules vary a lot. You need cell surface markers to do a good sorting by FACS. Another minor issue is the discussion about KPI. There are a huge number of Kazal-type proteinase inhibitors in crustaceans and it is not clear from this data if the authors discuss a specific KPI-gene, and there is a mistake in referring to reference 65 which is about a Kunitz-type inhibitor.

In summary, this paper is a very important contribution to crustacean immunology, and although a bit weak in lineage determination it will be of extremely high value.

Reviewer #2 (Public Review):

The goal of this study is to devise a means of promoting adult mouse auditory sensory cell development from supporting cells (SCs), as occurs naturally in birds and fish following sensory cell death. Previous studies indicated that activating Atoh1, an early acting transcription factor that specifies sensory cell fate during embryogenesis, was not sufficient for such regeneration. The authors hypothesized that adding a second transcription factor, Ikzf2, which maintains outer hair cell (OHC) fate, would synergize with Atoh1 and push adult SCs to differentiate as OHCs. They tested this hypothesis by over-expressing both Atoh1 and Ikzf2 in supporting cells after killing the endogenous OHCs in adult cochleae. The authors showed that the induced cells first express the general HC marker, Myo6, and only later become Prestin-positive, much as occurs during normal development. Unfortunately, these induced OHC-like cells had abnormal stereocilia and did not restore auditory (ABR) thresholds. Moreover, there was a loss of IHCs (the primary auditory receptors) suggesting that much more is needed to induce a real OHC and to protect IHCs than simply inducing the two selected transcription factors. Single-cell RNAseq (scRNA-seq) results showed that the induced OHC-like cells are enriched for HC genes and depleted for SC genes, but overall are most similar to neonatal HCs as defined in published scRNA-seq data from other groups. Overall, the scRNA-seq data did not offer a clear path forward, other than to identify and test additional transcription factors that might push the induced cells to the next stage. Nevertheless, the extent of SC transformation is impressive and has not been seen in previous approaches. This is an important contribution to our understanding of the control of OHC gene expression and differentiation contributed by two important transcription factors.

Reviewer #2 (Public Review):

The paper revisits the question of ligand discrimination ability of TCRs of T cells. The authors find that the commonly held notion of very sharp discrimination between strongly and weakly binding peptides does not hold when the affinities of the weak peptides are re-measured more accurately, using their own new method of calibration of SPR measurements. They are able to phenomenologically fit their results with a ~2 step Kinetic Proofreading model.

It is a very carefully researched and thorough paper. The conclusions seem to be supported by the data and fundamental for our understanding of the T cell immune response with potentially very high impact in many scientific and applied fields. The calibration method could be of potential use in other cases where low affinities are an issue.

As a non-expert in the details of experimental technique, it is somewhat difficult to understand in detail the Ab calibration of the SPR curve - which is a central piece of the paper. The main question is - what are the grounds (theoretical and/or empirical) to expect that the B_max of the TCR dose response curve will continue to be proportional to the plateau level of the Ab. Figure 1D does suggest that, but it would be hard to predict what proportionality shape the curve will take for lower affinity peptides. Given that essentially all the paper claims rest on this assumption, this should explained/reasoned/supported more clearly.

On the theoretical side - I think the scaling alpha\simeq 2 in Figure 2 is indeed consistent with a two-step KPR amplification. However, there are some questions regarding the fitting of the full model to the P_15 of the CD69 response. As explained in the Supplementary Material the authors use 3 global and 2 local parameters resulting in 37 (or 27) parameters for 32 data points. To a naive reader this might look excessive and prone to overfitting. On the other hand, looking at Figure S8 shows the value ranges of lambda and k_p are quite tight. This is in contrast to gamma and dellta that look completely unconstrained.

Finally, one of the stated advantages of the adaptive proof-reading model is that it is capable of explaining antagonism. It is hard to see how a 'vanilla" KPR model is capable of explaining antagonism.

Reviewer #2 (Public Review):

There is now a considerable body of knowledge about the genetic and cellular mechanisms driving the growth, morphogenesis and differentiation of organs in experimental organisms such as mouse and zebrafish. However, much less is known about the corresponding processes in developing human organ systems. One powerful strategy to achieve this important goal is to use organoids derived from self-renewing, bona fide progenitor cells present in the fetal organ. The Rawlins' lab has pioneered the long-term culture of organoids derived from multipotent epithelial progenitors located in the distal tips of the early human lung. They have shown that clonal cell "lines" can be derived from the organoids and that they capable of not only long-term self-renewal but also limited differentiation in vitro or after grafting under the kidney capsule of mice. Here, they now report a strategy to efficiently test the function of genes in the embryonic human lung, regardless of whether the genes are actively transcribed in the progenitor cells. The strengths of the paper are that the authors describe a number of different protocols (work-flows), based on Crisper/Cas9 and homology directed repair, for making fluorescent reporter alleles (suitable for cell selection) and for inducible over-expression or knockout of specific genes. The so-called "Easytag" protocols and results are carefully described, with controls. The work will be of significant interest to scientists using organoids as models of many human organ systems, not just the lung. The weaknesses are that they authors do not show that their lines can undergo differentiation after genetic manipulation, and therefore do not provide proof of principle that they can determine the function in human lung development of genes known to control mouse lung epithelial differentiation. It would also be of general interest to know whether their methods based on homologous recombination are more accurate (fewer incorrect targeting events or off target effects) than methods recently described for organoid gene targeting using non homologous repair.

Reviewer #2 (Public Review):

In this manuscript, Moncla et al. undertake a large sequencing and phylogenetic study to investigate the underlying epidemiology of the 2016-2017 Washington State Mumps epidemic. The authors generate 110 sequences and include 166 novel sequences in their analysis. This data set represents over a quarter of the publicly available Mumps genomes from North America.

They then apply a mixture of phylogenetic methods and intuitive data analyses to uncover, that i) Mumps was imported into Washington at least 13 times. ii) A disproportionate amount of transmission occurred in the Marshallese community in WA with limited transmission in the non-Marshallese community. iii) These heterologous transmission dynamics might be explained by historical and current health disparities within the community, but are not due to low vaccination coverage.

These conclusions are supported by a wide array of carefully controlled phylogenetic methods. The authors explore the sensitivity of their findings to sampling bias. Additionally, the conclusion that transmission occurred disproportionally within the Marshallese community is supported by multiple implementations of the structured coalescent as well as, more coarse but intuitive methods such as the rarefaction analysis and the "descendent" analysis in Figure 4. The "descendent" analysis complements the structured coalescent models and highlights how tips that are close to internal nodes inform the "state" of those unsampled ancestors. Each internal node represents an unsampled ancestor, and if transmission rates are higher in one population, then samples from that population are more likely to be close to those ancestors. The approach captures these processes; however, calling downstream tips "descendants" is unfortunate, as it is unknown if the tips that have "descendants" are direct ancestors of their "descendants" in the transmission chain. Inferring transmission dynamics from divergence trees is difficult, and variants of this approach are likely to be useful in other systems.

The finding that transmission disproportionally occurred in the Marshallese community leads the authors to propose several possibilities for why this may be. The authors should be commended for reaching out to Marshallese health advocates in this process and including the community in their study. This context is a major strength of the study.

Both the data generation and data analysis are achievements that advance our understanding of the epidemiology of Mumps. As can be seen in the tree in Figure 1 the 2016-2017 epidemic in North America was seeded by at least two divergent lineages that appear to have all contributed to the same outbreak. The large number of sequences contributed by this study will help future work uncover the dynamics that drive Mumps epidemics at larger scales. The findings also highlight how large outbreaks can persist in highly vaccinated populations and how an array of phylogenetic approaches can be employed to uncover the underlying population heterogeneity behind an outbreak. To have both of these achievements in the same manuscript sets this work apart.

Reviewer #2 (Public Review):

In this incredibly detailed effort, Hulse, Haberkern, Franconville, Turner-Evans, and coauthors painstakingly and patiently reveal the connectivity of central complex neurons within one "hemibrain" EM-imaged connectome of a fruit fly. This is best read as one of a series of such detailed papers including Scheffer et al., 2020 (which introduces the dataset) and Li et al., 2020 (which focuses on the mushroom body).

The authors achieve two major goals. First, they present a full account of all neurons (by type) present in the central complex and the connections between them (including to and from regions outside the central complex). By necessity, this work only examines such connections within a single animal from whose brain the hemibrain volume was imaged. Nonetheless, the relatively conserved morphology of fly neurons (at the scale of which regions they form arbors within) allows the authors to confidently relate their neurons to known examples from genetically labeled lines imaged at the light level. (And in some cases, they are able to show that some neurons with similar morphology can then be further subdivided into different types on the basis of their connectivity). Importantly, the hemibrain dataset contains both sides of the central complex, allowing for a complete analysis.

Secondly, the authors contextualize the observed connectivity patterns within the known functions of the central complex (particularly navigation and sleep/arousal). Appropriately and importantly, they offer detailed explanations for how the circuitry observed can support these functions. In some cases, particularly in their discussion of the fan body, they show how the connectivity patterns can support multiple variations of models of path integration (and more broadly how its architecture supports vector computation in general). These analyses make their central complex connectome a useful map - there is little doubt that it will inspire many future experiments in the fly community.

The only limitations of this work are rooted in the nature of the source material: it's only one animal's brain and because it's EM-based there's often no way to know whether a given cell type (if new) is even excitatory or inhibitory (though, notably, the authors take care to note where this is the case and to offer alternate interpretations of the circuit function). Synaptic strength is another relative unknown (not to mention plasticity rules or modulatory influences). For EM-based connectomes, the number of synapses made between two neurons is considered the basis for determining whether or not they are meaningfully connected. However, this precise number can vary as a function of how complete the reconstructions are (generally, as proofreading progresses, more synapses are found). This work improves on prior hemibrain studies by carefully demonstrating that it is possible to set a threshold on the relative fraction of synaptic contributions within a region in order to identify meaningful connections. (That is, they find that as the number of synapses discovered increases, the relative contribution remains relatively constant).

This is a massive work. There are 75 figures, not including supplements, and numerous region and neuron names to keep track of (not to mention visualize). It is impossible to read in a single sitting. So for the purposes of this public review, I highly recommend to any reader that they first find the region of the paper they're interested in and skip to that to view in side-by-side mode. The "generally interested" reader is best served by reading through the Discussion, which has more of the structure-function analyses in it and then referring to the Results as their curiosity warrants.

Scheffer et al., 2020 is available here: https://elifesciences.org/articles/57443#content Li et al., 2020 is available here: https://elifesciences.org/articles/62576#content

Reviewer #2 (Public Review):

Pupillometry is an increasingly accessible tool for the non-invasive readout of brain activity. However, our understanding of pupil-control circuits and of the relationship between changes in pupil size and perception, cognition or action, is far from complete. Therefore, any measurements that further this understanding are of great interest to a wide audience in the field of psychology and neurobiology.

This study used pupillometry to explore the neural processing that underlie perception and dissociate those from action-related neural processing. The authors use a novel and comprehensive task design, centered on binocular rivlary, that is likely to find wider use among researchers studying the neural processes that underlie perception and action. They used a non-invasive method (pupillometry) to disscociate putative processes and circuits that might drive perceptual switching. They found changes in pupil size that are reliably different depending on the task: for example - between the conditions that require reporting a perceptual switch versus not reporting it and between rivalrous and explicit changes in the visual stimulus.

Such approaches can be very useful in deciphering which of the myriad factors that can affect pupil size are in fact active under specific, controlled conditions and thus provide a basis for guided, direct measurements of these specific brain regions.

Overall, this study is well-conceived and executed. However, I have some questions and concerns about the analyses and conclusions made from the results shown. In general, I would encourage the authors to try and include more of what we do know about neuromodulation and the cortical control of pupil pathways to frame the hypothesis and interpret the results. Further, it is unclear to me whether the constriction/dilation dissociation is tenable with the presented data and analyses.

Reviewer #2 (Public Review):

Rhabdomeric Opsins (r-Opsins) are well known for their role in photon detection by photosensory cells which are commonly found in eyes. However, r-Opsin expression has also been detected in non-photosensory cells (e.g., mechanosensors), but their function(s) in these other sensory cells is less well understood. To explore the function of r-Opsins outside the context of an eye/head (non-cephalic function) as well as to investigate the potential evolutionary path by which sensory systems that rely on r-Opsins have evolved, Revilla-i-Domingo et al. have investigated gene expression in two distinct subsets of r-Opsin expressing cells in the marine bristle worm Platynereis dumerilii : EP (eye photoreceptor) and TRE (trunk r-opsin1 expressing) cells. The authors also generate two Pdu-r-Opsin1 mutant strains in order to investigate how the loss of r-Opsin function affects gene expression and behavior.

The question of what role r-Opsins play outside of photoreceptors is an interesting one that remains poorly understood. In this manuscript, the authors demonstrate a powerful protocol for FACS sorting and sequencing different cell populations from an important evolutionary model organism.

The transcriptomic analysis presented here demonstrates that both the cephalic EP cells and the non-cephalic TRE cells express components of the photosensory transduction pathway. This observation, together with heterologous cell expression data presented demonstrating sensitivity of Pdu-r-Opsin1 to blue light, suggests that both EP and TRE cells are likely to be light sensitive. The authors also suggest that they observe "mechanosensory signatures" in the transcriptomes, which, together with the analysis of undulatory movements in headless animals, lead them to suggst that r-Opsin in TRE cells functions as an evolutionarily conserved light-dependent modulator of mechanosensation, a conclusion that is not well-supported by the data presented.

Overall, many of the conclusions drawn from the transcriptome data are inferential and based on weak evidence. Key limitations are listed below:

1) The apparent overlap between the phototransduction and mechanosensory systems has already been shown (in Drosophila for instance) and the current work adds limited information to this story, and what is added is weakened by the absence of functional and physiological analyses. This is particularly true for supporting the claims of mechanosensory signatures in these cells. For example, genes whose expression is suggested in the text as being indicative of a mechanosensory function (glass and waterwitch) are, in fact, expressed in multiple sensory cell types. Glass (gl) is a transcription factor best known for regulating the expression of phototransduction proteins in photoreceptors. The function of waterwitch (wtrw) is not fully understood, but it is broadly expressed in sensory cells in Drosophila. It would be more compelling if mechanotransduction channels like Piezo and NompC were expressed in the TREs, but there is no mention of this.

2) The suggestion that the TRE cells share similarity with the mechanosensitive mammalian inner ear is provocative, but lacks strong support. For instance, physiological characterization of the response properties of these sensory cells or identification of anatomical similarities analogous to the stereocilia upon which hair cell mechanosensitivity is based would greatly increase plausibility of this claim. Particularly for a species that diverged from mice and flies many hundreds of millions of years ago, speculation based largely on transcriptome analysis is risky. Careful validation is required as identified genes might not share a conserved function with their assigned orthologs in mice and Drosophila.

3) The current analysis lacks sufficient power to make compelling claims with regard to potential ancestral protosensory cells. The investigators are examining a single species of marine worm and doing so without detailed anatomical and functional studies of the r-Opsin-expressing cells in the worm.

4) The behavioral experiments require more functional data to interpret unambiguously. The data indicate that r-opsin1 is required for light to surpress the undulation of decapitated worms. Does this mean that the TREs are photosensors whose activity inhibits locomotion or that the TREs are light-sensitive mechanosensors ?

5) It is assumed that the TREs constitute a homogenous cell population, but this is not demonstrated. This means that the TREs could be a mixed population (for example, distinct sets of photosensors and mechanosensors) and some of the TRE-expressed genes identified could be expressed in different specific subset of TREs.

Reviewer #2:

I like this type of multimodal study, and I think that the rationale for the study is good. I am not, however, convinced about the results/conclusions provided. Here are my main points:

I don't agree with your conclusion that the mediating role of GABA changes in aging. This requires longitudinal data, the cross-sectional approach in this study can only conclude differences between groups since only 1 time point is available.

No age interaction, this is surprising to me since there are age differences?

Compensatory explanation: Is there a correlation with performance? If there isn't, the proposal of compensatory mechanisms is unclear since it is then not obvious what the compensation is for?

COVID research updates: Coronavirus antibodies last for months — if you have them. (2021). Nature. https://doi.org/10.1038/d41586-020-00502-w

Reviewer #2 (Public Review):

This study traces the detailed excitatory connections of mouse forepaw sensorimotor circuits from the spinal cord, through brainstem, thalamus, sensory and motor cortical areas, and their motor outputs. This is a welcome and important contribution, considering the technical advantages of mice for circuit cracking and the increasing number of labs studying the functions of their limbs. Although the structure and function of forelimb sensorimotor circuits have been extensively studied in primates, they have been relatively neglected in the rodent, especially compared to the enormous scope of research that has been done on the rodent vibrissae system over the past 50 years. This study uses a variety of contemporary methods to reveal important similarities and differences between the forelimb and vibrissae sensorimotor circuits.

Overall, the results do not hold major surprises, although this is itself a noteworthy result. The authors did identify a few qualitative and quantitative differences between the forelimb circuit and the parallel vibrissae-related circuit; the functional significance of these differences is as yet unclear.

The weaknesses of the manuscript are few and minor. The study would have been stronger if it had performed comparable, parallel experiments on the hand and vibrissae circuits, however the scope of the study is already ambitious and strong enough as it stands. I do have a question about the identity of the cortical L4 neurons that were recorded, and this issue should be discussed.

Reviewer #2 (Public Review):

NICEdrug.ch integrates well-established previous methods/pipelines from the same group and provides an easy-to-use platform for users to identify reactive sites, create repurposing and druggability reports, and reactive site-specific similarity searches between compounds. Case studies provided in the manuscript are quite strong and provide ideas to the reader regarding how this service can be useful (i.e., for which kinds of scientific aims/purposes NICEdrug.ch can be utilized). On the other hand, there are a few critical issues related to the current state of the manuscript, which, in my opinion, should be addressed with a revision.

Major issues:

1) Two of the most critical drawbacks are, first, the lack of quantitative assessment of the abilities of the service and its analysis pipeline. Use cases provide valuable information; however, it is not possible to assess the overall value of any computational tool/service without large-scale quantitative analyses. One analysis of this kind has been done and explained under "NICEdrug.ch validation against biochemical assays" and "Comparison of NICEdrug.ch predictions and biochemical assays"; however, this is not sufficient as both the experimental setup and the evaluation of results are quite generic (e.g., how to evaluate an overall accuracy of 0.73 without comparing it to other computational methods that produce such predictions, as there are many of them in the literature). Also, similar quantitative and data-driven evaluations should be made for other sections of the study as well.

2) The second critical issue is that, in the manuscript, the emphasis should be on NICEdrug.ch, since most of the underlying computational methods have already been published. However, the authors did not sufficiently focus on how the service can actually be used to conduct the analysis they mention in the use cases (in terms of usability). Via use cases, authors provide results and its biological discussion (which actually is done very well), but there is no information on how a potential user of NICEdrug.ch (who is not familiar with this system before and hoping to get an idea by reading this paper) can do similar types of analyses. I recommend authors to support the textual expressions with figures in terms of screenshots taken from the interface of NICEdrug.ch at different stages of doing the use case analyses being told in the manuscript. This will provide the reader with the ability to effectively use NICEdrug.ch.

Reviewer #2 (Public Review):

Böhm et al. investigated the phosphorylation of the Ctf19CCAN component Ame1CENP-U by Cdk1 which forms a phosphodegron motif recognized by the E3 ubiquitin ligase complex SCF-Cdc4. They identify phosphorylation sites on Ame1 and demonstrate that phosphorylation of Ame1 leads to its degradation by the SCF with Cdc4 in a cell-cycle dependent manner. They also demonstrate that the outer kinetochore component Mtw1c shields Ame1 from Cdk1 phosphorylation in vitro. Finally, they propose a model in which at least one component, Ame1, is present in excess at S-phase in yeast to incorporate into high levels of sub-complexes for efficient inner kinetochore formation on newly duplicated centromere DNA. Then, in mitosis, phosphodegrons serve to mediate the degradation of excess Ame1 (and presumably other CCAN components) and in so doing protect against the formation of ectopic outer kinetochores.

This manuscript puts forth well-designed and thorough experiments characterizing the phosphorylation of Ame1 and its regulation by the SCF-Cdc4 complex. The writing is clear and the figures are generally easy to understand. The authors succeed in asking pertinent questions, designing experiments to answer them, and considering potential alternative explanations or confounding factors. As a whole this creates a generally convincing study regarding the phospho-regulation of Ame1. However, I also have some important concerns:

1) The authors begin the manuscript by mapping phosphorylation sites across Ctf19CCAN components but then largely narrow their experimental focus to Ame1 and to a lesser extent its binding partner Okp1. Without mutation of other components, the Ame1 mutant phenotypes are either absent or very mild. This would seem to implicate that, if this is an important process, that other targets for this quality control mechanism must exist. As it stands now, the focused investigation does not make the most compelling case for the broad conclusions that are claimed. More extensive investigation of phosphoregulation of CCAN subunits beyond Ame1 would certainly help justify the claim that phosphoregulation is used to clear excess CCAN subunits and protect against ectopic kinetochore assembly. Is there another lead from their initial mass spec work that could provide some molecular evidence that this is a general process? Failing that, the discussion could at least provide some hint at how the model could be tested in future studies.

2) The conclusion that the binding of the Mtw1 complex shields Ame1 phosphodegrons is arguably one of the most significant and interesting claims made in this paper. However, the evidence presented to support this claim seems to rely exclusively on in vitro data. Thus, this part is out of balance with other parts of the paper where some in vivo correlations are attempted/made.

3) The central model mentioned at the outset strongly predicts that the mitotic degradation of Ame1 doesn't impact its abundance at centromeres. That is not the only possibility, though, and some measurement (fluorescence of a tagged Ame1 or a ChIP on centromere DNA) of Ame1 at centromeres before and through mitosis would help instill confidence in the proposal.

Reviewer #2 (Public Review):

This study presents iteratively constructed network models of spinal locomotor circuits in developing zebrafish. These models are shown to generate different locomotor behavior of the developing zebrafish, in a manner that is supported by electrophysiological and anatomical data, and by appropriate sensitivity analyses. The broad conclusions of the study result in the hypothesis that the circuitry driving locomotor movements in zebrafish could switch from a pacemaker kernel located rostrally during coiling movements to network-based spinal circuits during swimming. The study provides a rigorous quantitative framework for assessing behaviorally relevant rhythm generation at different developmental regimes of the zebrafish. The study offers an overarching hypothesis, and specific testable predictions that could drive further experimentation and further refinement of the model presented here. The models and conclusions presented here point to important avenues for further investigation, and provide a quantitative framework to address constituent questions in a manner that is directly relatable to electrophysiological recordings and anatomical data. The study would benefit from additional sensitivity analyses, and from the recognition that biological systems manifest degeneracy and significant variability along every scale of analysis.

Reviewer #2 (Public Review):

Tu et al. submit a manuscript that evaluates the performance of the Abbott ID NOW SARS-CoV-2 test in an ambulatory cohort relative to RT-PCR tests. They enrolled 785 symptomatic patients, 21 tested positive for SARS-CoV-2 by ID NOW and PCR (Hologic) while 2 tested positive only via PCR. They also tested 189 asymptomatic individuals, none of whom tested positive by either ID NOW or PCR. The positive agreement between ID NOW and PCR was 91.3%, and the negative percent agreement was 100%. The authors also provide a review and meta-analysis of ID NOW performance across at least a dozen other named studies which is thorough and interesting. The cohort assessed in this study is small and localized. The data is undermined by sample size, with the most glaring example being the 100% negative percent agreement, which doesn't compare with the known performance of the test in broader populations.

Reviewer #2 (Public Review):

In this manuscript, Xue et al. assessed many AAV vectors and demonstrated that Thioredoxin-interacting protein (TXNIP) saves RP cones by enhancing their lactate catabolism. The results of this study were based on cone counting, IHC and reporter. While the authors focus on the cellular metabolism in the Txnip-mediated rescue effect, it is unknown whether anti-oxidative stress plays a role as well.

Reviewer #2 (Public Review):

Previously, Oon and Prehoda showed apically directed movement of aPKC clusters during polarization of the neuroblast prior to asymmetric cell division. They found that these movements required F-actin, but the distribution of F-actin has only been reported for later stages of neuroblast polarization and division. Here, the authors report pulses of cortical F-actin during interphase, followed by an apically directed flow at the onset of mitosis, a strong apical accumulation of F-actin at metaphase and anaphase, followed by fragmentation and basally directed flow of the fragments. aPKC clusters are shown to colocalize with the F-actin networks as they flow apically. The F-actin networks are also shown have partial colocalization with non-muscle myosin II, suggesting a possible mechanism for their movement. Finally, the authors solidify the results of actin inhibitor studies from their 2019 study by showing that reported effects on aPKC localization are preceded by F-actin loss as would be expected but was not previously shown. Overall, the Research Advance extends the past study by more directly showing the involvement of F-actin and myosin in the apical localization mechanism of aPKC, and by describing F-actin and myosin dynamics prior to this transition. The following concerns should be addressed.

1) The pulsatile nature of broad F-actin networks is evident during interphase, but these pulsations substantially subside upon entry into mitosis, and at this stage an apically directed flow of F-actin is the main behavior evident. This transition from pulses to flow is evident in both the movies and the kymographs of the F-actin probe. However, the authors state that the pulsations continue at the onset of mitosis and as the apical cap of aPKC matures. It is unclear whether the apical flow of aPKC and F-actin is associated with small-scale defined F-actin pulses, or small-scale random fluctuations of F-actin. The F-actin flow alone is an informative finding. The authors should consider revising their descriptions of these data (including in the manuscript title), or provide clearer examples of defined F-actin pulsations during the stage when aPKC polarizes.

2) I checked the main text, methods, figures and figure legends, but could not find listings of sample sizes. Thus, the reproducibility of the findings has not been reported.

Reviewer #2 (Public Review):

This work tests the ability of a kinase inhibitor to increase bone mass in a mouse model of osteoporosis. The inhibitor, which targets SIK and other kinases, was shown previously by these investigators to increase trabecular bone mass in young intact mice. Here they show that it increases trabecular, but not cortical, bone in oophorectomized mice and that this is associated with increased bone formation and little or no effect on bone resorption. In contrast, postnatal deletion of SIK2 and SIK3 increased both bone formation and resorption, suggesting that the inhibitor targets other kinases to control resorption. Indeed, the authors confirm that the inhibitor effectively suppressed the activity of CSF1R, a receptor tyrosine kinase essential for osteoclast formation. The authors also provide some evidence of unwanted effects of the inhibitor on glucose homeostasis and kidney function.

Overall, the studies are performed well with all the necessary controls. The effects of the inhibitor on CSF1R inhibition are convincing and provide a compelling explanation for the net effects of the compound on the skeleton.

1) The ability of the inhibitor to increase trabecular but not cortical bone mass will likely limit its appeal as an anabolic therapy. Indeed, the authors show that PTH, but not the inhibitor, increases bone strength. However, this limitation is not addressed in the manuscript. In addition, the mechanisms leading to these site-specific effects were not explored.

2) The mechanisms by which YKL-05-099 increases bone formation remain unclear. The authors point out that their previous studies indicate that the compound stimulates bone formation by suppressing expression of sclerostin. However, YKL-05-099 increased trabecular bone in the femur but not spine of intact mice and did not increase cortical bone in intact or OVX mice. In contrast, neutralization of sclerostin increases trabecular bone at both sites in intact mice as well as increases cortical bone thickness. These differences do not support the idea that YKL-05-099 increases bone formation by suppressing sclerostin.

3) The authors repeatedly state that the kinase inhibitor uncouples bone formation and bone resorption. However, the authors do not provide any direct evidence that this is the case. Although the term coupling is used to refer to a variety of phenomena in skeletal biology, the most common definition, and the one used in the review cited by the authors, is the recruitment of osteoblasts to sites of previous resorption. The authors certainly provide evidence that the kinase inhibitor independently targets bone formation and bone resorption, but they do not provide evidence that the mechanisms leading to recruitment of osteoblasts to sites of previous resorption has been altered. The resorption that takes place in the inhibitor-treated mice likely still leads to recruitment of osteoblasts to sites of resorption. Thus coupling remains intact.

4) The results of the current study nicely confirm previous findings by the same authors, demonstrating the reproducibility of the effects of the inhibitor. They also provide a compelling explanation for the net effect of the inhibitor on bone resorption (it stimulates RANKL expression but inhibits CSF1 action). While this latter finding will likely be of interest to those exploring SIK inhibitors for therapeutic uses, overall this study may be of limited appeal to a broader audience.

Edara, Venkata Viswanadh, William H. Hudson, Xuping Xie, Rafi Ahmed, and Mehul S. Suthar. “Neutralizing Antibodies Against SARS-CoV-2 Variants After Infection and Vaccination.” JAMA, March 19, 2021. https://doi.org/10.1001/jama.2021.4388.

Reviewer #2 (Public Review):

The authors analyze diminishing-return (beneficial mutations likely having a small effects for genotypes of high fitness) and increasing-costs epistasis (deleterious mutations likely having large effects for genotypes of high fitness). A framework is proposed where the fitness of genotype after a mutation at a single locus can be estimated from (i) the additive effect at the locus and (ii) a component determined by the fitness of the original genotype at the locus, referred to as "global epistasis". The concept of locus-specific global epistasis is new, even if variants of global epistasis have been discussed in published work. The manuscript shows that the locus specific assumption is empirically justified and it provides applications to a study of yeast.

Regression effects (diminishing returns and increasing costs epistasis) are quantified under the assumption that epistasis can be considered noise (idiosyncratic epistasis). The result is expressed in terms of Fourier representation for the fitness of a genotype, and the proof depends on a locus-specific analysis of correlations derived from the Fourier representation. In particular, the author clarify under what circumstances one can expect the regression effects. Several conclusions are very precise, and numerical results are provided as a complement to the analytical work.

The second part of the manuscript concerns historical contingency. Absence of contingency means that the expected fitness effect of new mutation for a genotype is independent of previous substitutions. A condition for minimal contingency in provided, and a new model (The Connected Network model, or CN-model) which satisfies is introduced.

A somewhat puzzling point is that the authors emphasize that their proposed frame workexplains diminishing-return and increased-costs epistasis. Diminishing return has been described as a "regression to the mean effect" of sorts in Draghi and Plotkin (2013) for the NK model, and it was argued that a similar regression effect applies to a broad category of fitness landscapes in Greene and Crona (2014). Moreover, "increased-costs epistasis" is likely to apply broadly as well with a similar argument also for landscapes that fall outside the category discussed by in the manuscript (an example is in the Recommendation section). On the other hand, a major strength of the manuscript is that it provides a superior quantitative precision, and some quantitative understanding for when one can expect diminishing returns and increased costs epistasis (that should be emphasized more in my view).

From a conceptual point of view, the locus specific framework, as well as the historical contingency discussion are valuable contributions. The fact that the author could construct a model (the CN model) that satisfy their minimal contingency condition is very interesting as well.

The weakness of the manuscript is the presentation of the work, especially for a general audience. More context and background, explanations of quantitative results and references would help. There are also a few cases of unclear claims and confusing notation (SSWM seems to be assumed without that being stated, the notation for Fourier coefficients is unclear in some cases) and the text has some other minor issues. Fortunately, a limited effort (in terms of time) would resolve the problem, and also improve the prospects for high impact.

The impact of reopening schools on SARS-CoV-2 transmission in England. (n.d.). LSHTM. Retrieved 10 March 2021, from https://www.lshtm.ac.uk/newsevents/news/2021/impact-reopening-schools-sars-cov-2-transmission-england

Perra, N. (2021). Non-pharmaceutical interventions during the COVID-19 pandemic: A review. Physics Reports. https://doi.org/10.1016/j.physrep.2021.02.001

COVID-19 Living Evidence. (2020, October 23). Weekly update of COAP As of 23.10.2020, we have indexed 80588 publications: 8902 pre-prints 71686 peer-reviewed publications Pre-prints: BioRxiv, MedRxiv Peer-reviewed: PubMed, EMBASE https://t.co/RaDy1Wm4Hq https://t.co/FYRaYPe8oG [Tweet]. @evidencelive. https://twitter.com/evidencelive/status/1319578431848353793

BDI-pathogens/covid-19_instant_tracing. (n.d.). GitHub. Retrieved 13 February 2021, from https://github.com/BDI-pathogens/covid-19_instant_tracing

Wang, P., Nair, M. S., Liu, L., Iketani, S., Luo, Y., Guo, Y., Wang, M., Yu, J., Zhang, B., Kwong, P. D., Graham, B. S., Mascola, J. R., Chang, J. Y., Yin, M. T., Sobieszczyk, M., Kyratsous, C. A., Shapiro, L., Sheng, Z., Huang, Y., & Ho, D. D. (2021). Antibody Resistance of SARS-CoV-2 Variants B.1.351 and B.1.1.7. Nature, 1–9. https://doi.org/10.1038/s41586-021-03398-2

Ashraf, B. N. (2020). Economic impact of government interventions during the COVID-19 pandemic: International evidence from financial markets. Journal of Behavioral and Experimental Finance, 27, 100371. https://doi.org/10.1016/j.jbef.2020.100371

David R Tomlinson 💙. (2021, March 5). Truth. 💙 @FreshAirNHS @theRCN @TheBMA @mancunianmedic @DrLindaDykes @Chakladar_A @KGadhok https://t.co/Ke2C84KuaT https://t.co/C469qvrSoK [Tweet]. @DRTomlinsonEP. https://twitter.com/DRTomlinsonEP/status/1367962251211202566

Reviewer #2 (Public Review):

This well-conceived and well-presented work has both originality and substance, and contributes important new ideas to the Hh signaling field with wonderful clarity.

AssayResult: 1.5

AssayResultAssertion: Abnormal

Approximation: Exact assay result value not reported; value estimated from Figure 3e.

AssayResult: <1

AssayResultAssertion: Abnormal

Approximation: Exact assay result value not reported; value estimated from Figure 3e.

AssayResult: 1

AssayResultAssertion: Abnormal

Approximation: Exact assay result value not reported; value estimated from Figure 3e.

AssayResult: 3

AssayResultAssertion: Abnormal

Approximation: Exact assay result value not reported; value estimated from Figure 3e.

AssayResult: 1

AssayResultAssertion: Abnormal

ControlType: Abnormal; empty vector

Approximation: Exact assay result value not reported; value estimated from Figure 3e.

AssayResult: 27

AssayResultAssertion: Normal

StandardDeviation: 14

ControlType: Normal; wild type PALB2 cDNA

Approximation: Exact assay result and standard deviation values not reported; values estimated from Figure 3e.

AssayGeneralClass: BAO:0000450 fluorescence microscopy

AssayMaterialUsed: CLO:0003684 HeLa cell

AssayDescription: HeLa cells were treated with PALB2 siRNA and transfected with peYFP-PALB2 expressing PALB2 variants (or empty vector), followed by exposure to 2 Gy of γ-IR. Six hours after irradiation, cells were subjected to immunofluorescence for RAD51 foci (where foci formation serves as marker of normal DNA damage repair function).

AssayReadOutDescription: The number of RAD51 foci per cyclin A-positive cells expressing the indicated YFP-PALB2 constructs.

AssayRange: foci/cell

AssayNormalRange: Not reported

AssayAbnormalRange: Not reported

AssayIndeterminateRange: Not reported

ValidationControlPathogenic: 0

ValidationControlBenign: 0

Replication: Three independent experiments with 50 cells per condition

StatisticalAnalysisDescription: Kruskal–Wallis test with Dunn's multiple comparison post-test

AssayResult: 5.3

AssayResultAssertion: Normal

StandardErrorMean: 0.46

HGVS: NM_024675.3:c.1010T>C p.(Leu337Ser)

Reviewer #2:

This study reports a new cell line model for Dyskeratosis congenita, generated by introducing a disease-causing mutation, DKC1 A386T, into human iPS-derived type II alveolar epithelial cells (iAT2). The authors found that the mutant cells failed to form organoids after serial passaging and displayed hallmarks of cellular senescence and telomere shortening. Transcriptomics analysis for the mutant cells unveiled defects in Wnt signaling and down-regulation of the downstream shelterin complex components. Finally, treating the mutant cells with a Wnt agonist, a GSK3 inhibitor CHIR99021 can rescue these defects and enhance telomerase activity. Overall, the study is well designed and executed. Data presented are generally clear and convincing. The new model presented here can be of great interests in the field to study the effects of DC disease causing mutants in diverse cell types.

AssayGeneralClass: BAO:0000450 fluorescence microscopy

AssayMaterialUsed: CLO:0003684 HeLa cell

AssayDescription: HeLa cells were treated with PALB2 siRNA and synchronized to G1/S phase by double thymidine block. Cells were then transfected with peYFP-PALB2 expressing PALB2 variants (or empty vector) and irradiated with 2 Gy. Four hours after irradiation, cells were subjected to immunofluorescence for RAD51 foci (where foci formation serves as marker of normal DNA damage repair function).

AssayReadOutDescription: The number of RAD51 foci per cyclin A-positive cells expressing the indicated YFP-PALB2 constructs was scored and presented as percentage change relative to the wild type mean RAD51 foci number per cell.

AssayRange: %

AssayNormalRange: Not reported

AssayAbnormalRange: Not reported

AssayIndeterminateRange: Not reported

ValidationControlPathogenic: 1

ValidationControlBenign: 3

Replication: Three independent experiments, each with 225 cells per condition

StatisticalAnalysisDescription: Kruskal–Wallis test with Dunn's multiple comparison post-test

AssayResult: 38

AssayResultAssertion: Abnormal

PValue: < 0.0001

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

AssayResult: -96

AssayResultAssertion: Abnormal

PValue: < 0.0001

ControlType: Abnormal; empty vector

AssayResult: 0

AssayResultAssertion: Normal

ControlType: Normal; wild type PALB2 cDNA

AssayResult: -34

AssayResultAssertion: Abnormal

PValue: < 0.0001

AssayResult: -11

AssayResultAssertion: Indeterminate

PValue: Not reported

AssayResult: -4

AssayResultAssertion: Indeterminate

PValue: Not reported

AssayResult: -14

AssayResultAssertion: Indeterminate

PValue: Not reported

AssayResult: -56

AssayResultAssertion: Abnormal

PValue: < 0.0001

AssayResult: -6

AssayResultAssertion: Normal

PValue: Not reported

AssayResult: -25

AssayResultAssertion: Abnormal

PValue: < 0.01

AssayResult: -31

AssayResultAssertion: Abnormal

PValue: < 0.0001

AssayResult: -16

AssayResultAssertion: Normal

PValue: Not reported

AssayResult: -10

AssayResultAssertion: Normal

PValue: Not reported

AssayResult: -21

AssayResultAssertion: Indeterminate

PValue: < 0.01

AssayResult: -20

AssayResultAssertion: Indeterminate

PValue: < 0.05

AssayResult: 8

AssayResultAssertion: Indeterminate

PValue: Not reported

AssayResult: -29

AssayResultAssertion: Abnormal

PValue: < 0.0001

AssayResult: -98

AssayResultAssertion: Abnormal

PValue: < 0.0001

AssayResult: -36

AssayResultAssertion: Abnormal

PValue: < 0.0001

AssayResult: 3

AssayResultAssertion: Indeterminate

PValue: Not reported

AssayResult: -32

AssayResultAssertion: Abnormal

PValue: < 0.0001

AssayResult: 85.76

AssayResultAssertion: Indeterminate

PValue: 0.0445

Comment: Exact values reported in Table S3.

HGVS: NM_024675.3:c.110G>A p.(Arg37His)

Reviewer #2 (Public Review):

In the manuscript Li and colleagues explored the mechanisms that potentially regulated the transcoelomic metastasis of ovarian cancer. By using the in vivo genome-wide CRISPR/Cas9 screen in human SK-OV-3 cell line after transplanted in NOD-SCID mice, the authors identified that IL-20Ra was a potential protective factor preventing the transcoelomic metastasis of ovarian cancer. SK-OV-3 cells with higher expression of IL-20R have lower metastatic potential in vivo. On the contrary, a mouse cell line ID8 with lower IL20Ra expression metastasized aggressively, which could be reversed by over expressing IL-20Ra in the cells. In human, the metastasized ovarian cancers had lower expression of IL-20Ra than the primary tumors. Mechanistically, the authors hypothesized that IL-20 and IL-24 produced by peritoneum mesothelial could act on tumor cells through the IL-20Ra/IL-20Rb receptor to promote the production of IL-18. IL-18 could drive the macrophages into M1 like phenotypes, which in turn controlled the transcoelomic metastasis of the cancer. The in vivo phenotypes in this study were consistent with these hypotheses. The role of IL-20Ra in this setting is potentially interesting and novel.

Virological. ‘Recombinant SARS-CoV-2 Genomes Involving Lineage B.1.1.7 in the UK - SARS-CoV-2 Coronavirus / SARS-CoV-2 Molecular Evolution’, 17 March 2021. https://virological.org/t/recombinant-sars-cov-2-genomes-involving-lineage-b-1-1-7-in-the-uk/658.

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 PARP inhibitor Olaparib for 48 h inhibits end-joining mediated by PARP and sensitizes cells to DNA damage; cell survival is measured by FACS 24 h after Olaparib washout

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

AssayRange: %

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

AssayAbnormalRange: PARPi resistance levels ≤30% of wild type

AssayIndeterminateRange: Not reported

ValidationControlPathogenic: 12

ValidationControlBenign: 9

Replication: 2 independent experiments

StatisticalAnalysisDescription: Not reported

AssayResult: 26.03

AssayResultAssertion: Not reported

ReplicateCount: 2

StandardErrorMean: 11.42