13 Matching Annotations
  1. Aug 2021
    1. Reviewer #4 (Public Review):

      This research fills a valuable gap in our understanding of neural cell populations. There is immense complexity in the neuron subtype landscape of the dorsal root gangion (DRG). Profiling had been previously conducted in mouse, but not within human. Providing the data and analysis of the human DRG is a valuable resource because substantial differences in cell populations and expression programs can exist between mouse and human. Any research that is focussed on the translational potential of a gene or pathway should verity its conservation across species.

      However, additional evidence is required to support a major claim of the manuscript: that there are mouse-specific and human-specific neuron subtypes. This claim is based on two major pieces of evidence. First, cluster comparison and co-clustering identify some cell populations that are species specific. Although this approach is suggestive, it is not definitive. Clustering separates populations of cells based major axes of variability, but those axes may not perfectly align across conditions or species. For example, excitatory cortical neurons may vary based upon cortical layer or whether they originate from the primary or secondary visual cortex. It is possible that one source of variation is stronger in one species and another source of variation is stronger in another species, leading to differences in clustering. The co-clustering may overcome of those limitations, but if the differences across species and experimental parameters are large enough, then even cells that come from the same population may not align. The in situ hybridization experiments also provide some support for species specificity, but it the overlap of specific markers could be confounded when the expression of individual marker genes evolve while the overall cell population remains consistent.

  2. Apr 2021
    1. Reviewer #4 (Public Review):

      In this paper, the author uses an impressive comparative dataset of 172 species to investigate the relationship between intraspecific genetic diversity and census (actual) population size. They find that even when they use phylogenetic comparative methods, the relationship between neutral diversity and population size is much weaker than predicted by theory and that selection on linked sites is unlikely to explain this difference. The paper convincingly demonstrates that the paradox of variation first pointed out by Lewinton in the 70s remains paradoxical.

      This paper is exceptionally strong in multiple ways. First, it is statistically rigorous; this is particularly impressive given that the paper uses methods and data from multiple fields (genomics, macroecology, conservation biology, macroevolution). This is the most robust estimate of the relationship between diversity and population size that has been published to date. Second, it is conceptually rigorous: the paper clearly lays out the various hypotheses that have been put forth over the years for this pattern as well as the logic behind these. The author has done a great job at synthesizing some complex debates and different types of data that are potentially relevant to resolving it. Third, it is exceptionally well-written. I sincerely enjoyed reading it. Overall, I think this is a major contribution to this field and though the paper does not resolve the challenge laid down by Lewinton, I think these analyses (and curated data/computational scripts) will inspire other researchers to dig into this question.

      I do however, have some suggestions as to how this paper could be strengthened.

      First, in phylogenetic comparative methods (PCMs) there has been a persistent confusion as to what phylogenetic signal is relevant -- when applying a phylogenetic generalized linear model with a phylogenetically structured residual structure (which the author does here), one is estimating the phylogenetic structure in the errors and not the traits themselves. The comparative analysis are well-done and properly interpreted but at some points in the text, particularly when addressing Lynch's conjecture that PCMs are irrelevant for coalescent times and comments/analysis on the appropriateness of Brownian motion as a model of evolution, that there is some conceptual slippage and I suggest that author take a close look and make sure their language is consistent. Strictly speaking the PGLM approach doesn't assume that the underlying traits are purely BM -- only that the phylogenetic component of the error model is Brownian. As such running the node-height test on the both the predictors and the response variable separately -- while interesting and informative about the phylogenetic patterns in the data (including the shift points you have observed) isn't really a test of the assumptions of the phylogenetic regression model. It is at least theoretically plausible (if not biologically) that both Y and X have phylogenetic structure but that the estimated lambda = 0 (if for instance, Y and X were perfectly correlated because changes in Y were only the result of changes in X). To be clear, I am fine with the PGLM analysis you've done and with the node-height test; I just don't think that the latter justifies the former.

      One note about the ancestral character reconstruction: I think it is a fine visualization and realize you didn't put too much emphasis on it but strictly speaking the ASR's were done under a constant process model and therefore they wouldn't provide evidence for (a probably very real shift) between phyla. I think it was a good idea to run the analyses on the clade specific trees (particularly given how deep and uncertain the branches dividing the phyla are) but I just don't think you could have gotten there from the ASR.

      I am not convinced that the IUCN RedList analysis helps that much here and in my view, you might consider dropping this from the main text. This is for two reasons: 1) species may be of conservation concern both because they have low abundance in general and/or that their abundance is known to have experienced a recent decline -- distinguishing these two scenarios is impossible to do with the data at hand; and 2) there is of course a huge taxonomic bias in which species are considered; I don't think we can infer anything ecologically relevant from whether a species is listed on the RedList or not (as you suggest regarding the lynx, wolverine, and Massasauga rattlesnake) except that people care about it.

      This is not really a weakness but I find it notable that recombination map length is correlated with body size. I realize this is old news but I was left really curious as to a) why such a relationship exists; and b) whether the mechanism that generates this might help explain some of the patterns you've observed. I would be keen to read a bit more discussion on this point.

    1. Reviewer #4 (Public Review):

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

    1. Reviewer #4 (Public Review):

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

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

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

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

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

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

  3. Mar 2021
    1. Reviewer #4 (Public Review):

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

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

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

      I cannot criticise any major issues in this manuscript.

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

    1. Reviewer #4 (Public Review):

      The goal of the manuscript was to add to the research on the rates of success of African American/Black PI in their pursuit of NIH funding. The authors specifically addressed variability in funding levels of NIH Institutes and Centers(ICs). The authors were successful in identifying that there are differentials rates of award rates by IC. The authors describe that topic choice was not associated with funding after accounting for IC assignment which vary in their funding rates.

    1. Reviewer #4 (Public Review):

      Using a transgenic line of Platynereis, in which GFP is expressed under the control of cis-regulatory elements for r-opsin, the study isolates r-opsin expressing cells from the head (eye photoreceptors) and trunk region (a population of segmentally repeated r-opsin expressing cells associated with the parapodia) by FACS. Subsequent RNA-Seq establishes that both populations of cells express genes for all components of the rhabdomeric phototransduction cascade, while the population of trunk sensory cells additionally expresses genes encoding proteins involved in mechanosensation. Using heterologous expression in a mammalian cell line, it is shown that the Platynereis r-opsin responds to blue light via coupling to Gαq suggesting that it mediates photoresponses via a canonical rhabdomeric phototransduction cascade. Transcriptomic analysis of an r-opsin mutant created by TALEN mediated gene editing then reveals that expression levels of the mechanosensory Atp2b channel are modulated by protracted exposure to blue light, a response abolished in the mutant. Behavioral analysis further suggests that undulatory movements of the worms are equally altered under these illumination conditions. Taken together this suggests that the r-opsin expressing trunk sensory cells act as both photo- and mechanoreceptors and that their mechanosensory properties are modulated in response to light. In combining the transcriptomic analysis of cell types with experimental studies of gene function and behavioral analyses, this study provides exciting new insights into the evolution of sensory cells. Several prior studies have found co-expression of photosensory and mechanosensory proteins in sensory cells of various bilaterians, and comparative studies suggested that photo- and mechanosensory cells may share a common evolutionary origin. However, the current study goes far beyond these findings in establishing a direct functional link between photo-and mechanosensation in a population of sensory cells suggesting that these sensory cells function as multimodal cells and that their mechanosensory properties are altered in response to light. Furthermore, the behavioral data (based on a novel machine-learning based tool of analysing the animals' movement) suggest that these cells have a behaviorally relevant function. Because r-opsin was found to be expressed in mechanoreceptors not only in lophotrochozoans (including Platynereis) but also in ecdysozoans and vertebrates (although functional studies are lacking here) and r-opsins belong to a large family of opsins, almost all of which are responsive to light, the present study suggests that r-opsins may have an ancestral bilaterian role in modulating mechanosensory function in response to light (in addition to their purely photosensory role in the photoreceptors of the eyes). Light-independent functions of r-opsin as recently revealed in Drosophila may, thus, be secondarily derived.

      The study is very carefully conducted and well presented. The only minor flaw is that in its present form, the discussion of the evolutionary implications of the finding lacks in clarity and specificity. The authors here often refer ambiguously to an "ancient" or "ancestral" role of r-opsins without specifying the lineage referred to (ancestral for lophotrochozoans? bilaterians? eumetazoans? metazoans?). The discussion should, therefore be revised with an explicit phylogenetic framework in mind.

    1. Reviewer #4 (Public Review):

      Coombs et al. aimed to establish a pharmacological tool to distinguish calcium-permeable (CP) AMPA receptors (AMPAR) from calcium impermeable AMPA receptors unambiguously. Towards this end, the authors examined the effects of intracellularly applied NASPM, PhTx-433, PhTx-74, and spermine. The authors showed that NASPM completely blocked outward glutamate-evoked currents with a desensitization blocker, cyclothiazide, from outside-out patch membranes from HEK cells expressing GluA1. In contrast, spermine and PhTx-433/74 partially blocked the outward currents in a voltage-dependent manner (Figure 1). TARPg-2 co-expression reduced potencies of spermine and NASPM, and altered shapes of their conductance-voltage relationship (Figure 2) as well as various kinetics of GluA1, including decay kinetics and recovery kinetics (Figure 3). Further, the authors showed that NASPM blocked GluA1 co-expressed with one of the AMPAR auxiliary subunits, TARPg-2, g-7, CNIH2 GSG1L (Figure 4). Finally, the authors showed that NASPM blocked AMPAR-mediated mEPSC events at +60 mV, but not -70mV, in cultured cerebellar stellate neurons from GluA2 knockout mice. Overall, this manuscript provides high-quality data and critical information about TARPg-2, GluA1, and GluA2 knockout mice.

      This provides a solid analysis of GluA1, TARPg-2, 7, CNIH2, GSG1L, and GluA2 knockout neurons. However, it remains unclear whether intracellular NASPM allows an unambiguous functional measure of CP-AMPAR, especially considering many combinations of AMPARs and auxiliary subunits, e.g., GluA1-4 with splicing isoforms, six TARPs, four CNIHs, GSG1L and CKAMP44, etc.

      Strengths:

      The experimental design to evaluate drugs and receptors with outside-out patch membranes and a piezoelectric device provides the highest-resolution analysis and meaningful information.

      Both experiments and analyses are rigorous and of high quality. However, it remains unclear if intracellular NASPM allows an unambiguous functional measure of CP-AMPAR.

      Weaknesses:

      Because the authors tested a limited combination of receptors and auxiliary subunits, it is difficult to conclude whether NASPM blocks all CP-AMPAR unambiguously.

      Slopes of the conductance-voltage relationships are changed upon TARPg-2 co-expression or different concentrations of NASPM.

    1. Reviewer #4 (Public Review):

      This paper describes the transmission of Trypanosoma brucei by the Tsetse vector. As part of these studies, the authors discovered that (i) a single parasite is sufficient for transmission and (ii) two stages of the Trypanosoma brucei life cycle (slender and stumpy forms) can be efficiently transmitted by the Tsetse vector. This was unexpected (as mentioned in the title) because only stumpy forms were known to be adapted for transmission.

      The life cycles of parasites are text-book knowledge that researchers rely on and rarely question. It's the slide #2 of every talk in parasitology. In the mammalian host, the life cycle of Trypanosoma brucei comprises two stages: the dividing slender forms and the cell-cycle arrested stumpy-forms, which are pre-adapted to survive in the midgut of the next host (Tsetse fly). In this report, Schuster, Subota et al. show that slender forms are sufficient to establish an infection in the Tsetse fly and thus ensure transmission. The claims and conclusions are justified by the data presented.

  4. Feb 2021
    1. Reviewer #4:

      This manuscript by Huss, P., et al, is a major technological step forward for high throughput phage research and is a deep dive into the deep mutational landscape of a portion of the T7 Phage receptor binding protein (RBP). The author’s develop a new phage genome engineering method, ORACLE, that can generate a library of any region of the phage genome. They apply ORACLE to do a deep mutational scan of the tip domain of T7 RBP and screen for enrichment in several bacteria. The authors find that different hosts give rise to distinct mutational profiles. Exterior loops involved in specialization towards a host appear to have the highest differential mutational sensitivity. The authors follow up these general scans in the background of phage resistant hosts. They find mutations that rescue phage infection. To demonstrate the utility of the approach on a clinically relevant task, the authors apply the library to a urinary tract associated clinical isolate and produce a phage with much higher specificity, creating a potentially powerful narrow scope antibiotic.

      Overall, the ORACLE method will be of tremendous use for the phage field solving a technical challenge associated with phage engineering and will illuminate new aspects of the bacterial host-phage interactions. It was also quite nice to see host-specialization validated and further explored with the screens done in the background of phage resistance mutations. The authors do a tremendous job digging into potential mechanisms when possible by which mutations could be altering fitness. We especially appreciate how well the identity of amino acids tracks host specialization within exterior loops.

      We have no major concerns about the manuscript but have some minor comments to aid interpretation. There are also some minor technical issues. We think this manuscript will be of broad interest, especially for those in the genotype-phenotype, phage biology, and host-pathogen fields.

      Minor comments:

      P5L20: In the introduction to the ORACLE section the authors mention homologous recombination then they mention using 'optimized recombination' that is done with recombinases. This contrast should be mentioned somewhere perhaps to highlight the benefit of having specific recombinases.

      P6L16: Using Cas9 to cut unrecombined variants is clever... Cool! This is a real 21st Century Dpn1 idea.

      P6L27 The authors state that there is a mild skew towards more abundant members after ORACLE. Why might this be? In iterations more abundant members simply become even more abundant? To be clear this isn't a substantial limitation and it's common to see these sorts of changes during library generation. Just curious. Overall looks like a fantastic method.

      P7L6: Authors mention ORACLE increases the throughput of screens by 3-4 orders of magnitude. How many variants can one screen? Is this screen of a little over 1k variants at about the threshold of the assay?

      P8L7: The authors assign functional scores based on enrichment and normalize to wild type. Is a FN=1 equivalent to wild type?

      P9L5: Awesome!

      P10L7: Authors mention R542 forms a hook with a receptor. There should be a citation here.

      P10L21: For N501, R542, G479, D540 there are wonderful mechanistic explanations. However, for D520 there is not. Any hypothesis for why this is distinct from the others? Are there other residues that behave similarly? I feel it would be really helpful to have a color scale that discriminates between FN 1 (assuming wild type) and enriched/depleted w/in figure 3A.

      P12L4: Authors note residues that are surface exposed yet intolerant to mutations in the previous paragraph. Authors also calculate free energy changes with Rosetta and state free energy maps pretty well with tolerance. What is the 93% based on? Perhaps a truth/contingency table would be useful here to discriminate/ compare groupings. What residues are in the 7% others. Can the energy scores help understand the mechanisms behind the mutations better?

      P12L7: Authors state substitutions predicted to stable and classified intolerant could indicate residues necessary for all hosts. What about those that fall outside of the groupings? Unstable residues can also be necessary.

      P14L22L Authors mention comparing systematic truncations, however they do not present any figure. This should be in a figure to aid in looking at the data and would surely be helpful to people in the phage field. A figure should be included here especially because this is one of the main discussion topics at the end of the manuscript.

      P16L2: The authors did the selection in the background of a clinically isolated strained and discussed 3 variants that were clonal characterized. Was this library sequenced similar to before?

      Figures:

      Barplots need significance tests.

      Figure 2C-E ; Fig 3A. All figures are colored white to red. With this color scale it's hard to appreciate which variants are neutral vs those that are enriched. A two or more color scale would be more appropriate. Log-scaling might be wise to get a better sense of the dynamic range that is clearly present in fig2F.

      FIg 4F: Needs a statistical test between bar plots.

      Fig6A-C: These figures have tiny symbols that represent the architecture at an insertion position. It's probably easier to look at if the same annotations from Fig 4B or C for architecture were used.

      Fig6D: needs tests for significance

      Supp fig 4E: This figure is the first evidence that the physics chemistry of amino acids w/in surface exposed loops determine host specificity. This is followed up by Figure 4D and E. I would consider moving this to one of the main figures.

      Supp fig 5: A truth table could be useful here to test for ability to classify based on rosetta compared to FD. It looks like here that the tolerant residues have a distinct pattern

      Why are these colored white to red?

    1. Reviewer #4 (Public Review):

      The authors have studied the effects of microstimulation in a single subject with 2 microelectrode arrays in the somatosensory cortex. They aimed to investigate the how altering frequency, current amplitude and train duration affected the elicited percepts. They report three new findings:

      1) Increasing stimulus frequency did not increase the intensity of the percept, in fact there was frequency selectivity of cortical regions and these were somewhat topographically organized on the cortical surface.

      2) The intensity of the subject's responses were similar using suprathreshold (higher) currents but using lowest electrical currents (perithreshold) required higher frequencies for detection similar to other somatosensory brain regions.

      3) Frequency-intensity variation could evoke different types of sensations, with higher frequencies more likely to evoke tingle or buzz (less natural), and lower frequencies eliciting more pressure, tap, or touch (more natural type sensations).

      The major strength of this work is the detailed testing performed over multiple sessions through the same microelectrodes, demonstrating consistent effects. It provides new methods to alter sensations by changing the parameters of stimulation to optimize the type of percept that they are trying to produce.

  5. Dec 2020
    1. Reviewer #4:

      General assessment of the work

      Gene drives can be used for sustainable control of disease vectors, and there is a need for a different gene drive strategies that can be tailored to the particular species, timescale, and desired spatial spread. Kandul and colleagues present a welcome new addition to the growing number of strategies for gene drive, called HomeR, that combines elements of killer-rescue and homing-based drive to exert spatiotemporal control over its spread, whilst counteracting the rise of resistant mutations. Whilst it is extremely promising, some major claims of this manuscript are inaccurate or unsupported by the evidence. The authors could easily address the most important concerns by expanding their sequencing analysis to better detect and quantify resistant mutations, paying careful attention not to overstress the potential of this drive to mitigate resistance, and by comparing the relative strengths of different drive strategies instead of focussing only on features that are most flattering to the HomeR strategy.

      Numbered summary of any substantive concerns

      1) The drive release strategy of Fig 4A + 4C is primed to underestimate and potentially mask resistance. In Fig 4A, where the authors search for signs of resistance, the population was seeded with males that were all homozygous for the drive, meaning that 100% of their G0 progeny will inherit it. As the rate of homing is close to 99%, only a small fraction of their G1 could have inherited a non-drive (potentially resistant allele) allele. In a realistic release scenario, resistant alleles will have ample opportunity to be generated and subsequently selected. Though still far from adequate, resistance testing would have been better performed on samples collected from the lower frequency releases in panel C. This experiment should not be used to draw strong conclusions about resistance to pHomeR, but should be used to make broader observations regarding the spread and stability of the construct.

      2) The strategy for sampling resistance will obscure almost all resistance in the population, and would fail to detect even a strong selection for it. Flies were only selected for resistance genotyping if they lacked GFP, meaning they carry two non-HomeR alleles (i.e. homozygous for the R1 allele or transheterozygous with another R1/R2/WT). One would expect most resistant alleles to be heterozygous in a population that was seeded with almost complete drive homozygosity. The authors could, and should, have done more to identify and quantify these. Amplicon sequencing was used to sample the full diversity of alleles in a larger pool of individuals (including GFP+ flies) collected at G10, why was this approach not used throughout? By adopting the approach earlier they would have been able to track the changing frequencies of R1 and R2 alleles over time.

      3) The impression given in the figure and main text is that R1 alleles were rare (or entirely absent), when they were not. In spite of the incredible advantage given to the drive, and a bias in sampling method that would mask the presence of resistant alleles, resistance was observed in every generation tested (G2, G3 and G10). The authors claim that because GFP-individuals were not observed in later generations, the resistant alleles had not come under positive selection. This logic is flawed, and indeed their own amplicon sequencing analysis performed on G10 flies revealed several resistant alleles, including an R1 present in 80% of non-drive alleles. The two most frequent mutant alleles detected were in frame, and I do not agree that these are likely to be deleterious recessive (as the authors speculated). These could be functionally resistant mutations. I believe there were many more R1 alleles in heterozygosity with the HomeR allele, these alleles could have been spreading, but were excluded from the genotyping analysis. Could these putative R1 individuals not have been specifically tested to see if they do, or do not confer resistance?

      4) The modelling takes a very limited approach to comparing different drive strategies, and by comparing proof-of-principle designs, important differences are obscured. For example, simple modifications that would mitigate resistance are likely to be included in many designs - such as multiplexing gRNAs. The nuances of each design are lost in a discussion focused on the rate of spread, which is largely irrelevant now because all of the drives are predicted to spread well.

      5) The authors did not discuss the relevance of having performed releases in a population that was already homozygous for Cas9. Do the release experiments and model really suggest the drive could spread if released into an otherwise WT population? I'm not sure the data presented in this manuscript can support that claim.

    1. Reviewer #4:

      PREreview of "Structural characterization of an RNA bound antiterminator protein reveal a successive binding mode" Authored by James L. Walshe et al. and posted on bioRxiv DOI: 10.1101/2020.09.27.315978

      Review authors in alphabetical order: Monica Granados, Katrina Murphy

      This review is the result of a virtual, live-streamed journal club organized and hosted by PREreview and eLife. The discussion was joined by ten people in total, including researchers and publishers from several regions of the world and the event organizing team.

      Overview and take-home message

      In this preprint, Walshe et al. use a structural approach to examine a bacteria's RNA-binding ANTAR protein, EutV, including how EutV's antitermination mechanism works to prevent transcription termination and thus regulate gene expression. In addition, the team examined how a single hexaloop with the conserved G4 is recognized in succession by conserved residues in the ANTAR domains, how conserved A1 helps with proper RNA folding, and how these interactions support RNA binding. Although this research is of interest in the field, there are some concerns that could be addressed in the next version. These are outlined below.

      Positive Feedback:

      -I appreciate the comment on how crucial it is to understand the system and structure of these proteins for therapeutic purposes. It helps exemplify the relevancy for people outside of this field.

      -I think it's interesting that there is potential for a new current model for ANTAR-mediated antitermination.

      -I found it interesting that the two domains of the dimer cannot bind to the P1 and P2 helices of the same RNA.

      -New data is used in this preprint and displayed openly in Supplementary Table 1.

      -This research is novel because it's the start of looking at specifics of the mechanisms ANTAR domain proteins use to prevent termination.

      -It will be interesting to look at bioinformatic analyses for the ANTAR domain across diverse bacterial strains. Especially in diverse ecological niches such as host-pathogen.

      -It would be interesting to look at the structure in the context of an RNA construct that includes the P1, P2, and all of the T-loop.

      -I am outside of this field of study, however, there are definitely a lot of details in this paper that it seems to be enough to reproduce. Though others possibly in the field have said, reproducibility is less likely in this type of work.

      -I'm outside of the field, but it is nice that they deposited the atomic sequences on a public repository. I wonder whether this is mandatory for acceptance?

      -Yes [the results are likely to lead to future research], now that there is more interest in mechanisms that ANTAR domain proteins use for antitermination.

      -Are these findings applicable for similar ANTAR proteins (homologues/orthologues) in other bacteria? What about more complex organisms?

      -Interesting topic!

      -First RNA bound!

      -Yes [I would recommend this manuscript to others and peer review], I think this is a promising manuscript.

      Major Concerns:

      -Lot of the details are included [in the preprint], lacking, however, is information in the method section about the modeling of the RNA using RNAComposer. It is mentioned in the results section, but not in the methods section.

      -It's not clear where the EMSA assay is used in the paper. It's mentioned in the methods section, but not anywhere else.

      -I think it would be helpful to see whether ANTAR mutants have anti-termination defects in a transcription reaction. Authors might consider being cautious talking about anti-termination without functional studies.

      Acknowledgments:

      We thank all participants for attending the live-streamed preprint journal club. We especially thank those that engaged in the discussion.

      Below are the names of participants who wanted to be recognized publicly for their contribution to the discussion:

      Aaron Frank | University of Michigan | Assistant Professor, Biophysics and Chemistry | Ann Arbor, MI Monica Granados | PREreview | Leadership Team | Ottawa, ON Katrina Murphy | PREreview | Project Manager | Portland, OR