On 2020-10-09 17:07:42, user Aura Raulo wrote:

Hello and thank you for your feedback! Really nice to hear that you enjoyed reading our manuscript! To answer your questions:

• Yes you got the SRI almost right, except for a few things: As you said, X is the number of cases (night-logger combinations) where two individuals crossed the same station within the specific time window of each other. in BI, this number was just 1 or 0 for whether they were ever observed within this time window from each other. This number (either X or 1/0) was then divided by the number of cases when either one of these individuals were observed but not together. The cool thing is that for the denominator, we included only those "not-together" observations for which we were certain that the other mouse was alive then, so that technically they could have been together. This is very similar to traditional SRI, just a proportion of observations of two individuals where they are observed together, but we are essentially excluding single observations of A or B from the denominator from time periods when the other was not born yet or assumed dead. This way the social associations do not reflect just the extent to which two mice co-existed on earth, but rather how much they interacted during the overlap of their existence. Almost identical adjusted SRI is published in Firth JA, Sheldon BC. Social carry-over effects underpin trans-seasonally linked structure in a wild bird population.Ecol Lett2016;19(11):1324-32

• "nodes" is a common social network term and means just the the nodes in the network. When using a network analysis, it's often a common practise to state what entities were the nodes and what were the edges connecting them in this network. This is because the network nodes are not always individuals, for example sometimes groups of individuals are considered a node. Or in an ecological network, a node would be species.

• Ah thanks for spotting! Thos should be 12 hours. I will fix it.

Thanks for such nice feedback. My co-author will be pleased to know that their snazzy supplementary material formatting received such complements.

On 2020-10-09 15:04:04, user Simone Giannerini wrote:

This is the first work where a link between the theory of circular codes and translation efficiency is established. The power of the theory gives you a brand new pair of glasses to see unexplained structure hidden within genetic information!

On 2020-10-09 14:15:41, user Hadrien Peyret wrote:

Do you know if you get budding of the recombinant protein from the plasma membrane of the insect cells? Could make subviral-like particles that might be even better immunogens!

On 2020-10-08 19:24:19, user gletreut wrote:

Please see our response to this comment at: www.biorxiv.org/content/10.1101/2020.10.06.315820v1.

On 2020-10-08 16:42:06, user Peng Xu wrote:

This work has been published on Metabolic engineering communications with DOI link https://doi.org/10.1016/j.m...

On 2020-10-08 16:11:30, user André Müller wrote:

I have a question regarding the definition of true/false positives/negatives used in the paper.

Did I understand it correctly that you define the false positives as the number of false taxa (on level species, genus, etc.)?

So, if I had a million reads and 99.99% of them were mapped to the correct 11 taxa (for dataset D7) and the last 0.01% (=10,000 reads) were all mapped to incorrect taxa, the precision = TP/(TP+FP) would be 11/(11+10,000) = 0.0011 !

If that indeed is how your definitiion works, I think the resulting precision would be disproportionally affected by a minority of wrongly mapped reads and not be practicable. Wouldn't one only want look at the mappings with significant abundances?

I think using per-read statistics are much better suited and many/most other publications use these.

If one doesn't have per-read ground truth mappings, but only knows what species went into a sample, couldn't one use an aligner to find out which read belongs to which species and then compute TP,FP, etc. on a per-read basis? For synthetic datasets the per-read ground truth should be available anyway.

On 2020-10-08 16:04:33, user Peng Xu wrote:

This paper has been published on the journal Bioresource Technology with the DOI link https://doi.org/10.1016/j.b...

On 2020-10-08 13:58:21, user Saoirse Foley wrote:

The published version of this manuscript is available at the following doi:

10.1098/rspb.2020.1688

On 2020-10-08 13:31:00, user Iratxe Puebla wrote:

This review was completed by Elizabeth Ing-Simmons (Max Planck Institute for Molecular Biomedicine), Sara Llorente-Armijo (MRC London Institute of Medical Sciences), Maria Rigau (MRC London Institute of Medical Science) and Virginie Tissières (Max Planck Institute for Molecular Biomedicine) as part of ASAPbio’s #PreprintReviewChallenge

In this preprint, Arrastia et al. present a new single-cell technique allowing the detection of chromatin contacts: scSPRITE. This method is based on split and pool recognition of crosslinked clusters of interacting genomic regions. Consecutive rounds of barcoding allows first the distinction of single cells and then of individual clusters of DNA molecules. This technique provides information about the 3D structure of the genome at a single-cell resolution like compartments, topologically associating domains and, most interestingly, higher-order chromatin features such as interchromosomal contacts and nuclear bodies.

Arrastia et al. introduce a novel strategy that extends their SPRITE method, which allows the mapping of multiway contacts, to single cell resolution. Single-cell SPRITE provides more contact information than single-cell Hi-C, for the same sequencing depth, which should make it an attractive alternative to scHi-C. Overall, the method provides complementary information to Hi-C in many aspects and has particular potential for the study of higher-order chromatin structures such as interchromosomal contacts and nuclear bodies. The schematics and model representations make the paper easily understandable.

Major points

• We had a number of questions relating to how the feature detection scores are calculated and interpreted. Firstly, the scores seem to depend crucially on the definition of the regions (e.g. TADs, regions associated with nuclear speckles). Therefore, these region definitions should be comprehensively validated. For example, in Fig S3b, it looks like a speckle region on chr2 has strong interactions with a non-speckle region on chr5, which could be affecting the detection score. Is there anything special about this region on chr5? For the TAD detection scores, it would strengthen the results to confirm that the TAD definitions used are consistent with those identified from other methods (i.e. scHi-C, Hi-C)
• In Fig 4c, some TADs appear to have very low detection scores. Are these TADs weaker, or is it possible that the TADs are well defined but contacts outside of the TADs are lowering the detection score?
• In general, showing examples from all parts of the distribution of detection scores, not just the top scoring examples, would help readers understand how to interpret these scores.
• How does the number of clusters vs size of the cluster contribute to the detection score? It looks like Cell 2 in Fig 2d should have a higher score than Cell 1, as there appear to be more contacts within the chromosomes, but maybe this is due to a few clusters covering a large portion of the chromosome whereas Cell 1 has more clusters?
• How are cells classified by cell cycle stage? This is not sufficiently described in the methods. It would also be helpful to provide the cell cycle stage alongside some of the plots e.g. Fig 4c.
• The text states that the cell cycle distribution in different clusters does not explain the differences in structure (Fig S4g, Fig S5b), but how this is justified by the figures shown is not clear. Further explanation would be helpful here.

Minor points<br /> - Can you clarify the % of cells that are from a single species? The 97% figure given in Fig 1b is more precise than the >90% stated in the text.<br /> - Can you explain why some cells have low detection scores? (Figures 2d,f, 3b, d, f, 4b) Is this due to technical reasons: are there quality differences between sequenced cells? Do the same cells have low scores for multiple features? Or is it due to a biological one like cell heterogeneity? <br /> - Is the cell labelled as “Cell 1” the same cell across all features (e.g: fig 2d, f, or 3b, d, f)? This is not clear. <br /> - How does the frequency of nucleolar contacts between pairs of chromosomes compare to what would be expected at random? An observed/expected score might be easier to interpret than the percentage of cells in which a contact occurs (Fig 3g,h). <br /> - You start with 100,000 cells but only sequence 1000 cells. Is it necessary to start with such high numbers for best results? This would be useful information for readers who would want to try the method.

Questions for the authors<br /> Cluster size, in terms of both the number of regions and size of genomic regions within the clusters, seems to vary a lot. Cluster size can impact the type of features you detect - for example, large clusters are useful for detecting features like nuclear speckles, but smaller features like TADs or enhancer-promoter interactions may be more readily detected by smaller clusters. Is it possible to control cluster size experimentally, for example by adjusting fixation conditions or sonication conditions, to optimise for detection of a specific class of interactions?

On 2020-10-08 13:20:27, user Iratxe Puebla wrote:

This review was completed by Sandra Franco Iborra (Columbia University), Luciana Gallo and Sharon Ahmad (Company of Biologists) as part of ASAPbio’s #PreprintReviewChallenge

In this manuscript authors aim to find in vivo evidence of the role that Fas expression plays in fibroblasts’ cell death after a fibrotic process in the lung. Prior in vitro evidence pointed at Fas expression as a key element in the activation of the fibroblast apoptotic programme after a fibrotic process. Authors show how knocking out Fas in either mesenchymal cells or specifically on fibrotic fibroblasts prevents the elimination of fibrotic fibroblast upon an acute pulmonary injury. This correlates with increased fibrosis in the tissue and expression of pro-fibrotic markers such as alphaSMA and Col1. This work helps to understand the underlying mechanisms that allow for the persistence of the fibrotic process that takes place in diseases such as Idiopathic pulmonary fibrosis.

This study uses a variety of animal models to delete Fas in fibroblasts: developmentally in lung mesenchymal cells and conditionally in Col1-expressing cells. Moreover, they also assess the expression of Col1a1 and alpha-SMA promoter activity in those different models using transgenic animals that express GFP or RFP conditionally. In terms of models, this study is very exhaustive.

Moreover, it also aims to answer whether Fas expression in fibroblasts is relevant to the persistent pulmonary fibrosis observed in diseases such as idiopathic pulmonary fibrosis, as some in vitro results suggested. Therefore, this study aims to fill this gap of knowledge and translate some in vitro findings into an in vivo setting.

The paper was very well written - clear, concise and effective. Really a pleasure to read.

General comments:<br /> Suggest avoiding the use of “data not shown” and instead refer to data or previous publications.

It would be interesting to also analyze the two models of Fas deletion at basal conditions (without bleomycin instillation) to put into context the results of the bleomycin instillation.

Figure 1E and Fig I - It seems that in both cases, deletion of Fas leads to a peak in the Picrosius Red staining at 6 weeks instead of 3 weeks. I think that the quantification of the Picrosius Red staining would help to contextualize the amount of fibrosis present at each time point in addition to the information provided by the images.

Minor comment - suggest clarifying in the legend for Figures 1 D-E and H-I that the second row of images are magnifications of the first row.

Figure 3G,H,I - Is there any explanation for the fact that the number of CD90+ and CD26+ Col 1 fibroblasts upon conditional Fas deletion are quite similar to the wildtype. This is not the case upon Fas deletion in mesenchymal lung cells. This is puzzling because it is apparent that the fibrosis and pro-fibrotic markers are still activated at 6 and 9 weeks.

There is a minor mistake on page 12 - “(0=0.02).

Suggest adding how many animals were used for each experiment in each figure legend.

Questions for the authors<br /> There is a lot of supplemental data - is it all necessary? It is so rarely looked at and journals are often strict about how much is allowed.

On 2020-10-08 12:53:38, user Iratxe Puebla wrote:

This review was completed by Pablo Ranea-Robles (Mount Sinai School of Medicine, New York) as part of ASAPbio’s #PreprintReviewChallenge

Wang et al. found a metabolic vulnerability of hepatic cancers after unveiling a downregulation of fatty acid metabolizing enzymes in hepatoblastomas and hepatocellular carcinomas. They demonstrate that this metabolic vulnerability can be exploited by treating mice with fatty acid substrates of these pathways and showing necrosis of the tumors and extension of the mouse models' lifespan.

Metabolic remodeling is a well-known characteristic of cancerous cells. Even though there is no common alteration that spans all types of cancer, nutrient substrate utilization changes such as switches between glucose and fatty acid metabolism are a common hallmark of cancer. Wang et al. found that mouse models of liver cancer and liver cancer patients display downregulation of fatty acid oxidation pathways, including mitochondrial and peroxisomal beta-oxidation and omega-oxidation. They also found an association in some types of cancers between the expression of these metabolic genes and patients' survival. The discovery that feeding animals with a diet enriched in C12 and/or C12DCA (DDDA) extends mice's lifespan with hepatoblastomas by promoting tumor necrosis is very relevant. This finding goes in line with the deficiency of the peroxisomal enzyme EHHADH, which renders these tumors vulnerable to C12 and C12-DCA (DDDA). These data open new possibilities to test diet supplements to treat cancers with decreased expression of genes involved in fatty acid beta-oxidation.

Major points:<br /> The authors show increased tumor necrosis in HB mice treated with C12 and/or DDDA. However, they only show H&E stainings. I think it would be more convincing if they show tumor size measurements and a marker of cell death in the liver preparations.

The authors conclude that mitochondrial mass and fatty acid oxidation are decreased based on the transcriptomics and OCR data. mtDNA quantification or another marker of mitochondrial mass (citrate synthase, for example) would better show the mitochondrial mass in HB mice. OCR with palmitoyl-CoA only shows a very mild decrease in the tumors. FAO decrease would be confirmed with direct FAO measurement in liver preparations. The fact of measuring OCR in mitochondrial preparations to deduct FAO rates misses peroxisomes' contribution to fatty acid oxidation, which can be very relevant in this Ehhadh deficiency model.

The authors do an elegant job by discerning the transcriptional signature that differentiates resistant tumors that express low Ehhadh and Cyp4a10 vs susceptible tumors. They found a signature characterized by extracellular matrix and cell adhesion genes, and some tumor suppressors. Are these genes also changed in resistant tumors that have normal Ehhadh expression? That could help to understand the reinitiation of these tumors under the effect of the diet.

Minor points:<br /> Which sex are these mice? This point may be important, considering that CYP4A enzymes show prominent sexual dimorphism and could affect omega-oxidation of fatty acids.<br /> What is the difference in Fig. 2B between Liver and WT columns?

Besides the EHHADH immunoblots, it would be interesting to know the tissue expression of EHHADH in liver slides by IF or IHC with an anti-EHHADH antibody, showing tumor and normal tissue.

Considering that global fatty acid oxidation is downregulated, some comment on the PPARA pathway in the discussion would be relevant. What do the authors think about the use of fibrates that activate fatty acid beta-oxidation but, at the same time, provoke hepatocarcinogenesis (in mice)?

Some of the preprint statements may require additional revision, for example, "endosomal omega-oxidation" or that peroxisomal/omega-oxidation is the main pathway to metabolize lauric acid. This may be true in stressed/pathological conditions that affect mitochondrial FAO, but is it true in the basal/healthy state?

Questions for the authors:<br /> Do the authors think that these mechanisms may apply to liver cancers in which diet plays a crucial role, such as HCC caused by NASH?

On 2020-10-08 11:37:12, user Iratxe Puebla wrote:

This review was completed by Michael Ailion (University of Washington) as part of ASAPbio’s #PreprintReviewChallenge

In this manuscript, the authors investigate the genetic basis of hybrid incompatibility between two wild strains of the nematode Caenorhabditis tropicalis and identify five new presumptive toxin-antidote gene pairs that reduce the fitness of hybrids. As a bonus, a previously characterized hybrid incompatibility in a different species (C. briggsae) is now suggested to also be caused by a toxin-antidote. In all of these elements, the toxin shows a maternal pattern of inheritance. Toxin-antidotes(TAs) are a type of selfish element that are common in bacteria but not widely characterized in eukaryotes (particularly in animals), so the discovery of five new elements is very exciting and suggests that TAs are much more common than previously recognized. Four of the five new elements cause postembryonic developmental delay or larval arrest, suggesting that TAs can act at different developmental stages, not just during embryonic development as in the previously characterized TAs. One of the gene pairs is molecularly identified and shown to encode a toxin that has some homology to the ligand-binding domain of nuclear hormone receptors (NHRs) but lacking a DNA-binding domain and instead having two putative transmembrane domains, an unusual feature of NHRs. Perhaps the most interesting finding is that two of the TAs (one from each strain) are closely linked on the same chromosome and can provide a novel genetic mechanism of balancing selection to maintain heterozygosity. Overall, the study is very well performed, the data are strong, and the manuscript is well written. I have relatively few minor critiques.

Minor Points

1. The genetic data showing the maternal-zygotic pattern of inheritance and mapping the identified incompatibility loci are very strong, but since only one of the pairs is molecularly identified, it remains possible that not all of these loci encode toxin-antidotes. The idea that these are TAs is a very reasonable possibility, but other possibilities are not considered, and even for the molecularly identified gene pair, there is no work on the mechanism to show that it indeed acts as a toxin-antidote. I would suggest toning down the language and refer to these as presumptive TAs.
2. The focus of this paper is on genetic mechanism, rather than molecular mechanism or developmental defects. That is fine for this preliminary study of these elements, but a bit better description of the developmental defects would be useful even in a preliminary study. For instance, for the molecularly identified slow-1/grow-1 element, is the developmental delay/larval arrest at a specific stage, or are all stages developmentally delayed?
3. Related to point #2 above, the assertion is made that the slow-1/grow-1 element is affecting postembryonic development, and does not disrupt embryonic development. However, it seems possible that a disruption to embryonic development could manifest itself as a postembryonic developmental delay. Without a better understanding of the mechanism or analysis of embryonic development (any embryonic phenotype? any delay in embryonic developmental timing?), this assertion should be toned down. Given that the maternally supplied toxin RNA is essentially gone by mid-embryonic development (Fig 3E), it seems very possible that the toxin is acting embryonically but its most obvious developmental consequences only appear later.
4. In the Discussion it is stated that “All TAs known to date are found within hyper-divergent regions”, but it is unclear if this is true for the slow-1/grow-1 element characterized here. Are the genes surrounding slow-1/grow-1 more divergent than is typical?
5. For the various crosses described (e.g. Fig 1, 2), it would be valuable to better describe the parental generation cross used to generate the hybrid F1 -- i.e. which parent was male and which was hermaphrodite? Does the direction of the parental cross matter? Given that some maternally inherited incompatibility genes show a maternal cytoplasmic pattern of inheritance, that possibility should be accounted for here.
6. Are the TAs found in the NIC203 and EG6180 strains present in other strains of C. tropicalis? Are any natural isolates heterozygous for a pair of conflicting TAs as proposed by the balancing selection model? A more complete analysis of these TA elements throughout the species would be an interesting extension to the work presented.
7. For the backcross strains sequenced in Figs 1D and S4, was the strain inbred for some generations before sequencing? Otherwise, it is hard to understand why there would be >50% NIC203 reads at the incompatibility loci. The same issue applies to Fig 2D for EG6180 reads.
8. Are the experiments in Fig 1F at 25° or 20°? This isn’t clear.
9. Given that the plate shown in the bottom panel of Fig 3B was started with a single egg that arrested as a larva, it is unclear how there can be two worms as shown in the photo.
10. What do the colors mean in the sequencing reads shown in Fig 3A?
11. There are a number of minor errors in the paper such as Figure call outs (e.g. wrong figure referred to), Figure legends, and other minor discrepancies (e.g. grow-1 protein is said to be 125 or 128aa, but actually looks to be 124 aa in Fig S9D).

On 2020-10-08 06:18:08, user chris wrote:

does anyone know where to find the copy number of the covid-19 spike proteins on the covid surface and where it is encoded? Or an EM picture of nice SYMMETRICAL, IDENTICAL covid-viruses, hard to find...not to mention high resolution crystallographic picture..

On 2020-10-07 23:47:22, user UAB BPJC wrote:

Review comments on “Staphylococcus aureus secretes immunomodulatory RNA and DNA via membrane vesicles” by the University of Alabama at Birmingham Bacterial Pathogenesis and Physiology Journal Club

Summary: This paper discusses how S. aureus is able to secrete extracellular membrane vesicles (MV) that contains immunomodulatory RNA and DNA and their delivery to intracellular host receptors.

This paper has very easy to read language and little use of jargon which is nice to find in scientific papers. The methods are described in much detail which is very nice. However, it is a bit long, especially the results section. Some of the details in the results may be better suited to be added to the either the introduction or discussion. For example, line 116-117 “it is conceivable that nucleic acids could be released through spontaneous phage-dependent or independent cell lysis” would be better suited in the discussion than in the results. The conclusion that RNA packaged in MVs from S. aureus has immunomodulatory effects is very interesting! It is also neat that there is currently a paradigm shift in that endosomal TLRs are not just signaling for IFNb in viral infections and this paper is playing a role in that shift. This paper could be more impactful if some of the current figures were added to the supplement and addition of experiments with more controls and addition of sequencing RNA/DNA as well as looking at which proteins are packaged in these MVs and whether of not they are playing any immunomodulatory roles. Additionally, the immunofluorescent images in this paper are beautiful.

Major points:

* Biological vs technical replicates are not clear which is used in each figure.

* What are these RNAs that are in the MVs? Are they random or purposely packaged specifically? Sequencing these RNAs as well as DNAs would add very valuable information.

* A readout of mRNA levels of IFNb is informational but there would be more value/impact if ELISAs to look at the effect on protein levels would be add value

Minor points:

Figure 1: S. aureus release MVs enriched in proteins and nucleic acids. <br /> * Panel A<br /> o Bradford assay is not the best at detecting levels of proteins in lipid rich conditions<br /> o This graph should be separated by each of the components protein, lipid, DNA, and RNA because relative fluorescent levels would not be the same for each of these components and comparisons between macromolecules cannot be done. This maybe better to show separated by macromolecule rather than by fraction.<br /> o Additionally, the label relative fluorescence levels is misleading because the Bradford readout is colorimetric and not fluorescent. <br /> o It is not clear what the normalization is. What is 100% in the peak fraction for each method?

* Panel B<br /> o Negative stained TEM images of purified MVs are not very clear. Are there better ways to show this? Perhaps just using membrane staining dyes and fluorescent images? <br /> o The scale bars are appreciated

* Panel C<br /> o This may be better suited as supplemental data

* Panel E<br /> o This is a common issue throughout the paper, it is not clear whether representative of “3 independent experiments” means that we are seeing all the data from all 3 experiments or just from 1. It would be nice to know whether these are biological replicates or technical replicates. <br /> o Another issue throughout the paper is that only mRNA is shown for the changes in IFN-b. It would be nice to show protein changes as well via ELISA. <br /> o This data are very interesting!

Figure 2: Purified S. aureus MVs induce significant IFN-b mRNA expression in cultured murine macrophages. <br /> * RAW264.7 cells are a macrophage-like cell line but are not actually macrophages. <br /> * A and B are nice to see how you decided on using 5ug and 3h timepoints but this data could be added to the supplemental <br /> * Again, not clear if technical or biological replicates and in C it would be good to add an ELISA to further validate your results.

Figure 3: Detergent, Benzonase, and Proteinase K sensitivity of MV-associated Nucleic Acids and Figure 6: Benzonase-treatment reduces MV-mediated IFN-b mRNA expression in macrophages<br /> * Combining of this figure and Figure 6 would make more sense, especially since your diagram of how Benzonase treatment of MVs affects the RNA isn’t until figure 6 but you use the Benzonase in Figure 3. <br /> * Additionally, in Figure 6b there is no untreated control which would be valuable. <br /> * How do you explain the decrease in nucleic acid levels in Benzonase treated MVs if you are claiming that MVs protect the RNA from this kind of degradation later on in the paper?

Figure 4: The RNA content of S. aureus MVs consists of Benzonase-sensitive and Benzonase-resistant subpopulations and Figure 5: the DNA content of S. aureus MVs is resistant to Benzonase treatment. <br /> * These figures could be combined into one figure because they address the same point. <br /> * Technical vs biological replicates?<br /> * Can you sequence the RNA/DNA isolated from these analyses? If C is averages of triplicate experiments wouldn’t it be more valuable to show all the data points and not just the averages?

Figure 7: Dynamin-dependent endocytosis is likely involved in MV-mediated induction of IFN-b in RAW264.7 cells<br /> * Figure 7a lacks an untreated control for dynasore and bMVs treatment<br /> * Dynamin is involved in other processes besides endocytosis, how can you be sure this is not affecting your results?<br /> * B would be nicer if you could show bafilomycin and chloroquine with the inhibitory arrow/line directly on the endosomal acidification instead of the arrow<br /> * C lacks bMVs only control <br /> * Bafilomycin has off target effects on IFNb so you would need to also do bafilomycin and chloroquine only controls to verify the effect you have is directly because of acidification.

Figure 8: MV-associated RNA induces IFNb largely through endosomal TLR signaling in murine macrophages<br /> * Again, it would be nice to see each data point represented and not just the means of the experiments. <br /> * 8A is very nice, especially the usage of the cGAMP control. <br /> * This figure has some really interesting data! <br /> * In B, why did you do only an IRF3 and IRF7 KO but did not include a TLR 9 as well?<br /> * The breaks in the axes in C-E can make the changes look more meaningful than they actually are especially since the scales are different in all 3 graphs

Figure 9: Treating MVs with benzonase reduces the IFNb mRNA expression in both WT & TLR3-/- macrophages compared to macrophages stimulated with untreated MVs<br /> * The difference in treatment of TLR-/- cells with MV vs bMVs was not explained<br /> * Neat results!

Figure 10: S. aureus bMVs and their associated RNA cargo is delivered into wildtype macrophages <br /> * Beautiful images!<br /> * It would be nice if you could include quantifications of the colocalization of MVs and RNA for both A and B<br /> * Are the scale bars for A and B accurate? Looking at the macrophages in the bottom right corner of both A and B, they look like vastly different sizes even though the scale shows that they are in the same scale.

Figure 11 <br /> * Nice schematic! <br /> * Are you showing Bafilomycin and chloroquine disrupting the TLR signaling separately of endosomal acidification? <br /> * Maybe show that the endosomal acidification impacts the levels of IFN

On 2020-10-07 22:50:26, user Federico Giorgi wrote:

How could I miss this? In vivo single cell analysis of neuroblastoma early development.

On 2020-10-07 16:39:14, user A B wrote:

The supplementary file associated with the paper is missing.

On 2020-10-07 15:31:21, user Alisson Gontijo wrote:

Dear authors, thank you for posting. We have discussed your interesting preprint in our lab meeting. A main issue is that we did not find a methods section in your preprint or here in the bioRxiv page. We encourage you to update your preprint and include the detailed methods as they are critical for the understanding and discussion of scientific findings. Below are other points raised in our discussions based on assumptions and inferences on the methods. We hope that they are helpful, nonetheless.

Gypsy retrotransposition reporter: an independent, alternative technique for de novo insertion detection or PCR with genomic DNA using primers across the inverted intron should be performed to certify that there is true retrotransposition occurring. This could help exclude alternative mechanisms for the generation of eGFP-positive cells/clones such as non-canonical translation mechanisms and/or somatic recombination**. For instance, from the scheme provided, the inverted intron looks really close to the eGFP translation start site and while the mutant version including the splice donor and acceptor mutations is interesting and supportive of retrotransposition, these mutations (unfortunately no sequence is provided to verify) could be affecting common or alternative translation initiation sites/mechanisms. In this sense, can you show by RT-PCR that the inverted intron is spliced as predicted in the Gypsy construct and not spliced in the SD,SA mutant?

**Somatic recombination can be surprisingly common in somatic tissues (e.g., please see work from Bardin lab (Siudeja et al., Cell Stem Cell 2015)) and hence a source of many unexpected expression patterns especially for multi-copy genes (from what we understood two copies of Gypsy reporter are used and the Gyspsy sequences can also recombine with endogenous copies/fragments).

A major issue with experiments using sh- or RNAi-based strategies against repetitive elements to infer causation is that the existence of copies and remnants of the elements throughout the genome and genes might lead to unintended silencing of genes that include these sequences in their transcripts. These can be required for specific developmental properties, such as immune function, and have nothing to do with the retrotransposition or retroviral properties of Gypsy. We see that you attempted to address this with multiple sh constructs against Gypsy. However, this is also limited. Have you checked the down regulated genes contain Gypsy-related sequences in their transcripts and/or loci? We acknowledge that this is hard to assess and disentangle. However, if "Gypsy activation" is causal for the reported phenotypes, then artificially expressing a sh-resistant form of Gypsy in the sh-Gypsy background during metamorphosis should rescue immune priming.

What do you mean exactly with "Gypsy activation"? Is it Gypsy expression, Gypsy viral particle production/shedding, and/or Gypsy retrotransposition (please see Josh Dubnau's papers and preprints)? Each mechanism could imply something different for priming the immune system. For instance, is env required?

We couldn't really assess the RT-PCR gene expression analyses as there was little detail provided.

Finally, the usage of the term "regeneration" for the developmentally-programmed tissue remodelling related with metamorphosis is a bit of a stretch in our opinion. Regeneration implies that the larval tissues have been damaged and the progenitor cells are once again generating larval tissue, which they are not (the imaginal cells are generating adult tissues). If necessary, maybe consider tissue remodelling, tissue substitution, organogenesis, etc.

Thank you for your attention and for sharing your interesting work as a preprint. We hope these comments help improve the work.

Alisson Gontijo, on the behalf of the Integrative Biomedicine Laboratory, CEDOC, UNL, Lisbon, Portugal.

On 2020-10-07 15:24:37, user Alisson Gontijo wrote:

Dear authors, thank you for posting. We have discussed your interesting preprint in our lab meeting. A main issue is that we did not find a methods section in your preprint or here in the bioRxiv page. We encourage you to update your preprint and include the detailed methods as they critical for the understanding and discussion of scientific findings. Below are other points raised in our discussions based on assumptions and inferences on the methods. We hope that they are helpful, nonetheless.

Gypsy retrotransposition reporter: an alternative technique for de novo insertion detection or PCR with genomic DNA using primers across the inverted intron should be performed to certify that there is true retrotransposition occurring. This could help exclude alternative mechanisms for the generation of eGFP-positive cells/clones such as non-canonical translation mechanisms and/or somatic recombination. For instance, from the scheme provided, the inverted intron looks really close to the eGFP translation start site and while the mutant version including the splice donor and acceptor mutations is interesting and supportive of retrotransposition, these mutations (unfortunately no sequence is provided to verify) could be affecting common or alternative translation initiation sites/mechanisms.

In this sense, can you show by RT-PCR that the inverted intron is spliced as predicted in the Gypsy construct and not spliced in the SD,SA mutant?

Somatic recombination can be surprisingly common in somatic tissues (e.g., please see work from Bardin lab (Siudeja et al., Cell Stem Cell 2015)) and hence a source of many unexpected expression patterns especially for multi-copy genes (from what we understood two copies of Gypsy reporter are used and the Gyspsy sequences can also recombine with endogenous copies/fragments).

A major issue with experiments using sh- or RNAi-based strategies against repetitive elements to infer causation is that the existence of copies and remnants of the elements throughout the genome and genes might lead to unintended silencing of genes that include these sequences in their transcripts. These can be required for specific developmental properties, such as immune function, and have nothing to do with the retrotransposition or retroviral properties of Gypsy. We see that you attempted to address this with multiple sh constructs against Gypsy. However, this is also limited. Have you checked the down regulated genes contain Gypsy-related sequences in their transcripts and/or loci? We acknowledge that this is hard to assess and disentangle. However, if "Gypsy activation" is causal for the reported phenotypes, then artificially expressing a sh-resistant form of Gypsy in the sh-Gypsy background during metamorphosis should rescue immune priming.

What do you mean exactly with "Gypsy activation"? Is it Gypsy expression, Gypsy viral particle production/shedding, and/or Gypsy retrotransposition (please see Josh Dubnau's papers and preprints)? Each mechanism could imply something different for priming the immune system. For instance, is env required?

We couldn't really assess the RT-PCR gene expression analyses as there was little detail provided.

Finally, the usage of the term "regeneration" for the developmentally-programmed tissue remodelling related with metamorphosis is a bit of a stretch in our opinion. Regeneration implies that the larval tissues have been damaged and the progenitor cells are once again generating larval tissue, which they are not (the imaginal cells are generating adult tissues). If necessary, maybe consider tissue remodelling, tissue substitution, organogenesis, etc.

Thank you for your attention and for sharing your work as a preprint. We hope these comments help improve the work.

Alisson Gontijo, on the behalf of the Integrative Biomedicine Laboratory, CEDOC, UNL, Lisbon, Portugal.

On 2020-10-07 09:42:53, user David Peters wrote:

Unfortunately, genomic studies too often recover false positives in deep time studies when compared to phenomic studies, the only studies that include a wide array of fossil taxa. In an online phenomic study Vulpavus, Protictis and Nandinia are basalmost Placentalia, the outgroups to the Carnivora, the basal-most of the placental clades. Talpa is an overlooked extant member of the Carnivora. Ursus arises apart from dogs and cats, which find last common ancestors in Tremarctos, Speothos and Borophagus. Arctodus, the short-faced bear, is a giant wolverine (Gulo). Seals and sea lions have separate terrestrial ancestors and became aquatic by convergence. Online cladogram here: http://reptileevolution.com...

On 2020-10-07 08:51:14, user Michael Schindler wrote:

Thank you for this comment, Indeed we calculated the UV-C dose assuming a focused beam. Taking into consideration the "real" distribution of the UV-C light underneath the HH-device the total integrated dose of the fast regimen is 20 mJ/cm². Hence, the lowest UV-C dose tested in our study that was sufficient for total 6-log reduction of SARS-CoV-2 infectious titer is 16 mJ/cm² (2 sec exposure with the hh-device). We updated the manuscript accordingly.

On 2020-09-30 18:25:51, user Holger Claus wrote:

I am almost sure that the calculated UVC dose for the moving regimens of 2.13 and 0.66mJ/cm2 are wrong (much too low). The actual dose must be measured. Also deactivating 6log with 0.66mJ/cm2 is magnitudes outside of other research

On 2020-10-07 06:08:28, user H Eric Xu wrote:

The PDB and map files are attached as supplementary information for those want to look into the structure immediately, and we are looking forward to feedback on the paper.

On 2020-10-07 00:15:41, user Noah wrote:

Really important fact people overlook in the archeological and genetics field. Thankfully it is now more well known

On 2020-10-06 17:55:49, user Anja Ollodart wrote:

Here is the link to the supplemental information :

https://figshare.com/s/abd3...

On 2020-10-06 17:21:55, user Mariam Gaid wrote:

A worth-reading article. The first reliable insight on plant aromatic O-PT.

On 2020-10-06 04:34:20, user Naveen Vankadari wrote:

Complete and extended work with functional studies of published version can be found in ACS Journal. https://doi.org/10.1021/acs...

On 2020-10-06 02:37:18, user Joseph Gleeson wrote:

Would love to hear your feedback! Joe Gleeson

On 2020-10-05 22:06:30, user Jamie Cate wrote:

The final MS now published in Nature Communications has new experiments that update the model. We now have evidence the mechanism of termination inhibition occurs prior to peptide release from the nascent chain-tRNA (NC-tRNA). We saw nascent chains trapped on the ribosome after peptide release due to the fact that some NC's are inherently "sticky" in the ribosome exit tunnel, at least in vitro.

See: https://www.nature.com/arti...

On 2020-10-05 19:44:46, user Dmytro V. Gospodaryov wrote:

It was believed that tardigrades did not have either alternative oxidase (AOX) or NADH dehydrogenase (NDH). At least, numerous database searches did not reveal any of these enzymes in sequenced tardigrade genomes. On the other hand, if we were right in our hypotheses expressed in our recent paper https://www.sciencedirect.c... , it would be logical for tardigrades to have an alternative respiratory chain enzyme. These animals may undergo hypoxia/re-oxygenation when fall into cryptobiosis during water deficit and recover after wetting, respectively. The research of Wojciechowska and colleagues is an important note in proof to our assumptions, as well as a considerable insight into the evolution of respiratory chains and the role of its alternative components in adaption of animals to the conditions that compromise proper operation of mitochondrial respiratory chain.

On 2020-10-05 17:16:32, user Esin B Sozer wrote:

The peer-reviewed and updated version of this article is now online at biophysical journal https://doi.org/10.1016/j.bpj.2020.09.028

On 2020-10-05 15:56:33, user Surajit Chakraborty wrote:

Study by Ghosh et al indicates the role of lysosome as a player of MHV egress. As per data presented in the study, viruses enter the ER-secretory pathway, proceed to Golgi and then gets shuttled back to lysosomes for release. Simultaneous with its participation in virus release, deacidification of the lysosomal milieu has been demonstrated to hamper the antigenic presentation via MHC-dependent antigenic presentation hence dampening the immune response against beta-coronavirus.

On 2020-10-05 10:23:10, user Surajit Chakraborty wrote:

Study by Ghosh et al indicates the role of lysosome as a player of MHV egress. As per data presented in the study, viruses enter the ER-secretory pathway, proceed to Golgi and then gets shuttled back to lysosomes for release. Simultaneous with its participation in virus release, deacidification of the lysosomal milieu has been demonstrated to hamper the antigenic presentation via MHC-dependent antigenic presentation hence dampening the immune response against beta-coronavirus. It would be interesting to investigate whether viral entry from ER-secretory pathway to lysosome is directly associated with the deacidification of lysosomes, or at earlier time points (6hpi, 9hpi) deacidification already gets started as a result of virus-mediated activation/deactivation of host signaling thus setting up a platform for virus egress.

On 2020-10-02 14:13:10, user Manish Kumar wrote:

The manuscript is potentially informative that the beta-coronaviruses, uses lysosomes to exit cells. The virus inducing deacidification of lysosomes, and therefore avoid degradation of viral particles. Importantly, pathway regulation by the Arf-like small GTPase Arl8b have an impact on antigen presentation pathways. This finding suggests Arl8b may be an important therapeutic target.

On 2020-10-05 12:42:45, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-10-05 12:20:04, user UAB BPJC wrote:

Review of Labana et al., “Armeniaspirols inhibit the AAA+ proteases ClpXP and ClpYQ leading to cell division arrest in Gram-positive bacteria” by the University of Alabama at Birmingham Bacterial Physiology & Pathogenesis Journal Club

Summary:<br /> This study focuses on elucidating the mechanism of action of the Gram-positive antibiotic armeniaspirol. Using a competitive proteomics strategy, this group identifies AAA+ proteases ClpXP and ClpYQ to be the target of armeniaspirol. Proteomics performed on clp mutants then shows that targeting of Clp proteases by armeniaspirol leads to dysregulation of important divisome and elongasome proteins, leading to cell cycle arrest. This is a novel mechanism of antibiotic activity, making armeniaspirol a promising compound for further drug development.

Overall, we found this to be a very interesting paper with valuable data and insightful conclusions. This paper was well-written, and the authors did a great job of focusing on both the chemical and biological perspectives throughout. With that said, we have some comments that may be beneficial for the authors to address, particularly in regard to data organization.

Introduction:<br /> May be beneficial to discuss Clp homology in other species either in the intro or elsewhere in the text to further demonstrate the relevance/importance of this antibiotic.

Figure 1:<br /> Including the abbreviations (1 and 2) in the Fig 1 A direct text would be useful for reference. Also, the use of various strains of staph shows a knowledge of the strains, which is appreciated.<br /> Showing the individual structures of each compound in 1A may be easier to understand visually.<br /> Might be beneficial to more clearly state in Results section “synthetic 5-chloro-armeniospirol will be referred to as ‘1’ throughout this paper.”

Figure 2:<br /> Figures 2B and 2C could be moved to the supplement to make room for more data, as the data can be interpreted without these figures in the main text.<br /> The volcano plot in Figure 2D is lacking context, and it is unclear if the highlighted genes/pathways are changed from published data or just from the drug. Side-by-side volcano plots, a heat map, or a table of the comparisons and functional analyses would make this data easier to interpret.<br /> The authors include some functional clustering (via coloring the proteins) but a table should be included at least in supplemental to better identify the relevant functional clustering which they use to support their conclusions later on (e.g. translation).

Figure 3:<br /> The goal of the electrophilic center noted in 3B (to covalently modify proteins, or covalent pull-down) should be explained better in the text...and why the competitive pulldown was surprising. (it is in the discussion but should be in the main figure-related text).<br /> Figure 3 could benefit, much like Figure 2, from a clearer functional clustering or a table which would clarify their rationale for choosing ClpP. <br /> Panels 3B and 3C could be moved to supplemental to make room for more data in the main text. For example, including data from the proteomics which supports their substrate modification or competition claims in the main text would be beneficial. Clarity on the rationale behind the path to looking at ClpP based on these data could be added so that the connection is more prevalent in the text.

Figure 4:<br /> The schematic to detail the methods can be moved to supplemental so actual data from supplemental (e.g. Fig S2 and Fig S3) can be moved to the main text as they are relevant to the main conclusions. If schematics are included, reducing the white space or making them less prominent in the figure would allow for more data to be included in the main text, rather than the supplemental.<br /> An E. coli protein is used, but it is shown previously that 1 is not active against E. coli. This is worth mentioning (it could be unable to enter the cell and therefore not function in this way for intact E. coli).<br /> Addressing the biology along with the chemical synthesis is important and adds to the impact of the results. We commend the authors for addressing both throughout. Considering the compounds as building blocks rather than THE answer, as comes across in the discussion, would couch the high IC50 and potential biological limitations.

Figure 5:<br /> Including the data on the divisome from Figure S4 seems important for the overall conclusions and could easily fit into Fig 5. <br /> The use of asterisks to denote "not significant” in 3B is confusing.<br /> The authors use data from supplemental and previous figures to support the hypotheses or conclusions of Figure 5. These previous figures should be referenced in the text with the citation (e.g. "the DivIVA expression with low transcript relating to the decreased proteolysis can be related to Fig S2 and S3 showing decreased proteolysis”).

On 2020-10-05 11:49:30, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-10-05 11:37:22, user Valerio Sbragaglia wrote:

Nice article, Congratulations!<br /> I leave below the link to two articles that could be of interest:

https://academic.oup.com/ic...

https://www.sciencedirect.c...

On 2020-10-05 03:16:07, user Neii wrote:

In the micro environment, kind of hard to replicate in assays. This paper shows where it could be binding to: https://www.tandfonline.com...

On 2020-10-04 12:53:46, user Michael Clerx wrote:

On the downside, CellML does not support full object orientation for composing models. This means that base classes that define an interface need to be imported as components of the model, which requires a more verbose syntax. It also does not support discrete variables, but only event triggers. To implement a variable like the interbeat interval d_interbeat, which stays constant between events, an additional equation would have to be added to the model, which sets the derivative of this variable to zero. This both introduces unnecessary code and makes the model less understandable as there is no clear distinction between discrete and continuous parts apart from the labels assigned to the variables

These concerns should be somewhat alleviated in the new standard, I hope: https://doi.org/10.1515/jib...

On 2020-10-04 07:31:08, user Sudit S Mukhopadhyay wrote:

published in MCB

On 2020-10-04 00:50:17, user David wrote:

Hi Danner et al., this paper https://bmcbiol.biomedcentr... by the Shendure lab seems to set a precedent for double-cut sequence replacement by NHEJ. What are your thoughts on this?

On 2020-10-03 18:05:49, user Chowdhury Mehedi Hasan wrote:

Very promising results! Good luck with the next phase of this vaccine!

On 2020-10-02 10:07:12, user Anon wrote:

The authors of these papers are spreading misleading information on their research using the national media of Bangladesh. They are claiming BioRxiv as a leading us-based journal.

On 2020-10-01 15:17:11, user Muhammad Zakiruddin Chowdhury wrote:

This research was conducted by members of Globe Biotech and Globe Biotech is presenting this as a publication in International Journal. They conveniently skipped mentioning that it has not been peer reviewed. Please google Bangladesh English News portals on 01 and 02 October and you will find more.

On 2020-10-03 06:47:27, user Holger Gerhardt wrote:

Super cool stuff Claudio! I believe this is a very important discovery that will hopefully pave the way towards selective interference for example in situations where excessive and unwanted vascular sprouting threatens tissue functions. Maybe in ocular conditions. The question is whether targeting such fundamental cell biological nodes that are present in essentially all cell types will ever become manageable in a therapeutic setting. Nonetheless, your work provides very important insights into the biology of angiogenic invasion. Congratulations! Cheers, Holger

On 2020-10-02 19:44:39, user Caitilyn Allen wrote:

This is a truly path-breaking paper that is going to excite bacteriologists in general, not just plant pathologists. The integration of several different datasets answer some important questions. Especially interesting to see the dramatic reprogramming when this bacterium switches environments!

On 2020-10-02 19:41:31, user David Ross wrote:

We received some feedback that this preprint contains too many ideas for one paper. So, we split the story into two parts. The first part, which includes a description of the measurement and a discussion of what the results can tell us about LacI allostery, is posted as a new preprint at https://www.biorxiv.org/con.... A subsequent manuscript will focus on the use of the results for precision engineering of genetic sensors.

On 2020-10-02 18:57:09, user ravi chandra wrote:

Is there a control for this experiment? tried to look for SARS COV2 sequences in a healthy and COVID negative individuals? What is the confirmation that the amplified rna sequences originated from SARScov2?<br /> Which was the first SARScov2 virus sample or gold standard that was considered in this study ? Any reference cited as such ?

On 2020-10-02 16:28:20, user David Ross wrote:

This preprint is related to an earlier preprint with the same title (https://www.biorxiv.org/con... We received feedback that the earlier manuscript contained too many ideas for one paper. So, we split the story into two parts. The part contained in this manuscript includes a description of the measurement and a discussion of what the results can tell us about LacI allostery. A subsequent manuscript will focus on the use of the results for precision engineering of genetic sensors.

On 2020-10-02 10:28:44, user B C M Ramisetty wrote:

Great work. But, the title seems too 'active' and misleading.<br /> "Bacteria that obtain BGC encoding prophages survive" or "prophages integrate to allow the survival of bacteria"? Is there a purpose ascribed?

On 2020-10-02 09:17:54, user Martin R. Smith wrote:

This sounds like a very useful package; are there any plans to add other tree distance measures beyond the problematic Robinson–Foulds, e.g. generalized RF distances? I'd be happy to share C++ implementations of some such distances if this would be useful.

On 2020-10-02 09:14:43, user Martin R. Smith wrote:

I'd be wary of relying too heavily on the Robinson–Foulds distance to evaluate the relative performance of the input / cleaned alignment; the difference you observe is relatively small, given the potential biases inherent in the RF distance. Do you see the same relative performance difference under more robust tree distance measures, such as the Quartet or generalized Robinson-Foulds distances? (See Smith, 2020, for discussion: https://doi.org/10.1093/bio...

On 2020-10-02 09:03:12, user Martin R. Smith wrote:

A thought on using the Robinson–Foulds distance to evaluate proposals: because moving a single tip or a small clade can lead to a disproportionately large RF distance, could this result in a certain class of potentially promising tree rearrangements being sampled less frequently than they ought to be?<br /> Generalized RF distances, which are still quick to calculate, better represent the degree of difference between trees and would seem more appropriate here; see Smith 2020, https://doi.org/10.1093/bio....

On 2020-10-02 08:57:59, user Martin R. Smith wrote:

A note on the distance values between nucleotide and amino acid trees: could their large size represent misplacement of a single taxon? The Robinson–Foulds distance can easily report disproportionately large distances in trees that are not as different as the metric suggests. I wonder whether distances calculated under a generalized Robinson–Foulds metric (Smith 2020, https://doi.org/10.1093/bio... would be a better representation of differences between topologies?

On 2020-10-02 08:54:08, user Martin R. Smith wrote:

A small question on this interesting study: is the Robinson–Foulds metric really a useful measure of topological stability? In this context, I'd worry in particular about its potential sensitivity to the misplacement of a single taxon, which could significantly distort results. A generalized RF metric (see Smith 2020, https://doi.org/10.1093/bio... would seem a more appropriate choice that might better reflect changes in topology.

On 2020-10-02 08:25:24, user Martin R. Smith wrote:

Congratulations on this interesting study. It's good to see due caution applied to the Robinson–Foulds metric, but other distance metrics can capture relationship information without running against saturation limits: did you consider using the quartet metric, or Generalized Robinson–Foulds metrics? (See Smith 2020, https://doi.org/10.1093/bio..., for some alternative distances.) These are implemented in the R packages 'Quartet' and 'TreeDist', so should be easy to calculate alongside the cophenetic distance.

On 2020-10-02 07:27:13, user jvarga wrote:

Nice work! By what criteria did you identified proteins? Did you use only unique peptides? How much per protein?

On 2020-10-02 01:29:37, user Robyn Hall wrote:

published as https://pubmed.ncbi.nlm.nih...

On 2020-10-01 17:42:25, user Xiaoping He 何小平 wrote:

Lines 201-204 described taxonomy assignment method: was taxonomy assignment to different levels based on the selection criterion (% sequence similarity x length of overlap) rather than similarity/identity? Does length of overlap mean length-of-amplicon x query-cover? Thanks.

On 2020-10-01 13:56:13, user Rajneesh Srivastava wrote:

Published version is available:<br /> https://doi.org/10.3390/ijm...

On 2020-09-22 21:51:06, user Řajneesh Srivastava wrote:

Now accept in IJMS!

On 2020-10-01 13:03:28, user Adam Smith wrote:

Major things are missing from this like the number count of animals or insects testing.

On 2020-10-01 08:26:23, user Tomas Takac wrote:

This article was peer reviewed and recently published with modifications (doi:10.1111/pce.13894).

On 2020-09-30 23:46:10, user Boya X. wrote:

Wondering how did you collect the data for "external content indexing" in table 2? Microsoft Academic seems is indexing arXiv, SSRN, peerj, vixra and etc. but your data shows not. https://academic.microsoft...., https://academic.microsoft.... , https://academic.microsoft...., https://academic.microsoft....

On 2020-09-30 23:23:31, user Joshua Mylne wrote:

This pre-print is now published in Journal of Natural Products https://doi.org/10.1021/acs...

On 2020-09-30 19:29:42, user Patrick O'Keefe wrote:

Supplemental Table 1 is mentioned in the text, but I did not see it.

On 2020-09-30 15:45:38, user Domingos Henrique wrote:

Please check:

https://dev.biologists.org/...

On 2020-09-30 10:20:45, user Emilian Stoynov wrote:

Interesting article. Can you provide information how long was kept in captivity the captive bred individual with the patagial tag prior to be released again with leg-mount tag replacing the patagial one? Frequently, captive bred birds perform better when re-released after sometime of refueling/rehabilitation following the original release. This fact may bias the data from switching between different type of tags. The best would have been if this result was obtained by marking wild experienced bird first tagged with patagial and afterwards switched to leg-mount tag.

On 2020-09-29 18:12:43, user Peng Yao wrote:

This BioRxiv has been published in Circulation Research as below.<br /> https://www.ahajournals.org...

On 2020-09-29 12:44:22, user William Moore wrote:

This looks like a very nice tool, having watched some of the tutorial videos at https://www.youtube.com/wat.... It provides nice flexible layouts but also supports fast alignment of panels. I'm curious if you were aware of the web-based OMERO.figure, which allows similar flexibility but with more limited alignment options?<br /> https://www.openmicroscopy....<br /> Regards,<br /> Will.

On 2020-09-28 01:25:22, user G wrote:

A version of the pre-print with a working download link is available<br /> https://www.biorxiv.org/con...

On 2020-09-25 17:17:09, user G wrote:

The link to youtube tutorials in the manuscript does not work. Tutorials and download link are on youtube.

https://www.youtube.com/wat...

On 2020-09-29 07:48:54, user Ramy Karam Aziz wrote:

1) This mutation has not taken place in an amino acid. Mutations take place in nucleic acids and cause *amino acid variations*<br /> 2) Why is this the same title with different author orders as https://www.researchsquare.... ? Is this normal?

On 2020-09-29 01:59:40, user Ravi kim wrote:

I hope the function of Rcn3 with a variety of strengthness will contribute to solve entire problem of knee.

On 2020-09-28 19:49:32, user Pramod pantha wrote:

Major findings;<br /> 1.Chitin metabolism and epithelial membrane-associated processes were suppressed in AcMNPV-infected hosts<br /> 2.Transcripts associated with hemocyte-induced defenses and immune responses were suppressed during systemic infection<br /> 3.Lipid metabolism and oxidative stress emerge as the most prominent functional processes induced in response to AcMNPV infection<br /> 4.Key host genes affected by the AcPNMV infection are targets of commercially available pesticides used against lepidopteran pests<br /> 5.Virals genes related to viral entry to cells, assembly, and egress were abundant in the hosts<br /> 6.Viral genes that influence host cell cycle and molting were found in abundance

On 2020-09-28 15:13:39, user Meng Cui wrote:

Thanks for the question. We chose quartz glass for its excellent optical quality. It can be made very thin (e.g. ~15 micron) so that it produces negligible aberration for the two-photon imaging. We have not tested Teflon tubing. If they were made to such wall thickness, it may be too soft for long-term longitudinal imaging.

On 2020-09-25 18:03:44, user Michael wrote:

Curious whether FEP / Teflon could be used in place of quartz as used in lightsheet microscopy or would the gas permeability be a problem or susceptibility to damage by IR laser?

On 2020-09-28 14:27:14, user Daniel Macqueen wrote:

Anibal,

I am not on Twitter, so will contact you through the comment box. I am not sure why you have posted this on bioRxiv in 2020, as it only serves to confuse the literature and could not be published in my opinion. The preprint makes several false claims, which is unsurprising when considering that the literature cited does not extend beyond 2007! A huge amount of work has happened in salmonid genomics since this time.

An example of one of the incorrect claims: “It is noteworthy that the present work estimates are the first for the 4R WGD in the Salmonine fishes’ ancestor since the pioneering work of Allendorf and Thorgaard (1984) and the first-ever based on nucleotide sequence data. Therefore, this is an update on an issue that awaited further refinement for almost 25 years (Gregory and Mable 2005).”

The 4R WGD has been studied extensively since Allendorf and Thorgaard (1984). See the following papers that have accurately dated the salmonid Ss4R and studied it at a genome wide level. This is far from a comprehensive list!

• Macqueen DJ, Johnston IA. A well-constrained estimate for the timing of the salmonid whole genome duplication reveals major decoupling from species diversification. Proc Biol Sci. 2014 Jan 22;281(1778):20132881. doi: 10.1098/rspb.2013.2881. PMID: 24452024; PMCID: PMC3906940.

• Berthelot C, Brunet F, Chalopin D, Juanchich A, Bernard M, Noël B, Bento P, Da Silva C, Labadie K, Alberti A, Aury JM, Louis A, Dehais P, Bardou P, Montfort J, Klopp C, Cabau C, Gaspin C, Thorgaard GH, Boussaha M, Quillet E, Guyomard R, Galiana D, Bobe J, Volff JN, Genêt C, Wincker P, Jaillon O, Roest Crollius H, Guiguen Y. The rainbow trout genome provides novel insights into evolution after whole-genome duplication in vertebrates. Nat Commun. 2014 Apr 22;5:3657. doi: 10.1038/ncomms4657. PMID: 24755649; PMCID: PMC4071752.

• Lien S, Koop BF, Sandve SR, Miller JR, Kent MP, Nome T, Hvidsten TR, Leong JS, Minkley DR, Zimin A, Grammes F, Grove H, Gjuvsland A, Walenz B, Hermansen RA, von Schalburg K, Rondeau EB, Di Genova A, Samy JK, Olav Vik J, Vigeland MD, Caler L, Grimholt U, Jentoft S, Våge DI, de Jong P, Moen T, Baranski M, Palti Y, Smith DR, Yorke JA, Nederbragt AJ, Tooming-Klunderud A, Jakobsen KS, Jiang X, Fan D, Hu Y, Liberles DA, Vidal R, Iturra P, Jones SJ, Jonassen I, Maass A, Omholt SW, Davidson WS. The Atlantic salmon genome provides insights into rediploidization. Nature. 2016 May 12;533(7602):200-5. doi: 10.1038/nature17164. Epub 2016 Apr 18. PMID: 27088604.

• Robertson FM, Gundappa MK, Grammes F, Hvidsten TR, Redmond AK, Lien S, Martin SAM, Holland PWH, Sandve SR, Macqueen DJ. Lineage-specific rediploidization is a mechanism to explain time-lags between genome duplication and evolutionary diversification. Genome Biol. 2017 Jun 14;18(1):111. doi: 10.1186/s13059-017-1241-z. PMID: 28615063; PMCID: PMC5470254.

Sorry I cannot be more constructive. The work is 12 years dated and should be taken as such.

All the best,

Professor Dan Macqueen<br /> Personal Chair of Integrative Fish Genomics<br /> University of Edinburgh<br /> Email: daniel.macqueen@roslin.ed.ac.uk

On 2020-09-28 09:25:57, user Maysa Vieira de Sousa wrote:

Interesting strategy to discriminate small RNA from all other species.

On 2020-09-27 04:32:46, user John Philip Vaughen wrote:

Excited to see this mechanism for non-autonomous GBA action and aggregate spread! Sharing a concern I've seen using multiple independent UAS-Gba1b transgenes (including those in attp2/attp40): GAL4-independent rescue of Gba-null fly phenotypes when UAS-Gba1b is present. Your FigS1 is reassuring, so I was wondering what temperature you do rescues at, and if you've noticed GAL4-independent rescue in other experiments? We think the fly brain is especially sensitive to GAL4-independent leak from UAS-GBA1b constructs, which makes cell-type specific rescue challenging (but is in line with human sensitivity to Enzyme Replacement Therapy)

On 2020-09-26 11:05:15, user Claudio Tennie wrote:

I congratulate Canteloup et al. on their new manuscript “Processing<br /> of novel food reveal payoff and rank-biased social learning in a wild primate”.<br /> Studying primate social learning (biases and mechanisms) continues to be a<br /> field that requires additional data, and this new contribution is very helpful.

My comment will refer to social learning mechanisms. To (hopefully) ease debate, I shall introduce a methaphor first. If we<br /> methaphorically compare social learning biases and –mechanisms to traffic system, we<br /> can compare social learning biases to description of roads, and we can compare social<br /> learning mechanisms to the vehicles that travel these roads (e.g. from<br /> horse-carts to Ferraris).

In their new article, Canteloup et al. claim to have found<br /> some social learning biases (in our metaphor: some new road designs) in monkeys, but<br /> they also claim to be able to pinpoint the social learning mechanisms at work - they claim to have found a certain type of vehicle on their roads.<br /> In particular, they claim to have found Ferraris, i.e. imitation (as Heyes 2018 has put it so well in her book: imitation is considered the “Ferrari” of social learning mechanisms).

My critique here does not relate to the authors' identification of<br /> the road designs, but to the identification of the Ferraris on these roads. That the<br /> auhors favour and promote Ferraris (i.e. imitation aka behavioural form copying) is clear from statements in this preprint such as: “acquire new skills … copying<br /> others’ behaviour”, or “monkeys copied … technique,” or “spread of novel food<br /> processing techniques”. It can also be gleaned by the very first sentence that states that “Cultural complexity is strongly shaped by the efficiency and accuracy [sic!] by which<br /> new knowledge is propagated”.

But there is a problem here, namely in that it cannot be<br /> inferred what type of cars run on roads by merely describing the shape of the roads. To<br /> determine social learning mechanisms (vehicle types), and in particular to detect Ferrais (

imitation), more is needed – what is needed is to have a look at the SL<br /> mechanisms (cars), to describe what kind of SL mechanisms are actually at work (i.e. to determine what kind of cars are actually on the roads).

When we look at the cars that drive the roads described in<br /> this paper – we can immediately see that the conclusion that Ferraris drive the<br /> road can not be ascertained – instead it can even be clearly rejected. Indeed, there is clear<br /> evidence for other types of SL mechanisms (other car types). In particular,<br /> there is evidence for non copying social learning mechanisms that merely trigger the individual reinnovation of latent solutions (Tennie et al. 2009 and later papers, e.g.<br /> Bandini et al. 2020).

Latent solutions can be identified by way of running baseline<br /> tests, to see what kind of behaviours develop without the need to see models (crucially, including without the need to imitate). See<br /> e.g. Bandini et al. 2020 Biol Lett for an update on the method and theory (and additionally<br /> between the differences of the transmissions of know-how, know-where, know-what<br /> etc). Here, the authors seem to have actually done this very baseline, at the start<br /> of their study (note: on this baseline there was little info, perhaps some<br /> could be added). The outcome was very clear. Three types of behavioural<br /> approaches came about spontaneously in technique-naïve monkey, i.e. entirely without<br /> the need to observe a model, and so without the need for imitation. Three peanut extraction behaviours/techniques were<br /> thus spontaneously and individually developed from scratch “crack with the hand<br /> (hereafter ‘CH’; Movie S1); crack with the mouth from the side of the peanut<br /> (hereafter ‘CMS’; Movie S2) and crack with the mouth from the top of the peanut<br /> (hereafter ‘CMT’; Movie S3).” That is, these techniques are techniques that<br /> monkeys can and do develop on their own, either as adaptations (unlikely) or as<br /> exaptations. Hereafter, and following this outcome, “road styles” (SL biases)<br /> can still be mapped, but what cannot be mapped anymore is that the type of car<br /> could even be a Ferrari (imitation). To map the latter requires instead a<br /> different type of experiment, which requiores the introduction of behavioural<br /> forms that do not come about in baselines (e.g. that arise over time, for<br /> example via cumulated effects of copying error; see e.g. Clay and Tennie 2018<br /> Child Dev).

And so, I suggest the authors keep their conclusions<br /> regarding social learning biases (the road part), but that they adjust their<br /> conclusions regarding social learning mechanisms (the type-of-vehicle part; in<br /> particular deleting the claims and hints at for imitation-like mechanisms). This adjustment<br /> should be easy to make (but would need to be done throughout the manuscript), and importantly, does not change the main take home message of the paper at all (it would not even change the title). But the change is required – the monkeys clearly did not need to copy technique – they merely needed to be socially cued to reinnovate the<br /> corresponding technique.

One small thing: at least when I myself tried to download the SM, it was empty.

On 2020-09-26 06:07:29, user Roeland KINDT wrote:

The article has now been published - https://bsapubs.onlinelibra... (or https://doi.org/10.1002/aps.... RcmdrPlugin.WorldFlora provides a graphical user interface, with guidelines available from https://www.researchgate.ne...

On 2020-09-25 20:56:57, user Bertold Kiss wrote:

Can you please explain how far this in vitro AFM experiment models the in vivo antiviral mechanism of granulocytes, with special regard to phagocytosis of antiviral agents? See: https://jlb.onlinelibrary.w...

On 2020-09-24 14:48:15, user Phil wrote:

If they had a camera to observe the test, where are the photos?

On 2020-09-23 06:03:19, user Péter Porosz wrote:

Pardon me, but what is the point? How does such a test contribute to better understanding? Is there anything to compare, i.e. have such mechanical tests been carried out on other types of virus?

On 2020-09-19 20:53:16, user Ruedi Matt wrote:

If we poke the virus with a spike it's not a surprise he will resist to burst or can be destroyed with heat. It can only be neutralisied by changing the molecular atomic structure.<br /> May we have to take a look into the ammonium molecule, based by nitrogen and hydrogen.

On 2020-09-25 14:59:07, user Michelle Momany wrote:

Very nice work! The increased propensity of fly septins to bundle in liquid vs yeast septins and interaction of fly w PIP2 and PS containing membranes vs just PIP2 for yeast are striking. The displacement of bundles along the membrane in your AFM studies (Fig5 suppl 4) reminded me of septin bundle dynamics we observed in Aspergillus nidulans. https://ec.asm.org/content/... Wondering how common bundles are in nonyeast organisms.

On 2020-09-25 11:29:28, user Chen Cao wrote:

This manuscript has been accepted for publication by Briefings in Bioinformatics

On 2020-09-24 14:52:39, user Quan Long wrote:

This paper has been accepted for publication by Briefings in Bioinformatics

On 2020-09-25 10:06:36, user Bart Lagerwaard wrote:

Is the data from figure 7 published somwhere? This data is not in the publication in cell metabolism https://doi.org/10.1016/j.c...

On 2020-09-24 17:54:01, user Michael wrote:

According to NYULH policy, when using data or tools generated in the core in publications, talks, or grant applications, please acknowledge the Microscopy Core at New York University Langone Health. <br /> Please amend the Acknowledgements accordingly.

On 2020-09-24 15:47:02, user ZhangLab_SLU wrote:

In our paper, we established several structural and evolutionary evidence on SARS-CoV-2 ORF8, including 1) it is novel version of immunoglobulin domain; 2) it is a fast evolving protein; 3) it shares a similar architecture as many other viral Ig proteins. Based on these sequence/structural similarity and experimental evidence on other viral Ig proteins, we made the prediction that one of the potential function of ORF8 is to disrupt immune response by interfering the MHC-I membrane presentation.

On 2020-09-24 00:30:47, user ZhangLab_SLU wrote:

We have proposed this idea on a manuscript deposited to bioRxiv on March 7, and a paper officially published in mBio (https://mbio.asm.org/conten.... We show that ORF8 is a novel Ig domain which is predicted to delay the immune response. We specifically propose that ORF8 will disrupt the MHC-I expression. This manuscript needs to cite the mBio paper! see bioRxiv link: https://www.biorxiv.org/con...

On 2020-09-24 14:46:08, user Carlos wrote:

I have red your interesting paper

I am a vascular surgeon, worried about periprosthesis infection. I would like to know more about this potential value in vivo. Are you using these devices in human beings?

Thanks in advance <br /> Carlos Paladino. Buenos Aires Argentina

On 2020-09-24 12:51:38, user Laurence Lafanechere wrote:

The results you present regarding the inhibition of 3CLpro are exciting.<br /> I was wondering if the cellular antiviral effect of Masatinib could also result from its<br /> ability to block cytoskeleton dynamics. Indeed, we have recently demonstrated<br /> that µM doses of Masatinib induce a stabilization of the microtubule network (Ramirez-Rios,<br /> Sacnicte et al. “A New Quantitative Cell-Based Assay Reveals Unexpected<br /> Microtubule Stabilizing Activity of Certain Kinase Inhibitors, Clinically<br /> Approved or in the Process of Approval.” Frontiers in pharmacology vol. 11<br /> 543. 30 Apr. 2020, doi:10.3389/fphar.2020.00543 )

On 2020-09-24 11:01:13, user Martin wrote:

I have commented on this work on Publons: https://publons.com/review/.... Perhaps you find it useful.

On 2020-09-23 13:57:40, user Manuel Espinosa wrote:

I would like you to know that the structure of the HUH-endonuclease RepB, encoded by plasmid pMV158, was solved a number of years ago: Boer et al, The EMBO Journal 28 (11), 2009

On 2020-09-14 15:20:53, user Manuel Espinosa wrote:

I would like to draw your attention that not all the HUH proteins use a Tyr residue to the covalent bond: the His-DNA covalent bond reported for the HUH-relaxase MobM from plasmid pMV158 is an example (Pluta et al, PNAS, 2017, PMID:

28739894).

On 2020-09-23 09:48:48, user Ed Rybicki wrote:

Great paper! But: <br /> 1. if there is energy production, it will surely only happen in virions inside cells, where the substrates are made<br /> 2. if it doesn't make ribosomes by itself, and makes particles without these to transfer its genome to new cells, it's still a virus B-)

On 2020-09-23 06:17:39, user Cassandra Leigh Williamson wrote:

How many samples were excluded whose reported sex did not match that inferred by PLINK? Why were they excluded?

On 2020-09-22 23:38:27, user Fraser Lab wrote:

Overall, this paper aims to demonstrate the ability to build high resolution Cyro-EM models from data collected in less than a day without recently reported new hardware upgrades, as well as establish two metrics for Cryo-EM model refinement. These metrics are a b-factor equivalent and a ‘goodness of fit’ of water molecules. As resolutions improve, the value of these metrics and procedures for water placement are becoming more urgent. <br /> This work expands on this group’s previous work on Cryo-EM model building evaluation, mainly the Q-score metric(Pintilie et al. 2020). However, we think that this paper could be stronger if the authors provided analysed their metrics to other high resolution structures (and perhaps even other ApoF maps/models) to improve the transferability of these metrics. Additionally, we would like to see more data provided to better understand the value of certain metrics shown including the change in the sigma value for Q-score and the chosen sigma value for the SWIM method. Finally, we believe this paper would be strengthened by comparing the proposed methods (updated Q-score and SWIM) with existing methods (original Q-score and phenix.dowser). We would like to see these comparisons both qualitatively with images and quantitatively with tables.

Clarifying comments:

1) Please explicitly state or name the two different models created from the two distinct maps (1.36 and 1.34 A resolution) - or is the same starting model real space refined into both? The wording throughout the paper makes it is unclear if there were two different models built. For example, in the introduction, you state ‘we achieved cryo-EM maps of apoferritin, reconstructed from the images collected from the commonly available 300-kV Titan Krios microscopes at 1.34A using K3 detector and at 1.36A using Falcon4 detector’. <br /> However, in the results you refer to both 2 different maps (the majority of the time) and 2 different models (once and a while). ‘per-residue Q-score plot for our two maps to range between 0.85 and 0.88’ and ‘The Molprobity and PDB reports of our two models are ranked very highly in all the assessment scores on the adherence of models to the chemical properties of proteins’.

2) Please draw strong semantic distinctions between atomic displacement parameters and Wilson B and sharpening B, specifically in the following sentences:<br /> a) ‘Both maps have comparable resolution based on the Fourier Shell correlation of 0.143 threshold6 and similar cumulative B-factor as estimated from reconstructions with varying numbers of particles.’<br /> b) ‘Traditionally, B-factor is used to assess the atom position uncertainty in crystallography and is a weighting factor to allow computing a model-based map identical to the experimental map.’<br /> c) ‘We thus introduce B’ factors derived from per-atom Qscores’<br /> d) ‘The B’ factor, which will be deposited to the PDB, serves the same purpose as the crystallographic B-factor in such a way that we can compute a model-based map that can match well with the experimental cryo-EM density map.’

Major comments:<br /> 1) Throughout the paper, we would like to see the authors use data from their structures as well as other high resolution cyro-EM apoferritin structures that have recently been published (https://doi.org/10.1101/202..., https://doi.org/10.1101/202.... While these structures are mentioned, this paper could be strengthened by comparing Q-score and water placements across this author groups collected apoferritin models and the two new high resolution apoferritin models. Specifically, commenting on the number and placements of water molecules as well as the Q-score of hydrogen atoms where modeled in the structure from Yip et al (https://doi.org/10.1101/202.... <br /> 2) We would also like the authors to analyze other high resolution Cyro-EM structures, such as β3 GABA-A and the recently resolved B1 GPCR complexes (https://doi.org/10.1101/202..., https://doi.org/10.1101/202... to better demonstrate the transferability of the metrics proposed. <br /> 3) Please clarify why and how a sigma value of 0.4 was chosen for Q-score. Please clarify what would happen if there are structures with resolutions better than 1.2A (see EMDB https://www.ebi.ac.uk/pdbe/.... One potential solution would be to provide a sliding scale of Q-scores so that Q-score could only be compared among structures with a similar resolution. However, we would still like to see justification of sigma values for each resolution ‘bucket’.<br /> 4) Please comment on if/how the Q-score can be used alongside metrics of model quality such as bond angles, length, ect.<br /> 5) We would like the authors to provide data on why certain scaling factor values were used (‘the calculation involves a single scaling factor; the optimum scaling factor is determined empirically by testing which value makes the resulting model-map matched the cryo-EM map better by FSC’). Additionally, instead of trying to match the scaling factor to the FSC, is it possible to compare it to an iso_bfactor for the overall map?<br /> 6) Are there any examples of modeled alternative conformers with better Q-scores than the ones shown in Figure 1f? Do the Q-scores of these residues improve with less map sharpening? <br /> 7) Please provide details on how the Q-score would change with varying sharpening factors. Specifically, as Q-score increases with ‘sharper peaks’, is there any correlation between a sharper map and an increased Q-score? In the context of the other two high resolution apoferritin papers, please compare the differences in map sharpening from improved hardware versus post processing updates. Please include comments on sharpening the B-factor using the Ewald Sphere in Nakane et al (https://doi.org/10.1101/202.... <br /> 8) Please comment on how you recommend evaluating or modifying Q-scores for alternative conformers. <br /> 9) Please provide data and/or rationale on why 2x sigma was chosen within the SWIM method.<br /> 10) It is unclear what the SWIM method would be replacing/improving. Will this be used during refinement or to adjust water molecule placement afterwards? Either way, please compare this method to the state of the art (Phenix.dowser and/or coot water picking). <br /> 11) In the results section, the difference between water placement from different ApoF structures are compared, but it is unclear how your method would improve these discrepancies. Please provide table of water and ion placement discrepancies between apoferritin structures (both those mentioned in the paper plus the two additional high resolution structures) using current methods and your updated method.

Provide the open source code (or link to said code) for the updated Q-score and SWIM analysis.

Minor comments:<br /> 12) There is an incomplete sentence in the last paragraph on the second page. ‘Since the resolution used in cryo-EM is neither a conventional optics criterion nor the detection of diffraction spots in a diffraction pattern of a crystal.’

Stephanie Wankowicz and James Fraser (UCSF)

On 2020-09-22 09:43:13, user Iain Wilson wrote:

A couple of comments:<br /> (i) In the abstract "Exostosin-1 (EXT1) glycosyltransferase, an enzyme involved in N-glycosylation" - rather EXT1 is involved in heparin sulphate biosynthesis - as actually stated in the introduction.<br /> (ii) In the results "EXT1, an ER-resident type II transmembrane glycosyltransferase" - this is probably a bit controversial as GAG biosynthesis is generally considered to be in the Golgi, see, e.g., doe: 10.1006/bbrc.2000.2219 "An immunocytochemical analysis showed that both EXT1 and EXT2 localized in Golgi apparatus".<br /> (iii) Also in the results "N-glycosylation in eukaryotes is co-translational" - actually not always - there are two flavours of OST, one rather acting "post-translationally".<br /> (iv) In the results "we comprehensively compared the glycome, proteome, and lipidome profiles of those ER membranes" Are the microsomes just ER or a mix of ER and Golgi? The data in Table S2 would suggest that it's a mix as also complex glycans are significantly present.<br /> Nevertheless, it is of interest that knockout/down of a key Golgi transferase may have affects in the ER and may interact with Notch.

On 2020-09-22 02:46:27, user yi wang wrote:

Dear Readers: please notice that the information for ethyl acetate production in this version of the manuscript is not accurate; please refer to our revised version of the manuscript (https://www.biorxiv.org/con... for details. Please let us know if you have questions. Best,

--Yi Wang, Corresponding Author.

On 2020-09-21 17:42:34, user Matthew Fisher wrote:

Peer review of this preprint now published: https://doi.org/10.5281/zen...

On 2020-09-21 17:20:33, user Jordan Berg wrote:

Metaboverse v0.3.0b is live!<br /> https://github.com/Metabove...

• Improved flexibility in metabolite, protein, and gene synonym mapping
• Other minor bugs fixed

See https://metaboverse.readthe... for a full description of updates.

On 2020-09-21 08:18:32, user Jouke- Jan Hottenga wrote:

See this: Am J Hum Genet. 2000 Jan; 66(1): 279–292. PMID: 10631157<br /> A General Test of Association for Quantitative Traits in Nuclear<br /> Families.

Interested in the comparisons between the TDT, classic linkage sib-pair analyses and these methods, because all are a different take on - but converge to - the same principle of explaining variation in human traits.

On 2020-09-21 06:29:09, user Vasily Zlatogursky wrote:

I wonder why the term Haptophyta is used, since the composition of what is called Haptophyta rather corresponds to "Haptista" (includes centrohelids), see e. g. Fig. 5

On 2020-09-20 21:02:32, user Bahauddeen Alrfaei wrote:

This paper has been published in peer reviewed Journal<br /> https://doi.org/10.1177/153...

On 2020-09-20 16:30:59, user David Ron wrote:

This paper brings up an interesting technical point: What was the basis of the disappointing results from the HTS campaign for suppressors of polymerisation? Was it due to absence of the right compounds in the library or due to a poor assay for the I˚ screen in the HTS? The answer to this could be apparent if GSK425 and GSK716 were part of the 1.7 M compound collection screened in the HTS, one might conclude that the I˚ assay in the HTS (suppression of polymerisation) was inferior to the assay used in the ELT-based screen (binding to Z momomers).

On 2020-09-19 22:36:55, user samhuff wrote:

Bad news is the microbes use the carbon for food and produce CO[2] or methane perhaps.

On 2020-09-19 05:56:34, user 胡汉三 wrote:

awesome work! I want to know where can I get the data in the paper

On 2020-09-19 03:03:44, user 윤유식 wrote:

I think this is very important finding about how tumor can proliferate while other tissues of the body is wasting. Tumor may induce the wasting of other tissues, but they can escape from wasting and grow continuously.

On 2020-09-18 14:01:06, user ladamo wrote:

Interesting study. Other studies have shown that in patients with SSC B there are alterations in circulating B cells. The authors found a difference in the prevalence of B cell subsets but do not provide much information about their findings on B cells gene expression. They state simply "In B cells, at the set cutoff, there were no genes differentially expressed.". This result is surprising (at least to me) and therefore I think it would be helpful for the reader if the authors could provide more information about their analysis of B cells (as this could help the reader to properly interpret this statement".

On 2020-09-18 02:09:42, user Maria Ingaramo wrote:

Summary: for now, we recommend using the S11 tag at the N-terminus of target proteins.

Details:<br /> We'd like to thank Dr. Abby Dernburg for pointing out that our S11 fragment, which ends in two glycines, might act as a C-terminal degron signal (doi.org/10.1016/j.cell.2018...:DdzbmEETvEUkkesPwEqFKBomMYw "doi.org/10.1016/j.cell.2018.04.028)"). We've successfully tagged proteins at both the N-terminus and the C-terminus, but we have not established that these yield similar expression levels. We take this concern very seriously, and we're checking this now. Results will be posted here and at andrewgyork.github.io/split_wrmscarlet. In the meantime, we recommend avoiding the potential issue by attaching the S11 fragment at the N-terminus. If C-terminus tagging is required, we suggest the alternative S11 sequence YTVVEQYEKSVARHCTGGMDELYK.

-Maria Ingaramo

On 2020-09-18 00:23:06, user Jun Wang wrote:

Thanks for the validation. We similarly found that Lopinavir and ritonavir had no inhibition against SARS-CoV-2 Mpro. Check the paper below. Although lopinavir has weak cellular antiviral activity, the mechanism of action is unknown. <br /> https://www.nature.com/arti...

On 2020-09-17 17:57:36, user Jaqueline wrote:

In the original preprint of this article, the author Serghei Mangul was erroneously listed with a second affiliation: Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, 90 090, USA, which has been removed from the author byline. The authors regret this error.

On 2020-09-17 15:38:21, user John O'Neill wrote:

Now published: https://rdcu.be/b7kLU

On 2020-09-17 14:25:23, user michalopoulosgk wrote:

Could this be done in the liver in vivo by injecting relevant plasmids?<br /> George K. Michalopoulos

On 2020-09-17 14:05:48, user Diana Duarte wrote:

Dear all, this is the link of the published version!!<br /> https://bmcplantbiol.biomed...

On 2020-09-17 11:34:26, user Bong Bóng Mưa wrote:

Hello, Im wondering what kind of SARS-CoV antigen was detected in the cells? Is that S or NP?

On 2020-09-17 02:19:03, user Özen Karaocak wrote:

Hi again. I see the paper is fully published and the dataset is available now, congrats on that. Is it possible to publish genotype files as well? Currently only BAM and FASTq files are available. Genotype files in PLINK format could be really helpful.

On 2020-09-17 01:57:25, user JP wrote:

"Importantly, however, it is currently impossible for us to distinguish Danish-like ancestry in the British Isles from that of the Angles and Saxons, who migrated in the 5th-to-6th centuries CE from Jutland and Northern Germany."

So how do you distinguish the "British component" from Celts and from partly Angle and Saxon descended English in Scandinavia or Iceland/Greenland and people descended from settlers of mixed ancestry? Y DNA I1 or U106 with some British ancestry could be of viking or thrall ancestry.

On 2020-09-17 00:15:08, user Malaria Unit wrote:

In this preprint, the authors show that a fragment of malaria parasite DNA derived from the liver of a Howler Monkey from the Atlantic Forest of Espírito Santo State does not contain a SNP in a short sequence of the mitochondrial genome that has previously been used to differentiate Plasmodium simium from other malaria parasites. This, combined with the amplification of a different nucleotide sequence thought to be specific for the closely related parasite P. vivax, leads the authors to conclude that this monkey was infected with a parasite that would be classified as “P. vivax” by the previous definitions and protocol described by Brasil et al (The Lancet Global Health, 2017) and de Alvarenga et al (Scientific Reports, 2018). This, in turn, leads the authors to assert that the de Alvarenga protocol cannot be used to differentiate between parasites that commonly infect monkeys, and those that infect humans.

We have two major concerns related to the methodology employed in this manuscript.

Firstly, the presence of malaria parasites in the liver of an animal cannot be considered unequivocal evidence that the animal can be infected at the blood stages and, thus, develop malaria caused by this parasite. The pre-erythrocytic stages of P. vivax, for example, can readily infect the livers of Duffy negative individuals within which it is unable to establish a blood infection. As shown in Mourier et al, 2020 (https://doi.org/10.1101/841..., the major genetic polymorphisms between P. vivax and P. simium are found in genes involved in the invasion of red blood cells. We would readily expect that in a region where P. vivax is endemic, liver stages of this species would be present in any population of primates exposed to the bites of infected mosquitoes, regardless of their susceptibility to blood stage infection and, therefore, to malaria.

Secondly, the protocol described in Alvarenga et al (2018) is not specific for discrimination between P. simium and P. vivax, but rather for the discrimination of P. simium from all other primate malaria parasites. The protocol is based on mitochondrial genome sequences, and primers were designed to amplify the region containing a SNP specific to P. simium, that can then be digested with the restriction enzyme HpyCH4III, which results in digestion of P. simium sequences only, but not of any other malaria parasite. Thus, there remains the possibility that the parasite identified by Buery et al, is P. malariae/P. brasilianum, another common non-human primate malaria parasite found in the Atlantic Forest (Yamasaki et al. 2011; Duarte et al. 2008; Deane 1964, 1967, 1972), and not P. vivax. In their discussion, Buery et al imply, erroneously, that the diagnosis of a ‘non-P. simium’ infection in ‘animal 4’ reported in Nunes et al. 2019 was made to avoid a diagnosis of “P. vivax”, however, as shown in the supplementary Table 1 of that paper, this animal had a P. brasilianum/P. malariae infection.

The authors do not describe their preliminary species-discriminating 18S-based PCR protocol in detail, except to mention it is based on that of Rubio et al (1999). The primers used for the amplification of P. vivax in that paper are identical at the last 10 base pairs of the 3’ ends to the sequence of P. malariae, thus raising the very strong possibility that these primers would also amplify the latter parasite. Moreover, the results of this identification were not shown in the manuscript.

Furthermore, the authors performed Sanger sequencing of the amplicon, to prove a P. vivax infection in the NHP liver sample. However, they sequenced only a small fragment of 28 bp containing the informative SNP in the cox1 gene, showing a T at position 3535 – which indicates that this is not P. simium infection, as defined by Brasil et al (2017). However, this small fragment is unable to differentiate between P. vivax and P. brasilianum, as these species are 100% identical at this locus fragment (as shown in Figure 5 of Alvarenga et al. 2018 - Alignment of partial mitochondrial sequences of Plasmodium).

We believe, therefore, that further, more robust evidence is required to show that the two SNPs suggested by Brasil et al. 2017 and Alvarenga et al. 2018 are not appropriate to monitor zoonotic malaria transmission and the dynamics of Plasmodium spp. circulation in the Atlantic Forest.

C Brito (Fiocruz, MG), D. Alvarenga (Fiocruz, MG), A de Pina-Costa (Fiocruz, RJ), MG Zalis (UFRJ), R Lourenço de Oliviera (Fiocruz, RJ), M Mutis (Fiocruz, RJ), C Peterka (SVS, MS), MF Ferreira-da-Cruz (Fiocruz, RJ), P Brasil (Fiocruz, RJ), CT Daniel-Ribeiro (Fiocruz, RJ) & R Culleton (Ehime University)

On 2020-09-16 19:48:45, user jcmcnch wrote:

I have a minor suggestion - Table 2 could include the years of the GO-SHIP transects analyzed here. The reason I suggest this is that we happen to be generating molecular data from the I09N transects from 2007, so it would help to distinguish your effort from ours when it is published in the near future!

On 2020-09-16 16:48:59, user Marishani Marin Carrasco wrote:

Really nice to be part of this work!! :).The sugarcane and sorghum kinomes provide new insights on the kinase superfamily expression and evolution of these two species with such complex genomes. :) amazing job !!!!! :)

On 2020-09-16 15:49:48, user YeastMan wrote:

Interesting paper. One thing that puzzles me is the use of the pYES vector for Y3H experiments. The authors show differences in interaction of SnRK1a1 and RAPTOR either in the presence or absence of FLZ8 driven by the GAL1 promoter and induction by galactose. However the yeast strain they use is delta GAL4 and delta GAL80. So how does galactose induction of FLZ8 work in the absence of these two proteins?

On 2020-09-16 15:30:36, user Raghu Parthasarathy wrote:

The observation of cool fungi is fascinating. However, I don't understand many aspects of the proposed "mushroom-based cooling device." Since the mechanism is evaporative cooling, how is putting mushrooms in a box any better than putting the equivalent amount of water in the box? Perhaps the argument is that the mushrooms have greater surface area, but this requires that the cooling be surface-limited rather than flow-limited, and this isn't discussed; moreover, if how is mushrooms-in-a-box better than water-soaked sponges in a box (or something else with a large surface area)? Clarification would be welcome!

On 2020-09-16 15:19:55, user Igor Schneider wrote:

This manuscript is now published in Proceedings of the Royal Society B.<br /> https://royalsocietypublish...

On 2020-09-16 13:50:59, user Dasiel Obregon wrote:

It is an interesting study. Please indicate which primers you used to sequence 16S amplicons. One suggestion, do not use OTU as a synonym of taxon (i.e. genus, family) in this manuscript. You are using dada2, therefore your analysis was done at the amplicon sequence variants (ASVs) level, not OTU. https://www.nature.com/arti...

On 2020-09-16 13:23:01, user FredG wrote:

China tested imported food and markets all around the country in June and found no virus. They focused on imported salmon and meat.

On 2020-09-11 23:02:05, user Brett Edgerton wrote:

Just wondering if authors might have accidentally overlooked acknowledgment of intellectual property input? - see https://macroedgo.com/2020/...

On 2020-09-16 10:14:37, user Hideo Watanabe wrote:

Although this is an important finding, is it specific to SARS-CoV-2?<br /> I wonder if the similar research has ever been done with seasonal flu viruses. If not, comparative studies will be more valuable. Thank you.

On 2020-09-09 22:46:14, user AJ wrote:

Two things: Given the lack of t cell infiltrates, is this due to a lack of MHC expression on the surrounding cells? This stain should have been done as a mechanistic enquiry, I know canonically neurons do not express it but there is cross presentation by surrounding cells. Perhaps it was and I read it wrong. Unknown; and wondering if the other viruses mentioned also have the leukopenic phenotype this does. The dying people aren't typical cases- there could be reasons they failed to have immune inflitrates, but the finding is significant and concerning. <br /> Also, adverse event in chadox- could it be that infected who did not seroconvert passed through the first sieve by testing neg for antibodies and were enrolled in the trial, received the vaccine, and it primed the cells again and cleared out remaining antigen in the cns? worth a thought. Figures a leaky efficiency would punish us in that way- need to absolutely rule out prior infection for such studies.

On 2020-09-09 21:30:45, user Craig Downer wrote:

Thanks for this informative evidence about the potentially serious consequences of Covid19.<br /> \

On 2020-09-16 09:32:40, user 多苍 wrote:

Hi.

Is the data available for us to try out?

On 2020-09-16 01:45:11, user Yuri Yépez wrote:

https://www.g3journal.org/c...

is this the same study in another journal?

On 2020-09-15 15:27:02, user Duggy wrote:

Can we get the exact link for the this dataset? <br /> I am unable to find the dataset in the provided link (http://gigadb.org/site/index):jUHaZ30ww4SxWrN7iNFdN5Rg7iQ "http://gigadb.org/site/index)")

On 2020-09-14 17:16:21, user Arlin Stoltzfus wrote:

The text says that "the original residue was randomly mutated to one of the 19 other amino acids." But the number of alternative amino acids accessible by mutating one nucleotide of a codon is typically 6 or 7, not 19. The other 12 or 13 require double or triple mutations. Furthermore, any heterogeneity in mutation rates, e.g., transition bias, necessarily increases the chance of parallelism. If effects like this are not taken into account, then the null distribution is mis-specified and greatly under-estimates the extent of parallelism.