On 2024-07-22 12:47:53, user Simon Crouzet wrote:

This article has been now published in CSBJ. Link to the publication: https://doi.org/10.1016/j.csbj.2024.06.029

On 2024-07-21 18:44:35, user Terri Mitchell wrote:

While the idea of AI condensing 500 million years of evolution into a few minutes sounds very grandiose, a protein mutated in isolation is not fast forward evolution--it's just a mutated protein. AI has provided the blue print for an artificial protein situated outside of evolved life. The marine organisms that actually evolved molecules to transduce blue wavelengths of light into longer wavelengths of other colors and reemit them for a reason having to do with natural selection have done it already. They evolved the fluorescent molecules. ESM3's value is solely commercial, and no doubt it will be a hop, skip and jump from making the sequence available to researchers to commercializing it as contrast dye. Its origins will soon be forgotten, but its effects on the environment, and therefore life, will undoubtedly be persistent and deleterious.

Wrapping the report in intellectual arguments about evolution doesn't scientifically validate the claim that an AI-generated mutant is "evolved". Even if the AI endgame was reached--replacing all life with a human-devised approximation, and it was somehow achieved by pseudo-evolution--ESM3 would still have no evolutionary value since it was synthesized in isolation. AI mutated a protein: it didn't evolve a protein. Making scientifically invalid claims doesn't advance the case for AI. Just the opposite.

On 2024-07-21 16:01:56, user Min Zhu wrote:

The full version of this manuscript is online in Science Advances. https://www.science.org/doi/10.1126/sciadv.adl6366

On 2024-07-21 00:09:37, user Meet Zandawala wrote:

Manuscript title: TRPγ regulates lipid metabolism through Dh44 neuroendocrine cells

Summary: This manuscript from Youngseok Lee lab examines the role of TRP gamma channel in regulating metabolic physiology. Specifically, it focuses on the regulation of lipid metabolism via DH44 neuroendocrine cells. It is a follow-up on the work from the same lab where they showcased the importance of TRP gamma in DH44 cells in regulating post-ingestive food selection (Dhakal et al 2022: https://doi.org/10.7554/eLife.56726 ). Overall, this work adds to the growing body of work on DH44 neuroendocrine cells which appear to be crucial internal metabolic sensors. We have a few major comments and suggestions on the preprint which could help clarify the mechanisms by which TRP gamma regulates lipid metabolism.

1. TRP gamma mutants exhibit higher TAG and protein levels compared to controls. Inhibition of DH44 neurons using Kir2.1 recaptiulates the phenotype of increased TAG however protein levels are unaffected. Since these manipulations are not restricted to the adult stage, it is not possible to rule out developmental defects. It would be beneficial to also include the fly weight for these manipulations to see if their size is altered by these manipulations. Also, is there any impact on developmental timing?
2. The experiments implicating the role of AMPK in DH44 neurons are quite interesting. However, the link between TRP gamma activation, AMPK and DH44 signaling is missing. How is DH44 release altered when TRP gamma is knocked down specifically in DH44 neurons?
3. The author rescue the increased TAG levels in TRP gamma mutants by driving UAS-TRP expression using DH44-GAL4. However, they also able to rescue the phenotype by expressing UAS-TRP in DH44-R2 expressing cells. As far as we are aware, DH44 and DH44-R2 represent two independent populations. This raises some questions. What is the identity of the DH44-R2 cells which normally express TRP? What is the importance of having TRP gamma in both the source (DH44 cells) and the target (DH44-R2 cells) to regulate lipid homeostasis? Wouldn’t modulation of DH44 release alone be sufficient to regulate lipid homeostasis?
4. DH44 is released as a hormone from both the PI neurons in the brain and endocrine cells in the VNC ( https://link.springer.com/article/10.1007/s00018-017-2682-y ). Neither this or the previous study on TRP gamma in DH44 neurons examined the presence or absence of TRP gamma in DH44 neurons the VNC. It is not clear if the DH44-GAL4 used in this study targets the DH44 neurons in the VNC.
5. General comment about structure: The manuscript could benefit if additional context was provided for some of the experiments. The experiments using metformin are interesting and a valuable addition. However, since the link between metformin and DH44 signaling was not explored, the rationale for conducting these experiments is not quite clear. Is the rescue of TAG levels with metformin in TRP gamma mutants DH44-dependent or is metformin directly acting on the fat body? Metformin treatment in DH44 > TRP RNAi flies can clarify this.
6. The manuscript would benefit from having a model which includes all the components in this inter-organ pathway (TRP gamma, DH44 neurons, gut etc).

Minor comment:<br /> 1. Stock numbers for fly strains have not been provided.

Signed by,<br /> Meet Zandawala <br /> Jayati Gera<br /> (Zandawala lab members)

On 2024-07-20 14:19:32, user Zach Hensel wrote:

The cluster described in Malaysia (C3) jumped out as having identical collection dates that are earlier than those typically associated with GISAID accessions this high (and consistent with those for Malaysia sequence circa November 2023) and also differ at multiple, overlapping positions despite being sampled on the same day.

Briefly, further investigation (using cov-spectrum and UShER phyloplace) identified four sequences from Mayasia with March 2022 collection dates containing both M:D3H and M:T30A:

EPI_ISL_18821484 (in C3)<br /> EPI_ISL_18821485 (in C3)<br /> EPI_ISL_18821546<br /> EPI_ISL_18821638

These all share G19677T, which is a mutation characterizing BA.2.40 in Malaysia which was common in March 2022 (760 out of 855 sequences worldwide with G19677T were found in Malaysia that month; 52% of sequences from from Sarawak, Malaysia collected in March 2022 have this mutation).

Further, the three sequences that are placed by phyloplace are found together with other sequences from Malaysia. This identifies another sequence with M:D3H and M:T30A sampled in Malaysia, EPI_ISL_18821317. Other mutations in these sequences are shared with various other lineages prevalent in Malaysia in March 2022.

One sequence with M:D3H and M:T30A, EPI_ISL_18821638, could not be placed by phyloplace, but NextClade calls it as BA.1.1 because it contains the BA.1-defining EPE insert in S.

The manuscript notes, regarding the period of Omicron emergence, "South Africa and Botswana, where genomic surveillance was more robust than in many other parts of Africa." BA.2.86 emergence in Southern Africa is well supported: <br /> https://www.nature.com/articles/s41467-023-43703-3

It is implausible that an intermediate between BA.2 and BA.2.86 would recombine with multiple lineages circulating in Malaysia and only be detected in Malaysia without leaving a trace in southern Africa given that this surveillance continued. Rather, I suspect that these observations are likely artifacts arising from processing samples collected in March 2022 together with samples collected in late 2023.

This can be tested by comparing mutations in these sequences to those observed in BA.2.86* strains common in Malaysia in late 2023. The most common of these is JN.1, which contains S:L455S as well as the S deletion shown for the C3 sequences here.

On 2024-07-19 19:06:04, user Maryam Foroozani wrote:

May I ask where I can find the supplementary figures?

On 2024-07-16 22:58:40, user Jim T wrote:

My congratulations to the authors on this impressive work! Your estimated 300kya date for the divergence of the ancestry of Khoe-San seems like a relatively good fit for the newer dates for the emergence of the Lupemban culture. Has this possible match been considered?

“Early Stone Age (ESA) archaeology is effectively absent from the rainforest zone, with the early Middle Stone Age (MSA) Lupemban industry representing the earliest sustained archaeological signature. Uranium-series dates of approximately 265 ka BP for the Lupemban at Twin Rivers (Zambia), although queried, suggest a precocious late Middle Pleistocene dispersal of early Homo sapiens into the equatorial rainforest belt.” - Taylor 2021

https://royalsocietypublishing.org/doi/full/10.1098/rstb.2020.0484

On 2024-07-16 09:31:08, user Sudin Bhattacharya wrote:

Very interesting work. However, analyzing distances in high-D space is problematic. Couldn't these findings be attributed to the curse of dimensionality, where far-away points all appear equidistant?

On 2024-07-15 17:30:04, user priyanka.bajaj3193@gmail.com wrote:

Reviewed by Priyanka Bajaj and Christian B. Macdonald (UCSF)

Summary:

Fusion oncoproteins occurring from genomic rearrangements are commonly observed in cancers and often drive oncogenesis. Although these fusions frequently involve kinases or transcription factors, they are a diverse group at both molecular and functional levels, and a unified description of their oncogenetic properties is lacking. Robust methods for predicting oncogenicity of unknown fusions would be immediately clinically useful, making this an important gap. At a more basic level, this points to a gap in our ability to describe a key biological phenomenon. Some recent work has tackled this problem by examining the physicochemical properties of fusion oncoproteins, notably [1], but this is essentially still an open question.

In this manuscript, the authors present a language model of fusion oncoproteins, FusOn-pLM, by fine-tuning ESM-2 with two recent databases of human fusion oncoproteins. They compare random masking vs. one using their previous fine-tuned ESM-2 model SaLT&PepPr and benchmark their results on a number of tasks, demonstrating reasonably increased specificity on specific tasks and improvement with non-random masking. The model training and benchmarking are sound and convincingly demonstrate the improvement.

Despite this, the lack of clarity about what unifies fusion oncogenes is a major challenge. Language models can be powerful ways to learn these sorts of definitions in a less biased way, and in that light this is an important step towards clarifying this basic gap. However, as written, the work uses a working definition of fusion oncogene that is based on physicochemical properties that may or may not be specific to oncogenes. Examining the benchmarking tasks the authors use makes this clearer: they are almost entirely predictions of condensate and IDR properties rather than oncogenetic ones. The one truly cancer-specific benchmark, differentiating carcinoma classes, is fairly narrow and no model performs particularly well here. As a result, we are unsure how strongly this model will perform in discrimination or generalization tasks.

Another general problem for the field is the lack of negative controls. Gene fusions are relatively common mutations, but bona fide oncogenic fusions are a small fraction of all fusions, making this a class imbalance problem. Even within tumors, the majority of fusions are thought to be passengers rather than driver mutations. Any predictor should be able to discriminate between these, but the lack of good data on non-oncogenetic fusions makes this challenging. This is evident in this work, where the model’s discrimination is not strongly tested.

In summary, we believe this is technically strong work which addresses a pressing need, and which also presents some general strategies for domain-specific language model fine-tuning, but which is unfortunately hamstrung by defects in the available data and conceptualization of the field that are outside of the authors’ control. As presented, it will be of interest to AI practitioners and oncofusion researchers, but the clinical utility is unclear.

Major points:

1) As discussed, we think the concept of an “oncofusion” is somewhat diffuse, as it describes an extremely heterogeneous set of proteins. This makes the prediction task particularly difficult. While the introduction discusses the barriers to prediction of fusion oncoproteins due to their intrinsically disordered regions and large size, we believe a bit more care with the effective definition they are using is warranted. Related to this is the choice of FOdb to train their model, which is essentially a database of condensate properties of oncofusions rather than oncogenetic ones. The implications of this choice also warrant a bit more discussion.

2) We wonder if there is a class imbalance problem. The databases used to fine-tune their model have a small fraction of possible fusion proteins, and don’t contain large amounts of negative training information. We are thus unsure if FusOn-pLM’s significant improvements over ESM-2 are specific to driver fusion oncogenes.

3) The method is not contextualized with respect to prior work in computational oncofusion prediction and characterization. Such methods are few ([2],[3],[4],[5],[6] among others) but important to understand FusOn-pLM’s performance.

4) Several experimental datasets for fusion oncogenes have been published, including [7], [5], and [8]. FusON-pLM’s performance on these would be a compelling way to show its utility, as well as a more specific oncogenetic task.

Minor points:

1) Figure 2D: Although FusON-pLM is doing a slightly better job at distinguishing carcinoma prediction into two classes (BRCA vs. STAD), the performance metrics are the worst across the board. What does this mean for the prediction problem overall? Does the fact that IDR and condensate properties are much better predicted mean that the model is actually not learning an oncogenetic task? This seems worthy of more discussion.

2) Figure 4A: The authors present a FusOn-pLM embedding visualization of fusion oncoproteins, along with the corresponding head and tail protein sequences. It would be beneficial to clarify whether the protein sequences used for the head and tail counterparts are full-length sequences or only up to the exon breakpoint that forms the chimeric fusion protein. This information can be included in the Materials and Methods section.

3) Figure 4A: The authors demonstrate that FusON-pLM is able to separate out fusions from their head and tail components. To demonstrate that it is learning more specific embeddings for fusion oncoproteins, a comparison of the embeddings with untuned ESM-2 would be appropriate.

4) Figure 4B: In the main text of results section the authors write “FusOn-pLM largely clusters sequences by key properties such as the fraction of polar, charged, and disordered residues as well as the propensity to form pi-pi and pi-cation interactions and prion-like domains, via the PLAC NLLR score.” From the data shown in Figure 4B, this conclusion seems fine for polar residues and NLLR scores, but not for disordered residues and pi-pi/pi-cation interaction propensity by eye. Without quantification of the clustering, we are not sure this statement is supported.

References:<br /> 1. Tripathi S, Shirnekhi HK, Gorman SD, Chandra B, Baggett DW, Park C-G, et al. Defining the condensate landscape of fusion oncoproteins. Nat Commun. 2023;14: 6008.<br /> 2. Shugay M, Ortiz de Mendíbil I, Vizmanos JL, Novo FJ. Oncofuse: a computational framework for the prediction of the oncogenic potential of gene fusions. Bioinformatics. 2013;29: 2539–2546.<br /> 3. Abate F, Zairis S, Ficarra E, Acquaviva A, Wiggins CH, Frattini V, et al. Pegasus: a comprehensive annotation and prediction tool for detection of driver gene fusions in cancer. BMC Syst Biol. 2014;8: 97.<br /> 4. Lovino M, Montemurro M, Barrese VS, Ficarra E. Identifying the oncogenic potential of gene fusions exploiting miRNAs. J Biomed Inform. 2022;129: 104057.<br /> 5. Li J, Lu H, Ng PK-S, Pantazi A, Ip CKM, Jeong KJ, et al. A functional genomic approach to actionable gene fusions for precision oncology. Sci Adv. 2022;8: eabm2382.<br /> 6. Liu J, Tokheim C, Lee JD, Gan W, North BJ, Liu XS, et al. Genetic fusions favor tumorigenesis through degron loss in oncogenes. Nat Commun. 2021;12: 6704.<br /> 7. Frenkel M, Hujoel MLA, Morris Z, Raman S. Discovering chromatin dysregulation induced by protein-coding perturbations at scale. bioRxiv. 2023. doi:10.1101/2023.09.20.555752<br /> 8. Kobayashi Y, Oxnard GR, Cohen EF, Mahadevan NR, Alessi JV, Hung YP, et al. Genomic and biological study of fusion genes as resistance mechanisms to EGFR inhibitors. Nat Commun. 2022;13: 5614.

On 2024-07-14 15:14:50, user Dd wrote:

Great work! <br /> We always wonder if the size of EVs is interfering with the binding capacity of the beads? Thus result in a lower detection maybe? Can you also share some images from the bead based flow cytometry? Thanks!

On 2024-07-13 07:51:37, user alexander_zlobin wrote:

Hi, I commented on the same issue before, but there is still one figure in the SI that retain the confusion between HID and HIE states of the catalytic His in serine triad proteases. This is figure S49, and it should be corrected.

On the unrelated topic, are you planning to provide your datasets later? I am particularly interested in all PDB entries you found and classified into GSA/TSA. As you of coarse are familiar, PDB searches are quite tiresome, and having this data already available would help tremendously.

Sincerely yours,<br /> Alexander Zlobin<br /> MeilerLab Leipzig, Germany

On 2024-07-12 23:46:41, user Alex wrote:

I hate myself for doing this, but apparently this is the only way to point this out: why doesn’t this benchmark include singleCellHaystack? Haystack was published in Nat Commun in 2020, has >75 citations now, is easy to install and run. An updated was published last year In Scientific Rep. Still, a part of this field that has apparently decided that it is completely fine to ignore this method.

On 2024-07-12 14:18:11, user Prof. T. K. Wood wrote:

1. DarT/DarG is better characterized as a type V TA system; this category is based on the fact that antitoxin DarG is an enzyme but does not alter the toxin (type VII). The first member of this group is GhoT/GhoS (please cite doi: 10.1038/NChemBio.1062).

2. Toxin/antitoxin systems were first shown to inhibit phage in 1996 (please cite doi: 10.1128/jb.178.7.2044-2050.1996).

On 2024-07-12 04:36:28, user Sam Buckberry wrote:

Our response to this preprint, titled ‘Transient Naive Treatment (TNT) iPS cells do not feature Sendai virus expression: Response to Sendai virus persistence questions the transient naive reprogramming method for iPSC generation’ is available here: https://www.biorxiv.org/content/10.1101/2024.07.10.602807v1

On 2024-07-11 13:25:32, user Pookey532 wrote:

A small correction in Table 1.<br /> CRISPR gRNA vector wrongly including PAM sequence, the consequence should say "gRNA plasmid becomes target of CRISPR cleavage" with the caveat that this would only be the case if the wrongly included PAM is followed by another PAM, which is not the case in many CRISPR plasmids such as the pX330 derived ones. This would obviously affect cleaving at the target if its PAM is not followed by a second PAM.

While some errors in the table are almost certainly errors in design (ex stop codons before a 2A sequence, mutations in ITRs, etc...) I'm curious why some of the other design "errors" are deemed errors. For example, using CMV in AAV vectors can be a perfectly acceptable choice depending on the use of the virus, especially if it isn't intended for long term expression. Likewise, use of "unstable" sequences in high copy plasmids can be a problem, however if those plasmids are maintained in bacteria that maintain plasmids at a low copy (Epi400, Stbl2, etc...), the replication origin of the plasmid becomes less relevant as the copy number becomes more dependent on the host strain. Similar to this, "Vectors containing toxic genes to E. coli host" is not necessarily a design error. Sometimes this simply the only option.

On 2024-07-10 18:49:28, user Björn Reumont wrote:

This preprint is published since Oct 2023 in BMC Biology, Open Access: Prevalent bee venom genes evolved before the aculeate stinger and eusociality, Link https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-023-01656-5

On 2024-07-10 03:40:12, user Zach Hensel wrote:

This article does not match my experience in Okinawa and the caricature of Okinawa here is not necessary to make the point.

Some of the claims are simply wrong (e.g. the description of civil marriage registration). Others are caricatures for rhetorical effect (e.g. "14 cans of SPAM" is not what the reference says). In general, the list of supposed ills in Okinawa today has no direct connection to the longevity of today's 100-or-so-year-olds.

I hope that the author can speak with people in Okinawa and perhaps reconsider this approach.

On 2024-07-08 22:32:03, user Tara wrote:

This manuscript is now published at the Journal of Infectious Diseases<br /> https://doi.org/10.1093/inf...

On 2024-07-08 16:13:36, user Agnieszka Lipinska wrote:

Just to clarify, the data provided for Fucus serratus does not correspond to 'released sperm and eggs'. We sequenced vegetative tissue as well as reproductive tissue (whole receptacles) containing sperm or eggs, but not isolated gametes. Please see https://doi.org/10.1111/nph... for reference.

On 2024-07-05 12:57:48, user Thomas BN Jensen wrote:

Please see https://zenodo.org/records/... for the additional datafiles (metagenome assemblies, metagenome bins).

On 2024-07-04 17:58:15, user Dhiman Pal wrote:

This preprint has been published recently.<br /> Please use following link to the final published version:<br /> Lin, Y., Pal, D.S., Banerjee, P. et al. Ras suppression potentiates rear actomyosin contractility-driven cell polarization and migration. Nat Cell Biol (2024). https://doi.org/10.1038/s41...

On 2024-07-04 16:16:55, user Michael F Miles wrote:

This article is now published in Neuropsychopharmacology. There is a change in the order of the first 2 authors and the first name of Jeremy Nguyen (Angel Nguyen) in the final published version.

Mignogna KM, Tatom Z, Macleod L, Sergi Z, Nguyen A, Michenkova M, Smith ML, Miles MF. Identification of novel genetic loci and candidate genes for progressive ethanol consumption in diversity outbred mice. Neuropsychopharmacology. 2024 Jun 29. doi: 10.1038/s41386-024-01902-6. Epub ahead of print. PMID: 38951586.

On 2024-07-04 13:19:42, user Gyawali, Rajan wrote:

Hi,

Could you please link this preprint to the published journal in Briefings in Bioinformatics titled "CryoSegNet: accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and attention-gated U-Net". The link to the published version is https://academic.oup.com/bi...

Thank you!

On 2024-07-03 16:05:42, user Jeffrey Duncan-Lowey wrote:

Congratulations on this interesting and important work establishing phage defense systems as a widespread and abundant source of gene cassettes of unknown function in functional mobile integrons.

Some work relevant to these findings -- a group has recently studied the type I CBASS system studied here (pic135AB) demonstrating that pic135B homologs, called Cap15 (interpro entries: PF18153/IPR041208), are cyclic di-nucleotide-activated beta-barrels that embed in and disrupt the bacterial membrane to cause cell death, validating the predicted role in membrane translocation (line 148). https://pubmed.ncbi.nlm.nih...

On 2024-07-03 15:10:50, user Peter Cattini wrote:

Our preprint manuscript on bioRxiv (doi: https://doi.org/10.1101/202... has now been published in final form in the Journal of Molecular Endocrinology under the title "Increased capacity to maintain glucose homeostasis in a transgenic mouse expressing human but not mouse growth hormone with developing high-fat diet-related insulin resistance, hepatic steatosis and adipose dysfunction". This paper can be found at: https://doi.org/10.1530/JME....

On 2024-07-03 13:55:44, user Jennifer Oyler-Yaniv wrote:

Hello, this paper has since been published in PNAS. Please see the accompanying link: https://www.pnas.org/doi/fu...

On 2024-07-02 23:49:22, user Brian wrote:

It has been reasonably well-established that if there is sufficient water, transpiration rate must not be restricted for the purpose of conserving water early season to gain benefits late-season. Even the current study shows "Early-season water use was positively correlated with above-ground biomass, challenging the assumption that early-season water conservation can be leveraged for late-season benefits". This study explores three treatments, all fully or partially irrigated. As authors' concluded that "We question the efficacy of LT traits, highlighting the physiological link between water use and carbon gain, and the potential opportunity costs of reduced early-season growth", I am unsure whether such treatments were the best choice. LT trait has been proved beneficial when soil moisture is scarce, and/or soil profile is deep enough to store sufficient water to be used late-season.

On 2024-07-02 15:48:40, user Donald R. Forsdyke wrote:

James Mallet's comments on an earlier version of this paper, noted the authors' claim that "This theory offers a level of parsimony and generality rarely seen in biology," and excused the absence of citation of his laboratory's study because it had come out "very recently, probably after you'd done most of this work."

However, the subjects of dosage compensation, Haldane's rule and speciation was covered together in the 1990s with some level of "parsimony and generality," which also included taking into account the immunological significance of collective gene functions (1-4). The growing evidence consistent with this viewpoint was more recently summarized in a textbook (5).

Perhaps, as part of their paper, the authors might more critically evaluate earlier work that so closely matches their own.

(1) Forsdyke, D. R. (1994) J. Theor. Biol. 167, 7-12 Relationship of X chromosome dosage compensation to intracellular self/not-self discrimination: a resolution of Muller's paradox?

(2) Forsdyke, D.R. (1995) J. Theoret. Biol. 172, 335-345. Fine-tuning of intracellular protein concentrations, a collective protein function involved in aneuploid lethality, sex determination and speciation?

(3) Forsdyke, D. R. (1996) J. Theoret. Biol. 178, 405-417. Different biological species "broadcast" their DNAs at different (G+C)% "wavelengths"

(4) Forsdyke, D. R. (2000) J. Theor. Biol. 204, 443-452. Haldane's rule: hybrid sterility affects the heterogametic sex first because sexual differentiation is on the path to species differentiation

(5) Forsdyke, D. R. (2016) Evolutionary Bioinformatics, 3rd edition. Springer, New York.

On 2024-07-02 11:48:52, user Hervé Acloque wrote:

The revised and peer-reviewed version of this paper is now available in the special issue of Genomics dedicated to FAANG:<br /> https://doi.org/10.1016/j.y...

On 2024-07-01 21:00:22, user Frank Koopmans wrote:

The manuscript has been published in Nature Communications and is available at https://doi.org/10.1038/s42...

On 2024-06-28 16:26:14, user Yat Ho Chan wrote:

Hi everyone,

We are excited to share that our article has been peer-reviewed and published in Nature Communications! You can find the article at the link below.

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

On 2024-06-28 13:20:02, user Jo Wolfe wrote:

Interesting preprint! Regarding the intro, indeed the oldest direct fossil evidence is Jurassic...but we recently found that the crown group of Brachyura are probably Triassic<br /> https://academic.oup.com/sy...

Also, in our 2021 Bioessays paper, we did suggest the pleon folding in metamorphosis may be due to Abd-A repression, so it's cool that you found support for that result

On 2024-06-27 16:17:06, user Ivana Jerkovic wrote:

The scripts from this study can be found at:<br /> https://github.com/MarcoDiS...

On 2024-06-25 00:34:52, user Jiazheng Miao wrote:

This manuscript has been published in Scientific Reports. Please access the latest published version.

Miao, J., Chen, T., Misir, M. et al. Deep learning for predicting 16S rRNA gene copy number. Sci Rep 14, 14282 (2024). https://doi.org/10.1038/s41...

On 2024-06-24 14:14:02, user Flo Débarre wrote:

Following up on my previous comment about the pangolin datasets featured in Figure 2:

As mentioned previously, according to INSDC medatadata, SRR11119760 and SRR11119761 were made public again on June 16, 2021. However, because the data were pushed to the cloud on June 18 only, which is the day the preprint was submitted to bioRxiv and shared via email with officials, it had been suggested that the data release could still be linked to the preprint submission. Careful inspection of the exact times of the different events on June 18 shows that this suggestion does not hold.

The preprint PDF was indeed generated at 17:52 EST (cf. pdf metadata), which corresponds to the time of the last Github commit on the preprint's associated repository. Communication of the results to NCBI/NIH officials took place at 19:00 EST ( source ). SRR11119760 was however public on the cloud at 14:00 EST ( source ), i.e. before the preprint's final version was compiled.

On 2024-06-21 10:40:11, user JamminOnTheOne wrote:

The materials and methods section regarding the production of mRNA is insufficient: "Subsequently, the mRNA and circular RNA (circRNA) were synthesized as described previously (10; 23)." The cited reference for mRNA production (Ref 10) gives two alternative types of protocols for mRNA production (post-transcriptional and co-transcriptional capping) and it is unclear which method was used. In the discussion section, it is also indicated that "GEMORNA-generated elements exhibit enhanced translation capacity with m1Ψ modification", however there is no mention in the text or in Ref 10 of the mRNAs being tested carrying this modification. It would be preferable to include the complete mRNA sequences and all of the reagents and procedures required to produce them.

On 2024-06-20 16:05:39, user Kishore Babu wrote:

1. I would also agree that the term “archetypical” in the title is wrong as the first structure of this class of proteins (PP2 family proteins) was published in 2023 (see Bobbili, KB et al. (2023) Structure, 31, 1-16) which reported the structure of Cus17 from the phloem exudate of Cucumis sativus. Therefore, the title should be modified by removing this word and reference should be made to the above publication and the structure of Cus17 in the Introduction as well as in the Discussion.

2. SEC- MALLS experiment (Supplementary Fig1a) appears strange: (a) While Nictaba is eluting much later than BSA monomer (Mr = 66,000) the authors claim Nictaba to be a tetramer in solution (Mr = 76,000; subunit mol.wt. = 19,000 Da), so that they can claim a difference in their protein from that of PP2 gene family of proteins all of which have been shown in at least dozen other studies to exist as dimers only. (b) Only two molecular weight markers have been used as the standards for calibrating the column. (c) Nictaba a PP2 gene family protein is expected to be impeded on the gel media of their column as in a number of studies in the past on PP2 gene family of proteins they have been shown to get retarded on on gel media ranging from Sephadex, Acrylamide, Superdex etc.(Read, SM and Northcote, DH (1983) Planta 158, 119-127; Anantharam, V. et al. (1986) J. Biol.Chem. 261,14621-27 and Bobbili, KB et al. (2023) Structure 31,1-16).

3. The location, geometry of the binding site, the stereochemistry of the bound chitotriose and its interaction in Nictaba are identical to that reported for Cus17- the founding member of the PP2 gene family fold (Ref. Structure (2023) vol 31 pp1-16). Moreover ,the key residues tethering chitotriose to Nictaba are Thr14, Trp15, Tyr21, Val39, Ala40 and Trp151 are identical and correspond with Thr18, Trp19, Tyr25, Val46, Ser47, Trp48 and Trp141. Given this remarkably striking level of identities of the binding residues and the groups in the sugar one fails to see any novelty in Nictaba-sugar interactions as compared to the fold founding member of the family, namely Cus17. In this context, the authors should discuss their results in comparison with the structure of Cus17.

4. Even the backbone Cα atoms of the subunit of Nictaba overlap within 1.06A of the Cα atoms of Cus17 indicating that Nictaba fold is not new and is a faithful copy of Cus17. This should be stated in the Results and Discussion sections of the manuscript as appropriate.

5. The InterPro site that curates protein folds has created a separate folder for PP2 gene family of proteins since the appearance of Cus17 structure recognising it as a novel fold. It is therefore not surprising that Nictaba fold is curated and subsequent to the fold of Cus17.

6. Authors do not report on study on the stoichiometry of binding by any method including ITC but they claim Nictaba has a single binding site per subunit for the sugar perhaps based on crystal structure which is not a conclusive evidence for their assumption as there are numerous examples of differences for the number of binding sites seen in crystal structure or modeling vis-a-vis what are found in solution. Extensive ITC studies on several PP2 type lectins have given a wealth of information on the binding constants and thermodynamic factors associated with the binding of chitooligosaccharides to them as well as on the binding stoichiometry (see Nareddy, PK et al. (2017) Int. J. Biol. Macromol. 95, 910-919; Bobbili, KB et al. (2018) Int. J. Biol. Macromol. 108, 1227-1236; Bobbili, KB et al. (2019) Int. J. Biol. Macromol. 137, 774-782).

7. Nearly 40% of the 60 references cited in this manuscript are citations to the publications of the corresponding author! On the other hand many important, relevant publications of other scientists (mentioned above) are not cited.

On 2024-06-07 04:15:26, user Swamy wrote:

This study reports the crystal structure of Nictaba, the Nicotiana tabacum lectin which is specific for chitooligosaccharides and also binds N-linked glycans. The tertiary structure of this protein is essentially the same as that of Cus17, the 17 kDa phloem exudate lectin from Cucumis sativus (Bobbili et al. (2023) Structure 31, 1-16). Hence the term “archetypal” used in the title is misleading and hence it should be removed.

Nictaba is homologous to PP2 family proteins from Cucurbitaceae and other species. Some background information about PP2 proteins is relevant here. Two major proteins, phloem proteins 1 and 2 (short form PP1 and PP2) in the sieve elements of plants were studied since early 1970s by several groups (Sabnis and Hart (1973) Planta, 142, 97-101; Allen (1979) Biochem. J. 183, 133-137; Read & Northcote (1983) Planta 158, 119-127; Read & Northcote (1983) Eur. J. Biochem. 134, 561-567). That PP2 is a lectin was well established in these studies and its activity was clearly shown to be inhibited by oligomers of GlcNAc and by N-linked glycopeptides by Allen (Biochem. J. (1979) 183, 133-137). The PP2 proteins have been found in a wide variety of plant species, both monocots and dicots. In 1986 Anantharam et al. (J. Biol. Chem. 261, 14621-14627) clearly established that the chitobiosyl core of N-linked glycopeptides is crucial for binding of the phloem exudate lectin from Luffa acutangula (ridge gourd) as treatment of the N-linked glycans with endo-β-N-acetylglucosaminidase H completely abrogated their binding. While some of these early workers did not consistently use the designation PP2 protein while describing these lectins, over the last 30 years the term ‘PP2 proteins’ is commonly used by scientists working on these proteins. In fact, Gary Thompson’s group, who have extensively investigated the PP2 family genes referred to these proteins as a Superfamily (Dinant et al. (2003) Plant Physiol. 131, 115-131) and also referred to the Nicotiana tabacum lectin as a PP2-like agglutinin. Now Els Van Damme’s group, which has been working on plant lectins for over 3 decades refers to other PP2 family lectins that were investigated from much earlier as Nictaba-related lectins. This is a gross violation of scientific propriety and Nictaba should be referred to as a member of the PP2 Superfamily of proteins.

Since the high-resolution crystal structure of Cus17 is already known, and the structure of Nictaba is highly similar to it, the same should be mentioned in the Abstract, Introduction and Discussion sections in an appropriate manner. In particular, in Discussion section (para 2) the sentence “A similar crystal structure was reported recently for the phloem lectin from Cucumis sativus (Cus17), a member of the Nictaba-related lectin family” should be modified to indicate that “the first crystal structure of the PP2 fold to which Nictaba belongs”. In fact, the narrative of referring to all PP2 family lectins as Nictaba-related lectins at various places in the manuscript should be changed and Nictaba should be referred to as PP2 family protein.

In the light of the fact that the structural fold of Nictaba is quite similar to that of Cus17, it would be appropriate to give a detailed comparison of the Nictaba structure with Cus17 structure. <br /> Since the glycan array investigations on Nictaba have been reported earlier by the same group (Lannoo et al. (2006) FEBS Lett. 580, 6329-6337; Schouppe et al. (2010) Glycoconj. J. 27, 613–623) the discussion on this aspect should be condensed. Importantly, the glycan array data is at best semi-quantitative and the authors should have used a method like ITC (Isothermal Titration Calorimetry) which gives reliable and quantitative information on not only the association (binding) constants, but also important additional information on the carbohydrate binding by the lectin, viz., stoichiometry, enthalpy and entropy associated with the binding.

Although extensive studies on the binding of chitooligosaccharides to phloem exudate lectins have been reported, especially employing ITC, the authors cited just one paper published in 2011 (Narahari et al. (2011) J. Phys. Chem. B. 115, 4110–4117). Since then several ITC studies on chitooligosaccharide binding to PP2 family lectins have been reported (reviewed by Swamy et al. (2022) Phytochemistry, Article No. 113251.) This information should be suitably cited. Also, the study of Bobbili et al. (2023) on Cus17 also reports extensive ITC studies on the binding of various chitooligosaccharides and the N-linked glycan, Man3GlcNAc2 to the lectin. It is important to cite this work while discussing the carbohydrate binding characteristics of Nictaba.

On 2024-06-19 16:20:06, user John wrote:

Where are the Supplementary Tables 1-7?

On 2024-06-19 07:46:07, user Guillermo del Angel wrote:

I was trying to see the actual list of variants referenced in Table S1 but there doesn't seem to be any link to view and download these?

On 2024-06-19 00:31:10, user Rajan K C wrote:

This article has been published. Please update the article.

K. C. R, Patel NR, Shenoy A, Scallan JP, Chiang MY, et al. (2024) Zmiz1 is a novel regulator of lymphatic endothelial cell gene expression and function. PLOS ONE 19(5): e0302926. https://doi.org/10.1371/jou...

Thank you!

On 2024-06-17 03:46:27, user mittimithai wrote:

I thought this sentence was odd:

"Excluding Physics, the highest presence of EP authors<br /> 300 after adjusting for the total number of authors in each country is seen in Arab countries<br /> 301 (Saudi Arabia, Iraq, United Arab Emirates, Pakistan) and in Malaysia and Philippines."

Pakistan is not an Arab country.

On 2024-06-16 07:52:20, user James wrote:

The tidyverse isn't exactly good and promotes truly awful coding practice. Why continue expanding it? It's also hard to understand what this adds besides bloat to these analyses.

On 2024-06-15 11:45:28, user Jiashun wrote:

Great works！Cogratulations, Dr Cheng. While I found there may be a small mistake. "Of the sixteen F1 double heterozygotes we derived from the cross between gpgp and the TILLING mutant heterozygotes, half had yellow pods, and all of these yellow podded F1s carried the ChlGW121* null allele (Fig. 3i-j)". I checked the figures and found 10 yellow pods rather than the half of sixteen (8). Please let me know if I misunderstood.

On 2024-06-15 08:59:37, user Marc RobinsonRechavi wrote:

In the manuscript you write:

"Supplementary information including the Python code used for the simulations is available at https://10.5281/zenodo.11562472"

but this link does not work and I did not find this data in Zenodo. Can you please provide the correct link?

On 2024-06-15 06:00:47, user Yongheng Wang wrote:

Pease check the demo here: https://youtu.be/3-RSjPakGH...

On 2024-06-11 13:08:39, user wonderfulponderfulponds wrote:

I feel the research design and some conclusion drawn is premature due to an overlook of the following aspects:

1. Lacking data on Fatty acids analysis of some representative wild capelin testis, semen or centrifuged spermatozoa: The regeneration of sperm and endurance of males largely relate to absence/ presence of enough "raw materials" (i.e., high quality lipids, phospholipids and long-chain polyunsaturated fatty acids to be precise). The original hypothesis tested would be dubious, irrespective of any male sub-cohorts or phenotypes, if the above basic requirement is missing.

2. Lacking data on a gastrosomatic index (gut/ visceral weight divided by body weight and expressed in %) or gut content analysis: if some capelins are not feeding in between their spawning, it is highly unlikely they would replenish depleted energy reserves, and bioenergetically channel 50% of such intake energy in food to invest in gamete/ milt production. As such the original experimental design does not take into account a careful bioenergetics point of view either. Some whole-body carcass analysis of some representative capelin would have been beneficial as an alternative.

I urge the authors to consider these in future work and good luck with the revisions.

On 2024-06-09 05:52:19, user Barend de Graaf wrote:

I found a level of confusion about the ‘SPH protein family’, and the working mechanism of the SI system in Papaver specifically, in this very interesting MS …..

In the introduction, authors mention:

“In poppy, when two members of the SPH protein family (PrsS1 and PrpS1) are cognate, they confer sporophytic self-incompatibility (Foote et al., 1994; Wheeler et al., 2009; de Graaf et al., 2012)”

This is not correct, poppy does not express ‘sporophytic SI’ but ‘gametophytic SI’.

Furthermore, authors also state ‘when two members of the SPH protein family (PrsS1 and PrpS1) are cognate’.

This is not correct either, PrpS proteins are not part of the SPH protein family, instead these are classified as the poppy SI membrane ‘receptor’ proteins that are essential for SI signalling in pollen, male component of SI system in poppy.

On 2024-06-08 16:21:26, user Reviewer 6 wrote:

I have read Gainey et al, the response by Coleen Murphy in the comments as well as the preprint here. I previously made a detailed assessment of these which are found in the comments of Gainey et al., bioRxiv 2024.

In my opinion the debate to whether the effect can be triggered under highly specific lab conditions is not particularly relevant. But I think the point is that if the effect is so sensitive to such artificial conditions (or one sRNA that is only expressed under even more specific conditions), then how physiologically important can it be in nature? CM's group points to their 2024 (Sengupta et al, plos genet) paper testing various bacteria C. elegans may be exposed to the wild. However, it seems that worms naively avoid OP50 (i.e. ‘prefer’ test bacteria) in essentially every comparison made by CM. This is contrary to reports by other labs (PMID: 38228683, PMID: 38228683) and potentially a more serious concern with the assay.

Furthermore, in the 2024 Sengupta paper, it seems that both pathogenic and non-pathogenic bacteria can trigger avoidance (or not), which is odd and makes me think the effect is rather random. If this is really something so specific or adaptive that prevents worms from infection or confers fitness - I believe CM refers to the effect as the worms "reading" bacterial sRNAs in their Nature papers (is there any evidence that the recognition sequence on maco-1 mRNA co-evolves with bacterial preference in wild nematode isolates?) - then wouldn't the worms have evolved at least some preference for avoiding pathogenic vs non pathogenic bacteria from their habitat?

To me it just seems that random bacterial sRNAs that may be complementary to some worm genes that regulate behavior (I mean, you would expect to see some matches among a panel of total bacterial small RNAs from many species worms are exposed to versus total worm mRNAs...) are being silenced through an RNAi like mechanism... It's not news that RNAi can be inherited, which was described nearly two decades ago and is a well understood process.

Perhaps Hunter et al should visit CM's lab to learn the technique, but given how meaningful the assay/readout is in my view (and how already overstudied/saturated sRNA based inheritance is), perhaps it is not the best investment of time and effort.

On 2024-06-07 16:53:51, user Reviewer 6 wrote:

I am a C. elegans researcher with some familiarity with the topics discussed. I do not personally know, nor have I interacted with any of the authors involved. I have read in detail both the preprint and the response in the comments. Below I provide some comments in the hope that they will hone arguments from both sides. For brevity, I refer to the authors of this preprint as “the authors” and Dr. Coleen Murphy as “CM”.

Summary:<br /> In my view, there are two issues here (1): the technical reproducibility of the choice assay; and (2) the physiological importance of CM’s results in a natural setting given the points raised by the authors. While CM makes some valid arguments on (1) – the authors should really have shown at least a few assays that attempted to follow the protocol exactly as stated by CM – the deviations here are in my view minor enough to raise significant questions about the choice assay and its interpretation. I believe the authors are justified in stating that (2) if the variables discussed here indeed significantly obscure detection of the phenotype, then the ecological significance of the inherited learned avoidance in a natural setting is in question. This is especially important given that, contrary to CM’s response, the authors do in fact see learned avoidance of PA14 as well as daf-7 expression at P0 and F1 in some experiments (indicating that the learning was induced) but not beyond in the F2 progeny of these same worms which displayed learned avoidance. Below is a detailed discussion of these points.

Specific comments:<br /> - CM states that the lack of naïve PA14 preference seen by the authors is a “serious cause for concern”. In CM’s 2024 paper (Fig 1, https://journals.plos.org/p... , worms are tested for bacterial food choice between OP50 (the lab food) versus bacterial species C. elegans may be exposed to in the wild. However, it seems that worms naively avoid OP50 (i.e. ‘prefer’ test bacteria) in essentially every comparison made by CM. This is contrary to reports by other labs (PMID: 38228683, PMID: 38228683) and in my view potentially a more serious concern with the assay. Contrary to CM’s assertion, while CM’s group and others see *mild* PA14 preference in naïve worms, other groups also do not observe such a preference in naïve worms or report more variable results (e.g., PMID: 21172617, PMID: 28877481, PMID: 31371455). Overall, the authors did replicate P0 and F1 learned avoidance in some runs and had a “learning index” consistent with prior reports in these experiments, so I do not see how the lack of purported naïve PA14 preference (which is quite minor and variable to begin with) is a significant concern here. <br /> - Looking at the authors’ raw data (table S2) for individual experiments, it seems the authors used <200 worms as advised by CM for most of their plates. The “up to 770 on a spot” was from a single plate, so I do not think this would change the conclusions of the authors. The authors compared worm density with choice index and found that there is no correlation within the ranges tested here.<br /> - “no azide or other paralytic used” (CM) – the authors claim to have tested this and state that addition of azide did not affect their results. They also claim that worms make a choice within 15 minutes and do not leave the respective lawn in the first hour of the assay. But none of this data is shown (it should be). <br /> - It seems that CM’s group counts worms in proximity to lawns “if they are within a few millimeters of the bacterial spot.” (STAR protocol). This may introduce systemic bias given the OP50 and PA14 lawns are clearly visibly distinct. Again, this raises questions to me regarding the reliability of this assay for interpreting minute effects and making broad generalizations.<br /> - Aspirating worms for counting would be unlikely to affect results.<br /> - The fact that conditions tested by the authors are varied between experiments is in my view a strength of this study given they did not observe F2 effects in any of their tests (you would normally change parameters rather than keep repeating the same protocol if you were unable to reproduce something, no?). However, testing variables/conditions such as temperature, light/dark etc. are informative only in a context where the authors have first fully followed through on the exact CM protocol with no deviations. So, I do think it is crucial to show a few attempts where the protocol is followed exactly as stated by CM.<br /> - The use of Triton X after bleaching may be a concern as CM points out. Though seemingly low (0.01%), this may hypothetically make bleached (i.e. already somewhat stressed) embryos or newly hatched L1s more vulnerable to pathogenic bacteria or alter their physiology. I do not see a point in including Triton X during or after bleaching, it is not standard nor required and is certainly a confounding variable. However, given the CMC of Triton X is 0.02% and the authors use below this concentration and only during plating, I would be surprised if this led to a dramatic change in the phenotype observed.<br /> - I do not find CM’s critique on daf-7 expression to be substantive. CM asserts that the authors do not see elevated daf-7p::gfp expression. Except they do! Which is especially evident with the single copy (SC) construct Fig 2 under SC at both 20 and 25oC. The magnitude of P0 daf-7 increase with the SC construct (~2 fold) is similar to what other groups observe at this generation (albeit with the multicopy strain, so it is hard to compare). I think the use of a single copy reporter is a strength of this paper, but in the future assays of daf-7 expression should really be done using endogenous CRISPR/Cas9 reporters. That an F2 response is not observed in runs where there is a high upregulation in the F1 generation is consistent with the authors’ interpretation.<br /> - The authors should show representative images of what is being quantified as CM states, as without this we do not know which neurons are being assayed. I do not think averaging both ASI neurons in a worm is a concern – even if there is an increase in one ASI, it would still be reflected in the average (as long as the correct neuron is being quantified). It may even reduce variability or bimodality to average the two, given the brightness of reporters on a confocal image can depend on the depth of the imaging plane as the authors state. <br /> - CM states that chunking is an unusual way to maintain the fluorescent strain. But this is a genomically INTEGRATED multi copy array (ksIs2), no? The point of the authors is that the fluorescence expression and associated Rol marker are unstable in their expression, which is not unusual for such integrated repetitive multicopy arrays. This is not an extrachromosomal array wherein fluorescent worms need to be picked to maintain the array, so CM’s statement that it is “standard accepted practice” to do so is simply wrong. In fact I find it quite concerning if CM’s group picks fluorescent worms to maintain this strain as it biases the worms for an epigenetic state in which the integrant is poised for expression, which may indicate other epigenetic issues in the strain’s background (i.e., lack of silencing of repetitive sequences). The instability of this strain I assume is why the authors obtained a single copy daf-7 reporter, which in any case would supersede any results obtained from a multicopy array. CM says nothing about the single copy integrant results, and I believe that given the authors observe P0 and F1 upregulation with the single copy integrant, I think the case is solid that there is no response observed in F2 worms from F1s showing daf-7 upregulation. An endogenous CRISPR/Cas9 reporter (e.g., transcriptional/SL2::GFP if a translational fusion is not possible) would really push home this point. <br /> - CM states that the authors replicates show poor “consistency”. However, we can only see this because the authors, unlike CM, show each experiment independently! We have no idea whether every experiment CM performed actually displayed learned avoidance behaviour, given the source data for CM’s choice assays is apparently not public. CM’s reports only show all learning experiments in aggregate, and I believe if the authors aggregated all their runs herein to a single plot, they would indeed see a seemingly ‘consistent’ avoidance effect. CM could easily address this by releasing raw/source data for choice/learning assays.<br /> - CM claims that in their hands behaviour from a set of training plates is ‘always’ consistent, but data are not shown. Both sides need to avoid making important claims without showing data.<br /> - CM states that the authors use of the same population to assay and then maintain for the next generation may confound the results. Again, the authors need to do the assay exactly as stated by CM, but if a few extra minutes of suspension in buffer really so obscures the phenotype beyond any detection, then how ecologically relevant can it possibly be? To my knowledge, there is no major phenotype that is completely ablated by a few minutes additional incubation in buffer. By this standard nothing involving washing off worms in a buffer would be interpretable.<br /> - It is interesting that sid-1 and 2 mutants do not show a learned F1 avoidance, but daf-7 expression is still elevated. It may be sufficient to have one SID protein for elevated daf-7 expression in progeny but require both for the behavior. Given both sid-1 and 2 are RNA transport channels, without double mutants and reliable daf-7 readout from an endogenous reporter, it is difficult for either group to infer any epistatic relationships between these genes. <br /> - I read the protocol file with notes from CM. I did not find any changes that are severe enough to cause concern and it seems that these are more clarifications/updates than changes to the fundamental principles of the assay. I also did not find the authors’ statements on this disingenuous, as there were clearly differences between the original STAR protocol and the updates provided. It is important for both parties here to refrain from personal attacks and address the substance of the arguments made.<br /> - I did find that some details in the STAR protocol were excessive, e.g., the height of plate stacks. I appreciate the detail but again, this raises the question that if such artificial variables really influence the phenotype so severely that it is no longer at all detectable, how physiologically relevant or robust can the phenotype be? <br /> - The statistical error in the STAR protocol pointed out by the authors: it seems either CM is misinterpreting a two-way ANOVA or that this was an oversight. I did not find this point too important overall as correcting such a statistical error would not change the conclusion of CM’s papers given the magnitude of effects previously described. <br /> - CM states that expression of P11 is essential for TEI. In CM’s 2020 paper (Kaletsky et al) it is stated that: “moreover, training on a P11 mutant that disrupts the perfect match to maco-1 but conserves P11 secondary structure induced no avoidance (Fig. 4e)”. As written, it seems essential not just for TEI (F2 effect) but also the P0 learning itself (unless CM can clarify that it is only required for the F2+ effect and that in Fig 4e only F2+ are being tested). So as I understand it, if lack of P11 expression is the issue, then there should be no P0 or F1 avoidance at all in any of these runs. Given the authors do not see an F2 effect in worms with robust P0 and F1 responses, it seems that this point is moot. I also do not think the authors can be blamed for any putative lack of P11 expression as it seems that for this portion (PA14 growth) they adhered to the protocol quite closely and explored various PA14 lines including those obtained from CM’s and other labs.

In summary, I think CM’s response is insufficient to alleviate many of the key concerns raised by the authors herein. I do not believe the lack of naïve PA14 attraction is a major concern, as there are literature examples where (a quite minor) naïve PA14 attraction is not observed. Furthermore, this is also confounded by CM’s recent (2024) paper wherein their worms prefer essentially every bacterium among a panel over OP50 in a naïve test, again contrary to prior reports from other labs. This makes me question the robustness as well as any broad conclusions that can be drawn from this assay.

The authors do also observe P0/F1 learned avoidance and elevated daf-7 expression contrary to CM’s rebuttal. I agree that the effects shown are not consistent between experiments here, but we cannot say whether this is simply because we are seeing here individual runs of inherently inconsistent assays whereas looking at an aggregate of data in CM’s papers (since the source data for the choice assays are not public). The major concern is that in those populations with P0/F1 responses (meaning the learning has been successfully induced), there is no further inheritance of avoidance beyond F1, and similarly for daf-7 wherein populations expressing high daf-7 at P0 and F1 do not transmit this to progeny. I believe this precludes “basic concerns about [the authors’] bacterial and C. elegans growth conditions, assay conditions, and assay techniques”. Overall, while it is important for the authors to show a few runs where the protocol is followed exactly as described by CM, I believe the deviations here are minor enough that even if they were able to replicate the transgenerational effect successfully, the sensitivity of the effect to such minutia would greatly diminish its physiological relevance to the worms - and its importance as an adaptive paradigm of transgenerational epigenetic inheritance - in a natural setting.

I also do not find it constructive for any party involved to address anything other than the scientific substance of arguments or engage in personal attacks. Given the attention and broad reach these studies have garnered, as well as the important implications, it is essential – and the normal course of the scientific endeavor – for such claims to be rigorously tested.

I also very much appreciate that the authors have shared these observations, and find it very commendable that CM has responded in a timely and comprehensive manner (as well as been responsive to the authors in refining their protocol).

On 2024-06-06 14:21:54, user Coleen Murphy wrote:

Gainey et al. failed to use experimental and assay conditions specified in the Murphy lab's protocols and added extraneous, damaging steps; together, these resulted in the Hunter lab's failure not only to test TEI, but also their failure to replicate previous, well-established preference of C. elegans for PA14, learned avoidance of PA14 in the P0 generation, increased daf-7 expression in the ASI and ASJ, and intergenerational avoidance of PA14. To summarize, Hunter and colleagues have not in fact attempted to faithfully replicate our protocols in a manner that would have tested transgenerational epigenetic inheritance of learned pathogen avoidance, and therefore cannot make any claims about reproducibility.

On 2024-06-05 18:16:30, user Coleen Murphy wrote:

Point-by-point critique of Gainey et al. 2024:

Figure 1: <br /> 1. (A-C) It has been reported by many groups that PA14 is mildly attractive to C. elegans, that is, given a choice between PA14 and OP50, worms choose PA141,2. However, in almost every assay shown in this paper, the worms prefer OP50 over PA14 – that is, they are already avoiding PA14 - prior to training (naïve preference), which is odd. This suggests that the authors are not using conditions that are standard, either in PA14 or OP50 growth or in choice assays (see note about choice assay performance). This is a serious cause for concern that is independent of any training conditions. In fact, as far as we can see, in only one case (Fig. 1C, F1) did their experiments replicate the naïve choice results observed by other groups. <br /> 2. Choice assays: their “choice assays” involve putting 3-4x the recommended number of worms on a plate (up to 770 on a spot!), letting them roam for variable amounts of time (“30-60 minutes”) without trapping them (no azide or other paralytic used), and then putting them in a 4°C incubator (which does not immediately halt worm movement), then counting them. None of this follows our published choice assay protocols, or the standard chemotaxis assay protocol3–6. Putting more than 200 worms on a single plate can lead to altered choice because of crowding. In the absence of a paralytic, worms change their preference due to various factors, including adaptation; therefore, in this case, the worms’ first choice (which is what we measure in all our assays) is not being measured. They also count the worms by “aspirating” the worms off of the plate, which is not standard in any behavioral assays, as far as we know.<br /> 3. Table 2 and Figure 1: There are almost no true replicates, as in each experiment, at least one or more condition is changed. (For example, the authors only tested the PA14 we sent them in one replicate - Exp 3). <br /> 4. daf-7p::GFP imaging experiments (Fig. 1D, F, H) – Hunter and colleagues do not report seeing increased daf-7p::gfp expression in the P0 generation. Increased daf-7p::gfp expression after exposure to PA14 has been reported by multiple groups7, not just ours, and is usually not small or highly variable, as it is due to the combination of bacterial cues and P11 small RNA; if they cannot replicate this basic result, it suggests that something is seriously wrong with their protocols or technique, or their worms are very sick, even before trying to use our protocol to train worms. <br /> 5. Additionally, they do not report the expression of daf-7p::gfp in the ASJ neuron7, which is very strange, since we have been able to reliably replicate Meisel, et al.’s finding in the P0 generation. Therefore, it is not clear from which neuron the authors are quantifying daf-7p::gfp levels. <br /> 6. Instead of imaging and reporting fluorescence levels in individual neurons, the authors averaged fluorescence intensity/worm, which is explicitly not what we did or others have done, because different neurons in each worm can have different intensities – particularly if they are the ASI rather than ASJ neurons. <br /> 7. While we see modest decreases in fertility after PA14 training, the authors report severe decreases in fertility: about one fifth of normal egg production, and a severe developmental delay) in their F1 generation that we do not observe. Both facts indicate that their worms are very sick, even the worms that have not been exposed to PA14. If their worms are extremely sick, it might account for the small number of progeny, poor imaging results, and a developmental delay that shifted the training times. This could be a result of overbleaching, which causes developmental delays; the bleaching protocol described in Gainey et al. deviates from our published protocol. Additionally, they add Triton X100 to their final M9 wash, which is used (although at a higher concentration) to permeabilize embryos in other protocols. We are not aware of any bleaching protocols that include Triton in a wash step, and our lab certainly does not; this addition might also damage the progeny.

Figure 2 <br /> 1. P0 imaging data suggest that the daf-7p::gfp response to PA14 is not reproducible in their hands; again, this has nothing to do with our paper or protocols, but rather appears that they cannot replicate previous results in the field that precedes our work. <br /> 2. Does “25°C” mean that the worms were grown at or assayed at 25°C, or both? This high temperature is generally hard on the worms. <br /> 3. Technical note: it appears that instead of consistently picking fluorescent daf-7p::gfp animals, the authors “chunked” large groups of worms, resulting in populations of non-fluorescent animals in their experiments. <br /> 4. Scale of P0 and F1 are extremely different (due to sickness of the P0s?).

Figure 3 <br /> 1. Notes that panels A, C, and D are repeated from Figure 1.<br /> 2. The authors discuss “OP50 aversion” but this does not make sense, since both trained and untrained animals are placed on HGs after bleaching. <br /> 3. Their naïve in F1 is sometimes even lower than in the P0 (Fig. 3D).<br /> 4. There is no consistency in their results across replicates, within experiments, or across figures of the paper – not just the inability to see an F2 effect, but in their naïve chemotaxes, P0 trained choice indices, and F1 results; the authors claim that their F1 assays are reproducible, but only 3 out of the 9 assays in this figure show F1 learned avoidance. <br /> 5. In 3J, data that are not replicates, as they have been performed using different conditions, have been pooled. <br /> 6. Gainey et al. observe substantial variation in behavior between training plates (Figure 3, table 2, S2 annotated protocol), and incorrectly treat each training plate as a biological replicate, rather than a technical replicate. (Each training plate is seeded and grown in the same conditions, and worms from the same bleached population are added onto the plates, therefore these are not biological replicates but rather technical replicates; biological replicates require starting with different worm populations and carrying out the whole experiment independently.) In our hands, behavior from a set of training plates is always consistent. <br /> 7. Additionally, we note that the authors use the same population of worms for the choice assays and subsequently for bleaching, meaning that worms are held in liquid for an extended time before bleaching; this may cause worms additional stress which may interfere with behavior.

Figure 4 <br /> 1. OP50 growth conditions: this would only matter if the controls and experimentals were grown on different plate types, which is not the case (but if the authors are in fact putting the controls on different plates from experimentals, then the experiment is done incorrectly).

Figure 5 <br /> 1. We also found that sid-1 and sid-2 are required, but since their controls are inconsistent (Fig. 3) in the first place, it is hard to know how to interpret their data. <br /> 2. Other mutants (rde-1, hrde-1, sid-1, sid-2) – still show increased daf-7p::gfp in F1 – again, these data are hard to interpret since they do not show a wild-type control that worked here. This also has little bearing on our work since other training paradigms (e.g., 4- and 8-hour training that engages small RNA-independent pathways) also induce daf-7p::gfp. It is also unclear which neuron (ASI vs ASJ) they are imaging.

Discussion <br /> 1. daf-7p::gfp - Picking fluorescent worms or rollers is standard worm husbandry; it is not a “result” to say that they noticed that Rol can be lost – but it does indicate that they should have discarded any results that they obtained before noticing that the array might have been lost in the worms they assayed. The fact that they have brought this up more than once suggests that they are not using standard accepted practices to maintain transgenic lines. <br /> 2. Dennis Kim’s work on phenazine-induced avoidance has been oddly neglected in this work7. Kim’s group found that phenazine-1-carboxamide induces Pdaf-7::gfp expression in the ASJ neuron, which we see quite reliably in our assays as well. No Pdaf-7::gfp imaging of the ASJ neuron is presented in this work, suggesting that either the PA14 they grew also did not make phenazines, or their image analysis is unreliable. <br /> 3. They made a lot of changes to our protocol (temperatures, light/dark, etc). We cannot find in this paper a single example of an experiment that followed our protocol entirely. <br /> 4. The authors make a point of calling OP50 a pathogen, which is odd; C. elegans grown on OP50 typically live for 2-3 weeks. They cite Garigan et al. 20028, which showed that when worms get old (past 15 days) eventually the pharynx stops grinding up bacteria and the gut will start to fill up with OP50, and killing bacteria does slightly extend lifespan - but this is not an effect observed in young (Day 1) animals on the short timescales used in the experiments here. In any case, since both control and trained animals are grown on HG plates with OP50, it cannot explain the behavior of the control animals. <br /> 5. The authors also never replicate the “bias towards Pseudomonas in choice assays ((Ha et al., 2010; Lee et al., 2017; Moore et al., 2019)” – Those papers also used OP50 vs PA14 to demonstrate this bias towards Pseudomonas, so it is unclear how the author think that their failure to replicate this basic finding is somehow supportive of any of their arguments. It is more likely that there is something fundamentally wrong in their initial conditions that have prevented the replication of all other groups’ findings, not just ours. Moreover, in our experiments, other than the 24 hrs of training on PA14 vs OP50, our control and trained animals are always on the same plates. This argument makes no sense, unless the authors have introduced an additional variable of plating control worms on one kind of plate/bacteria and their trained animals on a different plate/bacteria (which we do not do). <br /> 6. It is unclear why the authors grew worms at different temperatures. 20°C is the standard temperature for worm growth and assays. <br /> 7. In our hands, naïve OP50-PA14 choice index is not significantly different between P0 (when NGM plates are used) and the subsequent generations (when HG plates are used). The survival assay correlates well with the idea that their worms are very sick, much sicker than we see in our assays, although the sparse intervals in both assays make it difficult to draw any conclusions – not possible to draw the conclusion that the bacteria are “more lethal” since they are trying to compare two lifespans from different labs etc. - but if they are, it might be due to their PA14 cultivation conditions or the health of their worms. But the fact that they see massive leaving and desiccation of worms, they might indeed be growing PA14 under much more pathogenic conditions. <br /> 8. The authors state: “Near the conclusion of these experiments, we received an updated protocol that included several clarifying edits and additional deviations from the published protocols (C. Murphy, Personal communication).”

We clarified our protocols, we didn’t “deviate” from them. This is a concerning way to present our email communications in which we tried to correct errors in their protocol and offer constructive advice; we even extended an invitation to Hunter to visit our lab to learn the assay. We are happy to provide these emails if necessary.

In order to help others, we continuously update our lab’s protocols to make clarifications that will help future users. Any note from the Murphy lab is an example of this type of updating. For example, later we made a new bacterial construct that used a Kan marker and constitutive promoter instead of an Ara inducible promoter and Carb marker to streamline experiments. This is not a deviation, it is a natural progression of the research in our lab and our practice of continuously improving our assays and updating protocols.

It is disingenuous for the authors to present our updates to our protocols as if we have “deviated” from them – in every instance, we gave the authors all of the information that we had available to us at the time. Our suggestions were made genuinely and in good faith, with the assumption that the authors wanted to get the assay working rather than using it to point out changes in our protocol.

Moreover, this statement corroborates our assertion that all or most of the data in this paper seem to have been generated using a protocol that differs significantly from our lab’s, as the bulk of their experiments appear to have been done before contacting us: “Incorporating these changes into our procedures did not reliably alter our results.” (no data shown)

1. “[T]his example of TEI is insufficiently robust for experimental investigation of the mechanisms of multigenerational inheritance” – The authors failed to test the fundamental requirement for transgenerational inheritance, that is, the expression of P11 sRNA by PA14, which only happens on plates at 25°C. Since they cite our subsequent papers where we first identified P11 sRNA as the key to TEI9, then our finding that the Cer1 retrotransposon is also required for P11-mediated TEI10 and then our finding that other Pseudomonas species use a similar small RNA to induce TEI11, they are definitely aware of this fact. Thus, it is not clear to us why they have not attempted to test P11 sRNA levels while searching for conditions that would replicate our findings. As a result, we can never know whether P11 sRNA was produced in any of the conditions that the authors tested in the experiments shown.

Together, Hunter and colleagues’ failure to replicate the basic naïve attraction to PA14 over OP50 demonstrated by other labs, their failure to replicate the P0 daf-7 expression published by other labs, and their failure to reliably replicate the P0 and F1 behaviors shown by other labs suggests to us that there are more basic concerns about their bacterial and C. elegans growth conditions, assay conditions, and assay techniques independent of any of the attempts to replicate the findings from our work.

References <br /> 1. Zhang, Y., Lu, H., and Bargmann, C.I. (2005). Pathogenic bacteria induce aversive olfactory learning in Caenorhabditis elegans. Nature 438, 179–184. https://doi.org/10.1038/nat....<br /> 2. Ha, H., Hendricks, M., Shen, Y., Gabel, C.V., Fang-Yen, C., Qin, Y., Colón-Ramos, D., Shen, K., Samuel, A.D.T., and Zhang, Y. (2010). Functional Organization of a Neural Network for Aversive Olfactory Learning in Caenorhabditis elegans. Neuron 68, 1173–1186. https://doi.org/10.1016/j.n....<br /> 3. Moore, R.S., Kaletsky, R., and Murphy, C.T. (2019). Piwi/PRG-1 Argonaute and TGF-β Mediate Transgenerational Learned Pathogenic Avoidance. Cell 177, 1827-1841.e12. https://doi.org/10.1016/j.c....<br /> 4. Moore, R.S., Kaletsky, R., and Murphy, C.T. (2021). Protocol for transgenerational learned pathogen avoidance behavior assays in Caenorhabditis elegans. STAR Protoc. 2, 100384. https://doi.org/10.1016/j.x....<br /> 5. Kauffman, A.L., Ashraf, J.M., Corces-Zimmerman, M.R., Landis, J.N., and Murphy, C.T. (2010). Insulin Signaling and Dietary Restriction Differentially Influence the Decline of Learning and Memory with Age. PLoS Biol. 8, e1000372. https://doi.org/10.1371/jou....<br /> 6. Kauffman, A., Parsons, L., Stein, G., Wills, A., Kaletsky, R., and Murphy, C. (2011). C. elegans Positive Butanone Learning, Short-term, and Long-term Associative Memory Assays. J. Vis. Exp., 2490. https://doi.org/10.3791/2490.<br /> 7. Meisel, J.D., Panda, O., Mahanti, P., Schroeder, F.C., and Kim, D.H. (2014). Chemosensation of Bacterial Secondary Metabolites Modulates Neuroendocrine Signaling and Behavior of C. elegans. Cell 159, 267–280. https://doi.org/10.1016/j.c....<br /> 8. Garigan, D., Hsu, A.-L., Fraser, A.G., Kamath, R.S., Ahringer, J., and Kenyon, C. (2002). Genetic analysis of tissue aging in Caenorhabditis elegans: a role for heat-shock factor and bacterial proliferation. Genetics 161, 1101–1112. https://doi.org/10.1093/gen....<br /> 9. Kaletsky, R., Moore, R.S., Vrla, G.D., Parsons, L.R., Gitai, Z., and Murphy, C.T. (2020). C. elegans interprets bacterial non-coding RNAs to learn pathogenic avoidance. Nature 586, 445–451. https://doi.org/10.1038/s41....<br /> 10. Moore, R.S., Kaletsky, R., Lesnik, C., Cota, V., Blackman, E., Parsons, L.R., Gitai, Z., and Murphy, C.T. (2021). The role of the Cer1 transposon in horizontal transfer of transgenerational memory. Cell 184, 4697-4712.e18. https://doi.org/10.1016/j.c....<br /> 11. Sengupta, T., St. Ange, J., Kaletsky, R., Moore, R.S., Seto, R.J., Marogi, J., Myhrvold, C., Gitai, Z., and Murphy, C.T. (2024). A natural bacterial pathogen of C. elegans uses a small RNA to induce transgenerational inheritance of learned avoidance. PLOS Genet. 20, e1011178. https://doi.org/10.1371/jou....

On 2024-06-07 13:43:51, user marodon wrote:

Great job in line with works from other teams. May I suggest that the authors include two publications related to the subject in their discussion? <br /> Midavaine É, Moraes BC, Benitez J, Rodriguez SR, Braz JM, Kochhar NP, Eckalbar WL, Domingos AI, Pintar JE, Basbaum AI, Kashem SW. 2024. Regulatory T cell-derived enkephalin imparts pregnancy-induced analgesia. doi:10.1101/2024.05.11.593442<br /> Aubert N, Purcarea M, Fornier M, Cagnet L, Naturel M, Casrouge A, Dietrich G, Dieu-Nosjean M-C, Marodon G. 2024. Enkephalin-mediated modulation of basal somatic sensitivity by regulatory T cells in mice. eLife 13. doi:10.7554/eLife.91359.1<br /> Some other omments:<br /> -p3 Treg cells restrain exacerbated activation of peripheral neurons during early during inflammatory challenge<br /> -p6 CarboxypeptidaseE (Cpe) has been shown to process proenkephalin (Hook VYH, Eiden LE, Brownstein MJ. 1982. A carboxypeptidase processing enzyme for enkephalin precursors. Nature 295:341–342. doi:10.1038/295341a0)<br /> -Ref 50 is incomplete

On 2024-06-07 03:54:29, user Mohamed Hoballa wrote:

This preprint has been reviewed and published under the DOI: https://doi.org/10.30491/ja...

On 2024-06-06 17:27:56, user Prof. T. K. Wood wrote:

The first TA system found to inhibit phage was Hok/Sok in 1996 (that makes it seminal). So 26 years before retrons (your ref 47) and 25 years before ToxIN (your ref 48), Hok/Sok set the precedent of stopping phage by interpreting a phage process (transcription shutoff), rather than reacting to a specific phage protein. Curious as to why this discovery does not merit citation.

On 2024-06-06 12:01:12, user Elli wrote:

I am unable to see the suplementary tables the preprint refers to, where can I find these?

On 2024-06-06 12:00:30, user Elli wrote:

I am unable to see the supplementary tables the preprint refers to, where can I find these?

On 2024-06-06 09:51:33, user Asaf Levy wrote:

The paper is now published following peer review:<br /> https://academic.oup.com/is...<br /> Please cite this one.

Asaf Levy

On 2024-06-06 05:37:37, user Brian Junglen Jr wrote:

Can you please advise on the appropriate sample size required for a research study to conclusively determine the correlation between the spider gene and its impact on the snake's equilibrium?

On 2024-06-05 13:08:01, user Simon wrote:

This is an interesting analysis. I have two short comments/suggestions.

First, I wonder why authors made the more extreme Gly mutations for the binding site removal instead of the more conservative Ala mutations. Because of the special role of Glycine the quality of the input MSA is probably a lot worse for Gly instead of Ala mutations.

Second, I think authors should also report RMSD/pLDDT/PAE for the predicted structures itself and not just the ligand. Since AF3 performs the combined objective of structure prediction and docking mutation of the input has consequences for both objectives. It might be that it performs worse for docking because the overall quality of the structure prediction is reduced.

We've looked into the related problem of metal-protein interaction and there AF3 does a bit better to capture realistic physicochemical effects: https://x.com/simonduerr/st...

On 2024-06-05 07:04:18, user Si Hoffmann wrote:

The revised version of this article is published in Autophagy Reports.<br /> "Highlighting the hidden: monitoring the avidity-driven association of a fluorescent GABARAP tandem with microtubules in living cells"<br /> https://doi.org/10.1080/276...

On 2024-06-04 17:49:18, user phillip kyriakakis wrote:

Cool paper!

A few thoughts:

1) It would be great to see how this compares to the PhyB-PIF version<br /> 2) Blue light should activate PhyB/PhyA, it would be great to see different blue light doses to see how sensitive it is to blue light, not if it is sensitive to blue light. (See "Multi-chromatic control of mammalian gene expression and signaling" and "Multichromatic Control of Signaling Pathways in Mammalian Cells")<br /> 3) I am not sure what biological replicates means. Where three independent experiments done, or just three biological replicates, one experiment? If a single experiment, this should be made explicit and perhaps written as N = 1.<br /> 4) PhyA could be written as PhyA-NT instead of delta. Delta implies it is a knock out or something. Peter Quail used the "NT" notation and that has been used a lot since, so it would be easy for others to follow. <br /> 5) What are the effects of far-red light, perhaps with and without blue light? (See "Multi-chromatic control of mammalian gene expression and signaling" and "Multichromatic Control of Signaling Pathways in Mammalian Cells")<br /> 6) Would be nice to see blue and red systems multiplexed. Perhaps using DRE as in "Efficient photoactivatable Dre recombinase for cell type-specific spatiotemporal control of genome engineering in the mouse"

I am not suggesting these experiments or changes are needed to be published, but could improve the usefulness.

On 2024-06-04 07:43:30, user Wolfram Klapper wrote:

Excellent work! Congratulations! I wonder if you have checked the interdependence of HLA-I and EBV association and the effect on the microenvironment. Our own data show that TARC is a major driver and HLA-I is also an independent factor that associated with microenvironment features (see: https://onlinelibrary.wiley...<br /> Regards Wolfram

On 2024-06-03 17:49:15, user Joseph Wade wrote:

The following is a review compiled by graduate students participating in the Infectious Disease Journal Club, Department of Biomedical Sciences, University at Albany, SUNY:

This paper addresses differences in bacterial, archaeal, and fungal microbiomes within certain body sites as a function of pregnancy status. Prior to this work, changes in the maternal bacterial, archaeal, and fungal microbiomes in body sites other than the vaginal cavity and gut were poorly understood. The authors characterize the oral, urinary, stool, and vaginal microbiomes, and the urinary metabolome of non-pregnant, as well as pre- and postpartum women. They conclude that the microbiome of the oral cavity quickly rebounds after birth, whereas the vaginal microbiome takes longer to return to its pre-pregnancy state. The authors also conclude that the archaeal content of the oral microbiome correlates strongly with pregnancy status. We feel that most of the conclusions put forth in the paper are well supported. However, we have concerns about conclusions involving archaeal microbiome differences, and we have suggestions for changes in data presentation that may improve clarity.

Major Comments

1. The authors do not consider possible confounding variables associated with pregnancy status. Hence, correlation of microbiome differences with pregnancy status might not indicate a direct causative effect. For example, diet and hospital visits are likely to differ between the three groups assessed in the study. The authors should discuss the potential for these and other confounding variables, and, if possible, incorporate relevant metadata in their analyses.
2. Figure 1B, Figure 2A and Figure 2B are informative summaries of microbiome content but they do not indicate variability between participants. We suggest including corresponding supplementary figures with equivalent plots for each individual person in each cohort.
3. The authors state that Methanobrevibacter smithii in the oral cavity is almost exclusively observed for pregnant women. This is most clearly indicated by Supplementary Figure 10. However, Figure 2C appears to show Methanobrevibacter smithii as the most abundant Methanobrevibacter species in the oral cavity for all groups, regardless of pregnancy status. The authors should clarify this apparent disparity.
4. The observation that Methanobrevibacter smithii is found in the oral cavity almost exclusively in pregnant women is one of the most striking parts of the paper. Hence, we recommend moving Supplementary Figure 10 into the main figures.
5. The data in Supplementary Figure 10 indicate raw sequence read counts. Given that read counts are a function of total read depth, we recommend including a more thorough analysis of these data, including a statistical test of significance for the differences observed between cohorts.
6. The authors describe “semi-quantitative” and “qualitative” approaches to detect archaeal taxa. The potential weaknesses of these approaches should be discussed in the Results section. The authors should also indicate which method was used for each of the datasets shown in the figure panels. What does “semi-quantitative” mean? And is it possible to draw conclusions from data generated by “semi-quantitative” or “qualitative” approaches?

Minor Comments

1. It would be helpful for non-experts to include definitions of some of the jargon used in the main body of the paper, such as Shannon diversity, richness, alpha diversity and beta diversity.
2. The presentation of metabolomic data in Figure 4B would be enhanced by (i) including a table in which the raw numbers are reported, and (ii) flipping the x-axis to provide a more intuitive view (highlights regulation postpartum).
3. The most abundant bacterial genera in each sample type (stool, oral, urine and vaginal samples) are more clearly communicated in Supplemental Figure 3 than in Figure 1B. This is especially true for stool data, where the microbiome composition is quite different from that in the other body sites, meaning that most taxa are reported as “other”. Hence, we suggest using Supplemental Figure 3 in the main body of the text rather than 1B.
4. In Figure 4A, glucose, succinic acid and fumaric acid are decreased in the postpartum state urine compared with pregnant state urine. Glucose is the starting energy molecule for glycolysis, and succinic acid and fumaric acid are intermediates in the TCA cycle. Can the authors comment on whether their decrease in postpartum women is a function of the catabolic energy status during lactation?
5. It would be helpful to include text that explains the characteristics of each CST grouping to help the reader better understand (i) how the samples were separated into these groups, and (ii) the significance of CST of the different groups.
6. Figure 3B. This representation is confusing, especially since there are lines with a single point on one side and multiple points on the other. The authors should provide a more detailed explanation of the data representation in the figure legend.
7. We recommend that the authors consider using color palettes that will make it easier for people with color blindness to distinguish between colors.
8. The representation of data in Figure 2C is difficult to follow. Can the authors use the same representation as Figure 2B?
9. Many of the references to supplementary figures are incorrect.

Suggestion for a Future Experiment

A further experiment may include collection of stool samples postpartum as well to look at multiple time-points postpartum. It would be particularly interesting to include later postpartum samples to observe how long it takes to return to the prepregnancy microbiome in each body site.

On 2024-06-03 17:29:27, user Joseph Wade wrote:

The following is a review compiled by graduate students participating in the Infectious Disease Journal Club, Department of Biomedical Sciences, University at Albany, SUNY:

This manuscript looks at the role of the TofI/R and QsmR regulators on virulence in Burkholderia glumae. Previous work suggested that TofI/R was the dominant regulator over QsmR in B. glumae quorum sensing. Here, the authors identify independent roles of TofI/R, distinct from QsmR, and suggest a regulatory effect of QsmR on TofI/R. Moreover, the authors conclude that the QsmR variant T50K substantially reduces B. glumae virulence. The conclusions regarding the hierarchy of TofI/R and QsmR are generally well-supported across multiple independent assays, although there are some inconsistencies between the RT-qPCR and the RNA-seq data. The conclusions regarding the importance of the genotype of qsmR rely heavily on control data that are not shown; in the absence of these data, it is impossible to draw a strong conclusion on the contribution of qsmR to virulence.

Major Comments

1. There are some inconsistencies between the RT-qPCR and the RNAseq data. While the RT-qPCR data indicate ~4-5 times higher toxJ transcripts in 411gr-6 vs 336-gr1, the transcriptomic data from Figure 10 show no difference between the two strains.This is similar for toxA, but the difference between the two isn’t as big. Could the authors provide an explanation for these apparent inconsistencies between experiments? It would also be helpful if the authors could indicate in Figure 10 the variability between replicates.
2. In Figure 9, the authors show that introducing qsmR from a virulent strain into an avirulent strain that produces the T50K variant of QsmR restores virulence. But the key control of introducing the variant qsmR is listed as “not shown”. Data for this control are critical to support the authors’ conclusions and should be included in the figure. We also recommend that the authors complement qsmR deletion mutants with qsmR (both T50K and wild-type) to test whether these can restore virulence phenotypes.
3. In Figure 9 A, C, and D, can the authors include a positive control to indicate the extent to which introducing qsmR restores virulence?

Minor Comments

1. Why and how were qRT-PCR data normalized to two housekeeping genes?
2. Can the authors include a brief discussion on the subset of virulence genes chosen for the RNA-seq?
3. It would be informative if the avirulent strain could be used as a control in Figure 2.
4. The method used by the authors to quantify disease severity does not appear to be quantitative and could be difficult to reproduce. Could the authors provide a quantitative assay to allow other groups to reproduce these experiments? Additionally, can the authors comment on whether or not samples were blinded before assessing disease severity, given the possibility for unconscious bias?
5. Could the authors clarify their use of statistical significance markers (Figures 2B and 2D).
6. Figure 10B might be better represented as a heatmap.
7. Figure 9B. For those outside the field, it would be helpful to describe in the text the phenotypic differences for infected virulent vs. infected nonvirulent vs. uninfected.
8. It would benefit the reader if there was a final model figure to tie everything together.

Suggestion for a Future Experiment

Are strain phenotypes specific to the species of rice being used in experiments, i.e., is the hypervirulent strain more of a generalist whereas the “wild-type” strain is a specific pathogen for this rice species?

On 2024-06-03 08:51:10, user Texugo wrote:

Can I have access to the raw data ?

On 2024-05-31 18:55:39, user Julio Neto wrote:

Could you provide additional references that support the adverse outcomes of metaplasia in endothelial cells and the transformation of these cells to have connective and contractile properties? Also, I'd like to know if the relaxation of endothelium-dependent from isolated blood vessels is impaired in hypertensive animals. What are the basal blood pressure values of these awake animals? Are SOX-2 and KLF-4 transcription factors related to pluripotent-induced stem cells so, among these, how play a major role in determining endothelial drive instead of other cell types? (cardiac, for example). Preliminary data presented here could be from the transcriptome of single-cell RNA, which led to the sequence of the study. Lastly, I suggest reducing the range of graphs in Figure 6 (e.g., U46619 from 30 pM to 10 µM) to generate the best fit Hill sigmoid with more reliable efficacy and potency values. Congratulations and good luck!

Mark Johanson in this source as a main idea states that the messages employees send and receive on company work devices are always very accessible and monitored by their employers. Employees overall need to be very cautious when using their work devices for personal use. One key detail that Johanson mentions is that particular workplace platforms like Slack and Microsoft Teams often allow companies that use them to closely monitor their employees by allowing them to record and keep track of employees' activities and messages.

On 2024-05-30 23:46:19, user Clashing_titans wrote:

Cogent, clear, well-written and an interesting piece to the Legionella effector story:<br /> Chadha et al should be proud of this work!

On 2024-05-30 19:17:48, user Sharath Tippur Narayana Iyenga wrote:

Hi,<br /> Interesting work for rapid separation and detection of bacteria from blood. However, the 2nd step of ''Selective cell lysis'' is used from the work which is already published and has a patent approval in progress. This original work has not been cited in this pre-print. This has to be added in the original paper before publishing. It is highly important to properly cite the original paper if their method is utilized in another work. The original paper citation details is below:

Narayana Iyengar S, Dietvorst J, Ferrer-Vilanova A, Guirado G, Muñoz-Berbel X, Russom A. Toward Rapid Detection of Viable Bacteria in Whole Blood for Early Sepsis Diagnostics and Susceptibility Testing. ACS Sens. 2021 Sep 24;6(9):3357-3366. doi: 10.1021/acssensors.1c01219. Epub 2021 Aug 19. PMID: 34410700; PMCID: PMC8477386.

On 2024-05-30 17:30:49, user Pawan wrote:

Where is the supplementary material of this paper?

On 2024-05-30 16:57:13, user sanalkumar rajendran wrote:

Dear Authors, this quite interesting and great effort to bring published HiChip work in one single platform. We, Riggi lab has published couple of articles in sarcoma models which includes HiChIP profiling from relevant cell lines, mesenchymal stem cells and specific oncogene knock-down condition. It would be nice to have those datasets also included in the Loop Catalogue. <br /> 1. Nature Communications volume 13, Article number: 2267 (2022). https://www.nature.com/arti...<br /> 2. SCIENCE ADVANCES, 31 Mar 2023, Vol 9, Issue 13, DOI: 10.1126/sciadv.abo3789<br /> Thanks

On 2024-05-30 16:51:25, user Djo Hasan wrote:

Dear authors of the article, entitled: “Molecular Mimicry as a Mechanism of Viral Immune Evasion and Autoimmunity” (https://doi.org/10.1101/202....

Thank you for sharing this very interesting article.

In this article, you stated that “Molecular mimicry explains portions of the multiple sclerosis auto-antibodyome”. I suggest that you should take the findings of Martins, et al. (2023) [1] into consideration. These authors pointed out that although the amount of antigenic sharing between hosts and both pathogenic and non-pathogenic parasites and bacteria is massive, molecular mimicry by itself is not a sufficient factor to disrupt intact self-tolerance mechanisms.

In this context, in my recent paper (https://www.aimspress.com/a... [2], I provided the missing link between molecular mimicry and the activation of autoimmune responses. I figured out that activation of the purinergic P2X7R expressed on regulatory T cells is potentially required for molecular mimicry to activate autoimmune responses. This occurs only after repeated or high-dose microbe infection and not after a single low-dose infection. I also presented a novel model of immune responses in mammals to foreign antigens, self-antigens and antigens with molecular mimicry.

I hope that these comments will help to improve the quality of your paper.

Kind regards,<br /> Djo Hasan

References

1. Martins YC, Jurberg AD, Daniel-Ribeiro CT. Visiting Molecular Mimicry Once More: Pathogenicity, Virulence, and Autoimmunity. Microorganisms. 2023;11(6). doi: 10.3390/microorganisms11061472.
2. Hasan D. Purinergic P2X7R expressed on regulatory T cells potentially links molecular mimicry to autoimmune responses. AIMS Allergy and Immunology. 2024;8(2):80-123. doi: 10.3934/Allergy.2024006.

On 2024-05-30 10:18:07, user Prof. T. K. Wood wrote:

Again, growth on methane was achieved by reversing methanogenesis in 2016 by obtaining active Mcr for the first time in a pure culture (ref 20), so the Introduction remains misleading (line 48, 'in their natural state') and line 323 is misleading:

"...ANME-1 MCR may allow Methanosarcina to perform AOM (20 )." We used multiple lines of evidence to demonstrate growth in 2016 and subsequent papers converted methane to lactate and even created a microbial fuel cell using methane, hence both uncited works again confirm active Mcr so it is not appropriate to write "may allow" growth. Should the field refer to all of your results as "may be' valid?

Moreover, in effect, the recombinant strain we produced was the way Nature created ANME, according to your work here (by altering Mcr) so our work is relevant to your report here as validation of this study, and the Introduction in this second version is still misleading.

On 2024-05-26 21:40:46, user Prof. T. K. Wood wrote:

Line 47: this statement is false: "Though there is not yet strong evidence that backwards carbon flow can be coupled to growth in either methanogens or ANME,.." as ref 20 was seminal in reversing methanogenesis in a methanogen by cloning Mcr and showing growth for the first time on methane in the engineered methanogen.

On 2024-05-30 09:18:41, user Sam Calis wrote:

Is there list of the peptides identified with the subtiligase biotin-assay availlable somewhere?

On 2024-05-30 05:42:05, user cong wrote:

We are trying to install Nanomotif in our server. We tried all of the install methods, and the major install looks good. However, when we tried nanomotif MTase-linker install, the following error was shown. It seems that module 'snakemake' had some issues. We then checked the 'snakemake' install and found we had snakemake==8.12.0. Is there any method to solve the problem for MTase-linker install?

Thank you very much!

$ nanomotif MTase-linker install<br /> /home/miniconda3/envs/nanomotif/lib/python3.12/site-packages/nanomotif/mtase_linker/setup.smk<br /> Traceback (most recent call last):<br /> File "/home/miniconda3/envs/nanomotif/bin/nanomotif", line 10, in <module><br /> sys.exit(main())<br /> ^^^^^^<br /> File "/home/miniconda3/envs/nanomotif/lib/python3.12/site-packages/nanomotif/main.py", line 513, in main<br /> mtase_linker(args)<br /> File "/home/miniconda3/envs/nanomotif/lib/python3.12/site-packages/nanomotif/main.py", line 475, in mtase_linker<br /> snakemake_create_environments(args)<br /> File "/home/miniconda3/envs/nanomotif/lib/python3.12/site-packages/nanomotif/mtase_linker/dependencies.py", line 24, in snakemake_create_environments<br /> status = snakemake.snakemake(snakefile,<br /> ^^^^^^^^^^^^^^^^^^^<br /> AttributeError: module 'snakemake' has no attribute 'snakemake'

On 2024-05-02 21:22:33, user Alex Crits-Christoph wrote:

Nanomotif looks like a fine tool, especially for metagenomics, and I have no doubt it will push the prokaryotic methylation nanopore field further!

I was curious if you would be able to benchmark it more, as the benchmark presented in Fig 1C is quite limited in scope, and could be expanded. We have shared R10.4.1 data for a variety of microbes that you might be interested in benchmarking on, include some with paired PacBio data in REBASE (which is not ground truth, but a good comparision):

aws s3 cp --recursive s3://cultivarium-sequencing/MICROBEMOD-DATA-NOV2023/mapped_bams/ . aws s3 cp --recursive s3://cultivarium-sequencing/MICROBEMOD-DATA-NOV2023/reference_genomes/ . aws s3 cp --recursive s3://cultivarium-sequencing/MICROBEMOD-DATA-NOV2023/pod5/ .

It is also worth noting that the comparison to MicrobeMod is a bit limited due to the reason that you note here: "The low motif recall of MicrobeMod, at high coverage and high motif occurrence settings, primarily stems from identification of similar motifs that are not identical to the benchmarking motif, e.g. SNGAm6TC instead of GAm6T".

Despite this, overall my sense is very that nanomotif's motif calling will be likely superior in many circumstances to STREME in the context of prokaryotic methylation. Probably the best way to evaluate methylation motifs would be with some manual inspection after running multiple tools (and parameters).

On 2024-05-30 05:18:38, user Severin Lechner wrote:

Thanks for looking into the downstream effects of HDAC inhibitors in various cells and on several levels.

It would be interesting to compare the results to recently published data on proteomics and phosphoproteomics response to a broad panel of HDAC inhibitors, such as: <br /> - Decrypting lysine deacetylase inhibitor action and protein modifications by dose-resolved proteomics, Cell Rep. 2024<br /> - Decrypting the molecular basis of cellular drug phenotypes by dose-resolved expression proteomics, Nat Biotech. 2024

Further, the Abexinostat selectivity data in the main text lacks a reference. The statement also does not fully agree with the recently updated target selectivity landscape of HDAC inhibitors, where Abexinostat is shown to bind HDAC10 and the off-target MBLAC2 with substantially higher affinity than HDAC1: <br /> - Target deconvolution of HDAC pharmacopoeia reveals MBLAC2 as common off-target, 2022. Nat Chem Bio

On 2024-05-29 13:09:14, user Alex Crits-Christoph wrote:

KrakenUniq reports the coverage ("breadth of coverage", the percentage of the reference genome that is covered by sequencing reads) of each reference genome that may be present in the sample. Breadth information is a key way of determining true positive from false positive hits in metagenomics: almost all false positives are characterized by low breadth of coverage, as in these cases, reads only mapped to a fraction of the reference.

The authors should report coverage from KrakenUniq for their analyses; only counts are provided in the supplementary, but coverage values would be more informative for determining whether a microbial genome is present in a sample. For a brief discussion of this see:

https://instrain.readthedoc...

Further, the authors could then consider employing a minimum breadth cutoff to further separate true from false positives.

Finally the authors could also consider comparing to approaches that incorporate breadth automatically, such as:

https://github.com/bluenote...<br /> https://sourmash.readthedoc...<br /> https://instrain.readthedoc...

On 2024-05-29 08:20:26, user Alexey Belogurov Jr. wrote:

Manuscript has been published Chernov AS, Rodionov MV, Kazakov VA, Ivanova KA, Meshcheryakov FA, Kudriaeva AA, Gabibov AG, Telegin GB, Belogurov AA Jr. CCR5/CXCR3 antagonist TAK-779 prevents diffuse alveolar damage of the lung in the murine model of the acute respiratory distress syndrome. Front Pharmacol. 2024 Feb 21;15:1351655. doi: 10.3389/fphar.2024.1351655. PMID: 38449806; PMCID: PMC10915062.

On 2024-05-29 07:40:40, user PengLong li wrote:

Dear professor Bahlburg,

Hello. I'm very sorry to bother you in your busy schedule.

My name is Penglong Li, and I am a master's student at Dalian Ocean University in China. I have been focusing on the analysis of Antarctic krill resources using echogram images, a topic that greatly interests me. I recently came across your paper titled "An open and lightweight method to analyze the vertical distribution of pelagic organisms using echogram screenshots," which has been immensely inspiring for my research.

I am currently attempting to replicate the methodology presented in your paper. However, I have encountered some difficulties, particularly with accessing the source code. The link provided in your paper (https://sandbox.zenodo.org/... appears to be inactive.

I would be extremely grateful if you could share the echogram color matching program and other source code mentioned in the paper. Having access to these resources would greatly assist me in my research and help me better understand and apply your methods.

Regardless of your decision, I wish you the very best. Thank you for your time and consideration. Your help would be immensely appreciated, and I am deeply grateful for any assistance you can provide.

Wishing you good health and continued success in your work.

Best regards,<br /> li.pen.long0506@gmail.com<br /> Penglong Li<br /> Dalian Ocean University

On 2024-05-29 06:50:45, user theNiessingLabs wrote:

The authors show in Figures 3 - 4 and in Table 2 in silico-docking studies with an alphafold2-model of PURA as template. In this model, PUR-repeat III is shown as a monomer with an awkward-looking fold. The authors use this docking to suggest a direct interaction between PURA and GLUT1. <br /> Unfortunately, the authors seem to ignore that PUR repeats do not exist as single, monomeric repeats but require dimerization. For repeat III of Drosophila PURA, a high-resolution structure of its homodimeric domain has been reported already in 2016:<br /> https://elifesciences.org/a...<br /> PDB-ID: 5FGO<br /> For repeat III of the human PURA (as used in this study), more recently the homodimeric high-resolution domain structure has also been published:<br /> https://elifesciences.org/a...<br /> PDB-ID: 8CHW<br /> Considering these experimental structures, Figures 3-4 and Table 2 refer to unphysiological folds. As a result, conclusions drawn from these figures have to be considered as entirely wrong.

On 2024-05-28 03:20:10, user Samuel W. James wrote:

As a specialist in earthworm phylogenetics and taxonomy, I would really like to see an expanded taxon set within earthworms, including several cases where aquatic to terrestrial (and back) habitat shifts have taken place. There are even some where earthworms have colonized marine shore habitats. My colleague Christer Erseus who works on non-earthworm clitellates could also make some intelligent suggestions for future work on lineages that have transitioned from fresh to marine water environments or vice versa, as well as aquatic / terrestrial shifts. <br /> Nevertheless, terrestrial soils are basically aquatic environments, in that earthworms and Enchytraeidae ( and the soil-dwelling polychaete Hrabiella (I think) depend on free water and water films on soil particles and their body surfaces.

On 2024-05-25 13:05:40, user Leando Cruz wrote:

Very intersting work. I would like to know the opinion of the author on the paper "Modulation of alpha-synuclein phase separation by biomolecules", since the authors were the first to propose the formation of biomolecular condensates of the protein mediated by spermine

On 2024-05-24 11:13:50, user Marcelo R. S. Briones wrote:

This paper was peer reviewed and published (May 27, 2024) in the journal "Viruses" https://www.mdpi.com/1999-4...

On 2024-05-22 17:31:24, user lf wrote:

A version of this paper is now published in the journal Epigenetic at the following link https://www.tandfonline.com...

On 2024-05-22 15:01:07, user Donald R. Forsdyke wrote:

The authors cite a paper in PLOS Biology that was first posted as a preprint paper here in bioRxiv see Johri et al. 2021. The four comments I added to the Johri preprint paper have now been updated, noting the intriguing new k-mer analysis of Roberts and Josephs (2024).

On 2024-05-22 14:43:59, user Donald R. Forsdyke wrote:

The SSRN preprint mentioned previously (see four comments) has now (2024) been formally published under the same ("three historians") title in Theory in Biosciences (143(1): 1-26). One of the three (William J. Provine) having died in 2015, I now sadly report the passing of Mark Boyer Adams (May 9th, 2024). The paper included the work of the remaining historian (myself). This built on the DNA studies of Erwin Chargaff and my k-mer and nucleic structure analyses.

The formally published final version of Johri et al. is available in PLOS Biology. Their admonition to "carefully define ... underlying uncertainties" has resurfaced regarding "Lewontin's paradox." Citing this, Roberts and Josephs have posted a new bioRxiv preprint (May 19th 2024) entitled: "Previously unmeasured genetic diversity explains part of Lewontin’s paradox in a k-mer-based meta-analysis of 112 plant species" (see: Roberts and Josephs 2024).

On 2024-05-22 06:46:04, user Marc RobinsonRechavi wrote:

In the methods, you write<br /> "HDF5 files with the approximations for each organism are available on FigShare at https://figshare.com/accoun...."

But the figshare link doesn't work :(

On 2024-05-21 11:13:03, user dirkfaltin wrote:

Interesting paper. However, I'm afraid you are mixing up a lot of concepts that should be kept separate. Ethnonyms like Goths, Langobards and Frisians are political terms. They are not biological categories. Terms like "Wielbark Goths" (847) are entirely nonsensical. We simply don't know how the people of the Wielbark culture identified or were identified by others. Most likely they had never heard the name Goths and were not called so by outsiders. The name Goths appears only later in an entirely different region. We also don't know of the earlier "Gutans" had anything to do with the Wielbark-people or the later historical Goths. If you investigate the remains of a person buried in a cemetary that may have belonged to the Langobards, you still don't know how the individual identified whose DNA you extracted. Historians have been very careful to workout the problems that arise from mixing up ethnic/political terminology with archaeology. This paper revives the old mistakes by mixing up ethnic/political terms with archaeological material culture and biology.

On 2024-03-22 13:47:09, user Georg wrote:

As for the identification of the so-called East Scandinavian cluster associated with the I1 Y haplogroup, conclusions about the Baltic source are premature - the isolated autosomal complex is characteristic of groups of hunter-gatherers found from the Mesolithic Iron Gates, Neolithic Northern Germany ostorf003 and possibly EastBaltic

On 2024-05-20 00:22:12, user Alexis Rohou wrote:

I was asked to review a version of the manuscript for a journal. Below are my comments to the authors.

In this manuscript Shub and colleagues present MIC, a new tool for the analysis of three-dimensional macromolecular structures obtained using x-ray diffraction or cryogenic electron microscopy (cryoEM). Given an atomic model featuring water molecules and/or ions, and the corresponding 3D map data, MIC automatically labels each putative water and/or ion location with a guess water or ion identity.

Thanks to improvements in cryoEM instrumentation and data analysis, 3D maps can now frequently be obtained at resolutions better than 2.7Å, so that the problem of correctly labeling small map features as either water or ion presents itself more frequently to practictioners, myself included. In that context the availability of a well-characterized, open-source and highly performant classifier of ion/water density features is timely and most welcome.

I found the manuscript to be well written, the description of the method and how it was tested clear (though as a non-expert I had trouble following the description of the network architecture and of the feature attribution methodology), and the results convincing. Most questions that arose as I was reading early parts of the manuscript (e.g. regarding the influence of slight errors in modeling of protein atoms on the labeling of ions/water) were answered in later parts. I only have minor feedback & suggestions for the authors and otherwise am supportive of publication.

Here's the feedback I have:<br /> - lines 47-48: Perhaps... but I wonder if the authors are aware of Ravera et al (Nature, 2022)... and there may be a few other reports I'm unaware of myself... I wonder whether a radial average profile could be used as part of the fingerprint in future versions of MIC to improve the quality of the labeling by the network. Perhaps the authors could comment (in the discussion?) about whether incorporating experimental map features could be possible in future work?<br /> - lines 189-190: "this simple metric showed statistically significant separation between<br /> test set predictions that agreed and disagreed with the deposited PBD label": I found this sentence confusing at first, and still do to an extent. To me the syntax did not convey the meaning of the figure unambiguously. To spell it out: <br /> -- My understanding on first reading was: when a site lands near a boundary (i.e. the model is uncertain), the model's prediction tends to disagree with the deposited PDB label. Doesn't that mean that if the user gets a result that is assigned low confidence, they should actually assume that the identity assigned by MIC is wrong? That would seem to be problematic... wouldn't it?<br /> -- Having reviewed Fig 2e, I now think the text was a bit confusing. My interpretation of the figure: at low confidence scores, say around 0.5, it's only slighlty more likely than not that there's a disagreement with the PDB; this is more in line with the behavior I would have hoped for... i.e. when there's uncertainty, it's a close call - not necessarily was the wrong label given...<br /> - line 213: "The revised overall test set accuracy following manual annotation is 83.3%". This is impressive. How will this translate to lower-resolution structures, where there will be more error in atom positions... I am thinking back to the fact that they authors found that fingerprinting worked best when using very fine shells... Perhaps this should be discussed somewhere [I think this is touched on in the paragraph ending line 269]<br /> - line 269: Right. That touches on my earlier question. When you get to the ~3Å range, unless you happen to have your coordinating side chains modeled really well, MIC is going to be quite prone to error. [I still think this topic of accuracy as a function of resolution should be returned to in the discussion]<br /> - line 432: "MIC achieves incredible accuracy". Hah! I think the authors should aim for the readers to actually believe the claimed accuracy, and it is unhelpful to characterize the accuracy as "incredible". I found the manuscript credible overall ;)<br /> - line 495-498: "limiting all calculated (...) to be identity agnostic" - I don't understand this. Might be worth spelling things out for non-specialists like myself<br /> - line 512: "non/prune-fifp" I think should be "non-prune/fifp"<br /> - line 513: "prune-eifp" I think should be "prune/eifp"

Alexis Rohou<br /> May 2024

On 2024-05-16 13:44:41, user Jiri Hulcr wrote:

Nice article, we are hoping that we could use the genome sequence for our current work. <br /> I would recommend that the authors do not replicate the standard adage of how we have to study this beetle because it is a pest that is difficult to control. There are piles of literature on this topic, including two comprehensive compendia by the Forest Service, dedicated to the biology and management of this species. Foresters know very well how to manage forests to avoid outbreaks of the Southern pine beetle. There is a multi-state program for monitoring the population of the species, and predicting its local outbreaks. Case in point - the beetle was pretty much eradicated from the state of Texas, which is presumably why the authors had to use material from Mississippi. <br /> Studying genetics in order to "kill the pest" seems mistargeted. We know nearly all bark beetle outbreaks are a symptom of excessive stand density, warming climate, or introductions of invasive species, i.e., human-caused. So justifying this great research by the need to control the insects is not very convincing. The many incredible biological features of this insect would make a much more interesting justification.

On 2024-05-15 13:15:57, user Ruben Perez wrote:

This preprint has been published in Virus Evolution (10.1093/ve/veae031). The title has been slightly modified: “Highly pathogenic avian influenza H5N1 virus infections in pinnipeds and seabirds in Uruguay: implications for bird-mammal transmission in South America”.

On 2024-05-14 04:54:31, user Erick Nedd wrote:

1. Limited Sample Size: In Figure 1A-E, I noticed that only 18 out of the 54 subjects were included in the comparison of biological age versus chronological age in EL subjects. Due to the already limited nature of having a large sample size of centenarians for research studies, it may be more effective to include more participants because having a smaller sample size may limit the generalizability of findings and necessitate caution in applying results to broader populations or conclusions about aging.

2. Including more controls: In Figure 3 where the forward programming of EL-specific iPSCs into cortical neurons was analyzed, I noticed that there was no control of an established cortical neuron cell line. Including this control, would be helpful in comparing the differences and similarities between the differentiated iPSCs and the cortical neurons, and would further help to confirm the pluripotency of the iPSCs created in this experiment.

3. Inclusion of Demographic Information: Including the demographic information of centenarians or the countries from which they come from would be helpful in contextualizing the study and understanding the possible influences on biological age. Considering that factors such as, geographical location, lifestyle habits and access to healthcare can all influence the aging process, including the demographic information of participants may illustrate environmental or genetic factors that may lead to exceptional longevity. Furthermore, having iPSCs from diverse populations may lead to more robust findings in that they would better represent the genetic variation across the globe.

4. Supplemental figures: While the inclusion of the supplemental figures was interesting in seeing the different population of immune cells in centenarians, there was not sufficient information for readers to see how these immune cell populations compared to offspring or their spouses. Having a control of an offsprings’ spouse would help your audience understand how the population of immune cells in centenarians may have lead them to living a longer life, and provide a clearer picture of the role of immune cells in longevity.

On 2024-05-13 11:39:30, user Alice Risely wrote:

This is a great experiment for looking at changes in serum metabolites over migratory stages. This is valuable information. However, it is a shame that the gut microbiome part of the study is only rudimentary - the study would be much stronger if it could link changes to metabolites with gut microbiota variation, which requires measuring the gut microbiota across all stages and using metagenomics or universal primers. As such, I think the 'gut microbiome adaptions' in the title is misleading.

On 2024-05-10 21:05:06, user disqus_gM8nbME1Vj wrote:

In discussion, para 2, 19th line, the author mentioned that the expression of "the induction of Fe uptake related genes such as FRO2, IRT1 was also not compromised in the pye mutant unlike hy5 mutant" and refered to the "Figure S2".<br /> But, in "Figure S2", it appears that FRO2 and IRT1 levels are lower in pye than in hy5 mutant as compared to the WT.

On 2024-05-10 08:11:47, user Stefano Vianello wrote:

Dear Dr. Blotenburg,

I'm Stefano, the author of REF 20 re endoderm-rich gastruloids. In the Discussion section of your manuscript you write that

[REF20] maintained mESCs in 2i-medium and reported faithful emergence of endoderm cells

. Given the importance of mESC culture conditions in your analyses and possible future interpretations (at least, re endoderm), I wanted to point out that — following the practice of the lab I was working in at the time — mESCs were not grown in the classic 2i medium (2i in N2B27), but in fact in a 2i in ES+LIF medium (exact recipe in REF20's Materials & Methods > Cell culture). Based on gastruloid end-phenotype alone (of those shown in FigS1), I would guess this atypical mESCs culture medium is most closely matched by your culture condition 3 (and possibly condition 4), and that those conditions (though they were not selected for scRNAseq) are giving rise to endoderm-rich gastruloids.

Sincerely,<br /> Stefano Vianello

On 2024-05-09 13:09:41, user Peter Ellis wrote:

Fascinating work but it doesn’t complicate the central dogma in any way regardless of what inaccurate textbooks say.

https://www.researchgate.ne...

Crick was quite specific: the central dogma simply says that translation is irreversible.

On 2024-05-09 08:52:36, user Mihail Halachev wrote:

Apologies for the late reply. The variant in the LOXHD1 gene<br /> (as all other enriched variants listed in Tables 1-5) can be found in ClinVar by searching for AlleleID = 176561, rather than by variation ID.

https://www.ncbi.nlm.nih.go...

On 2024-05-08 16:54:50, user Jorge Soberon wrote:

Many people have used ellipsoids (Mahalanobis distance) for niche modelling in the past. I think you need to consult, at least:

Farber, O. and Kadmon, R., 2003. Assessment of alternative approaches for bioclimatic modeling with special emphasis on the Mahalanobis distance. Ecological modelling, 160(1-2), pp.115-130.

Jiménez, L., Soberón, J., Christen, J.A. and Soto, D., 2019. On the problem of modeling a fundamental niche from occurrence data. Ecological Modelling, 397, pp.74-83.

Drake, J.M., 2015. Range bagging: a new method for ecological niche modelling from presence-only data. Journal of the Royal Society Interface, 12(107), p.20150086.

Hirzel, A.H., Hausser, J., Chessel, D. and Perrin, N., 2002. Ecological‐niche factor analysis: how to compute habitat‐suitability maps without absence data?. Ecology, 83(7), pp.2027-2036.

On 2024-05-08 12:59:32, user Yuri Pavlov wrote:

"The artifacts of the dataset were removed by the authors while publishing the dataset" statement is false. The data you downloaded are raw EEG data (as stated in the dataset description. All analyses are therefore performed on data containing a lot of eye movement and muscle artifacts making your classification algorithm useless.

On 2024-05-07 17:57:42, user Dimitrius Santiago P. S. F. Gu wrote:

Hi, the manuscript has just been published with open access in the Journal of Sarcopenia, Cachexia and Muscle! Please access https://onlinelibrary.wiley... for the latest version.

On 2024-05-07 17:37:12, user Julian C wrote:

(From the author) To appear as a book chapter in: Ruiz Romero C, Calamia V and Lourido L (Eds), Protein Arrays: Methods and Applications, Electronic ISSN 1940-6029, Print ISSN 1064-3745, Springer Nature.

On 2024-05-07 17:33:00, user Julian C wrote:

(From the corresponding author) To appear in PLOS ONE.

On 2024-05-06 11:02:33, user Roman wrote:

There is already another CRISPR method out there, called CREATE (https://pubmed.ncbi.nlm.nih... so maybe the authors want to pick a different acronym so that readers don't get confused.

On 2024-05-06 08:20:36, user Liheng Luo wrote:

Your work, CRISPR-GPT is groundbreaking, bridging the gap between complex gene-editing technology and researchers from various fields. The potential for accelerating biological discovery is immense, and I’m excited to see its impact on future research. <br /> I am eager to see this technology in action and would greatly appreciate the opportunity to explore a demo of the author’s website. Such hands-on experience would provide invaluable insight into the practical applications of CRISPR-GPT.

On 2024-05-06 08:02:44, user Daniel Guzman Llorens wrote:

Congratulations to the team, it is a great article. I found the results very insightful.<br /> While reading, I seem to have found an error in the insulin intensity histogram in Figure 3, as both A and C seem to be the same histogram.

On 2024-05-04 01:52:43, user priyanka.bajaj3193@gmail.com wrote:

In this study the authors comprehensively examined the mutational effects on PLpro proteolytic activity and stability. The authors have designed a FRET-based assay composed of N-terminal mClover3 donor and C-terminal m-Ruby3 acceptor fluorophores separated by a linker containing the Nsp2/3 PLpro cleavage motif to measure the proteolytic activity of PLpro. From DMS data the authors infer PLpro active site mutations ablates activity. Their study also revealed residues required for cleavage of the Nsp2/3 site, identified features of substrate binding pocket and the sequence requirements of the blocking loop. The authors have given explanations for their observations in the Discussion section. Overall, the paper is supported with follow-up enzymology and crystallography experiments of key residues. The major limitation of this study is leaky expression of mutations can mask clinically relevant mutations that can arise due to viral evolution and might have the potential to evade inhibitor treatment. Study of such mutations can provide more information about the potential escape routes open to the virus to evade developing therapeutics. Moreover, incorporation of statistical analysis could strengthen the confidence in inferences drawn from the deep sequencing data and improve the quality of the manuscript. <br /> The following points can improve the quality of the manuscript:<br /> Major points<br /> 1. In Figure 1f, it is unclear that why is the PLpro activity increasing with increase in inhibitor concentration? Perhaps the Y axis is mislabeled as while inhibitor concentration is increasing, PLpro activity should decrease. However, FRET signal would increase (and maybe should be the axis label), since there will be no cleavage. <br /> 2. Line 175 – 178 - What does 0 represent in the normalized dataset? What is the rationale used for selecting minimum 10 reads in the unselected library as the read cut-off. 10 reads is pretty low cut-off. From the data, it seems the distribution tails off before cut-off chosen for the s.d.- by eye. 0.3 s.d and 20 as read cut-off might be a better option to eliminate sequencing artifacts.<br /> 3. Line 187-190 – What is the number of reads for the mutants that showed lower activity scores? There is a possibility that due to low read cutoff, these mutants might be lying in the range with low reads in the unselected library.<br /> 4. Line 223-224 – Authors mention they find a good correlation between activity and abundance score. Although this is noticeable from the scatter plot but supporting high-throughput data with statistical parameters like pearson correlation coefficient, a metric that provides comparison between 2 datasets will make this data reporting more quantitative and informative.<br /> 5. Line 1340- Figure 3b- What do authors mean by variants with small enough error? Please be precise.<br /> 6. Line 315-319/ Line 1550-1556- Extended data Fig 15c – It is difficult to interpret the inference reported that is based on the data in Extended Fig 15. There is no data reported for Normalized AMC cleavage for Y268W. Interpretation can be more comprehensible by plotting a scatter plot between the Normalized Activity Fitness Scores obtained from DMS data and Normalized AMC cleavage (%). Through this plot, the reader can easily make out the outlier.<br /> 7. Line 364-367 – Authors mention “M208W strikingly increases the protein melting temperature by over 5C, indicating a substantial improvement in thermal stability. Increased stability, and thus reduced turnover in cells, may provide a mechanism to explain leaky expression in our cellular assay and increased yield of recombinant protein for E.coli expression.” Since, leaky expression is a different issue, it is confusing why will leaky expression be a plausible reason for increased stability but less activity? <br /> 8. Since Extended data Fig 11a shows that variants display substantial amount of leaky expression, how have the authors taken this information into account while inferring results from DMS activity scores, especially since they are quantifying at the RNA level and not at the DNA level? Can the activity scores obtained for the mutants be normalized to leaky expression scores in some way, for example by subtracting the scores obtained from the leaky expression dataset in order to measure the true activity of each mutant?<br /> 9. Solvents are known to affect an enzyme’s activity, selectivity and stability. In Figure 5, authors should consider and comment about the role of solvent in understanding the mechanism of Michelis-Menten kinetics of M208 variants using substrates Z-RLRGG-AMC, Ubiquitin-Rhodamine and ISG15-Rhodamine.<br /> Minor points<br /> 1. Figure numbers need to be reformatted. Figure 3 onwards they are incorrectly labelled. For eg. ‘Fig 3’ is labelled as ‘Fig 1’.<br /> 2. Line 426-429 – In Figure 3b, L and R domains of papain should be labelled or highlighted in separate colors for the ease of understanding for the reader.<br /> 3. Overall, different DMS datasets obtained from different assays in the paper have different read cut-offs such as 10, 13 and 18. A consistent statistical logic for obtaining different read cut-offs across different DMS datasets will be helpful. Also, increasing the read cut-off might improve the data quality and minimize sequencing artefacts.

• Reviewed by Priyanka Bajaj and James Fraser (UCSF)

On 2024-05-03 10:36:51, user Samvid Kurlekar wrote:

Really great and useful work and an astounding tour de force - enjoyed reading it!<br /> I was wondering if the authors might please comment on whether the iPT cells expressed significantly higher levels of lncRNAs such as Neat1 or Malat1 when compared to 'normal' PT cells. We have seen these Neat1/Malat1-rich PT cells in our own scRNA-seq dataset (from mice) but were unable to detect HAVCR1 or VCAMI. However, genes associated with VCAM1-positivity were detected and these PT cells (which we called PT Class A) were present in Control kidneys and were seen in situ by RNAScope too. <br /> I would be quite interested to know if the iPT-VCAM1+ population you have comprehensively described matches the PT Class A cells we saw.

On 2024-05-01 23:26:21, user Guei-Sheung Liu wrote:

The article has published in Hum Gene Ther.<br /> Utility of Self-Destructing CRISPR/Cas Constructs for Targeted Gene Editing in the Retina.<br /> Li F, Hung SSC, Mohd Khalid MKN, Wang JH, Chrysostomou V, Wong VHY, Singh V, Wing K, Tu L, Bender JA, Pébay A, King AE, Cook AL, Wong RCB, Bui BV, Hewitt AW, Liu GS.<br /> Hum Gene Ther. 2019 Nov;30(11):1349-1360. doi: 10.1089/hum.2019.021. Epub 2019 Oct 25. PMID: 31373227

On 2024-05-01 23:23:51, user Guei-Sheung Liu wrote:

The article has now publshed in Nucleic Acid Ther.<br /> An Integrative Multi-Omics Analysis Reveals MicroRNA-143 as a Potential Therapeutic to Attenuate Retinal Angiogenesis.<br /> Wang JH, Chuang YF, Chen J, Singh V, Lin FL, Wilson R, Tu L, Ma C, Wong RCB, Wang PY, Zhong J, Hewitt AW, van Wijngaarden P, Dusting GJ, Liu GS.<br /> Nucleic Acid Ther. 2022 Aug;32(4):251-266. doi: 10.1089/nat.2021.0111. Epub 2022 Mar 31. PMID: 35363088

On 2024-05-01 23:21:11, user Guei-Sheung Liu wrote:

The article has now published in Pharmacol Res. 2023 Jan:187:106617. doi: 10.1016/j.phrs.2022.106617. Epub 2022 Dec 16.

On 2024-05-01 17:47:25, user Kevan Shokat wrote:

Fantastic study of the clamping effects of Rocaglamide analogs across the helicase family. I particularly like the different effects of nucleotide and that ATP rather than AMP-PNP can stabilize different helicases as shown in Figure 5F. I wonder if mixtures of ATP and smaller concentrations of AMP-PNP could let helicases work (ATP) and then trap (AMP-PNP). Great study of so many dimensions of this assay! Congratulations!

On 2024-05-01 16:53:49, user Timothy Tomkins wrote:

This manuscript explores the mechanisms involved with decreased immunity due to aging. It does this by looking at the microbiome found within mice at different ages, then looking at increased inflammation utilizing immunoassays. The results show increased innate immunity and inflammatory signals, showing that an older mice’s microbiome acts differently on the TLR signaling system. The manuscript excels on the immunological side; however, the microbiome side of the study lacks quality control and explanation. I would recommend contacting a microbiome expert to get more insight into the field, as there is much missing from this report.

This review focuses on the microbiome side of the report, which has quite a few problems to work on.

1. Polymerase Chain Reaction (PCR) details are missing, such as the primers used, the polymerase used, and the procedure used, such as the temperature, timing, number of cycles. The PCR is not repeatable based on what is said in the methods. Refer to the paper ‘The Impact of DNA Polymerase and Number of Rounds of Amplification in PCR on 16S rRNA Gene Sequence Data’ at DIO: https:// doi.org/10.1128/mSphere.001....

2. When sequencing the PCR data, the number of reads per sample is not stated. This is important to know if the coverage of the communities is measured. The authors also do not include the observed number of taxa to compare to the Chao1 numbers to determine coverage. This is shown in Figure 1B, where the chao1 number is very low. The taxa used for this graph is not stated, leaving the reader confused if it is the number of genera, or phyla. The lower number assumes phyla, while genera is the more commonly used in the field and should be in the hundreds. Showing the number of ASVs/OTUs in each sample is a common measure of alpha diversity.

3. The method to which the sequenced data was sorted is not given. The authors do not state if ASVs or OTU’s were utilized which leads to confusion in the way the dominant taxa were determined. It is also impossible to know if the sequenced data was corrected for 16S copy number, or for variance in genome size. Refer to ‘The Variability of the 16S rRNA Gene in Bacterial Genomes and Its Consequences for Bacterial Community Analyses’ at DIO: https://doi.org/10.1371/jou...

Next are some more minor issues that can be addressed.

1. At line 340, the instrument and settings for the zirconia beads are not explained.

2. No reference is given for the CLR method at line 385.

3. In the statistical analysis, it states that data was rarefied to the minimum library size, but that size is not mentioned.

4. Figure 1A states P=0.002, however no statistical test is given to get that P.

5. Figure 1C utilizes the words upregulated and downregulated which are used with expression data. I believe this is abundance of taxonomic groups, so different word choices would be more accurate, such as more or less represented. Further, you don’t know that age caused that, as implied by the phrase “by age.”

6. Timothy Tomkins, SHSU biomedical student.

On 2024-04-30 20:15:37, user Austin McIlhany wrote:

Fascinating paper both on the topics of cutting edge field of microbiomes and still-ongoing issues of human-caused environmental crises, specifically oil spillages. Here are a few ideas and questions I have that I think could possibly improve this paper:<br /> 1. Since this study focuses on N and P levels and hydrocarbon degradation, then the levels of N and P in the collected seawater need to be quantified.<br /> 2. I understand that it must not be under lab-ready circumstances to extract AND analyze the samples of the artic waters at the same exact location for a more accurate sampling of the microbiome<br /> 3. The levels in the three growth conditions of high, low, and ambient. It would be helpful to list all ingredients and their final concentrations for the three conditions as well.<br /> 4. Perhaps in the future, the use of transposons could also be used to track the genes that correlate with biodegradation of crude oil, and which strains/taxa of bacteria they originate. Making a isolated, pairwise and community could also help narrow it down.<br /> 5. PCR conditions for the V4 amplification should be described.<br /> 6. In the “differential abundance analysis,” were there adjustments made for different 16S copy numbers in different taxa? Different genome sizes? If not, this must be mentioned as a limitation.

大多数人认为分布式训练由于需要同步和通信，必然比单机训练慢，但作者认为Decoupled DiLoCo比传统同步方法快20倍以上，这挑战了人们对分布式训练速度的固有认知，展示了异步计算的潜力。

大多数人认为混合不同代际的硬件进行训练会降低性能或效率，但作者认为即使不同代际、不同速度的芯片混合使用，仍能达到与单一芯片类型训练相同的机器学习性能，这挑战了硬件必须同质化的行业共识。

大多数人认为分布式AI训练需要高度同步和紧密耦合的系统才能保证效率，但作者认为通过解耦的'计算岛屿'架构，即使局部硬件故障，系统其他部分仍能高效学习，因为故障被隔离了。这挑战了传统分布式训练必须保持同步的主流认知。

On 2024-04-30 14:51:04, user Luca Jovine wrote:

Published on 26 April 2024 as:

Elofsson A., Han L., Bianchi E., Wright G. J. & Jovine L.<br /> Deep learning insights into the architecture of the mammalian egg-sperm fusion synapse <br /> eLife 13:RP93131 (2024)<br /> DOI: https://doi.org/10.7554/eLi...<br /> PubMed: https://pubmed.ncbi.nlm.nih...

On 2024-04-29 21:54:07, user Yeshveer Singh wrote:

Final version of this article is published at MPMI. Here is the link: https://doi.org/10.1094/MPM...

On 2024-04-29 15:59:37, user Amber Gonzalez wrote:

Additional comments<br /> Hello! I am a Sam Houston State University graduate student enrolled in a microbiome course, BIOL5394. My overall impression of the manuscript is that it is excellent, and the information presented is helpful for forensic science. <br /> Abstract and Introduction<br /> The introduction is very clear and to the point. Does this study consider where the individual's death occurred, indoors or outdoors? I believe the environment in which a death occurred could alter the microbial community succession and potentially influence your given results. What ethical guidelines were followed when handling the cadavers?<br /> Methods<br /> Quality control <br /> • Need specific PCR protocol, like # cycles, temps, polymerase, etc. https://journals.asm.org/do... <br /> • There is no mention of the correction of the 16S copy number and variances in genome size for taxa identified ASVs. https://journals.plos.org/p... <br /> • Was coverage of communities measured and representatively sampled equally?<br /> o Include coverage measures such as Good’s coverage or Chao1.<br /> • Line 115 explicitly states the protocol for USA samples. What about the samples from Finland and Italy?<br /> • Lines 113-114 mention sections of the dissected internal organs but fail to mention the specific section. Is there a measured area region that was dissected? This could help with consistency in sampling.<br /> DNA Extraction and Sequencing <br /> • Lines 130-131 state the Greengenes database used to assign taxonomy was last updated in May 2013. More accurate identification results would come from a more recently updated 16S gene database.<br /> Statistical analyses<br /> • Good practice to include the complete list of packages and codes used for the analysis.<br /> Results<br /> Figure 3<br /> • PCoA assumes normal distribution of data. Need to show normality test or use NMDS.<br /> Figure 4<br /> • The taxa in this figure show excessive “unknowns.” I believe updating the database could improve this.<br /> Figure 5<br /> • Lines 256-259 mention the findings of ASV family are in class Bacteroidia, however after reviewing I believe the correct class is Betaproteobacteria.<br /> o Bacteroidia, Betaproteobacteria, and Clostidia are color-coded with very similar colors. Consider making the jump to the next class a bit more distinctive.<br /> Additional comments<br /> • The article mentions the sample size was 265, but when I add up the four category sample sizes, I get a sum of 262. Is this intentional, or a typo?<br /> • Of the 20 Finnish cadavers, why was the liver the only organ supplied?<br /> • Overall, it was a very interesting study!

On 2024-04-29 13:34:46, user Oyinoluwa wrote:

Abstract & Introduction

The abstract is good overall and structured well. It gives a clear insight into the concern at hand and summarizes the key findings to show its significance. However, the research question is not clearly stated. This is something that can be elaborated on in the introductions, but it should also be stated briefly in the abstract. I understand that there is not a lot of research done on the effects of pregnancy to the female microbiota, but it is unclear as to what the research question is. This introduction gives a good background on the issue of pregnancy altering the microbiome. Below are my comments on what could be added or further explained.<br /> 1. Are you trying to determine which organisms are lost/recovered?<br /> 2. Is this study for a general understanding of the effects of pregnancy on women (pre/post birth)?<br /> 3. Are you looking at a specific family of bacteria, fungi or archea that has a meaningful change during both phases (pre- and post-maturation) to see their effects? <br /> 4. Are the women from diverse backgrounds (race)?<br /> 5. Was this study conducted in the same location?<br /> 6. Include more previous studies, if any, stating what others have claimed to be the reasons behind “side effects” of pregnancy <br /> 7. Include your own claims on what you would expect and not expect to see.

Figures and Tables

The table heading and description are good and easy to read. However, make sure to explain all abbreviations.<br /> Table 1. <br /> 1. What is the difference between an t-test and a Fishers exact t-test?<br /> 2. What does SS-days mean? <br /> 3. Also explain why each test was used for the p-value result.<br /> Figure 3. <br /> 1. I am not sure what these diagrams represent.<br /> 2. 3B- What is a vaginal community state type? How were they classified? It is stated that software clusters them, but what are the criteria?<br /> 3. 3A- What do the colors represent? Explain the unknown portion? <br /> General comments for figures<br /> Figures captions need to be clear, like in 1C ii) the title is “Rchress.” Is that observed ASVs? Include clear titles for your figures as well as axis labels. References to the figures from the results section show that the figures support the findings. You can include the reasoning behind choosing these graphical analyses. <br /> Methods, Materials & Analysis

1. In your sample and collection section, it is mentioned that the participants collected their own samples. Is there a reason for this? Why did a professional, specifically a doctor, not collect the samples? Was there a way to determine that the samples were collected and stored properly?
2. Does the study look at women across different races? Consider this please!
3. How was Lctobacillus classified to species level using only the V4 region? That is not possible, the full 16S sequence is required. https://www.nature.com/arti...
4. How did your account for chimeras and PCR errors before going onto further filtering steps?
5. Further explain your controls. It was mentioned that positive and negative controls were removed, but there is no mention of their identity.
6. What analysis was used to determine dominant and rare taxa? This was mentioned in the paper but was not included in the methods section.
7. This study used 16S rRNA analysis to determine species present as different body sites, when the sequencing step is conducted to determine the species what the sequencing coverage is. Is it complete? In addition, how many reads per sample? <br /> a. This was not stated at all in the paper, and it would be good to include it in the analysis portion.

Results, Discussion, Conclusion

In the limitations section, it is mentioned that the samples were not collected from the same women before and after pregnancy. Wouldn’t this affect the results you have presented? It is hard to say whether your results and conclusion support the claims which were made due to this aspect of sample collection. Although your results and conclusions are significant to the study, the validity is questionable.

On 2024-04-25 23:14:15, user Michelle Wille wrote:

This is an important study rapidly presenting key findings of sampling for HPAI in the Antarctic region. We have some concerns around the interpretation of the results - we beleive the diagnostic used is excellent at detecting a broad diversity of H5 viruses and is not specific to HPAI H5 and therefore it is unclear whether the authors detected HPAI H5N1 or LPAI H5Nx. A summary of our concerns has been presented here: https://www.preprints.org/m...

On 2024-04-25 07:12:08, user Rafal Mostowy wrote:

Hi, it’s very interesting work! I was wondering if you could make your supplementary material available? I don’t see it posted with the preprint. Thank you

On 2024-04-24 16:37:09, user Q Li wrote:

Please link this preprint to the published paper https://rdcu.be/dFAvb <br /> Thank you!

On 2024-04-23 21:44:35, user Mattia FM Gerli wrote:

The peer reviewed version of this article has been published on Nature Medicine in March 2024 at the following DOI:

https://doi.org/10.1038/s41...

On 2024-04-23 17:09:36, user Moira C wrote:

Hello, the Yadav lab has shown data on a protein structurally similar to SLK in humans called TAOK1, I have linked their paper here https://www.science.org/doi.... Do you have any comment on the similarity between these two proteins especially since this other kinase seems to have an I-bar domain as well.