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

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

On 2025-05-23 12:04:52, user Malte Bødkergaard Nielsen wrote:

The preprint has been peer-reviewed and published (open access) in 'FSI: Genetics' with DOI:<br /> 10.1016/j.fsigen.2025.103291

On 2020-11-07 09:32:44, user N-fixer wrote:

Nice work! One small query - why do you state that "B. rhizoxinica" is a single lineage? It's clearly in Mycetohabitans, and is sister to M. endofungorum (Estrada de los Santos et al. 2018). Or perhaps you have additional evidence to refute this?

On 2022-01-28 20:16:36, user corihuel wrote:

Dear Authors,

Your work was recently reviewed and discussed by the Bacterial Pathogenesis and Physiology Journal Club here at the University of Alabama at Birmingham (UAB). As part of our review of pre-prints, we compile comments from our discussion that we think may better your publication.

Overall, our group found the manuscript to be a very interesting read with detailed information on the structure/function of SteD emerging. We can tell that considerable thought that went into each experiment as well as figure production. Your lab has shown an exceptional amount of rigor in your experimental designs that made it difficult to refute your findings. This study was very well done, and we all enjoyed discussing it.

Below we point out some comments and aspects that we feel could improve on the manuscript.

1) We felt that the text was a little difficult to follow. Though it is probable that this will be alleviated once the paper has been properly formatted, as the figures help a great deal in understanding the text.

2) We very much appreciated the short anecdotes in the manuscript explaining the specific actions of the chemicals used for your experiments. None in our journal club work this closely with transport systems and it made understanding your work much easier.

3) We were curious about your justification for using a melanoma cell line in your studies rather than an APC line like BMDMs? We’ve noticed that it has been used for other Salmonella studies, but we think it necessary that you justify in the text why you use this cell line.

4) The order of your figures is a little confusing, specifically figures 1-3. We think it would really help if you were to either combine Figures 1 and 3 in some manner or reorder them so that Figure 3 comes just after Figure 1, rather than being interrupted by Figure 2. This would streamline the reading and comprehension of your data greatly.

5) On the topic of Figure 3, we were curious as to why you found the specificities you did and yet continued to use the region 13 mutation rather than the S68A G69A mutations in your experiments for Figure 4. Especially given the problems you had with region 13 mutation expression and release from Salmonella.

6) Our group wanted to extend our compliments to your inclusion of the protein diagrams you had throughout your paper. The visualization made it easy to understand the mutations made and really helped with the overall comprehension of the paper and the experiments you were completing. On this note, however, we don’t think it necessary to highlight the F and Y residues in Figure 7. They are discussed in the text but are not tested in the figure. That depiction would be better included in a supplemental figure showing the experimental results from those mutations.

7) Lastly, we believe Figure 5C should be moved to supplementary since it only confirms that your siRNA worked as intended.

Sincerely,<br /> UAB Bacterial Pathogenesis and Physiology JC

On 2022-12-06 10:39:04, user SzBszan wrote:

Great work!<br /> Some comments: Fig2D: vGLuT1 and vGlUT2 main titles are switched (likely accidentally), maybe doublecheck for similar typos in the cell type titles too.

On 2025-10-15 11:10:48, user Schratt lab wrote:

REVIEWERS' COMMENTS

Reviewer #1 (Remarks to the Author):

Soutschek et al. have included new data in this revised manuscript that address the points that I have raised. Interestingly, these new data suggest that miR-1229-3p inhibition leads to lower mitochondrial activity and mitochondrial dysfunction, although not to the extent that mitophagy is increased, ultimately leading to altered Ca2+ regulation and dendritic arborisation. Despite this mitochondrial dysfunction, miR-1229-3p inhibition enhances synaptogenesis, and so this contrasts with Iwata et al. (ref. 32 in their manuscript) where it is reported that decreasing mitochondrial metabolism slows neuronal development. How can the authors’ findings be reconciled with Iwata et al.? Mito-ER interactions are considered in the discussion and indeed they mention that MERC regulation by miR-1229-3p could be a mechanism in human neurons contributing to slowing of mitochondrial metabolism and associated neuronal maturation. However, their findings don’t appear to support the premise that impaired mitochondrial metabolism slows neuronal maturation. I would be interested in additional discussion on this point.

Reviewer #2 (Remarks to the Author):

The authors have sincerely addressed all reviewer comments, and the revised manuscript is considered to be of a much higher quality compared to the initial submission. While they have not performed a specific experiment for every point I suggested, their explanations for not doing so are convincing, which I find acceptable. The current version of the manuscript is suitable for publication and does not have any major issues.

Reviewer #3 (Remarks to the Author):

The authors have addressed all concerns sufficiently. Congratulations.

On 2023-09-29 21:10:20, user disqus_mtg7x7eXMb wrote:

The pre-print Kim et al. [1] shows promising pharmacodynamic data for an LRA (Galunisertib, given as 20 mg/kg for 14 days, in multiple cycles) to purge the SIV reservoir in HAART treated SIV infected monkeys. They corroborate the finding with the use of a radiolabeled anti-env imaging probe, which the authors claim can be used to detect tissue areas of increased viral replication in the body when the probe uptake in those areas increases.

This radiolabeled probe was already used in a 2022 publication [2] from the same team (Samer et al. JCI-Insight, reference number 35 of the Kim et al. pre-print [1]), to show the increase in SIV viral production in monkeys treated with HAART following the LRA administration given for a shorter period of time at lower doses (5-10 mg/kg for 7 days).

In the 2023 pre-print, however, the first cycle of the LRA did not induce those very high increases in probe uptake in lymph nodes or the gut of animals as reported in the 2022 [2] paper in which the animals received the LRA at lower doses for a shorter period compared to the 2023 pre-print [1].

Why higher doses and longer duration of the LRA in 2023 are not revealing those high increases in probe uptake seen in the 2022 paper?

One possible explanation is that those high increases in SUV uptakes seen in the 2022 paper are the result of non-specific uptake of the probe, based on certain details of the 2022 published images (more in note-*1).

The latter seems a reasonable explanation for the Samer et al. paper [2] given that the LRA showed a systemic effect (see Suppl Fig S6 in Samer et al., in which the effect of the LRA is measured in the peripheral blood mononuclear cells), hence it is unlikely that the red spots in the PET images show up only in the Axillary cluster of lymph nodes ipsilateral to the injection sites and not in any other lymph nodes clusters in the body. See for instance video 3 and video 4 of the [2] paper in the links repasted below for quick access (under note-1*), the two animals with the highest increase in Lymph nodes uptakes show red spots only in the Axillary nodes ipsilateral to the injection sites, but not on the opposite site of the Axillary nodes nor in other lymph nodes clusters of the body, such as the inguinal lymph nodes.

The authors however showed that the LRA did induce an increase in viral replication from PCR of inguinal nodes tissues shown in Table 2 and Fig 4A of [2]. For instance, the A14X064 showed ~100 fold increase in CAVL-RNA in inguinal nodes, yet from video 4 https://insight.jci.org/art... the only nodes that light up in red in the PET images are the Left Axillary nodes ipsilateral to the extravasation of the injection site on the Left arm of the monkey. <br /> Lack of increase in specific binding of the probe in the lymph nodes, despite the evidence of increase in viral RNA in some of those tissues based on PCR, points to a lack of reproducibility of the anti-env imaging system, or, at most, to a poor sensitivity of the imaging system.

On the other hand, this lack of reproducibility that comes across by comparing the 2022 and 2023 imaging data, which are based on previous two publications from the same imaging team in 2015 [3] and 2018 [4], questions indeed those earlier two nonhuman primates papers [3, 4] too, in which the authors showed evidence that this imaging system is capable of detecting residual levels of viral replication in HAART treated animals, the latter being an attribute of an imaging systems very sensitive to detect changes in target (gp120) molarity. <br /> The latter consideration assumes that the binding affinities of the radiolabeled F(ab’)2 fragment of the 7d3 used in [1, 2] and the radiolabeled 7d3 used in [3, 4] are similar, which seems a fair assumption based on what is implied in the Methods sections of the [1, 2] articles, although in vitro binding data have not been reported in the [1, 2] articles (more in note-*2).

Similarly, there is no evidence of increase in gut SUV levels after the first two weeks of Galunisterib administration in the 2023 pre-print Kim et al. [1]. This contrasts with the observation reported in the 2022 [2] of a substantial increase in gut uptake seen for instance in animals (A14X027 (video 1) or A14X013 (video 7) or A14X064 (video 4)) after only one week of the LRA administered at lower doses for shorter periods compared to the [1] 2023 study in pre-print.

An alternative explanation for the increase in gut radiotracer uptake seen in the 2022 paper is that the increase in bowel uptake reflects a well-known phenomenon of non-specific intraluminal uptake of the radiolabeled antibody\F(ab’)2 fragments [5, 6] (see more under note-*3.1)).

In other words, the hypothesis that the increases in probe uptake in lymph nodes and the gut seen in the 2022 paper after the first LRA-cycle are fully explained by non-specific uptake of the probe appears consistent with the lack of increase in probe uptake in the same anatomic compartments in the 2023 study in which animals received the same LRA at higher doses for longer periods of time.

Finally, another important difference between the 2022 [2] and the 2023 [1] imaging data is that in the latter [1], the increase in probe uptake (given as SUV, standardized uptake value) is now seen in the lymph nodes, gut (and many other regions of the body, the latter a feature that points to a non-specific nature of the biodistribution) at later cycles of the LRA administration, when also the heart (blood pool) probe uptake is increasing, which is happening in all animals at the later cycles of [1]. As described by mathematicians in the 80’s, when changes in input function take place, the use of the SUV to quantitate changes in probe uptake could be misleading and requires mathematical modeling of the time activity curves generated in serial imaging along with an input function [7], or at least a normalization on the blood pool (heart).

This phenomenon of the increase in heart uptake (which indicates blood pool) following Galunisertib administration was not noted in the animals of the [2] publication. Only in one animal of the previous 2022 publication, the authors claimed increase in the radiotracer uptake of the heart. However, the figures and videos associated to this particular animal (A14X004) reveal a potentially important inconsistency, as described under note-*3.2.

It would be helpful, in general, to standardize measurements of covariates generated in different labs, especially in studies that are closely related to each other like the [1] and [2] studies. Note under note-*4 lack of standardization in measurements of CAVL viral RNA and CAVL viral DNA between [2] and [1] studies.

Notes and Bibliography

note-*1. Figure 3H of [2] shows that the highest increases in lymph node (LN) uptake are in monkeys A14X060, A14X064, and A14X013. In these animals, PET images show clear evidence of significant extravasation of the injected probe into the subcutaneous tissues ipsilateral to the high LN uptake. A fourth animal (A14X027) has a slight increase in LN uptake with smaller extravasation. The four animals above are 4 of the 5 animals that the authors claim in the Abstract of [2] show increase in probe uptakes in lymph nodes caused by the administration of the LRA. It is well known, however, that when radiolabeled antibodies\fragments are intentionally or unintentionally (infiltration) administered by subcutaneous or intradermal route, they find their way into the lymphatics and then non-specifically concentrate in regional draining nodes [8], as it appears to be the case for the animals listed above since the radiotracer uptake in LNs was much higher on the side of infiltration than the contralateral side or than in distant nodes. For this reason, the SUV analysis should not have included those LNs in Figure 3H. Without those LNs, however, it looks like from the published images that there is no increase in probe uptake in the LNs, as claimed in the abstract of [2] in which a causal link between Galunisertib administration and increase in LNs in probe uptake is inferred from the data analysis.<br /> Video1…8 from the JCI-I link<br /> Video 1 (A14x027, suv-scale=1.5) : https://insight.jci.org/art...<br /> Video 2 (A14x037, suv-scale=1.5): https://insight.jci.org/art...<br /> Video 3 (A14x060, suv-scale=1.5): https://insight.jci.org/art...<br /> Video 4 (A14x064, suv-scale=1.5): https://insight.jci.org/art...<br /> Video 5 (A14x004, suv-0.3, kidney and liver removed) : https://insight.jci.org/art...<br /> Video 6 (A14xX005, suv-scale=1.5): https://insight.jci.org/art...<br /> Video 7 (A14x013, suv-scale=1.5): https://insight.jci.org/art...<br /> Video 8 (A14x004, suv-scale=1.5): https://insight.jci.org/art...

note-*2. In Samer et al. [2] we read “The 64Cu-DOTA-F(ab´)2 p7D3 was previously validated in SIV-uninfected macaques (62)”, with ref 62 being ref [3] of this document. In the pre-print Kim et al. for the characterization of the probe the Samer et al. [2] reference is provided. <br /> However, in ref [3] only the intact 64Cu-DOTA-7D3 and not the F(ab’)2 was tested in vitro in data presented in Suppl Figure S1 of [3]. In particular, Suppl Fig S1C of [3] shows approximately 2-fold only difference in radiotracer uptake in a competition assay using gp120 expressing cell lines, that are known to express gp120 at much higher levels than primary cells (e.g. pbmc, spleen or lymph node cells). Of note, the competition assay of S1C shows rapid loss in binding when the radiotracer is incubated with only 25% more of the non-radiolabeled (cold) ligand, which is unexpected for high affinity binding ligands. <br /> All these four imaging NHP studies [1-4], produced by the same imaging team, lack autoradiography analyses. The latter ex-vivo technique generates powerful data for the in vivo inference, as typically done in oncological pre-clinical research, because, as mathematicians have shown when they first attempted to extract quantitative information from the PET images [7], what the in vivo imaging is revealing is not a signal proportional to the absolute concentration of the target, but rather a signal that is proportional to the binding capacity of the probe, which is the product of the probe affinity times the concentration of the target. The implication is that if the latter is very low, we can have in our hands a very high affinity ligand, yet we will not be able to generate an SUV level that is predominantly explained by specific binding of the probe. In absence of autoradiography data, some evidence of binding capacity can be generated by implementing in vitro cell binding assays using primary cells (e.g. PBMC or splenocytes or lymph node cells from infected animals and compare the binding to same cells from uninfected animals). This was done only in the first publication [3] in Nature Methods, in which a two-fold (only) difference in SUV uptake was observed by comparing uninfected and infected spleen and lymph node cells (S1B) ex vivo incubated with the radiotracer. <br /> However, the binding data were generated using cryopreserved cells without cold-competition assays; the latter would be useful to rule out, for instance, putative higher non-specific uptake due to higher cell death in the infected cells following their thawing. In general, it would be helpful to increase the sample size of S1B of the 2015 publication [3] (for instance only one well for uninfected lymph node cells were used for that piece of data, which precludes any robust conclusion from the data), as well as to produce autoradiography studies, as mentioned above, in which tissue sections are incubated with close to kd concentration of the probe and after washing, the tissue sections uptakes are compared to the non-specific uptake generated by pre-incubating the adjacent tissue sections with large amount of the cold (i.e, non-radiolabeled) probe to block all gp120 receptors in the tissue sections.

Additional validations of the observed increased SUV uptakes in SIV infected animals, or following the administration of an LRA, as claimed in the 4 NHP publications, is particularly warranted given the state-of-art research in this area and given that, because of the high costs and demanding resources associated to these studies, few laboratories in the world have the capacity of reproducing these experimental data.

note-*3.1 The study [2] did not appear to exclude in the analysis of the gut areas that are consistent with the intraluminal antibody excretion in bowel segments, because details of how the gut SUV uptake was obtained were missing from the Methods section of the [2] publication. The new [1] pre-print states “To quantify the signal in the gut tissue, the body segment below the stomach to above the cervix was initially isolated. The Gut’s SUV was then calculated by extracting the spleen, both kidneys, liver, and bones within the designated body region using Boolean operations.” The latter approach , if adopted also in the [2] publication with those high levels of gut SUV probe uptake seen soon after cycle 1 in some of the animals, again proves that those areas are consistent with the intraluminal antibody excretion (e.g. stools) in bowel segments were not excluded in the analysis, however, this is not what is commonly done in antibody imaging studies [5], because it is known that this phenomenon of non specific uptake in the gut due to the excretion of these types of radiotracers can occur.

note-*3.2 Figure 3F in [2] is a figure obtained from Supplemental Video 5 (https://insight.jci.org/art... ), with baseline and post-LRA images displayed with SUV-rainbow-scale = 0.3 for animal A14X004. Based on the legends, all other animals are displayed at SUV-scale =1.5. (baseline is before LRA (i.e first panel to the left) and middle and right panels are images at week 1 post LRA for one week and week 2 post-LRA for another week, respectively). <br /> Based on the legends, and consistent with the CT anatomy of the Video 5, the Video 8 https://insight.jci.org/art... shows the same images, before subtracting liver and kidney, displayed at 1.5 SUV. If we try to picture how the Video 8 (baseline, left panel) would look like by putting our hand on top of the liver and kidney to mask these two organs, it appears that what is left is an image that is the same image displayed in Video 5 (first panel to the left) or Figure 3F (first panel to the left). However, the legends state that the scales are different for Video5\Fig3F (0-0.3) and Video 8 (0-1.5), hence also the colors of the baseline images of Video 5 and 8 should be the different.<br /> In other words, Video 8 and Figure3F\Video 5 are incompatible. The evidence that Video 5 and Video 8 of reference [2] require to be harmonized for the validity of the whole dataset, can be also deduced by looking at the rainbow scale of the images. The rainbow scale goes from black to blue to green to yellow to red. So if we fix it to max SUV=1.5, it means that if an SUV uptake is 1.5 or higher, it will show up as red, and all the other colors would indicate levels below SUV=1.5 …for instance green is around 0.7. Now, if the rainbow is fixed to max Suv=0.3, it means that whatever is 0.3 or higher will be red, and green is in the middle, around 0.15. If Figure 3F\Video 5 is correct but the mistake was done in Video 8 (left panel was set at SUV scale 0.3 instead of 1.5 like written in the legends), then once displayed on a scale 1.5, the left panel of Video 8 should show a liver and kidney in color bluish...(which is not seen in the liver and kidney of any other animals, hence the latter scenario would point to a very fast biodistribution of the probe, which is consistent with a damage of the probe). <br /> Two different versions of the Samer et al. paper can be found online, the PMC version and the JCI-Insight version, which differ primarily on the biodistribution of the A14X004 (video 5 and video 8).

Some of the differences between JCI-Insight link (https://insight.jci.org/art... current version online modified in June 2023) and the PMC-link (dated November 2022 https://www.ncbi.nlm.nih.go... ) are here outlined:

A)<br /> JCI-I: However, a probe generated using a rhesus IgG1 Fab against an irrelevant antigen 64Cu-DOTA-F(ab´)2 pIgG1 in an SIV-infected macaque was used as further control (Supplemental Figure 8 and Supplemental Video 9). <br /> PMC: However, a probe generated using a rhesus IgG1 Fab against an irrelevant antigen 64Cu-DOTA-F(ab´)2 pIgG1 in an SIV-infected macaque was used as further control (Supplemental Figure 8 and Supplemental Video 2). <br /> Note, Suppl video 2 in PMC link shows images of A14X037, hence unrelated to the sentence above in the PMC link.<br /> B)<br /> JCI-I: A smaller increase in the gut was also present in A14X004 and A14X060 (Figure 3G, Supplemental Video 5, and Supplemental Video 8).<br /> PMC: A smaller increase in the gut was also present in A14X004 and A14X060 (Figure 3G, Supplemental Video 5, and Supplemental Figure 1). <br /> In this case too, Supplemental Figure 1 in PMC link shows figure title TGF-? inhibits HIV-1 latency reactivation by PMA in ACH-2 cells, hence unrelated to sentence in the PMC link.<br /> Consistent with the changes above, the supplemental materials in PMC do not show Supplemental Video 8 and Video 9. The videos legend, however, is the same on both links, i.e. points to the existence of additional two videos (called movie S1 and movie S2) in both the PMC and JCI-I links. None of the two links however call movie S1 or movie S2 in the main text, so this is an editing mistake probably propagated for 3 corrections made on the JCI-Insight publication, the last one dated June 14th 2023 based on the Version History section linked to the publication https://insight.jci.org/art... .

C) Video 1 link of the PMC link does not contain the animal ID listed in the legend, but animal A14x004 displayed at 0-1.5 SUV scale (what became Video 8 in the JCI-I link)<br /> D) the PMC link date is Nov 2022, the JCI-I link shows that changes were made in June 2023 based on the third upload of the supplementary material file.

note-*4. Figure S3 from the [1] pre-print shows CAVL-RNA in the gut with unit measurement [copies/ml] and ranges 0-30; in the 2022 [2] paper Fig 4A shows the same covariate but with unit measurement [copies/10to6] cell-eq and ranges (0.1-1,000) log-scale; <br /> Figure S4A from the [1] pre-print show CAVL-DNA in different organs with unit measurement [log copies/10to4 cell-eq, range 0-5]…; in the 2022 [2] paper Fig 4C shows the same covariate but with unit measurement [copies/10to 6] y-axis transformation unclear;

1. Kim, J., TGF-? blockade drives a transitional effector phenotype in T cells reversing SIV latency and decreasing SIV reservoirs in vivo. 2023.<br /> https://www.biorxiv.org/con...

2. Samer, S., et al., Blockade of TGF-beta signaling reactivates HIV-1/SIV reservoirs and immune responses in vivo. JCI Insight, 2022. 7(21).<br /> https://insight.jci.org/art...

3. Santangelo, P.J., et al., Whole-body immunoPET reveals active SIV dynamics in viremic and antiretroviral therapy-treated macaques. Nat Methods, 2015. 12(5): p. 427-32.<br /> https://pubmed.ncbi.nlm.nih...

4. Santangelo, P.J., et al., Early treatment of SIV+ macaques with an alpha(4)beta(7) mAb alters virus distribution and preserves CD4(+) T cells in later stages of infection. Mucosal Immunol, 2018. 11(3): p. 932-946.<br /> https://www.ncbi.nlm.nih.go...

5. Beckford-Vera, D.R., et al., First-in-human immunoPET imaging of HIV-1 infection using (89)Zr-labeled VRC01 broadly neutralizing antibody. Nat Commun, 2022. 13(1): p. 1219.<br /> https://pubmed.ncbi.nlm.nih...

6. Hnatowich, D.J., et al., Pharmacokinetics of the FO23C5 anti-CEA antibody fragment labelled with 99Tcm and 111In: a comparison in patients. Nucl Med Commun, 1993. 14(1): p. 52-63.

7. Mintun, M.A., et al., A quantitative model for the in vivo assessment of drug binding sites with positron emission tomography. Ann Neurol, 1984. 15(3): p. 217-27.

8. Keenan, A.M., et al., Immunolymphoscintigraphy and the dose dependence of 111In-labeled T101 monoclonal antibody in patients with cutaneous T-cell lymphoma. Cancer Res, 1987. 47(22): p. 6093-9.<br /> https://pubmed.ncbi.nlm.nih...

On 2018-11-14 01:28:31, user Manuel Kleiner wrote:

This is a very nice comprehensive dataset on transmission mode. The interpretation of the results is, however, problematic as there are some serious biases in the data. For example, for vertically transmitted insect symbionts every single symbiont strain and host species has been included in table S1 as a separate entry, while for horizontally transmitted rhizobial symbionts, where hundreds of symbiont strains and host species are known, everything was lumped together at genus level and on the host side just "legumes". So there is a strong bias leading to underestimation of horizontal transmission in terrestrial settings. <br /> Also, the definition of symbiont is unclear. The manuscript seems to only include obligate mutualists. What about the broader definition of symbiosis which includes parasites, or what about mixed mode and horizontally transmitted symbionts of vertebrate animals. Particularly for the intestinal microbiota of many mammals, we do know quite a bit about transmission mode, but not much about the ecological impact of the symbionts.<br /> As mentioned above, I think this is a very valuable dataset, but the mansucript would need to be recast in terms of conclusions, more precise definitions what was looked at, inclusion of additional data and better data grouping to avoid biases caused by use of different taxonomic levels.<br /> Thank you for making this available!

On 2019-06-11 08:51:06, user Zhou Xu wrote:

During the revision process, we found that a subset of Chlamydomonas reinhardtii telomeres have blunt ends by hairpin ligation assay, which is an important finding with the regards to the evolution of such structures in plants. Because this assay is very sensitive to the quality of extracted genomic DNA, we were able to detect blunt ends only using a strain that does not have a rigid cell wall and gives higher quality DNA. The results in this preprint have now been refined and updated in the version published in Life Science Alliance (https://www.life-science-al... "https://www.life-science-alliance.org/content/2/3/e201900315)").

On 2020-12-18 17:33:30, user Leandro de Mattos Pereira wrote:

In my opinion, this type of research that involves the genetic manipulation of sarcov-2 should be banned, given the little knowledge we have of genetic recombination between sarcov-2 and exogenous sequences is yet limited. The ethics committee should not approve this type of exploratory research using vectors with reverse transcriptase together with cells infected with sarcov-2.

On 2025-08-18 13:24:12, user Thijs J.M. van den Broek wrote:

This manuscript has now been published and can be found here: https://doi.org/10.1093/neuped/wuaf002

On 2025-02-18 17:40:04, user Dongyao Li wrote:

Great work, thank you! I have one comment regarding comparison of cell segmentation. The paper states:

Overall, the CosMx 6K segmentation tool produced cells with larger sizes, higher solidity, and better circularity, indicative of more regular and convex cell shapes compared to other platforms

I would argue "larger", "more circular", and "convex" do not necessarily mean better segmentation. For example, "more circular" and "convex" could be the bias of the particular segmentation model. If more circular is better, then "nucleus segmentation + large expansion" (basically the first version of Xenium segmentation prior to multi modal cell segmentation stain) is the better segmentation since it's large, circular, and convex.

The paper later states:

Among the ST platforms, Xenium 5K showed better separation of marker gene pairs after segmentation

We further visualized marker gene expression across the annotated cell types. Xenium 5K exhibited the most distinct expression patterns (Supplementary Fig. 8d), facilitating more accurate cell type annotations.

and concludes:

Despite exhibiting more irregular segmentation shapes, Xenium 5K effectively distinguished different cell types and minimized transcript mixing between adjacent cells

"Better separation of marker gene pairs", "distinguish different cell types" and "minimize transcript mixing" are indeed the more principled and biologically meaningful metrics to evaluate cell segmentation. The word "despite" in the conclusion suggests circularity means better segmentation, which I don't necessarily agree.

On 2023-05-09 22:54:47, user Daphne wrote:

Summary

CryoET allows researchers to view biomolecular complexes in their native state via in situ imaging of cells, as opposed to purifying such complexes and viewing them in isolation via single particle cryoEM. However, traditional sample preparation of thin cell sections is laborious and can introduce artifacts. In this paper, the authors quantify the sample damage introduced by cryo-FIB milling, a newer technique proposed as an alternative to traditional sample preparation. Although much has been done to understand how radiation damage affects particles in single particle cryoEM, the same cannot be said for understanding how cryo-FIB milling damage affects samples in cryoET. As cryo-FIB milling becomes more widely used, downstream workflows and data interpretation will be helped greatly by understanding the mechanism and characteristics of damage caused by cryo-FIB milling itself.

This paper aims to characterize the damage caused by cryo-FIB milling by using a model system, the ribosome. The authors utilize a previous template matching method they developed, 2DTM, as a broad metric for comparing and quantifying damage. The major success of this paper is highlighting practical considerations for sample preparation based on quantification of damage from cryo-FIB milling. The major confusion we have with this paper is in interpreting the data for the mechanism of cryo-FIB milling damage, and whether alternate explanations for the data have been explored. This paper provides helpful insight into what one can expect from cryo-FIB milling their samples, and lays the groundwork for optimizing sample preparation.

Major points

We are unsure what new information the plots in Figures 1G and 1H provide relative to Figures 1E and 1F. It makes sense that the 2DTM SNR values plateau in the thicker lamella, as there is a larger region of undisturbed particles. However, because the 2DTM SNR is also included in Figures 1E and 1F in an untransformed and more intuitive coordinate frame, we are unsure what new interpretations come out of Figures 1G and 1H, and why this coordinate frame is necessary.

Although Figure 3 shows that damage from FIB milling is distinct from typical radiation damage in cryoEM, the statement “This is consistent with a model in which the FIB-damaged targets have effectively lost a fraction of their structure, compared to undamaged targets, possibly due to displacement of a subset of atoms by colliding ions” currently relies solely on the use of 2DTM SNR as a measure. This could be more strongly supported by solving structures or 2D classes of LSUs from varying depths in the lamella to observe the damage. Solving these structures from the datasets and low pass filtering them to different spatial frequencies (as done in Figure 3) would be helpful to see if there are consistent changes at the molecular level that could explain the propagation of radiation damage as a result of displacement of atoms due to colliding Ga+3 ions.

Figure 2A illustrates how variable 2DTM SNR values can be. Outlining potential workflows for experimental validation of this ribosome dataset and other datasets would be helpful for those wishing to benchmark cryo-FIB milling damage on their own particular system of interest.

In the discussion section, the practical recommendation that particles more than 30 nm deep can be used for subtomogram averaging seems at odds with the earlier observation that damage can be seen up to 60 nm deep from each lamella surface.

The 2DTM approach and the estimation of radiation damage relies on the reduction in correlation between the ideal, undamaged template and SNR. Can there by any other reasons for a reduction in this correlation other than FIB milling and/or electron exposure? e.g. stacking of multiple particles in Z-direction in case of higher ice thicknesses leading to reduced detectability.

The fact that the damage makes it impossible to pick entire particles suggests that the scale of radiation damage due to Gallium ions makes the entire particle explode. Are there any instances in which you see differential damage meaning that only a part of the particle is damaged and the rest is intact? I would expect this since different parts of particle are exposed at different points along Y axis. Otherwise, it can be concluded that the damage is always of “whole” particles but there is no conclusion clearly stating this. This could be worth exploring.

Figure 3F: it is well established that at low scattering angles, the electron scattering factors of negatively charged atoms can be negative. Was this considered when calculating the SNR from phosphorous atoms in the phosphodiester bond?

Minor points

In Figures 1G and 1H, we suggest using nanometers instead of Angstroms as the x-axis label. This will make it easier to interpret the blue dotted lines as the edges of the lamella.

In Figure 1, it is unclear if “relative 2DTM SNR” of Figures 1E and 1F are exactly the same as “2DTM SNR” of Figures 1G and 1H.

How does the 2DTM method take into account the radiation damage in template structure? Would it be possible to recapitulate the template model based on the known mechanisms of radiation damage due to exposure to electron beam during imaging so as to use a close to ideal model for template matching?

It would be helpful if the rationale for selection of undamaged, ideal templates in 2DTM is explained in more depth. For example, are these from single particle cryoEM data with the minimum possible radiation damage or are they from other experimental techniques?

What does the dotted line in Fig 2D represent?

Why are there more outliers (outside the lamella thickness) in 120 nm sample (fig 1G) than in the case of 200 nm lamella? What does the existence of these outliers indicate?

Are particles averaged over Y-axis in fig 1G and H?

What is the error in measurement of electron exposure and how is it propagated in measuring the radiation damage?

-Tushar Raskar, Daphne Chen, and James Fraser

On 2018-04-28 01:00:25, user Pat Schloss wrote:

Thanks for including mothur in your comparisons. FWIW, I went back and found a bug that explains the poor performance of mothur's implementation of SparCC. I've posted my comparison on the authors' repository: https://github.com/scwatts/...

On 2017-05-11 20:31:38, user AvitoholX wrote:

"Interestingly, the population of Euskera speakers shows one of the maximal frequencies (87.1%) for the Y-chromosome variant, R1b-M269 [12], which is carried at high frequency into Northern Europe by the Late Neolithic/Bronze Age steppe migrations [4,5,13]"

What??? Where did they take this? There is no Yamnaya ancestry present in Portugal until 1500 BC, very little in the contemporary Basques, and yet Basques obtained their 87% frequency of R1b from the Yamnaya-infected Central Europeans?

On 2020-05-25 20:02:19, user Lambodhar Damodaran wrote:

Nice paper! Just wanted to share another paper where An. stephensi was detected in Ethiopia. Work is currently being done to characterize insecticide resistance observed in the population there. https://www.sciencedirect.c...

On 2019-11-29 09:21:32, user Mun-Gwan Hong wrote:

It is an interesting paper about pQTLs identified with WGS. <br /> An intriguing observation is the (probable) overrepresentation of intron\_variant and amino-acid changing variants (missense\_variant, splice\_region\_variant, stop\_gained) among the replicated cis-pQTLs. Limiting to the top cis-pQTL per protein, 28 out of 80 pQTLs (35%) were intron\_variant, while 9 of 80 (11%) were amino-acid changing variants, according to Supplementary Table 1a. The proportion is surprisingly high. It is maybe worth to discuss in this manuscript.<br /> Those amino-acid changing variants might be identified due to affinity difference toward different proteoforms of one protein as we saw in our study (doi: https://doi.org/10.1101/464... "https://doi.org/10.1101/464909)"). I don't have a clear idea on the enrichment of intron\_variant yet. But, I would check first if some of the intron\_variants are, in fact, the variants related to alternative splicing, or LD proxies of amino-acid changing variants.

On 2017-04-26 20:22:11, user Brian Martinson wrote:

Thanks for the opportunity to review and comment on this very interesting research. I've got one suggestion and a question for you. (I offer these in addition to supporting Chris Mebane's suggestions, particularly those with respect to further explication of methodology and more detailed description of the Lee & Schrank hypothesis)

Suggestion: At various points in the paper you use of the term "questionable" or "questionable practices" or "questionable research practices," to refer to the image manipulation falling into your Category 2. However, from the description in the paper of what you classified into that category, that type of thing clearly violates currently accepted standards for <br /> allowed types of image manipulation, whether due to ignorance, <br /> carelessness or malfeasance, meaninging the behaviors themselves are not really questionable at all. So, in keeping with the recently introduced terminology of the NASEM report - Fostering Integrity in Research (disclosure: I was a member of the authoring panel of the report), it would seem more correct to reference these as "detrimental," "detrimental practices," or "detrimental research practices."

Question: It's great to see efforts to empirically test hypotheses derived from a variety of theoretical perspectives, positing potential influences arising at multiple levels ranging from systemic, to local, to intra-individual. At the same time, this raises an interpretation question in particular about the country-level associations observed, but perhaps also about the team-level associations observed. It's not clear to me how the methodology you've employed helps to avoid making the exception fallacy - the error of "exceptional cases" leading to conclusions being reached about the larger groups from which the cases are drawn. In most multi-level analyses this concern is typically addressed through the use of multi-level models where the units of observation are distinguished from the units of analysis and the latter are specified at two, three or sometimes four different "levels" in the context of generalized linear modeling of some sort. It may be arguable whether, or to what extent, such methods help to avoid the exception fallacy, but they do represent an explicit recognition of the issue. Perhaps I missed it, but I don't think you've employed such multi-level modeling techniques here, nor do such techniques appear to have been employed in the 2017 Fanelli et al. PNAS publication? To give just one example of how this might lead to an interpretation problem, if a case arises from, say, a country in which there are institutional level policies about misconduct, one doesn't know whether the individual who engaged in the image manipulation was employed at an institution with or without such a policy. And regardless of whether their institution had such policies in place, one doesn't really know the extent to which the individual was even "exposed" or subject to the influence of the policy's presence or absence. If I'm right, then more caution is warranted in the interpretation of the associations beyond those at the individual-level.

On 2014-07-23 12:06:43, user Víctor Raggio wrote:

read it yesterday. excellent work. great review.

On 2018-01-04 21:08:32, user Jeffrey Ross-Ibarra wrote:

Although current data strongly suggests a single domestication of maize (Matsuoka et al. 2002), knowing the geographic location of domestication is of interest for a multiple reasons. It may be of use agronomically, allowing us to identify portions of the range of maize’s wild ancestor teosinte most likely to harbor novel genetic diversity. But it is also of interest scientifically in terms of our understanding of how domestication occurs. Is maize descended primarily from a single population on one hillside and spread from there? Or was maize domestication a more dispersed process, involving selection and gene flow across a number of populations by multiple groups?

By studying a nice sampling of maize and teosinte populations from across Mexico, Moreno Letelier et al. (2017) seek to reasses the genetic evidence for specific geographic origins of maize domestication. Using a number of different methods, they claim “the likely ancestor of maize may be an extinct population of teosinte from Jalisco or the Pacific coast”.

I should state from the start that I don’t know where maize was domesticated. The SouthWest Mexican lowlands <1800m<br /> seems pretty likely given all the evidence, but whether Jalisco or Michoacan or Balsas I don’t think the genetic data have yet said with any certainty.

Below I detail some concerns with the analyses presented here.

Jalisco as ancestor

Moreno Letelier et al. (2017) build dendrograms of genetic distance (Figure 3) among all their samples, finding that parviglumis from Jalisco is closer to maize than populations from the Balsas. I don’t doubt this result, but as we discuss in Van Heerwaarden et al. (2011), this could be due to gene flow instead of ancestry. Current gene flow from parviglumis to maize is known in Jalisco (see e.g. discussion in Serratos (1997)), and should be discounted as an explanation before trying to infer ancestry from genetic distance alone. Indeed, in their own TreeMix analysis (Figure 4), Jalisco populations of teosinte form a single group with other teosintes, and are thus no more “ancestral” than any other (but see below for issues with TreeMix analyses). Given the really nice data the authors have, I’d be tempted to do something like redoing the analyses of Van Heerwaarden et al. (2011), especially if combined with denser geographic sampling.

I’m not sure where the inference of an “extinct” population comes from, as this idea seems mentioned only in the abstract.

TreeMix

The authors use TreeMix (Pickrell and Pritchard 2012) to test for gene flow. This method first builds a population tree using allele frequencies, then adds edges (arrows) of migration to account for excess covariance in allele frequencies between populations. However, the authors chose to compare all domesticated maize as a single group to individual populations of teosinte. This means any post-domestication gene flow between maize and teosinte (which is presumably restricted to sympatric populations) is either missed entirely or interpreted as gene flow between all maize and teosinte. Indeed, the gene flow shown on Fig. 4 is between maize and mexicana, as has been well documented in the highlands of central Mexico (Hufford et al. 2013), but is limited to populations there and perhaps the Southwest US (Fonseca et al. 2015).

A clue that this analysis might be problematic comes from the monophyletic grouping of all teosinte (both mexicana and parviglumis) separate from maize. Taking this at face value would suggest those subspecies split after domestication, which seems somewhat unlikely given both genetic (Ross-Ibarra, Tenaillon, and Gaut 2009) and ecological (Hufford et al. 2012) evidence they’ve been distinct for some time.

I think it would be preferable to sample a number of maize populations and include each in the analysis, hopefully allowing TreeMix to do a better job building the correct tree and localizing gene flow. SeeDs of Discovery data, for example, provides publicly-available SNP data for ~5,000 maize landraces.

ABBA-BABA

The authors then apply the ABBA-BABA test (Durand et al. 2011), which tests for assymetry in counts of shared derived alleles between two taxa in an ingroup with a third taxon. If the tree depicting the relationship between species is correct, then both ingroup taxa should share similar numbers of derived alleles with the third taxon. Asymmetry in numbers of shared derived alleles then suggests gene flow. Here, the authors use only maize from the highlands of central Mexico for this test, citing Freitas et al. (2003) that these landraces were likely the first to be domesticated. But the widespread gene flow from mexicana into highland maize makes a problematic choice to use for understanding the origin of maize domestication (Van Heerwaarden et al. 2011). Moreover, both trees show teosinte populations sharing a common ancestor more recently than either do with maize, which seems problematic. The first tree (((Jalisco,Balsas),maize),Tripsacum) shows the two parviglumis populations splitting post maize domestication, which is only plausible if one is a very recently derived colonist. The second tree (((*mexicana*,Balsas),maize),Tripsacum) shows parviglumis and mexicana diverging after their common ancestor with maize, which as discussed above is likely wrong. Significant D (or fd) statistics here may thus mainly reflect that the tree is wrong. Perhaps instead the questions of maize origin might be one of comparing a “Jaslico-ancestral” tree (((Jalisco,maize),Balsas),Tripsacum) to a “Balsas-ancestral” tree (((Balsas,maize),Jalisco),Tripsacum) – I’m dubious ABBA-BABA is the appropriate way to go about this though.

From the lit

Both Van Heerwaarden et al. (2011) and Hufford et al. (2013) are papers produced by my lab, so I’m clearly not objective, but in several places the authors seem to ignore or misinterpret results from these papers, highlighting instead results from their own work which are pretty similar.

Recognizing that gene flow from mexicana likely causes biases in identifying ancestral maize populations, Van Heerwaarden et al. (2011) used a broad sampling of >1,000<br /> landraces to estimate ancestral maize allele frequencies. We identified numerous samples from Western Mexico (including multiple samples from Jalisco) as those most genetically similar to the putative ancestor of modern maize. Notably, however, we did not suggest “ancestral teosinte alleles in the Western region, rather than the Balsas Basin” (emphasis mine) – we actually didn’t have the resolution to really say one way or the other (see our Figure 3B). In fact, in spite of our lack of resolution, we mostly interpreted our data as consistent with archaeology and previous genetics as supporting a Balsas origin. In spite of its inclusion as evidence supporting a possible Jalisco origin, Moreno Letelier et al. (2017) seem to forget our paper later, however, claiming that “dense enough sampling in the mountains of Jalisco… were not considered in previous studies as a potential center of domestiation”, and noting “the inclusion of Jalisco populations here, which have not been used previously in other studies”.

Hufford et al. (2013) used the same genotyping platform as Moreno Letelier et al. (2017) to test for gene flow between mexicana and highland maize. But while Moreno Letelier et al. (2017) claim “previous studies could not differentiate between contemporary processes and ancestral introgression”, we explicitly used HapMix (Price et al. 2009) to estimate the timing of admixture from tracts of inferred ancestry. Our analysis was problematic for a number of reasons – for example assuming a single bout of admixture – but nonetheless revealed that maize alleles in mexicana were mostly young while mexicana alleles in maize could be quite old, consistent with adaptive introgression from mexicana into maize upon colonization of the highlands and selection against gene flow from maize into mexicana (see Fig. S4 in Hufford et al. (2013)). The authors later compare their inferred 9.6% introgression from mexicana into maize to experimental results showing 1-2% (citing our review (Hufford et al. 2012), but presumably referring to results from Ellstrand et al. (2007)), but don’t mention the nearly identical 9.8% estimate from Hufford et al. (2013) using STRUCTURE (Pritchard, Stephens, and Donnelly 2000) (our HapMix estimate was 19.1%). Their result that “there are more introgressed alleles from mexicana to maize than in the opposite direction” also echoes our finding that “gene flow appeared asymmetric, favoring teosinte introgression into maize”.

Fnally, Moreno Letelier et al. (2017) seem to imply that climate data pointing to the existence of refugia in Western Mexico favor a Jalisco origin for maize. But the paper they cite – Hufford et al. (2012) – instead argues “there has been little change in the subspecies’ ranges from the time of domestication to the present”, and at least by my reading makes no reference to specific geographic areas as more likely domestication origins.

References<br /> Durand, Eric Y, Nick Patterson, David Reich, and Montgomery Slatkin. 2011. “Testing for Ancient Admixture Between Closely Related Populations.” Molecular Biology and Evolution 28 (8). Oxford University Press: 2239–52.

Ellstrand, Norman C, Lauren C Garner, Subray Hegde, Roberto Guadagnuolo, and Lesley Blancas. 2007. “Spontaneous Hybridization Between Maize and Teosinte.” Journal of Heredity 98 (2). Oxford University Press: 183–87.

Fonseca, Rute R da, Bruce D Smith, Nathan Wales, Enrico Cappellini, Pontus Skoglund, Matteo Fumagalli, José Alfredo Samaniego, et al. 2015. “The Origin and Evolution of Maize in the Southwestern United States.” Nature Plants 1. Nature Publishing Group: 14003.

Freitas, Fabio Oliveira, Gerhard Bendel, Robin G Allaby, and Terence A Brown. 2003. “DNA from Primitive Maize Landraces and Archaeological Remains: Implications for the Domestication of Maize and Its Expansion into South America.” Journal of Archaeological Science 30 (7). Elsevier: 901–8.

Hufford, Matthew B, Paul Bilinski, Tanja Pyhäjärvi, and Jeffrey Ross-Ibarra. 2012. “Teosinte as a Model System for Population and Ecological Genomics.” Trends in Genetics 28 (12). Elsevier: 606–15.

Hufford, Matthew B, Pesach Lubinksy, Tanja Pyhäjärvi, Michael T Devengenzo, Norman C Ellstrand, and Jeffrey Ross-Ibarra. 2013. “The Genomic Signature of Crop-Wild Introgression in Maize.” PLoS Genetics 9 (5). Public Library of Science: e1003477.

Matsuoka, Yoshihiro, Yves Vigouroux, Major M Goodman, Jesus Sanchez, Edward Buckler, and John Doebley. 2002. “A Single Domestication for Maize Shown by Multilocus Microsatellite Genotyping.” Proceedings of the National Academy of Sciences 99 (9). National Acad Sciences: 6080–4.

Moreno Letelier, Alejandra, Jonas A. Aguirre Liguori, Maud I Tenaillon, Daniel Piñero, Brandon S Gaut, Alejandra Vazquez Lobo, and Luis E Eguiarte. 2017. “Was Maize Domesticated in the Balsas Basin? Complex Patterns of Genetic Divergence, Gene Flow and Ancestral Introgressions Among Zea Subspecies Suggest an Alternative Scenario.” BioRxiv. Cold Spring Harbor Laboratory. doi:10.1101/239707.

Pickrell, Joseph K, and Jonathan K Pritchard. 2012. “Inference of Population Splits and Mixtures from Genome-Wide Allele Frequency Data.” PLoS Genetics 8 (11). Public Library of Science: e1002967.

Price, Alkes L, Arti Tandon, Nick Patterson, Kathleen C Barnes, Nicholas Rafaels, Ingo Ruczinski, Terri H Beaty, Rasika Mathias, David Reich, and Simon Myers. 2009. “Sensitive Detection of Chromosomal Segments of Distinct Ancestry in Admixed Populations.” PLoS Genetics 5 (6). Public Library of Science: e1000519.

Pritchard, Jonathan K, Matthew Stephens, and Peter Donnelly. 2000. “Inference of Population Structure Using Multilocus Genotype Data.” Genetics 155 (2). Genetics Soc America: 945–59.

Ross-Ibarra, Jeffrey, Maud Tenaillon, and Brandon S Gaut. 2009. “Historical Divergence and Gene Flow in the Genus Zea.” Genetics 181 (4). Genetics Soc America: 1399–1413.

Serratos, J Antonio. 1997. Gene Flow Among Maize Landraces, Impoved Maize Varieties, and Teosinte: Implications for Transgenic Maize. CIMMYT.

Van Heerwaarden, Joost, John Doebley, William H Briggs, Jeffrey C Glaubitz, Major M Goodman, Jose de Jesus Sanchez Gonzalez, and Jeffrey Ross-Ibarra. 2011. “Genetic Signals of Origin, Spread, and Introgression in a Large Sample of Maize Landraces.” Proceedings of the National Academy of Sciences 108 (3). National Acad Sciences: 1088–92.

On 2019-02-28 09:21:59, user Seyed Yahya Shirazi wrote:

This paper is accepted at the IEEE EMBS Conference on Neural Engineering (NER) 2019.

On 2020-01-30 16:57:21, user Ying Zhang wrote:

In general, single-cell analysis is still at very early stage, especially for cell-type identification (how many markers were used for each cell-type?). Also, as Seurat developers stated the clustering result is sensitive to the parameters chosen (and they are still using Seurat v2 vs the current version is Seurat 3), it is not convince to draw some of the conclusions in the paper, such as the composition of cell types in the samples.

Given all these technical details, I don't think they could meaningfully comment on the comparison between ONE Asian sample with other samples.

On 2020-01-12 08:17:28, user Mikhail Katkov wrote:

published ( https://journals.aps.org/pr... )

On 2016-01-04 05:48:45, user Mike Gandal wrote:

It would be interesting to see how much of the technical variation you see across batches is due to differences in sequencing depth. Also, it would be good to report how the quantification and normalization methods used by the different studies.

On 2019-06-30 13:58:35, user S2 wrote:

I like this paper. I have two questions. If the authors would like, please answer them.

1. Which do you think epithelial arroy is mainly depend on composition of placozoan epithelial sheet (very thin cells, AJ only junctions, etc) or specific features of placozoan molecules ?

2. Do not Individual placozoas bond after they fission ?

Thank you.

On 2020-03-04 14:58:04, user Jackie wrote:

Congratulations to your great finding in your recent paper<br /> Unfortunately we reported this mutation and the furin cleavage site on 21th,Jan on researchgate<br /> https://www.researchgate.ne...

Although our paper was written in Chinese, the figure 1 and the English abstract clearly tell readers what we found.<br /> This virus killed many Chinese. So this finding has political meaning to our country and people.<br /> I hope you can cite our paper in your published version.

Xin Li, Guangyou Duan, Wei Zhang, Jinsong Shi, Jiayuan Chen, Shunmei Chen, Shan Gao, Jishou Ruan.<br /> A furin cleavage site was discovered in the S protein of the 2019 novel coronavirus.<br /> Chinese Journal of Bioinformatics (In Chinese), 2020, 18(2): 1-4. doi: https://doi.org/10.12113/20...

If you have any requirement, I would like to listen and try my best to accept.

Thank you very much<br /> Best regards

On 2023-12-07 12:33:31, user kamounlab wrote:

The method / software looks interesting but I cannot find a link or a citation. Perhaps a Gihub? Otherwise, the preprint isn’t useful.

On 2019-06-04 22:05:53, user Charles Warden wrote:

I overlooked this link the first time that I saw this paper, so I hope this is useful for other people:

https://www.nygenome.org/bi...

I think having raw data from dbGaP is also important, so I hope that will be available soon.

Thank you very much for posting this very interesting paper!

On 2020-01-15 10:33:10, user qx jiang wrote:

This paper is now published by JBC with a different, more informative title. <br /> https://www.ncbi.nlm.nih.go...

On 2024-05-29 13:04:29, user Fares SAYEGH wrote:

very elegant demonstration

On 2021-02-01 20:57:24, user Crypto Microbiology wrote:

Finally!!!!<br /> I was modelling the COVID infection and I thought that the infection would have been ended before.... How could I know people was submitting crappy assemblies!!! I have downloaded too many SRA sequences and I have found serious assembly problems :) Thank you for your nice work...

On 2020-05-19 10:20:56, user Vikas Bansal wrote:

We have published with the similar name. Please have a look here https://github.com/imsb-uke...

On 2017-05-26 11:09:15, user David Posada wrote:

Hi Iñigo, yes amazing paper.

Regarding your exchange with Rob, and also related to some analogies you make in the paper, I believe it is important to distinguish between a dN/dS poisson for counts in cohorts, and a markov model for state changes through time in a single tumor sample genealogy. They are not totally equivalent, although is clear that model selection could be (and should be, as Rob said) used in both cases. Iñigo you are giving verbal arguments for model fit, but ideally they should be supported by AIC differences, for example. I can say that model selection among 4x4 markov models in single-cell tumor data tends to choose "complex" models (i.e., GTR+G). Whether a trinucleotide markov model within individual would give a better AIC scores than a GTR+G or a "standard" codon model, is something to be tested...the "low" number of mutations in coding regions expected within a tumor might suggest that 192 parameter might be overfitting... will see.

Another interesting point is that you measure here overall dN/dS in tumor cohorts, while selection acts on single tumors. Your estimate is a an average of the individual dN/dS for each tumor, correct? and these tumors, even for the same cancer type, could be quite distinct (age, pathways involved) So is it possible that a few individual tumors are under negative selection while most are under strong positive selection?

On 2020-05-11 10:51:11, user Chiara Crestani wrote:

Nice work but you list LMG15085 as animal isolate, it is actually a human isolate https://eid2.liverpool.ac.u...

On 2017-10-07 19:04:04, user AdamMarblestone wrote:

-"Attractor dynamics in networks with learning rules inferred from in vivo data" https://www.biorxiv.org/con...

On 2017-01-02 02:45:20, user AdamMarblestone wrote:

-"Theta Coordinated Error-Driven Learning in the Hippocampus" http://journals.plos.org/pl...

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 2023-04-08 05:50:30, user Qianmu Yuan wrote:

I think it's unfair to claim that LMetalSite was over-trained for the proposed reasons by the authors in Section 3. First, LMetalSite is a binding site detection method, which is not designed for binding protein identification. Most methods in this field are only trained on binding proteins. If the authors want to use LMetalSite to distinguish binding vs. non-binding proteins, they should re-design the output scores, just like [PepNN Commun Biol 2022]. However, they did not mention any details about how to convert the residue-level predictions by LMetalSite into protein-level predictions. Second, the datasets of LMetalSite removed redundant sequences sharing identity >25% over 30% alignment coverage, which is a strict and common threshold used in this community. The new datasets in M-Ionic collected sequences with no more than 20% identity, which is similar to LMetalSite. However, M-Ionic used a loose threshold for the alignment coverage (90%). Besides, the negative set collected by the authors only removed redundant sequences sharing 100% identity, which might be another potential problem.

On 2020-07-03 06:49:53, user Wolfgang Jarolimek wrote:

The paper is technically very well executed. The data are nicely presented and described. The results and in particular the conclusions are not convincing. It is claimed that the antibody inhibits lysyl oxidase like 2 activity, but already the initial reports show that it is a very weak inhibitor. The authors did not try to show its primary activity and therefore, all results and conclusions could derive from effects other than inhibition of LOXL2 activity. Given that the reported effects are opposite to the literature, it may be more likely that the problem is in the antibody and not the superiority of the primary cell system. This should be discussed.

On 2020-02-04 16:45:35, user Clive Thomason wrote:

Not being a medical person, the statistics and analysis interest me. The EG, ML an<br /> d R are so variable at present due to reliability of reporting. As the confirmations can run from 4 to 14 days from first presentation to clinic, then the R value can be from 1.195 to 2.2 as so far extrapolated. When putting this into my spreadsheet, it can give a figure by 29th feb of EG from 550,00 to ML 2.2m. So very variable. Then also as within a given % of infected how many of those are positive or negative within a 4 day window that would not show in statistics. I also take into account on R value, the infrastructure of a country, the health care available and the lifestyle at large, which gives the R value of China compared to USA as 1.196 to 1.091. So very difficult to tell until 6 weeks statistics are available

https://docs.google.com/spr...

On 2021-07-12 19:00:49, user Taj Azarian wrote:

Great idea and a very nicely implemented tool! We particularly appreciated the analysis vignettes with strep pnuemo and staph aureus. We started experimenting with it last week as it provides a nice addition to one of our current studies.

We do have a couple practical questions about "best practices"...<br /> 1) Are there any considerations for how the initial time-scaled tree is inferred? For example, if you performed model testing with BEAST and found that a skygrid demographic model with relaxed clock best fit, would this violate the underlying assumption of constant background population size? In this scenario, would it correctly identify the expanding lineages but underestimate the effective population size of the expansion? Would a better approach be to infer an initial tree using a relaxed clock and constant population size?

2) Regarding the analysis of SPN, are there any thoughts to how recombination would impact the performance of the tool? I am less concerned about ancestral recombination that is generally shared by all members of the population of interest. However, if the expansion of a lineage was associated with a recombination event (e.g., a capsule switch in pneumo or even just a large recombination block impacting protein antigens), how would that bias the detection of the expansion or the population size in relation to the background? (We can assume these events were detected and censored before coalescent analysis)

I don't expect endless simulations to address all the different possible population dynamics, but I think some comments about the recombination questions would be of general interest to those of us that work on highly recombining bacteria.

Thanks again and great work!

On 2021-02-04 17:08:43, user Daoyu Zhang wrote:

https://www.nature.com/arti...<br /> Computer modeling is the #1 worst way to spin fake results. None of your alleged “higher than human” affinities are actually higher than human when actual experiments are conducted using untagged ACE2 and RBD in a Surface Plasmon Resonance (SPR) assay. Intact spike trimers https://www.researchsquare....<br /> and transduction Assays using real virus <br /> https://www.biorxiv.org/con...<br /> Unanimously rank human ACE2 at the highest binding affinity and transduction efficiency of all ACE2 used in the experiment.

On 2016-07-07 12:56:24, user Christina K. Pikas wrote:

I'm not clear on "key" - does this refer to the accession number for the article or is it something else found only in the paid version? Also, in appendix 1, why not do an index browse to find the journal? if you're looking for variations, also need to truncate?

On 2020-01-23 14:45:06, user Silas Kieser wrote:

Interesting article. What about using the CAMI 1 dataset to test the plasmid prediction. It would be a simulated dataset from an independent source. If I remember right it contains a lot of plasmids.

On 2020-06-04 03:06:08, user Ron Conte wrote:

They say the S-enantiomer was 60% more effective than the R-enantiomer. Yes, but it was (by my quick calculation) only 17.6% more effective than the mix of R- and S-HCQ that is used as medicine. Is that not correct?

On 2022-05-11 01:49:49, user bioRxiv wrote:

This preprint is participating in the Comment-a-thon pilot initiative by bioRxiv/medRxiv at the Biology of Genomes CSHL meeting. If you are registered for this conference you can enter the competition by signing up using the link provided at the meeting. Remember to add #BoG22 to your comments.

On 2021-12-13 03:40:59, user kellen westra wrote:

Pre-print Review on “Copper(II) Gluconate Boosts the Anti-SARS-CoV-2 Effect of Disulfiram In Vitro”

Summary:<br /> The research done here in this paper does a great job at studying how we can better fight COVID-19. Here they look at two different medications, Disulfiram, an anti-alcoholism drug, and copper gluconate which is a common food additive or copper supplement. The study looks at how the mixture of these two drugs effects the anti-SARS-CoV-2 activity at the cellular level. They compare this to how well the two drugs effect the activity of the anti-SARS-CoV-2 activity on their own. They found that a 1:1 ratio of these drugs does a very good job, going from around 67% for each drug on their own to over 90% against -SARS-CoV-2 when joined together. They also found that the EC50 for the 1:1 combination was even lower than that of the two on their own.<br /> This research is very applicable to todays day because of the pandemic we are going through, although we are obviously most interested with what happens in vivo. I thought the research was very interesting though, and if it could be replicated in vivo could be very useful in fighting COVID-19. I thought the paper itself could use some work, but the information was there and it is important to get it out there.<br /> Areas for improvement:<br /> Major: I thought some of the major things that needed some improvement was the lack of an introduction and conclusion. The paper had great information and did a great job explaining the research, but an introduction is necessary to give background knowledge of research done in the past, why the research is important, etc. A conclusion was also missing and that would have helped the paper flow more and wrap up the research in a concise and understanding manner.<br /> Minor: There were a few grammar issues, but nothing too terrible. The author also went off on a tangent that was hard to understand, and didn’t really fit in the last paragraph of the results and discussion (starting at line 37).

On 2024-08-12 15:52:52, user Swathi Sukumar wrote:

Are the IF images in Figure 7, Z-stacks? Max projection of Z-stacks and more technical replicates are required. Additionally, how were the puncta counted?

On 2017-02-08 06:13:46, user Martin Modrák wrote:

Paying for submitted (not accepted) manuscript is not unheard of in the economic sciences ( http://cofactorscience.com/... "http://cofactorscience.com/blog/submission-fees)")<br /> On the other hand, The Cryosphere and a few other geo journals used to have APC per submission, but now have APC per published paper (http://www.egu.eu/news/195/... "http://www.egu.eu/news/195/changes-for-egu-interactive-journals-new-library-and-payment-concept/)").

Maybe ask there for their experience?

On 2020-04-17 01:20:14, user Ksenia Krasileva wrote:

Dear Ethan and Madhav,

We read your pre-print in our lab journal club and collected comments that we hope might be useful: https://docs.google.com/doc...

Do not hesitate to contact me if you have questions or comments.

-Ksenia

On 2023-02-07 09:28:11, user Sjors Scheres wrote:

This preprint poses a hypothesis that extra densities that have been observed in many cryo-EM reconstructions of amyloids from brains with neurodegenerative diseases are caused by a straight form of RNA (ortho-RNA). This hypothesis is purely based on the fitting of atomic models in the extra densities. No experimental evidence to support this hypothesis is provided.

The idea that RNA assembles together with amyloids has been proposed; the new contribution here is the fitting of atomic models. But, because of disorder and a possible mismatch with the imposed helical symmetry, the quality of the extra densities is generally poor. Therefore, the potential fit of atomic models of molecules in these densities is not enough evidence to conclude that such molecules are responsible for these densities.

More experimental data will be needed to find out what molecules are responsible for these extra densities, which would have potentially important implications for our understanding of these diseases.

On 2021-02-09 12:18:08, user yon katz wrote:

Interesting study. How can I see the Supp. Movies? Thanks.

On 2025-01-30 09:58:54, user Dorit Hockman wrote:

This pre-print is now published in Nature Genetics https://www.nature.com/articles/s41588-024-01990-6

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

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

On 2017-08-24 14:01:00, user Jack Wilkinson wrote:

On further reflection, the sequential 'model check' we describe for the latent variable method here (Fig 3) *probably isn't* a sensible model check. If anything, it is a way to assess the apparent predictive performance of the model, which is obviously a bit different. A more appropriate model check would compare the posterior predictive for each submodel against the observed responses at the corresponding stage. I'm currently working on implementing this. The idea of using multistage models to make multivariate predictions still holds, but this isn't a good way to check your model.

On 2022-05-12 06:39:17, user Lei Yang wrote:

These results provide the first description that a HSC70 chaperone binds its own mRNA via the C-terminal SVR domain and by this means regulates its own translation. Note that this finding explains for the first time the discrepancies found between transcription and translation of HSC70 chaperones. This let us propose that a post-transcriptional auto-regulatory HSC70 feedback loop exists regulating chaperone activity within and between tissues.

On 2018-05-29 18:30:53, user Meera Sundaram wrote:

This paper clearly shows the importance of actin and myosin<br /> in patterning apical extracellular matrix structures (C. elegans alae). But I<br /> think a better model to explain the data might be patterned matrix secretion<br /> that depends on contractile actin-myosin that coats secretory granules and<br /> helps expel their contents– e.g. as shown by Shilo’s group for glue-protein<br /> secretion in Drosophila (Rousso et al 2016, Nature Cell Biology 18, 181-191).

What does your seam>UTRNCH::GFP reporter look like after<br /> actin or nmy RNAi? Does the orientation of actin bundles become disorganized in<br /> a way that matches the alae “mazes”? (Figure 4 appears to be showing animals at<br /> too late of a stage to address this).

Btw, two papers from my group have demonstrated that<br /> mutations in sheath aECM cause alae gaps similar to what you show here for<br /> noah-1 (Gill, Cohen et al 2016 PLoS Genetics 12(8):e1006205; Forman-Rubinsky et<br /> al 2017, Genetics 207, 625-642).

On 2025-02-07 21:55:49, user Patricia Legler wrote:

We made FRET substrates for CHIKV PMID: 30742841. They are in the Suppl. Info.

On 2023-07-14 16:23:14, user John Smith wrote:

This conclusion doesn’t quite make sense.

No question bat ZC45 & ZXC21 from Zhoushan island, Zhejiang province (the most east part of china) were chimeras between bat SARS-Cov-1 lineage and *ancestral* bat SARS-CoV-2 lineage. Ancestral bat SARS-CoV-2 viruses may once existed in central region

The Germany estimation of ~90 years of split makes sense.

On 2020-03-15 17:25:33, user Andy wrote:

Based on the study design shown in Figure 1, the conclusion that reinfection cannot occur is based on N=2 monkeys. And 1 of those 2 was euthanized 5 days after re-exposure.

Please read the paper carefully before jumping to conclusions based on 1 monkey.

On 2020-04-29 06:44:37, user Kijeong Kim wrote:

Haplotracker: a web application for simple and accurate mitochondrial haplogrouping using short DNA fragments

On 2025-04-18 04:12:11, user Luca Jovine wrote:

Published on 11 April 2025 as:

Dioguardi E., Stsiapanava A., Fahrenkamp E., Han L., De Sanctis D., Inzunza J. & Jovine L.<br /> Structural basis of ZP2-targeted female nonhormonal contraceptionProc. Natl. Acad. Sci. U.S.A. 122 (15):e2426057122 (2025)<br /> DOI: https://doi.org/10.1073/pnas.2426057122 <br /> PubMed: https://pubmed.ncbi.nlm.nih.gov/40215272/

On 2019-05-16 21:13:18, user Anasto wrote:

So much wishful thinking in one paper. Lots of claims of ‘proof’ where no such thing was shown. Here’s a list from reading just the first half: <br /> - 16S for identification of an actinobacteria strain is like from the 1990s. We know more about ribosomal sequence diversity these days. <br /> - Sampling effort is weak. The insects were pooled? - likely mixing environmental microbes. Not replicated over time and space. Cannot make claims about symbiosis from bugs caught in two traps. <br /> - The chitin media seem to be super low nutrient. The strep was isolated because this media was used, not because the bacteria is biologically meaningful. Why did you not use any other method? <br /> - The sampling from the nests was not really replication. A contaminant would turn up in exactly the same pattern if sampled from insects and nest material from the same three tubes. <br /> - The supposedly “mutualistic” streptomyces was detected in trace amounts (a few CFUs form large chunks of material or pooled bug samples). Why believe that it is biologically relevant? <br /> - The putative closest relative strep on genbank is a common soil bacteria. Makes me think that this really is a contaminant. <br /> - No attempt to work with the fungus of one of the insects. It was not even isolated or confirmed. Any conclusion about that system is moot. <br /> - Using some artificial agar and sawdust goo for the nests. Bound to be totally different than dead wood, and support/select an unnatural set of microbes.<br /> - I pull this one out verbatim first because it sums it all… line 381: “The consistent isolation of this Nectria sp. suggests that it is vectored by the ambrosia beetles”. There is a hundred years of literature showing that you cannot infer a vector, not to mention a symbiont, from just occurrences in media. You cannot ignore a century of research and throw around these vague claims. Nectria is common in sick plants, and there is no evidence that it interacts with these bugs, other than simply living in the same substrate. What if the bacteria and the Nectria are simply in the same habitat as the bugs, and THAT’s why they are in the samples? You need environmental control samples.

I stopped here – too many problems.

On 2022-08-27 09:19:57, user Jacques Fantini wrote:

Interesting.<br /> By the way it would be fair to cite previous articles on SARS-CoV-2 and ganglioside GM1<br /> e.g. Biochem BiophysRes Commun. 2021 Jan 29;538:132-136. <br /> J Infect. 2021 Aug;83(2):197-206. <br /> Best regards.<br /> Jacques Fantini.

On 2017-06-15 01:13:21, user John Belmont wrote:

Really nice work and highly relevant to the clinical interpretation problem. The result shown for CDKL5 is impressive.<br /> Did you consider developing the constraint model based on protein domain annotations?

On 2020-06-09 11:18:46, user BlackWinny wrote:

An excellent paper that should even be distributed (translated into vernacular languages if needed) as early as in junior high schools, without waiting to enter high school and university.

On 2022-10-24 15:59:40, user Nyah Johnson wrote:

Hi! I thought that this was a great paper. It was very informational and allowed for easy understandings of the goal of the research which i thought was pretty interesting. I did, however, have some questions about the main points. I think a point being proven was in regard to the question about parasites expressing antigenic variation while in extracellular spaces. I was curious on when exactly the antigenic variation occurs. Is there a signal that notifies the parasite when to change the VSG coat also? I think ultimately these were questions the paper posed as well, however i think it could be beneficial to list some speculations on what you might think is occurring. It could help with providing some context in relation to this in the background. Also I was a bit unclear on the plan for future work. I didn't see it really discussed in detail, either that, or I wasn't sure if the questions you posed at the end of discussion was where the future work was headed. Overall, the paper was great i was able to fully emerge and and take interest in the topic despite this not being my primary discipline. Explanations were amazing, I would just narrow down on the points you weren't sure about because I also wasn't sure about it and others may not be as well.

On 2021-05-08 13:50:11, user johnshop wrote:

See the comments here: https://pubpeer.com/publica...

On 2016-07-22 21:26:46, user Jim Bouldin wrote:

This seems to be a restricted use of the term "distance sampling", one focused mainly on issues of detectability, and hence most applicable to moving or otherwise cryptic objects. I think this focus should be made clearer. There is a large literature on "distance sampling" that is much more general than this usage.

I think it is also imperative that the issue of non-randomness of objects be made clear and that your package is designed only to deal with situations where objects are randomly distributied, spatially. In your description of "main assumptions" (page 5, middle) this issue is not even mentioned, but it has very important effects on estimates of density whenever distance sampling is used.

On 2017-10-28 16:46:37, user Lionel Christiaen wrote:

Student #1

Prior investigations on the activity of low-affinity binding sites within enhancers implied that there might be a mechanism that mediates the brief binding of specific transcription factors to their low-affinity targets. Moreover, because of the sequence similarity between different enhancer binding sites, the existence of a mechanism by which transcription factors can distinguish their low-affinity targets is plausible. <br /> In this study, Crocker et al. ventured to untangle such a mechanism by proposing a model in which low-affinity transcription factor binding to specific loci is dependent on the concentrations of their respective transcription factors along with their co-factors. This concentration dependence then mediates the local microenvironment near the transcription sites to be enriched for their relevant transcription factors and cofactors.<br /> Crocker et al. draw five conclusions based on the obtained pieces of evidence in this work which suggest that such a mechanism exists in the D. melanogaster embryo. As the transcription factor and cofactor: Ubx and Hth, respectively, are enriched around their target svb locus. The team employed the use of both old and new methods to draw these conclusions. <br /> First, the authors claim that Ubx is present in microenvironments of varying local concentrations. They utilized IF staining along with confocal imaging in fixed embryos to show that Ubx protein distribution was not uniform in the nucleus and that instead, there were regions of high and low intensity, which implies areas of high and low TF occupancy. To make this claim more substantial, the authors expanded the images 4-fold in each direction to increase resolution, developed a threshold for what constitutes enrichment based on normalized fluorescence intensities, and indeed saw local enrichment of Ubx in specific subnuclear regions. It is essential to clarify the colored regions in supplementary figure 1 B and C. The authors did a good job showing that this mechanism may be specific to this locus and that other transcription factors did not show a similar distribution pattern and did not overlap with Ubx expression. Furthermore, the authors showed that Ubx does not just cluster around areas of active transcription, they did this by looking at the overlap between active transcription (active RNA pol, methylation marks) and Ubx localization. <br /> Secondly, they determined that these observed results above do indeed happen in living embryos. They could recapitulate the same expression enrichment that was observed in fixed nuclei, inside of live samples. They did this elegantly by using single molecule imaging generating a HaloTag-Ubx transgene coupled to fluorescent dyes. The data recapitulated the expression patterns in living embryos, with overexpression leading to expected developmental defects, and they also fused Ubx to a Nanos promoter to recapitulate early developmental expression patterns. This is substantial evidence of functional Ubx. By injecting the HaloTag ligand, Janelia Fluor 635, which increases signal when bound to its target, the authors found an efficient and innovative way to overcome the signal to noise ratio challenge that is present in samples with free-floating fluorescent molecules. The live imaging results suggested that these local environments exist in vivo. Importantly they could visualize the dynamics of the transcription factor binding by measuring fluorescent intensities as a function of time. This result recapitulated known transcription factor residencies similar to mammalian cells. It is important to note that Figure 1G is missing the green circle in all the panels after the first one. <br /> Thirdly, the authors concluded that transcriptionally active svb loci and enhancers correlate with regions of high Ubx concentration. They did this by expressing tagged Ubx along with FISH staining of svb mRNAs and then measuring the distance between high-intensity regions of Ubx localization and mRNAs. They were able to conclude that active svb transcription correlated with high level of Ubx within a few hundred nanometers. They tested whether this localization pattern was specific to svb and Ubx by driving expression of an unrelated transcript with a synthetic enhancer containing binding sites for another TF, TALEA, this did not show Ubx enrichment. Thus, suggesting that this is a specific property of Ubx-svb dynamic interactions. However, because of the high fluctuations in the TALEA data Figure 3G (0.02 +- .63), you cannot draw this conclusion confidently. It may be that the N number is too low for this sample. I would typically not be concerned with this but because your Ubx/svb sample is .6 +- .1 results in a broad overlap if you are conscious of the plus and minus. <br /> Next, the authors show that manipulation of the number of svb binding sites can change the level of Ubx enrichment. By manipulating three independent svb enhancers (DG3, E3N, and 7H) that contained multiple low-affinity sites the authors showed that even if you relocate these enhancers, the microenvironment enrichment still happens. Moreover, increased affinity sites abolished the Ubx microenvironment. However, if you delete high-affinity sites the enhancer becomes more low affinity, resulting in a restored enrichment of Ubx (recapitulating the low-affinity binding svb microenvironment.) It would be interesting to see if you can recapitulate the microenvironment with synthetic svb low-affinity sites liken to the synthetic high-affinity site experiments. <br /> Finally, Crocker et al., checked if the known Ubx cofactor is also enriched in this local transcriptional microenvironment. They find that Ubx indeed requires Hth and Exd to bind low-affinity sites in enhancers 7H and E3N (Figure 4AD). More importantly, Hth was co-enriched with Ubx around active transcription sites in the enhancers above. This result suggests that the presence of the cofactors in the microenvironment is also required for the formation of efficient transcription. It would be interesting to see the same assay done with Exd and both Exd and Hth gone. <br /> Overall this account was very well presented and convincing. However, more can be done to make this manuscript better. I believe this study will undoubtedly shed light on the question of how temporally brief, low-affinity interactions can drive transcription through the formation of enriched transcription factor microenvironments.

On 2017-04-30 03:44:36, user ???? wrote:

This was accepted as oral presentation at <br /> http://ic-ic.tongji.edu.cn/...<br /> 7. Special Session on Protein and Gene Bioinformatics: Analysis, Algorithms and Applications (Michael Gromiha et al., India)

On 2021-05-11 13:23:18, user Piotr Skorka wrote:

Dear Authors, Thank you for discussion our paper (Lenda et al. 2019) in your manuscript. However, we noted that this paper was incorrectly cited. Please, find the correct citation below:<br /> Lenda, M, Skórka, P, Knops, J, et al. 2019. Multispecies invasion reduces the negative impact of single alien plant species on native flora. Diversity and Distributions 25: 951– 962. https://doi.org/10.1111/ddi...

On 2025-10-08 09:45:42, user Ayansh R wrote:

Published in Computational Biology and Chemistry, 120, 108647. https://doi.org/10.1016/j.compbiolchem.2025.108647

On 2024-03-18 17:57:45, user Martin Rundkvist wrote:

I have a few suggestions for making this fascinating paper more comprehensible to archaeologists and historians. I have edited about 120 issues of two journals. Feel free to get in touch.

On 2016-08-18 05:43:43, user merylnass wrote:

http://peterbaxterafrica.co...<br /> "Anthrax was also used by the Selous Scouts to infect cattle in the Malvernia area. The spore would then be passed on to humans consuming the meat. The distribution of anthrax was carefully controlled in Gaza lest infections move to the Kruger National Park to the detriment of wildlife and South African goodwill. Selous Scouts intelligence officer Jim Parker, in his book Assignment Selous Scouts, confirms that anthrax was deployed in Matabeleland north to infect or kill cattle, in order to deprive infiltrating ZIPRA forces of food. Veterinary Department personnel, unaware that the Scouts were responsible, had a very difficult time containing outbreaks in the Tribal Trust Lands. The disease was more easily controlled on white ranches where there was better access and security.

By October 1979 the isolation unit at Gwelo Hospital was overflowing with cases of anthrax poisoning. Patients from Lower Gwelo, Que Que, Zhombe, Gokwe, Selukwe, Shangani – even some from as far away as Fort Victoria – were treated. There were 10, 753 recorded cases of anthrax poisoning in 1979 and 1980, and 182 confirmed deaths. These figures were compiled from treated cases and in reality must have been much higher.5 Whichever way, they far exceeded cases recorded in the rest of the world combined."

On 2024-05-11 17:42:26, user Thierry Grange wrote:

A revised version of this manuscript has been published in October 2023:

https://doi.org/10.1038/s41...<br /> Genome sequences of 36,000- to 37,000-year-old modern humans at Buran-Kaya III in Crimea<br /> E. Andrew Bennett, Oguzhan Parasayan, Sandrine Prat, Stéphane Péan, Laurent Crépin, Alexandr Yanevich, Thierry Grange & Eva-Maria Geigl <br /> Nature Ecology & Evolution<br /> volume 7, pages 2160–2172 (2023)

On 2018-03-10 18:21:51, user Colin Hawco wrote:

What a nice study. Very elegant, not shocking considering the author list. Admittedly, I read it pretty quickly, but since the point of the bio archive is a chance to comment on things before they go up for review, so I thought I'd share some thoughts.

Firstly, if I understood the methods correctly, only 11 people were actually included in the study. I, more than most, can appreciate how difficult it could be to acquire this kind of data. Both in terms of time and money. But with a sample of 11 it's hard to think of the data as anything other than extremely preliminary Highly Questionable pilot data. Which is such a shame, given that the design is so eloquent, the questions are so topical, and the results potentially very intriguing and important.

And so is difficult as it may seem at the stage, I really think it's the best thing that could be done is to add another 10 people to the study. This is especially salient given that, if I understood correctly (and I will admit here I read it pretty quickly with children in the background) this is a counterbalanced design with only one session of intervealed TMS fMRI, but an interest in the effect of 5hx vs 1hz.

The othrr thing that popped into my mind immediately, and this is kind of just a quirk of how people do TMS, is why am I going to spot on a bathing cap? You get very nice grease pencils that will leave a very clear acts directly on somebody's scalp, which in my experience remains easily identifiable over the course of two or three hours even. Bathing caps can shift, even in the best of circumstances, and the case of people with a lot of hair poofy hair can be quite difficult to fit tightly. It also adds an extra bit of stuff on top of the participant that they might not find very comfortable. I would never, of course, make this is a criticism of the study, maybe more of a discussion point for my own edification as to how people do different things in different TMS labs.

I never had a chance to look at the analysis in thorough detail, but from my quick read it looked pretty good. Very nicely done.

Overall the great study, Highly publishable, but really in need of a larger sample size. With a sufficient n this could be a very impactful study.

On 2020-08-30 07:59:11, user Matsuura Laboratory wrote:

Thank you Prof. Jerala for using our ”beta-annulus peptide” for scaffold of vaccine candidate for SARS-CoV-2 !

On 2018-03-05 05:37:12, user Chris Gorgolewski wrote:

Great to see more defacing algorithms! The one presented in this preprint seems to be very similar to https://github.com/poldrack...

On 2018-04-02 11:06:06, user Mikhail Arzamanov wrote:

Supplementary Materials line 282 misspelling "Poptapovka" instead "Potapovka"

On 2019-08-29 16:43:25, user Bastian Hornung wrote:

I'm sorry to say, but the reported microbiome (Figure 2B) looks essentially like a list of common contaminants, see https://www.ncbi.nlm.nih.go... or https://www.ncbi.nlm.nih.go... . While some organisms could probably be there (like the Staphylococcus or Streptococcus), some of the others really indicate that they are not derived from a host-associated microbiome (Delftia, Geobacillus, Aquabacterium), and I don't think this could be untangeld without the proper use of controls (see https://www.ncbi.nlm.nih.go... "https://www.ncbi.nlm.nih.gov/pubmed/30997495)").

On 2020-03-26 10:38:29, user L A wrote:

Either there's some problem with Protein E, or there's some new biology there. In the work it's found to interact with DNA-related proteins. In Fig 4D some similarity to histones is proposed, but the Protein E sequence compared (there in the figure to H2A) is incorrect. OK yes even with the correct sequence there is some similarity, but it looks quite low and limited just to half of what the authors propose. The match could just be casual because this is a putative transmembrane segment (Protein E is probably membrane bound) and the corresponding element in the H2A sequence is quite hydrophobic.

On 2021-01-14 16:28:49, user Simon Zinkhan wrote:

Our manuscript has since been accepted by the Journal of Controlled Release. Find the updated version here: https://www.sciencedirect.c....<br /> Thank you for showing an interest in our research!

On 2020-08-02 20:00:50, user pb2145 wrote:

In Table S1, the sequence for FtsZ-linkerQrich is missing the C-terminal helix sequence. Is that the actual sequence of the construct or just an omission in the manuscript?

On 2021-12-21 11:08:56, user Yoshitaka Moriwaki wrote:

Your statement-1: <br /> "The interaction analysis of spike-omicron has revealed that this variant has strong interactions with the ACE2 receptor compared to WHU strain and delta variant. The spike-ACE2-delta complex has almost similar binding affinity with spike-ACE2-WHU complex".

Comments:<br /> For the affinity, Did you used the gbsa (?G bind) technique or is it just docking energy? Because the binding strength or affinity(Ki) is determined by the binding free energy, ?G bind such as gbsa, pbsa or more ensemble TI, FEP extracted via molecular dynamics simulations. Also docking which may be efficient but not particularly accurate; they can be used to predict binding modes not affinity.

statement-2<br /> The strong binding affinity of spike-omicron with ACE2 may result in increased transmissibility and infectivity of the variant. This can be explained by looking at the spread of virus...

Comments:<br /> Again what does "stronger binding affinity" means here because I can't see any validation of your statements or results based on dynamics. Better to validate via dynamics.

On 2015-10-03 00:07:38, user Optimistix wrote:

We solicit feedback from the community - all comments are most welcome!

On 2017-03-25 19:41:50, user FinMin Taylor Swift wrote:

Fascinating.

On 2021-05-04 18:52:49, user Milka Kostic, PhD wrote:

Dear authors,

Thank you for sharing this interesting preprint with the community. Below are some more detailed comments that ay have others appreciate your work better. I congratulate you on an excellent piece of work.

Kind regards,

Milka

COMMENTS ON THE PREPRINT BY Dölle, Adhikari et al.

Targeted protein degradation is a very active field at the moment. Many efforts in this area are focusing on transforming known ligands (binders, inhibitors) of proteins with a clear disease relevance into bifunctional (PROTAC-based) degrader molecules. Unlike the traditional antagonist/inhibitor based compounds in preclinical and clinical use that diminish (inhibit) activity of the target, these degrader molecules induce selective degradation of the target. Thus, they remove the target from the proteome. This type of pharmacological activity could be a real benefit when the target in question plays significant scaffolding roles, by engaging multiple binding partners using different regions and binding sites. In such a case, inhibiting individual protein-protein interactions would be highly impractical. However, if the target is degraded, all these PPIs would disappear together with the target!<br /> Dölle, Adhikari et al. select one such target - WDR5, a protein that performs different scaffolding roles (i.e. binds different partners) in the context of epigenetic regulation. Because of this, WDR5 has been implicated as a target for drug development and couple of compounds that inhibit WDR5 mediated PPIs have been described. These compounds served here as a starting point for WDR5 selective degrader development.<br /> The authors used existing structures of WDR5 bound to the PPI inhibitors to identify surface exposed areas of the molecules that could be modified for degrader molecule development. In brief, each PROTAC (bifunctional degrader) includes a ligand for the target and a ligand that recruits an E3 ubiquitin ligase, connected via a linker. The linker is known to have an impact on the performance of PROTACs and the authors use three chemically distinct types of linkers (PEG based, aliphatic and aromatic). The nature of the E3 ligase is also a major factor that affects degraders' activity, and the authors start by incorporating ligands for cereblon (CRBN), VHL and MDM2. Altogether, they generate number of PROTAC-based degraders featuring different linkers and different ligases.

They describe detailed validation steps of their degraders which included:<br /> - Measuring in vitro (biochemical) affinity between WDR5 and degrader molecules using ITC, showing Kd values in low nM.<br /> - They also tested binding via DSF<br /> - The authors tested that degraders were cell permeable and that they engaged the target using BRET. This is an important step in validation as degrader molecules tend to be larger, leading to concerns that may have difficulty entering cells. <br /> - They provide evidence that their degraders induce target degradation in cells, including under endogenous conditions. Importantly, they show that negative control compounds (always critical to have on hand) show no activity, and that inhibiting proteasome rescues observed degradation. Additionally, they confirm that mRNA levels of WDR5 did not change, thus further validating that the reason for decrease in protein levels is due to degradation. (They also include additional pieces of evidence that effects on WDR5 protein levels are degradation dependent)<br /> - Also importantly, the authors show selectivity by quantitative proteomics and demonstrate that WDR5 is the only protein depleted out of more than 5800 identified after 9 hours of treatment (while treatment with individual ligands did not have this effect)<br /> - Lastly, they show anti-proliferative effects in MV4-11 cells of their best performing degraders (these compounds were VHL-based PROTACs). However, the concentration needed for cellular effects was high (10uM). The authors then showed that this is due to low levels of VHL present. When they overexpressed VHL, the growth inhibitory activity improved.

Overall, the work is of high quality and includes appropriate steps for degrader validation. This gives high confidence that WDR5 degraders described in this work are useful as probes for WDR5 biology. For example, what happens to histone methylation once WDR5 is removed? Does removal of WDR5 lead to destabilization (or stabilization) of some of its binding partners (proteomics results suggest that this may not be the case, but would be interesting to dig deeper into this question)? What happens to transcription? What effect does this have on MYC activity (MYC family is known to engage with WDR5)?

I am sure the authors and the community have these and many other questions in mind, and I look forward to seeing what new biology they and others can discover with this new generation of tool compounds in hand.

On 2023-11-06 09:30:19, user Mario Van Poucke wrote:

A revised version of this preprint has now been published in Journal of Veterinary Internal Medicine (http://doi.org/10.1111/jvim... "http://doi.org/10.1111/jvim.16892)").

On 2023-02-12 11:51:50, user Prof. T. K. Wood wrote:

Should credit previous TA system Hok/Sok for first report of phage inhibition via transcription shutoff (ref 17, 1996) along with Laub (ref 19, 2021). Also, 27 years before, Hok/Sok also failed with T7 due to time to lysis (ref 17), so this has been reported previously, and should be indicated.

On 2023-04-13 19:38:43, 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:

The authors previously showed that CRISPR-Cas systems prevent the spread of antibiotic resistance plasmids in populations of Enterococcus faecalis, but that CRISPR-targeted plasmids can be maintained transiently. The current study investigates the mechanisms by which plasmids can be transiently maintained, in the presence of targeting by a CRISPR-Cas system. The paper supports the idea that the plasmid and intact CRISPR-Cas system are not compatible within a single bacterium, and identifies loss of CRISPR spacers and mutations in cas9 as mechanisms that facilitate plasmid retention. The experiments in this paper are straightforward and the conclusions drawn are well supported by the data. Some more description of the WT1 population would be helpful. Also, we found the format of Figure 2 to be confusing, and we suggest a simpler representation that focuses on the specific spacers that are lost rather than the order of spacers in the CRISPR array. Lastly, the discussion does not compare the mechanisms described here with those observed in previous studies looking at bacteria with self-targeting spacers.

Major comments<br /> 1. It is unclear which genetic changes in the WT1 population lead to plasmid retention. The authors should discuss this in more detail.<br /> 2. The layout of Figure 2 is difficult to understand, for three reasons. First, the way the data are represented is not clearly explained in the Figure. If the authors choose to keep Figure 2 as a main figure, we recommend adding Figure S3 as a panel in Figure 2. Second, as the authors discuss, there are weaknesses associated with using p-values rather than simple abundance (i.e., Figure S4). The abundance numbers appear to tell the full story. Third, the authors do not explain why the order of spacers in the array is important, as opposed to the presence/absence of specific spacers. We suggest showing only the frequency of detection for individual spacers, rather than spacer pairs.<br /> 3. The authors do not compare the mechanisms discovered here to those described in previous studies that look at self-targeting spacers. We suggest including PMID 23637624 in that discussion as well as the authors’ own work.

Minor comments<br /> 1. In Figure 1a, we suggest showing individual lines for each of the populations, rather than averaging five of them.<br /> 2. Lines 79-80, typo: “In this study, we tested the investigated the fate of…”.

On 2016-04-28 14:13:53, user Natasha wrote:

This is a preprint. The peer-reviewed published version appeared in <br /> Biology letters today. http://rsbl.royalsocietypub...

On 2023-02-27 15:17:45, user Ramon Crehuet wrote:

This is a very nice papers studying the physics of protein phase separation. It deals with two questions that are well-known for homo-polymers but not so obvious for proteins 1) to which extent single-molecule properties correlate with phase-separation propensity 2) how does multi-valency lead to phase separation and why is it required? Non-computational scientists should read beyond the comparisons of the HP and HP+ models to understand the connections between strength of interactions and number of them (multivalency). <br /> The only thing I would change from this work is the size of the protein chains used in the simulations. In my opinion, using N=20 is a bit too small, and leads to "discretization" errors for low and high fractions of polar vs hydrophobic residues. I think the behaviour at values above to 0.85 would be better described if they had chosen N=50, which is also more realistic in the case of intrinsically disordered proteins that phase-separate.

On 2021-05-24 21:45:57, user Michael Torres wrote:

Hello, I really enjoyed reading your paper! I particularly liked that you consistently quantified the number and volume of AChR clusters, the length of primary and secondary branches, and the number of primary and secondary branches throughout your assays. I also have several suggestions. Consider rewriting the fifth to last sentence on page 4 to enhance readability. In figures 1B and 2A, please show the original gel for more authenticity. In figures 1D, 2C, and 6C, consider adding legends to connect genes to their respective colors. In figure 1H, please address the “Length” misspelling. Consider including a higher resolution image of a-GAPDH in figure 2B. Additionally, I suggest realigning 2D-2I. Making 2A-2B smaller could help accomplish this. Consider including HB9 values in the written results section on page 15. In figure 3A, consider using lines or arrows to show discontinuity in the pretzels, and please explain the pretzel shape’s importance. For figures 3B-3G, I recommend enlarging the bar graphs to balance their size with the page’s other images. For 5A, consider using a different color than yellow to improve contrast with white. Consider making the 6C images larger, especially in width. I suggest placing these six images in two rows, each with three images. They are insightful images, and seeing them in detail would be helpful. I suggest annotating 7A with arrows to show clusters, and I also recommend enlarging 7B, 7C, and 7F. Lastly, I recommend including a time graph for 7D to illustrate MuSK internalization over time.

On 2018-02-08 17:46:12, user Maximilien Robespierre wrote:

Mobile phone use and risk of brain neoplasms and other cancers: prospective study

Victoria S Benson Kirstin Pirie Joachim Schüz Gillian K Reeves Valerie Beral Jane Green for the Million Women Study Collaborators

International Journal of Epidemiology, Volume 42, Issue 3, 1 June 2013, Pages 792–802, https://doi.org/10.1093/ije...

Results from some retrospective studies suggest a possible increased risk of glioma and acoustic neuroma in users of mobile phones.

During 7 years’ follow-up, 51 680 incident invasive cancers and 1 261 incident intracranial CNS tumours occurred. Risk among ever vs never users of mobile phones was not increased for all intracranial CNS tumours (RR = 1.01, 95% CI = 0.90–1.14, P = 0.82), for specified CNS tumour types nor for cancer at 18 other specified sites. For long-term users compared with never users, there was no appreciable association for glioma (10+ years: RR = 0.78, 95% CI = 0.55–1.10, P = 0.16) or meningioma (10+ years: RR = 1.10, 95% CI = 0.66–1.84, P = 0.71). For acoustic neuroma, there was an increase in risk with long term use vs never use (10+ years: RR = 2.46, 95% CI = 1.07–5.64, P = 0.03), the risk increasing with duration of use (trend among users, P = 0.03).

In total, 791 710 women with a mean age at baseline of 59.5 years (standard deviation 4.9) were included in analyses of tumour incidence. During an average of 7 years’ follow-up, 51 680 incident invasive cancers and 562 incident non-invasive intracranial CNS tumours occurred; neoplasms were diagnosed on average 4.2 years after baseline report of mobile phone use.

On 2017-10-30 20:41:38, user Lena Hileman wrote:

Great study!!!<br /> With an alternative phylogenetic method (e.g., likelihood/bayesian), you may find a different tree topology. It looks like your tree may suffer from long branch attraction of pseudogenes. Also, should consider rooting with outgroup TAC gene(s).

On 2019-04-24 10:16:54, user Dan B wrote:

Hi - this example may be of interest for this manuscript:<br /> https://www.biorxiv.org/con...

On 2023-03-07 12:51:34, user LS wrote:

Super interesting- the authors could do a cool extension if the authors examine the possibility that Spike is activating transcytosis through caveolae or a transcellular pathway across endothelium allowing the infection to establish within stromal cells in lung and other tissues. Of interest is the capture and potential efflux dependent on region within the microvasculature.

On 2017-10-11 18:08:09, user Matt Barber wrote:

I noticed a bit of discussion about relationship between rodent transferrin and evolution of longevity. Total self-reference here, but you might compare to our previous studies of transferrin family evolution in primates (https://www.ncbi.nlm.nih.go... "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4455941/)"). In that case strong evidence for selection in response to bacterial iron acquisition receptors. Would be interesting to know if rapidly evolving regions of rodent transferrin overlap with primates.

On 2024-11-19 11:21:21, user Daniel Rodríguez wrote:

This preprint has been published as:<br /> Rodríguez-León, D.S., Uzunov, A., Costa, C. et al. Deciphering the variation in cuticular hydrocarbon profiles of six European honey bee subspecies. BMC Ecol Evo 24, 131 (2024). https://doi.org/10.1186/s12862-024-02325-z

On 2017-09-29 06:16:39, user guillemaud wrote:

PREPRINT PEER REVIEWED AND RECOMMENDED by PCI EVOL BIOL

This preprint by Pirie et al. has been peer-reviewed by Hervé Sauquet and Thomas Couvreur and recommended by Hervé Sauquet for Peer Community in Evolutionary Biology. Peer-reviews, decisions, author's replies and the recommendation can be found here: https://evolbiol.peercommun...

On 2022-12-05 13:37:18, user Erik Marklund wrote:

Cool! I'd be interested in reading more about how you actually did the MD. I also think you need to soften the claim in the abstract that your approach "exploration of all conformational space confirming the experimental data". I strongly suspect that it is too enormous to be anywhere near fully explored even with your enhanced sampling approach. That said, I applaud the use of enhanced sampling for gas-phase proteins.

On 2017-12-12 16:56:29, user Alex J Noble wrote:

The Supplementary Videos may be found here:

https://drive.google.com/op...

On 2020-05-12 01:20:50, user Geoff wrote:

Could you add a scale bar to figure 2C? Also, would be great to add to your methods section your protocol for infecting the other cell lines. Was just looking at this for guidance on how to infect Calu-3 cells and was sad to see it wasn't there.

On 2020-11-10 12:24:28, user Bassem Hassan wrote:

Very nice work! Sorry if I missed it, but did you examine non heat shocked control flies at 2 weeks and 3 weeks also? Thanks.

On 2025-01-31 19:40:14, user Dagan Segal wrote:

Dear Oscar,

Thank you so much for your comments and feedback - it took a while to see this on this forum so apologies for the delayed response.

We incorporated some of your suggestions to add more context to discoveries made by your group and others regarding Cav1 in Ewing Sarcoma- appreciate the suggestions!

In our studies, the subset of CD99 High cells we characterized do indeed have caveolae- so in contrast to previous studies- we believe the signaling in these cells do depend on caveolae per se and not just Caveolin-1 expression.

Thanks also for the comment on other signaling pathways- MAPK levels were low in our hands for all these cells (CD99 High or Low) but maybe worth revisiting.

Cheers,<br /> Dagan (first author)

On 2022-10-24 15:12:40, user Marlise Amstutz wrote:

But the great advantage of Type IIS restriction enzymes is, that if I assemble fragments using these enzymes I don't need to leave the recognition site of these enzymes in. It let's me create scareless, seamless sequences. So when I use Type IIS RE why would I let them in? The only reason would be for further modifications. But then, why would I leave the same site several times and not use different sites, so I can direct my future modifications specifically? I can't imagine someone, would have designed this like that.

On 2024-04-22 08:04:35, user Daniel Ryan wrote:

Now published in Nature Microbiology.<br /> https://www.nature.com/arti...

On 2015-12-02 22:12:32, user Bob Goldstein wrote:

This paper reports an independent genome for the tardigrade Hypsibius dujardini and raises some reasonable concerns about contamination in our recent paper (1). We thought seriously about the possibility of contamination—it was of course the most likely initial explanation for the large amount of foreign DNA found in our assembly—and much of the analysis in our paper was designed specifically to address this issue. We view the independent data and analysis of Koutsovoulos and coauthors, including their analysis of our data, as valuable toward resolving questions of broad interest. We will work now to try to further resolve the issues that were raised. We plan to refrain from commenting more until we've done additional analyses that can shed more light on this issue, and we'll be happy to share what we learn between groups.

We appreciate that the bioRxiv preprint server is a valuable way to move science like this forward without delay, and we're grateful to Koutsovoulos and coauthors for making use of it.

-Thomas Boothby and Bob Goldstein

(1) Evidence for extensive horizontal gene transfer from the draft genome of a tardigrade. Boothby TC, Tenlen JR, Smith FW, Wang JR, Patanella KA, Osborne Nishimura E, Tintori SC, Li Q, Jones CD, Yandell M, Messina DN, Glasscock J, Goldstein B. Proc Natl Acad Sci USA. 2015 Nov 23. [Epub ahead of print]

On 2022-04-17 09:05:13, user Prof. T. K. Wood wrote:

Additional evidence that antitoxins are malleable and arise rapidly, see 2014 https://doi.org/10.1038/sre... where antitoxin ToxI was evolved (not cited). Also, should cite the first paper linking toxin/antitoxin systems to phage inhibition (https://journals.asm.org/do... "https://journals.asm.org/doi/10.1128/jb.178.7.2044-2050.1996)").

On 2023-09-09 08:58:15, user Fabian Westhaeusser wrote:

Great work! But really wondering why you did not mention the work of Dietrich et al. ("Towards Explainable End-to-End Prostate Cancer Relapse Prediction from H&E Images Combining Self-Attention Multiple Instance Learning with a Recurrent Neural Network") or Walhagen et al. ("AI-based prostate analysis system trained without human supervision to predict patient outcome from tissue samples"), which are super relevant to this topic?

On 2023-05-11 11:08:19, user Lorna Role wrote:

This is beautiful work - I am really intrigued by the possibility of functional diversity of cholinergic projection neurons that this may underscore--- your studies target anterior SI (DB?) to BLA- whereas others targeted more caudal NBM/ posterior SI projections to BLA. Could different states and valence cues engage different BFCNs?

On 2020-04-20 17:09:02, user Arjun A Bhaskaran wrote:

Nice work Maria, Xavier and all other members. This method will definitely help many scientists and projects!

On 2017-03-31 16:31:11, user Jonathan Eisen wrote:

OK am not sure if this is the right thing to do but it seems right so am doing it.

I just reviewed this paper for a journal. The version submitted is the same as this latest version here. And so I am going to post my review here. I guess this is very meta - posting at the preprint site reviews I made for a preprint about preprints for its publication in a journal

Review by Jonathan Eisen, UC Davis

ORCID ID 0000-0002-0159-2197

First a disclosure. I am on the Advisory Board of BioRXiv and am an editor at PeerJ.

Overall and general comments

1. This is overall an excellent and very useful paper on preprints in one general area (microbiology)

2. I believe the overall analysis is sound and the claims are generally supported by the evidence.

3. There are a few areas of the manuscript that could use modification – mostly just additional detail. I provide line by line comments for any part of the manuscript that I believe could use some modification below.

4. It would be good if all references to web sites include a data when the web site was accessed and in many cases it would be good to include some information from the site as supplemental material. After all, web sites can change and referencing them is tricky. More detail in line by line comments below.

5. The posting of a reproducible version of the analysis in Github is great and I would recommend highlighting this in the text when the analysis is first mentioned.

Line by Line Comments Below

Lines 18 and 19.

“Preprints were initially adopted among physicists and biologists in the 1960s as a method of sharing interesting research amongst colleagues (5)”

I think it would be good to discuss a little bit more the history of preprints and of sharing manuscripts prior to publication. The text here makes it seem like preprint sharing started in the 1960s when I believe this is untrue. The reference cited here appears to focus on organized efforts to share preprints in the 60s (e.g. an attempt by NIH and efforts by the physics community). While important these were not the beginning of preprints and thus the text is a bit inaccurate

In addition, I think it is not quite accurate to say this was a means of "sharing interesting research among colleagues". The efforts in the 60s were attempts to make preprints more broadly available and with less of a bias where some people saw preprints but others did not.

Furthermore, I think there were probably many reasons why people did this including establishing priority and staking out territory (i.e., it was not just about sharing interesting research among colleagues).

Line 36

“preprints are typically publicly available in about 24 hours”

Is there data supporting this claim

Lines 42-45

“This difference can be meaningful to authors since some journals, including the American Society for Microbiology (ASM) Journals, will only accept submissions that have been posted on preprint servers hosted by non-profit organizations”

Is there a reference for this claim?

Lines 48-49

“their work to a journal as journals will not consider manuscripts posted as preprints under a CC-BY license (e.g. Proceedings of the National Academy of Sciences). “

Should this read “to a journal as SOME journals”

Lines 53-55.

“Compared to the bioRxiv site, the PeerJ Preprint site is more fluid, gives readers the ability to “follow” a preprint, and provides better access to article keywords and the ability to search preprints.”

This should include a date of accessing the sites and I would recommend perhaps submit screenshots as supplemental material.

Lines 55-57

“With broader acceptance of preprints by traditional journals, many journals, including all of the ASM journals, have established mechanisms to directly submit manuscripts that are posted as preprints on bioRxiv.”

and

Lines 57-58.

“The only direct submission mechanism for manuscripts submitted at PeerJ Preprint is to the PeerJ journal”

A reference / web link would be useful here. Plus a date when these policies were examined would be helpful. I would recommend including the text of the policies as supplemental material if possible.

Lines 68-71.

“A relatively new example of what this might look like is PrePubMed (http://www.prepubmed.org/) "http://www.prepubmed.org/)"), which seeks to index preprints from numerous sources. A more organized effort is being initiated with funding through ASAPbio to create a “Central Service” that would aggregate preprints in the life sciences (http://asapbio.org).”

It would be good to include the date of access of these sites and if possible some additional information in supplemental material such as text from the site or screen captures to support the text.

Lines 72-73

There is also hope that the National Institutes of Health (NIH) will renew their interest in indexing preprints as separate research products than peer-reviewed publications.

Please clarify. Hope from whom?

Lines 81-83.

“First, a significant amount of attention has to be given to the potential dual use research of concern (DURC) since posted results in microbiology research could offer insights to individuals seeking to engage in terrorist activities”

DURC is not just about terrorist activities – it is about any possible misuse of research information.

Lines 83-85

“Second, for researchers engaging in research that involves human subjects it is critical that assurances be made that institutional review boards have been consulted and have approved of the research.”

I would suggest adding a line here about animal research too and how it is critical that appropriate reviews were done for such research.

Lines 89-90

“Again, while hoping to maintain the efficiency of the preprint format, traditional microbiology journals have policies for these issues in place that should be easy to implement by preprint servers”

Could you clarify or provide examples of what you mean by policies.

Lines 100-101.

“Each take a generally permissive stance towards posting of preprints prior to submission.”

Given the following sentences about journal policies changing it would be good to mention how the journal policy was inferred and if possible include references and or web links with dates and such.

Line 145-146.

“Considering the preprint is a citable work with a DOI, it would, in fact, be the preprint author that scooped the second.”

I personally agree with this statement but many in the community do not. This only works if people view preprints as valid publications. There are many examples where peer reviewed papers have been published claiming priority on some novel finding when preprints existed on the topic and where the authors of the peer reviewed paper and sometimes the editors of the journal have stated something akin to “we do not consider preprints valid papers”.

Lines 160-162.

“Some fear that the use of preprints will allow scientists to circumvent page limits by posting preliminary manuscripts.”

It would be helpful to reference examples of people expressing such fear if they are available online anywhere.

Lines 173-176

“In fact, several funding agencies including the Wellcome Trust and the UK Medical Research Council encouraging fellowship applicants to include preprints in their materials; meanwhile, the NIH is in the process of soliciting input from the scientific community on their role in grant applications.”

References / links would be very useful here.

Lines 193-195

“Any manuscript that was published went through several month delays in releasing information to health care workers, the public, and scientists needing to learn new methods to study a previously obscure virus.”

I think the wording here and in a few other places in the paper is a bit confusing or awkward. Preprints are published too. So I would suggest using some alternative wording to “published” for papers that have gone through peer review vs. preprints for those that have not.

Lines 207-214

Discussion of NY Subway metagenomics.

I think an important part of the story has been left out. On February 17, 2015 Chris Mason wrote a long blog post (at the request of this reviewer) for microBEnet about their paper and about some of the challenges in inferring the presence of anthrax in the subway. See

https://www.microbe.net/201...

Lines 265-267

“Although the hosted commenting is only one mechanism for peer review, this result was somewhat disturbing since the preprint model implicitly depends on people’s willingness to offer others feedback.”

Any information you could provide on the relative balance of commenting in other systems (e.g., Twitter, Facebook, Blogs) would be very helpful here. Or perhaps at least refer to the discussion later in the article

Lines 301-303

“Although it is impossible to quantify the quality or impact of research with individual metrics, it is clear that preprints and the publications that result from them are broadly accepted by the microbiology community”

I would love for this to be true. But I am not sure I completely follow the logic of this claim here. Could it not be that the citations for preprints are mostly coming from the authors of those preprints? I know we in my lab cite our preprints quite a bit. Or could it be that a subset of the community cites preprints a lot but others do not?

On 2021-01-13 08:46:01, user Ivan Molineris wrote:

If I understood well the annotation "ENSEMBL" used in this paper correspond to the _comprehensive_ GENCODE annotation. What about _basic_ GENCODE annotation?

On 2019-04-06 05:52:40, user Jonathan Gressel wrote:

Great tour de force. Now all that is needed is genetic confirmation: the backcross from an R X S cross to the S should have near 100% R individuals, instead of the Mendelian 50%. Worth finding out as it has implications vis a vis the rate of spread of R through a population - even one no longer treated with glyphosate.

On 2024-03-09 21:05:59, user L Miguel wrote:

This paper shows all-atom simulations can capture all that is already known in the literature about RNA-RRM binding of TDP43.

On 2024-09-22 20:05:49, user Fraser Lab wrote:

Based on: https://www.biorxiv.org/content/10.1101/2024.07.24.604935v1.full

The manuscript from Lehner and colleagues presents a wealth of mutagenesis information on amyloid aggregation. The central premise of the paper is to use a yeast selection based around the oligmerizaton/aggregation of Sup35 fused to a peptide (in this case abeta) as a proxy for amyloid forming potential. This is cool information on its own and the experimental analysis and computational framework for linking to energies is top notch. The point of using double mutants to enhance the dynamic range is very well explained and will solidify their approach to impactfully link DMS experiments to thermodynamic concepts.

The major framing of the paper revolves around an analytical protein folding/engineering concept of phi-values that highlights energetic differences in the importance of interactions for forming the transition state vs. the ground state. For the textual interpretation of the results, one must buy into the energetic effects of the Sup35 system as a readout of the transition state (and secondarily for FoldX calculations on various PDBs of abeta polymorphs to be a readout of the ground state). The major issue is that an alternative (and perhaps simpler) explanation is that mutations in APR2 are more disruptive to the Sup35 oligomerization process in the screen and that this reflects amyloid/oligomerization propensities and not strictly TS of initial nucleation. The data from previous studies that is used to draw correlations to justify their interpretation around the TS is buried in extended data figures and is a bit all over the place, especially the deconvolution of primary vs. secondary nucleation. The existence of multiple polymorphs in human cells (and populations), which may or may not have related transition states - and the exact conformational requirements of the Sup35 activation mechanism - further complicate this interpretation.

In summary - this contains amazing data, but I do not see the language of the interpretations lining up with the strength of the _specificity_ of the claims about the transition state. A fuller discussion of the limitations of the prior low throughput assays that are referenced in extended data 1 and 2 and detailed kinetic characterization of some of the more surprising mutants in a biochemically defined system would greatly improve the match between the data and the claims. These issues should not stop others from building on this beautiful work - but in doing so, other investigators should note that there remains ambiguity as to whether the effects are truly on the TS or on the ground state.

Avi Samelson and James Fraser

On 2015-02-21 13:57:34, user Teej wrote:

Sweeet

On 2017-06-18 08:19:51, user Davide wrote:

Will Poly-A libraries work?

On 2018-06-07 13:30:38, user Emmanuel Bourinet wrote:

A tour de force study in the elucidation of molecular mechanisms of painfull mechanosensation!

On 2025-11-21 18:49:01, user Max Seldes wrote:

To the authors, I very much enjoyed reading this manuscript! I found the introduction and discussion sections to be particularly well structured and were very logical in their progression. I wanted to know if you have any way of retrieving or calculating the specific locations of calls or relative density of calls along your transects? I think this project could really grow from investigating the correlation of call frequency/density with canopy cover as an explanatory variable in something like a GLMM or ANOVA. If you were able to do this, I think it would be really cool to create some additional layers like percent road coverage within a 1km buffer or percent surface water to test some of the suggested explanations for the shift in overall abundances. You could even quantify stuff like distance to forest patch or something like that to really hone in on patch effects. I think this project has a really great direction and could really easily be expanded upon! Best of luck to the authors on your future endeavors and I hope to see more from you!

On 2020-08-03 01:30:54, user Carlos Moura wrote:

Maybe I'm just stunned but I understand that maybe there is a way to trigger the nitrogenase production without the nifA and the cascade that it creates? That's it? If so this was proposed before by anyone?