On 2017-12-19 18:53:05, user C. Andrew Frank wrote:
The contents and the author list on this biorxiv page reflect the state of the paper upon initial submission. A completed revision is published at eNeuro. The revision also reflects the final authorship list, arrived at through revision experiments and editing: Catherine J. Yeates, Danielle J. Zwiefelhofer, C. Andrew Frank.
On 2020-05-24 15:17:35, user Reviewer#2 wrote:
RoHo is the ratio of # offspring of a node carrying the derived mutation to the # offspring carrying the ancestral state. So the latter ("NonA progeny"), the white leafs in Fig. 2) are a central ingredient of the statistic. If there is a inherent bias in offspring towards being ancestral because sub-trees branching off before the "red" mutation (that are not resolvable if there is no mutation), then the assumption of RoHO being 1 under neutrality (Fig. 3) does not hold. And I fear that for realistic mutation rate this bias can be very substantial.
On 2022-09-01 04:23:39, user Titos wrote:
Published: https://www.nature.com/arti...
On 2019-10-24 03:48:44, user Victor Higareda Alvear wrote:
hello I am a little confused. OTU counts means pair-end sequences right?
On 2020-05-29 00:16:40, user Kalon Haduian wrote:
Dear Ashot, thank you very much for your work, I'm sorry that I didn't ask in the context of this article, but do you know if Arshakid Mausoleum samples will be examined?
On 2025-01-03 18:39:13, user Ryan wrote:
I believe this manuscript is now published in Nature Methods:<br /> https://www.nature.com/articles/s41592-023-01779-1
On 2015-09-23 09:35:14, user Peipei Xiao wrote:
In fMRI data processing and analysis step, after MNI normalization, you calculating mean signal time course for brodmann ROI. As brodmann atlas in Talairach space. Does there any problem?
On 2017-10-20 07:45:23, user PerSister wrote:
Nathalie Balaban noted the importance of the initial inoculum already at 2004 (with a bi-phasic mathematical model that emphatize the importance of measuring the whole death-curve). <br /> See: http://science.sciencemag.o...
On 2020-02-01 18:29:02, user Donna Carpenter Smythe wrote:
Insightful read..congratulations on your methods for sterile inner core sampling..looking forward to further research on these extremely important glacial novel microbes and their implications.
On 2022-02-23 08:23:27, user Mingyue Wu wrote:
Very interesting work that complements our understanding of the role of the ancient intraspinal sensory neurons in mammals. To be clear, zebrafish CSF-cN are known to send ascending projections up to targets in the hindbrain , but not midbrian according to (Wu et al., 2021). The sensory neurons are found to project to somata of hindbrain motor neuron and axons of reticulospinal neurons.
On 2015-08-03 15:43:14, user Kathryn Miller wrote:
These are serious problems and this article contains a great deal of information that is helpful in sorting out the many issues. The data presented is useful and important, as are the perspectives about impact on career progression. Quantitating how much is required now vs. in the past is extremely difficult for all the reasons raised in the article and in the comments. However, many of us who have been 'around' for a long time have experienced the issues that are described in this article. It important to continue to the dialog and to work towards solutions, both short term and long term. Thank you, Ron.
On 2024-02-12 14:59:12, user Dianne Little wrote:
Nice set of recommendations that will be useful for the field. Some of the citations in text don't match correct citations in the refence list.
On 2017-08-10 06:51:42, user clare turnbull wrote:
Nice paper from James Brenton on on copy-number signatures, with sigs of HRD, BFB, WGD and others. Sigs can also predict survival https://t.co/h91WZqOG0i
On 2018-07-17 15:25:10, user Rob Beynon #FBPE wrote:
OK, I have never done this before, but a straw poll of the twitterverse suggest that this is the point of preprint servers. I have previously reviewed this manuscript for another journal, and found some problems. This is what I have done:
=Run a PDF comparison tool to compare the previous and this version - the changes are modest, at best<br /> =Look to see whether new data were included - none are included<br /> =Check to see if my comments previously had influenced the revision. not to any great extent
I always sign my reviews, so the authors know me, and I have no worries about doing this, other than the fact that I am making this prior review public. If I have missed a situation where my previous comments were acted on, I apologise, because I have not 'reviewed' this manuscript in the same way.
(The second review also had major concerns, but it is not my place to comment on those here)
Here is my review, verbatim. Where the authors have responded to my previous review, I have marked with ***<br /> EDIT: 18/7 - I should have acknowledge that the authors now cite two prior papers on exactly the same topic that I had to point out to them
==========================================<br /> ACCEPTED REVIEW 5TH FEB, 2018<br /> REVIEW SUBMITTED 16th FEB 2018<br /> ==========================================
Comments for the Author (Required):<br /> Please note that from 2016, I will only review papers if I can sign my name at the bottom. I am perturbed by the increasing hostility and pettiness of reviewers, let alone some quite unacceptable delaying practices. Inevitably, this raises the question of poor reaction form authors, but I have NEVER experienced this. Rather, I believe that open reviewing greatly enhances the quality of the dialogue. Hence, my review is signed below.<br /> Rob Beynon
This is a straightforward application of QconCAT technology for quantification of allelic variants of UDP glucuronosyltransferase 2B15. There are only two variants that need quantification, and it could be argued that QconCATs are overkill. I would certainly recommend removal of the term 'novel', it really isn't.
More seriously, the authors do not address this paper: https://www.ncbi.nlm.nih.go... or this paper: https://www.ncbi.nlm.nih.go... that also quantifies, using QconCATs, the same enzymes, and includes activity determination.
The authors must therefore find more novelty that they are currently claiming. It is disappointing that they do not refer to these papers, and it is not really acceptable to fail to search the literature fully. Even the 2017 paper was published last October.<br /> *** The papers are now cited in the biorxiv version
There are number of issues that, to my mind, have not been adequately addressed -
QconCAT design<br /> Why such a large QconCAT when this could have been completed with a single peptide containing both variant peptides? This is not well explained, and the properties of the complete QconCAT are not described.
Digestion efficiency<br /> The authors rightly included multiple flanking amino acids to increase equalisation of digestion rates. Arguably 15 amino acids is overkill, but that is not critical. What is critical is direct evidence of the digestion rates of the two proteins (analyte/standard) are in harmony - although I stress that the kinetics of the digestions do not have to be the same, for absolute quantification the important factor is that both digestions reach a plateau. I recommend that the authors have a look at this paper: <br /> https://www.liverpool.ac.uk... in which we discuss the issues of differential proteolysis. The goal is not to equalise digestion rates but to ensure that both reactions reach completion. I would ask that this be included - the time dependence of digestion is not a difficult experiment. The rate of digestion is not solely the domain of primary sequence, but also, the intrinsic structural digestibility of the native versus the standard protein.
Quantification of the standard<br /> The authors do not describe quantification and handling of the QconCAT. I note there are cysteine residues in the sequence, which can lead to issues of aggregation, making quantification more challenging. How do the authors know they added 75ng of the standard? We would usually use a common peptide, added to the QconCAT, against which we quantify using an accurately determined 'light' common standard peptide.
Analysis of data. It is good to see the Skyline pages, and the assays look robust. However, presumably the raw data will be deposited somewhere also?
A further point, the results are really underwhelming, not in the data per se, but in the presentation. Why not produce a detailed analysis of the relationship between RNA (with errors) and protein (with errors) for both homozygotes and heterozygotes? I felt the paper fell flat and lacked insight or detailed interpretation. These areas (ASE) are a domain where QconCATs could really add value - I also believe that the evidence for cis regulation is at present tenuous.
Overall, the relationship (of) this paper to previous work renders it much less strong. I think the pressure on the authors is to produce significant added value. It is hard to be 100% enthusiastic about publication at the moment.
One last request - the figure legends are woefully inadequate. They should be much more informative.<br /> *** To be fair, the legends are now better.
On 2018-02-01 08:54:01, user Nancy Yu wrote:
This package seems to require R >= 3.5.0 which is not available yet (as of 2018-02-01) ?!
On 2024-04-29 13:34:46, user Oyinoluwa wrote:
Abstract & Introduction
The abstract is good overall and structured well. It gives a clear insight into the concern at hand and summarizes the key findings to show its significance. However, the research question is not clearly stated. This is something that can be elaborated on in the introductions, but it should also be stated briefly in the abstract. I understand that there is not a lot of research done on the effects of pregnancy to the female microbiota, but it is unclear as to what the research question is. This introduction gives a good background on the issue of pregnancy altering the microbiome. Below are my comments on what could be added or further explained.<br /> 1. Are you trying to determine which organisms are lost/recovered?<br /> 2. Is this study for a general understanding of the effects of pregnancy on women (pre/post birth)?<br /> 3. Are you looking at a specific family of bacteria, fungi or archea that has a meaningful change during both phases (pre- and post-maturation) to see their effects? <br /> 4. Are the women from diverse backgrounds (race)?<br /> 5. Was this study conducted in the same location?<br /> 6. Include more previous studies, if any, stating what others have claimed to be the reasons behind “side effects” of pregnancy <br /> 7. Include your own claims on what you would expect and not expect to see.
Figures and Tables
The table heading and description are good and easy to read. However, make sure to explain all abbreviations.<br /> Table 1. <br /> 1. What is the difference between an t-test and a Fishers exact t-test?<br /> 2. What does SS-days mean? <br /> 3. Also explain why each test was used for the p-value result.<br /> Figure 3. <br /> 1. I am not sure what these diagrams represent.<br /> 2. 3B- What is a vaginal community state type? How were they classified? It is stated that software clusters them, but what are the criteria?<br /> 3. 3A- What do the colors represent? Explain the unknown portion? <br /> General comments for figures<br /> Figures captions need to be clear, like in 1C ii) the title is “Rchress.” Is that observed ASVs? Include clear titles for your figures as well as axis labels. References to the figures from the results section show that the figures support the findings. You can include the reasoning behind choosing these graphical analyses. <br /> Methods, Materials & Analysis
Results, Discussion, Conclusion
In the limitations section, it is mentioned that the samples were not collected from the same women before and after pregnancy. Wouldn’t this affect the results you have presented? It is hard to say whether your results and conclusion support the claims which were made due to this aspect of sample collection. Although your results and conclusions are significant to the study, the validity is questionable.
On 2020-11-18 06:44:18, user kdrl nakle wrote:
Very slim results. 3 types of produce only and nothing to do with SARS-CoV-2. Even the formatting was done in a lazy way. Looks like a paper my students would do.
On 2020-05-05 20:42:00, user Taekjip Ha wrote:
Thank you very much for sharing your interesting manuscript!<br /> We used your preprint as one of the journal club papers in the Single<br /> Molecule & Single Cell Biophysics course for graduate students of Johns<br /> Hopkins University during the Covid-19 lockdown. Students also practiced peer<br /> reviews as the final assignment. I am submitting their formal reviews here <br /> and hope you find them useful.
Taekjip Ha
Reviewer 1.
The authors develop an ?-hemolysin nanopore-based sequencing by synthesis assay<br /> which can be used to interrogate the kinetic properties of single DNA<br /> polymerases. Their method is novel and addresses the problem of increasing the<br /> throughput of polymerase screening methods. Previous techniques only allowed<br /> kinetics of polymerases to be screened one at a time. This new method is a<br /> clever integration of existing nanopore sequencing technologies that addresses a<br /> longstanding problem in development of specialized polymerases in biotechnology.<br /> The paper is interesting to read and not especially difficult for someone<br /> outside of the field to understand.
Each polymerase-pore complex could be uniquely tagged with a circular barcode<br /> template, allowing the assay to be multiplexed and scaled up to accommodate 96<br /> complexes at once. Convincing proof of concept data is shown highlighting the<br /> ability of the method to distinguish between barcodes, as well as the stability<br /> of the circular template. The title and abstract are appropriate, concise, and<br /> clearly lay out the aims of the paper. Introductory figures showing assay design<br /> and low throughput tests are very well presented and easy for the reader to<br /> follow. Low throughput tests show clear clustering, in both two-dimensional<br /> plots and PCA, of data obtained from each tested polymerase which could be used<br /> to distinguish and characterize them. Later in the paper, however, there are<br /> confusing inconsistencies between what is stated and what is shown in the data.
Figure 3a shows how each kinetic parameter is defined by the voltage trace. Only<br /> four of the five kinetic parameters are shown: dwell time, tag release rate, tag<br /> capture rate, and full catalytic rate. Tag capture dwell time (TCD) is not<br /> shown, yet it is featured in the principle components analysis and is shown to<br /> have a relatively high coefficient for some polymerases. How this parameter is<br /> defined by the trace and how it differs from dwell time is not clearly addressed<br /> in the main text of the paper. This figure (3a) and the subsequent analysis<br /> could be improved by explaining how each parameter is calculated and how they<br /> differ to clear up any ambiguity. Explanations of how each parameter correlates<br /> to polymerase fidelity, processivity, speed, etc. may also help convince the<br /> reader of the utility of their method. This is done well for some but not all of<br /> the described parameters.
Figure 5 shows the distribution of counts associated with each of 96 unique<br /> circular barcodes over three polymerases. RPol1 is associated with relatively<br /> few read counts which are not much higher from background off-target signal from<br /> RPol33. The uneven distribution of barcode counts is attributed to the low<br /> processivity of polymerase 1. Later (figure 6), in the 96-plex screen of<br /> polymerase mutants, less than twenty mutants in the screen have detectable<br /> barcode counts and those that do have few counts. This observation is again<br /> thought to be due to poor processivity of the polymerases. Polymerase fidelity<br /> very likely also plays a role in the ability of the assay to identify<br /> polymerases. Since barcode assignment is alignment based, and nanopore<br /> sequencing platforms are known to have a relatively high error rate as well, one<br /> can imagine that a more error-prone polymerase will also escape detection. There<br /> is no benchmarking data to define a polymerase detection threshold. It is clear<br /> that the efficacy of the method decreases for polymerases with lower fidelity<br /> and processivity, but what might be designated as ‘low’ is never defined. What<br /> subset of polymerases make it through this new screening process and what are<br /> their defining kinetic characteristics? How widely applicable would this method<br /> be for identifying desired features in polymerase variants? What kinds of<br /> polymerases would be expected to be missed by the screen?
There are some minor inconsistencies in the data that should be addressed.<br /> Supplemental table 5 shows the calculation of the proportion of mapped reads in<br /> the low throughput 3-plex experiments. The number of total raw reads used to<br /> calculate the 67% CBT mapping as described by the main text is 418, the value<br /> for RPol1 alone rather than a sum of the total read values for all three<br /> columns. Similarly, the text states that 20 polymerase variants were identified<br /> in the screen while figure 6a shows only 17 polymerases were associated with<br /> barcode counts.
The method described in the paper is conceptually strong and should be very<br /> helpful in identifying polymerases with desirable kinetic properties when<br /> coupled to mutagenesis screens. It has the potential to be improved upon as<br /> nanopore sequencing technology is further developed and the error rate that is<br /> currently innate to the platform is decreased. It is likely that general<br /> improvements to nanopore sequencing itself would greatly decrease false positive<br /> rates in the described method. This technique could also be more applicable if<br /> its points of failure were addressed and proper thresholds defined. The higher<br /> false positive rate observed in RPol2 (supplemental figure 11a) is more likely<br /> to be a fault of the polymerase fidelity rather than a characteristic of the<br /> barcode set. What kind of polymerase misincorporation rate is permissible to<br /> still allow confident barcode assignment? At what point does polymerase<br /> processivity become an issue and cause ambiguity in barcode identification?<br /> There appears to be a set of kinetic parameters that must be met in order for<br /> differences in polymerases to be resolved by this assay. Clearly defining what<br /> it is good at and what it is going to miss is essential before it can be used<br /> reliably for screening.
Reviewer 2.
Summary<br /> In this article, the authors expand upon their previously published system of singlemolecule<br /> nanopore sequencing-by-synthesis and investigate whether it can be scaled-up to be<br /> used as a screening method downstream of polymerase directed evolution experiments. The<br /> major advancement in this paper is that as a screening tool for polymerases, it also has the<br /> capability to provide detailed kinetic information on each of the polymerases, something that<br /> prior methods struggled to do. As a proof-of-principle, the authors simultaneously screen 96<br /> polymerases with 96 barcodes and extract kinetic data from their single-molecule profiling.<br /> This work has multiple merits. Notably, although the general framework is the same, the<br /> authors have made a series of changes to improve their system since their previously published<br /> work, that played a role in allowing them to make multiplexed measurements. The authors also<br /> creatively pull a variety of kinetic parameters from their single-molecule voltage traces that<br /> allow them to easily separate different polymerases after principle component analysis.<br /> On the other hand, the work has a couple of issues, detailed below, with regards to<br /> controls and clarity that would be helpful if addressed.<br /> Major Issues<br /> 1. The authors utilize DNA bases that are tagged to generate unique signals for recognition<br /> when captured and blocking the nanopore. From the principle component analysis<br /> tables (Supplementary Table 4a-c), it appears that the polymerases vary quite a bit with<br /> regards to processing different bases. At present, it is unclear whether these kinetic<br /> differences are being caused by differences between structures of the bases, or whether<br /> they are caused by differences between structures of the tags. One control would be to<br /> repeat one set of experiments with the tags shuffled between the bases and observe<br /> how reproducible the results are. This would give the reader a sense of how much<br /> measurements are being affected by the tags used for this technique.<br /> 2. For the experiment in Fig. 5, the authors end up showing that barcodes can be identified<br /> with a false positive rate of 13%. This is with a pilot experiment of 96 barcodes. From<br /> this data, it suggests that this technique would be difficult to scale-up any further, which<br /> may limit its usefulness – in fact even 96 barcodes may already be pushing the limit.<br /> From reading the paper, it is unclear if what is dominating this problem is the length of<br /> the barcode (i.e. limited sequence divergence due to 32-nt), or if nanopore sequencing<br /> accuracy is still a limiting factor. It would be great to see a small pilot experiment with<br /> longer barcodes to see if this could allow for improved accuracy, or some in silico<br /> statistical modeling extrapolating from their current data (e.g. length of barcode x<br /> required to accurately separate number of polymerases y with a false positive rate of z).
quite flexible, it still is unaddressed whether this repeated jostling of the tag<br /> (linked directly to the base) would affect kinetic measurements. Overall, it would be nice<br /> to see some measurements compared or benchmarked against a more well-established<br /> technique side-by-side (e.g. single-molecule optical trap), just to see if the data matches<br /> up or not. Notably with a parallel technique, you can also do the control of tagged vs.<br /> untagged nucleotides, thus unambiguously determining the potential effect of a tag on<br /> polymerase kinetics.<br /> Minor Issues<br /> 1. In the abstract the authors mention they “develop a robust classification algorithm that<br /> discriminates kinetic characteristics of the different polymerase variants.” It is unclear<br /> what this is referring to in the paper. If it is simply the principle component analysis then<br /> saying “develop” may be a bit overreaching.<br /> 2. Rather than referring to prior publications this publication should have in the<br /> supplement and/or methods the exact nucleotide + tag combinations used in this paper.<br /> 3. It is unclear after reading the methods why there are three separate PCA tables per<br /> polymerase in the supplement.<br /> 4. It is unclear what is the difference between tdwell and tag capture dwell from the written<br /> descriptions in the paper. Highlighting the difference visually in Fig. 3a (as was done<br /> with the rest of the kinetic variables) would help the reader clearly understand exactly<br /> what is being measured.<br /> 5. A table of the 96 barcodes used for Fig. 5/6 should be added to the supplementary<br /> materials.<br /> 6. The numbers in Supplementary Table 5 do not add up correctly – the authors should<br /> take a look again and make sure the correct numbers are present.<br /> 7. In Fig. 2 the authors experimentally calculate BMPI cut-offs for 3 different barcodes and<br /> get 0.8, whereas in Supplementary Fig. 8 the authors do an in-silico calculation for BMPI<br /> cut-off and still get 0.8. One would imagine that increasing the number of barcodes<br /> would require a stricter BMPI cut-off. Some sort of commentary on this, or perhaps<br /> reanalysis of the multiplexed data with a stricter BMPI cut-off could be helpful.<br /> 8. In Supplementary Fig. 12 the authors show a protein gel of their pore-polymerase<br /> conjugates. The bands show that post-linking, there is still a decent amount of nonlinked<br /> polymerase. In the methods there is no mention of a size exclusion purification<br /> step post-conjugation. Are the authors loading a mixed population onto their chips? This<br /> needs to be clarified.<br /> 9. In Supplementary Table 7 the tag capture dwell (TCD) variable missing.
Reviewer 3.
In the study titled Multiplex single-molecule kinetics of nanopore-coupled<br /> polymerases, Palla et al. developed and demonstrated the use of a<br /> single-molecule sequencing technology for the high-throughput identification of<br /> DNA polymerases with desired kinetic properties. Nanopore sequencing reactions<br /> were carried out on complementary metal-oxide-semiconductor (CMOS) chips, each<br /> of which contains over 30,000 individually addressable electrodes, thereby<br /> allowing sequencing reactions to be carried out on each chip in a multiplex<br /> fashion. Each DNA polymerase was coupled to an ?-hemolysin pore and bound to a<br /> 51 bp circular barcoded ssDNA template (CBT). The template is bound to a primer,<br /> thus enabling the incorporation of the appropriate nucleotides by the polymerase<br /> into the ssDNA template. Since each ssDNA template is circular, multiple<br /> iterations of the barcoded region can be observed during the sequencing of each<br /> template. Furthermore, each of the four nucleotides are uniquely tagged. When a<br /> nucleotide is being incorporated into the template ssDNA, the tag attached to<br /> the nucleotide is captured in the nanopore, thereby decreasing the conductance<br /> through the pore. Such a decrease in conductance is measured by an analog to<br /> digital converter (ADC) placed parallel to the sequencing circuit, and the<br /> recorded ADC values are then converted into a fraction of open channel signal<br /> (FOCS). Because the four tags are different from each other, the corresponding<br /> FOCS generated differ from each other as well, and can thus be used to<br /> distinguish the nucleotides from each other. Using a software, the FOCS is<br /> converted into raw reads. Then, using a barcode classification algorithm, each<br /> qualified raw read is compared to any template of the experimenter’s choice.<br /> Aligning a raw read to the correct template will more likely generate a higher<br /> barcode match probability index (BMPI) value for that read, while aligning a raw<br /> read to an incorrect template will more likely generate a lower BMPI value for<br /> that read. As such, for each sequencing experiment, the average BMPI value<br /> (derived from comparing raw reads to a template) can be used to identify the<br /> template to which the polymerase is bound. And if each polymerase-template pair<br /> is unique, the average BMPI value can then be used to identify the polymerase as<br /> well. Lastly, the authors defined a set of five kinetic parameters that can be<br /> measured during the course of a sequencing reaction. Because different<br /> polymerases are likely to differ from each other with respect to these kinetic<br /> parameters, comparison of the parameters between polymerases can help identify a<br /> polymerase with the desired properties.
To develop their nanopore sequencing technology, the authors first showed that<br /> the BMPI value can be used to identify a CBT. Thereafter, the authors showed<br /> that, after a polymerase is loaded with a particular CBT, the loaded CBT will<br /> not get replaced by another CBT that is present in the same reaction volume,<br /> thereby demonstrating the potential for multiplexing this sequencing platform.<br /> Then, as stated above, the authors defined five kinetic parameters that can be<br /> measured during sequencing. Using Principle component analysis (PCA), the<br /> authors showed that these kinetic parameters differ between polymerases, thus<br /> indicating the ability of this platform to distinguish polymerases based on<br /> these parameters. To demonstrate the multiplex potential of their platform, the<br /> authors conducted multiplex experiments in which different sets of CBTs were<br /> loaded onto three different polymerases. These pore-polymerase-CBT conjugates<br /> were then pooled prior to loading onto the CMOS chip. Notably, these experiments<br /> showed that CBTs can be identified in a pooled format. Finally, as a practical<br /> demonstration of the capability of the platform to identify, in a multiplex<br /> format, polymerases with properties of interest, the authors generated 96<br /> polymerases, each of which was then loaded with a unique CBT. In this multiplex<br /> reaction, the authors identified four polymerases that are potential candidates<br /> for further development for use in DNA amplification methods.
Here are some thoughts I had while going through the preprint:
The authors state that, in their pooled 3-plex sequencing experiment, about<br /> 67% of the raw reads (n = 418) were identified as any of the three barcodes used<br /> in the experiment. In Supplementary Table 5, it can be seen that, for total<br /> RPol-CBT, [the percent of raw reads with BMPI > 0.8] = [the number of raw reads<br /> with BMPI > 0.8] / [the total number of raw reads]. That is, 66.9% = 280 / 418.<br /> However, the table shows that the total number of raw reads for the RPol1-CBT1<br /> alone is 418. If this is the case, it is unclear to me how the total number of<br /> raw reads for all three RPol-CBTs (RPol1-CBT1, RPol2-CBT2, and RPol3-CBT3) can<br /> be 418 if that of RPol1-CBT1 alone is already 418.
On p19, line 1, I believe that “Experiments 1 and 3” should say “experiments<br /> 1 through 3”, since in all three of these experiments, the raw reads were<br /> compared to the correct template, as noted in the legend below the figure<br /> (Supplementary Figure 6b).
In Supplementary Figure 6a, the color-coding legend indicates that the<br /> barcode region of the ssDNA template is highlighted in grey. However, nothing in<br /> the ssDNA sequence was highlighted in grey.
The data presentation for Supplementary Figure 6b along with the associated<br /> text description are a bit confusing too me. It is stated that, in experiments<br /> 1-3, the reads were compared to the correct templates, while the reads in<br /> experiment 4-5 were compared to the incorrect templates shown in Supplementary<br /> figure 6a. In this part of the study, the three pore-polymerase-CBT conjugates<br /> (RPol1:CBT1, RPol2:CBT2, and RPol3:CBT3) were first individually assembled, and<br /> then pooled and loaded onto the CMOS chip. Assuming that this has been done for<br /> each of the five experiments indicated in Supplementary Figure 6, then there is<br /> really no universally correct template (e.g., comparing CBT1 to the raw reads of<br /> a pooled experiment would only yield higher BMPI values for a third of the reads<br /> (i.e., only for RPol1:CBT1-derived raw reads). Are the raw reads from experiment<br /> 1, 2, and 3 compared to CBT1, CBT2, and CBT3, respectively? This wasn’t<br /> specified anywhere in the text.
Regarding Figure 6a, the authors stated that, out of all of the 96<br /> polymerases screened in this multiplex experiment, 20 polymerases were<br /> identified as having detectable activity (p23, bottom). However, as depicted in<br /> Figure 6a, there are only 17 polymerases for which the associated barcodes were<br /> counted (i.e., there are only 17 yellow bars). Thus, it is unclear to me where<br /> the number “20” is derived from.
In the PCA analysis in Supplementary Figure 11, the authors tried to map the<br /> sequencing data derived from the multiplex experiment back to those derived from<br /> the singleplex experiments involving the same three polymerases. The sequencing<br /> data set for the second barcode set (CBT33-64) could not be mapped back well,<br /> and it was stated that this might be due to the high false positive rate of<br /> barcode identification for that barcode set. That being said, as indicated in<br /> Supplementary Table 6, the false positive rate for RPol1:CBT1-32 and<br /> RPol2:CBT33-64 are 11.94% and 16.06%, respectively. Thus, if the author’s claim<br /> is true, the inability to map back is due to a 16.06% – 11.94% = 4.12%<br /> difference in the false positive rate. It is unclear to me if a 4.12% difference<br /> in false positive rate would really lead to such a dramatic difference in the<br /> ability to map back. Also, it is unclear if this higher false positive rate<br /> arose due to polymerase (RPol2), the templates (CBT33-64), both, or neither.<br /> Logically, it seems unlikely that the rate would be due to the CBTs since it is<br /> unlikely that the middle third of the set of 96 CBTs would just happen to give<br /> higher false positive rates in comparison to the other two thirds. An easily<br /> accomplished comparison between two polymerases would be to load both<br /> polymerases with the exact same set of CBTs, and then compare the derived false<br /> positive rate for each polymerase. Then, one can repeat the experiment but using<br /> a different CBT set. This will help narrow down whether the observed false<br /> positive rate is due to the polymerase or the CBTs themselves.
Regarding Figure 5, it is unclear to me the exact differences between 5a and<br /> 5b. I see that the data presentation is a little different, but I’m not sure if<br /> both figures are necessary here given that both deal with the same three<br /> polymerases as well as the same set of 96 CBTs.
It is stated that the surface of each individual CMOS chip contains 32,768<br /> electrodes (p30) and that the chip contains thousands of pores (p4). Now, as<br /> mentioned in the measurement setup (Figure 1a legend), the measurement setup<br /> requires two electrodes (a counter electrode and a working electrode). Given<br /> this, it is unclear to me what proportion of those 30,000-some electrodes are<br /> working or counter electrodes. I believe that clarification on this would help<br /> the reader get a better sense of the number of pore-polymerase-CBT conjugates on<br /> each individual CMOS chip, and thus, a better sense and appreciation of the<br /> multiplex scale.
On p30, under the section Pore-polymerase-template complex formation,<br /> “SpyCather” should say “SpyCatcher” (i.e., a “c” is missing).
On 2020-11-02 14:34:03, user Think4OnceNurLife wrote:
A later version of this article is published in the Journal of General Virology
On 2019-12-04 10:14:33, user Laurent Guyon wrote:
I have just created a quick video tutorial for first steps in miRViz. You can access in YouTube or in the Download file section (top right on the website http://mirviz.prabi.fr/):: "http://mirviz.prabi.fr/):")<br /> https://www.youtube.com/wat...
A comprehensive pdf tutorial is also available on the miRViz website.
On 2023-05-04 15:56:59, user Jordi Torres-Rosell wrote:
This manuscript has been published and is available at:
On 2025-06-28 20:15:21, user Prof. T. K. Wood wrote:
Line 222: the first antitoxin shown to regulate a loci other than the one that encodes it was MqsA, so there is precedence for antitoxin HicB regulating more than its loci (Nat Chem Biol 2011, doi: 10.1038/NChemBio.560).
On 2016-03-05 03:58:49, user disqus_hlLLDdz9K1 wrote:
similar idea in http://www.nature.com/ejhg/... could be interesting if can be compared with ARTP2
On 2017-05-11 22:07:52, user asademiloye wrote:
Please cite as:<br /> A.S. Ademiloye, L.W. Zhang, K.M. Liew, Element-free Multiscale Modeling of Large Deformation Behavior of Red Blood Cell Membrane with Malaria Infection, Proceedings of the 5th International Conference on Computational and Mathematical Biomedical Engineering (CMBE2017), Pittsburgh, PA, United States, 2017: pp. 316-319. doi:10.1101/136648.
On 2021-07-14 14:04:52, user Sandrine CHARLES wrote:
This paper has just been published on-line:
Title
Taking full advantage of modelling to better assess environmental risk due to xenobiotics—the all-in-one facility MOSAIC
Journal
Environmental Science and Pollution Research, (), <br /> 1-14
DOI
10.1007/s11356-021-15042-7
Available as 'Online First':
On 2023-12-13 01:25:27, user MBIO 600 class wrote:
We reviewed this paper as part of the SDSU graduate course MBIO 600: Seminar in Molecular Biology. Our expertise ranges from ecology, cancer biology, and molecular biology.
In this preprint, Sanhueza et al investigated the effect of different thermal conditions on stress response and welfare of captive fish (Atlantic Salmon). We all agreed that the paper was very interesting and was onto something, but feel that it may not have provided enough evidence to support all of their conclusions. Additional genes or stress pathway components would strengthen their work. To reach a broader general audience (that combines ecology and molecular fields for example), a more general description of some of the methods used would be helpful. More insight on how the natural and farm-raised populations behave would enable readers to put the authors’ results into context. It remained unclear what the final outcome was for the fish. We feel additional metrics to fish health and “wellness” would improve the manuscript. How do farmers “grade” their fish (for example weight; length over width; presence or absence of lesions? Overall, the manuscript offers potentially interesting findings that would be improved by additional clarifications and controls, as detailed below in further comments to follow.
On 2022-08-27 06:56:43, user Iratxe Puebla wrote:
Review coordinated via ASAPbio’s crowd preprint review
This review reflects comments and contributions by Claudia Molina Pelayo, Demetris Arvanitis, Pablo Raneo-Robles, Sónia Gomes Pereira. The comments were synthesized by Vasanthanarayan Murugesan.
In this preprint, Hughes et al. describe the interaction between the ER protein PERK and the mitochondrial protein ATAD3A. During ER stress, PERK phosphorylates elF2a leading to reduced global protein synthesis. The authors show that increased interaction between PERK and ATAD3A during such stress attenuates elF2a phosphorylation locally around mitochondria, resulting in continued translation of mitochondrial protein despite a reduction in global protein translation. The authors present multiple lines of evidence to support this claim and the experiments were well performed. The findings may have important implications for the understanding of mitochondrial protein synthesis and the interactions between mitochondria and the ER.
The following suggestions were raised:
Experiments
The manuscript would benefit greatly by measuring protein translation explicitly showing that mitochondrial protein translation is retained despite a reduction in global protein synthesis under certain conditions. That would help determine whether mitochondrial protein translation is protected under certain conditions driven by ATAD3 expression.
The specificity of ATAD3A towards PERK activation requires further experimental validation. Some specific suggestions are:
Manuscript
Recommend providing more details about the experimental protocol when treating cells with ER stressors. Different treatment durations are found throughout the manuscript (30min, 1h, 8h…). More information would be helpful in understanding the election of those time points for different experiments.
In Figure 2, recommend including the blots for the downstream targets ATF4, GADD34 and CHOP at the 30 minutes time point, where the upstream activation starts.
In Figure 2, the differences shown in the representative images for p-eIF2a and ATF4 appear milder than what is shown in the graph. In particular when compared with the interpretation of blots in Fig. S2. It is suggested to include all the blots used for quantification in Figure 2 in a supplemental figure so it can be clear how overexpressing/downregulating ATAD3A has a meaningful effect on this signaling pathway.
Figure 2B shows 5 different (phospho)proteins using the same loading control blot. This approach would require stripping of the membrane after each blotting, can this be specified in figure legends and in the Materials & Methods. Was the membrane stripped after each blot or were different membranes used? If different membranes were used, please indicate so and present the individual beta-actin blots corresponding to each protein as a supplemental figure.
In Figure 3A, arrows indicating the contact sites between ER and the mitochondria would be helpful in highlighting the colocalization of the two proteins. Please also provide scale bars for the images.
In Figure 3D, the #contacts per mitochondria, it is important to specify the area of images analyzed. It is unclear that n=45 images from 3 separate experiments refers to 45 images per experiment or a total of 45 images pooled from 3 experiments. Please clarify.
Recommend discussing the limitation of experiments using a single siRNA for loss-of-functions studies and experiments using cell culture.
On 2018-11-22 11:39:25, user Martin R. Smith wrote:
If you used a random tree instead of a molecular tree to generate hypothetical ancestors, would your approach improve the congruence of morphological results with the random tree?
On 2017-05-03 15:33:36, user Lynn Amon wrote:
Has the raw sequencing data been deposited to any public databases?
On 2017-02-02 21:20:17, user Miriam Goodman wrote:
Cool beans! Excited to dive into the data. Thanks for posting the preprint.
On 2020-12-02 12:49:37, user Alan Herbert wrote:
The P193 residue is in the Z? domain of human ADAR . Along with N173, it is directly involved in binding the left-handed Z-conformation. Mutations to these human residues directly map to Mendelian disease confirming a biological function for the left-handed alternative Z-DNA conformation and its role in regulation of interferon responses. Citing the earlier work would help readers and strengthen the case made by the authors.<br /> "Herbert, A. Mendelian disease caused by variants affecting recognition of Z-DNA and Z-RNA by the Z? domain of the double-stranded RNA editing enzyme ADAR. Eur J Hum Genet 28, 114–117 (2020). https://doi.org/10.1038/s41..."
On 2024-07-02 23:49:22, user Brian wrote:
It has been reasonably well-established that if there is sufficient water, transpiration rate must not be restricted for the purpose of conserving water early season to gain benefits late-season. Even the current study shows "Early-season water use was positively correlated with above-ground biomass, challenging the assumption that early-season water conservation can be leveraged for late-season benefits". This study explores three treatments, all fully or partially irrigated. As authors' concluded that "We question the efficacy of LT traits, highlighting the physiological link between water use and carbon gain, and the potential opportunity costs of reduced early-season growth", I am unsure whether such treatments were the best choice. LT trait has been proved beneficial when soil moisture is scarce, and/or soil profile is deep enough to store sufficient water to be used late-season.
On 2018-08-12 06:34:58, user Rod Whiteley wrote:
Love the idea and approach, but I'm having issues actually running the analyses. <br /> I'm getting a login requester and an error on the webpage (http://www.estimationstats.com)<br /> https://uploads.disquscdn.c... https://uploads.disquscdn.c... <br /> So I tried R and reticulate isn't compatible with R 3.3.2.
Do you have a standalone R package for dabest or is there a way to get the webpage working I don't know about?
Thanks again for your work,<br /> Rod
On 2019-04-10 15:54:17, user Fraser Lab wrote:
The idea is that by examining the structural context of these mutations you might arrive at a simple biophysical explanation for the results.
On 2020-02-13 10:47:13, user Priyanka wrote:
Am I only one who can't find the tool or package they used for their simulation and modelling
On 2020-02-01 12:00:59, user jvkohl wrote:
That would be true only if everyone continues to ignore these 2 facts
1) Koel et al. (p. 976) show that major antigenic change can be caused by single amino acid http://science.sciencemag.o...
2) A two-amino acid change in the hemagglutinin of the 1918 influenza virus abolishes transmission https://science.sciencemag....
On 2020-02-01 14:53:18, user ????????? ??????? wrote:
No. This "bat coronovirus" https://www.ncbi.nlm.nih.go... was submitted after this article was written. "Submitted (27-JAN-2020) CAS Key Laboratory of Special Pathogens"
On 2025-06-26 05:37:17, user DS wrote:
You might be interested in our previous work identifying the role of SerpinB2 in ECM remodelling in PDAC<br /> https://www.nature.com/articles/onc201763
On 2017-03-02 08:39:31, user Sujai Kumar wrote:
Please add comments/criticisms here. We're also happy to schedule a Skype QA if you have a journal club that wants to discuss this paper. There are a few new analysis types that we would especially like feedback on.
On 2018-06-02 07:41:27, user jinghuazhao Zhao wrote:
I made an attempt with depict, which shows impressively tissue enrichment at the nervous system reported earlier. See https://github.com/jinghuaz....
On 2016-07-06 08:46:09, user Alejandro Fernandez Woodbridge wrote:
What about "non identifing" data: Can we get a nice bigwig with the mutation rate that highlights regions with high functional (low snp count) or a dbSNP type of file with all 150M variants (I work on a proteogenomics lab, we need to consider all possible coding mutations)
On 2016-06-16 09:11:09, user Jerven Bolleman wrote:
The paper has been published in the Journal of Biomedical Semantics.<br /> http://jbiomedsem.biomedcen...
On 2024-10-01 15:34:23, user Thierry Alquier wrote:
A revised version of this article is now published in Brain, Behavior and Immunity at the following link (Open Access): https://pubmed.ncbi.nlm.nih.gov/39326768/
On 2025-05-05 07:05:57, user Roberto wrote:
Good morning, and thank you for your comment. Copying and pasting should work by using Ctrl + C and Ctrl + V.
On 2020-04-28 14:40:28, user Andi WIlson wrote:
Great paper.
From your TE-mediated remodelling in P. citrichinaensis figure, it appears you are suggesting that its "original" MAT locus looked like that of the homothallic species P. capitalensis where both MAT1-1 and MAT1-2 information is harboured at a single location. It is then suggested that a transposable element mediated the transfer of the MAT1-2 information to a different scaffold.
However, from the figure showing the evolution of thallism in this group of fungi as well as and statements elsewhere in the manuscript (eg: the reconstruction suggests that homothallism of P. capitalensis and P. citrichinaensis does not originate from the same evolutionary event), I think it is more likely that the "original" locus harboured only MAT1-1 information, with MAT1-2 information having been introduced into the genome elsewhere (probably during sexual recombination with a MAT1-2 individual).
Can you comment on this?
On 2017-06-23 20:23:57, user Gennadi Glinsky wrote:
Interesting concept linking the evolution of human-specific regulatory loci associated with control of pluripotency to development and function of human brain.
On 2019-03-11 20:52:31, user Julius Adler wrote:
March 11, 2019
Add the following to the end of the Discussion of October 1, 2018
Drosophila Mutants that Are Motile but Respond Poorly to All Stimuli Tested:<br /> Mutants in RNA Splicing and RNA Helicase, Mutants in The Boss<br /> Lar L. Vang and Julius Adler
Relation of this paper (October 1, 2018) to the idea of generalized arousal:
Specific responses have now been recognized as different from a generalized response, also known as generalized arousal.
The idea of a generalized arousal was described by Donald Pfaff, Lars Westberg, and Lee-Ming Kow (2005): “Many of the major advances in neurobiology during past decades dealt with specific central nervous system responses to particular environmental or internal stimuli…Fundamental to all emotional and motivational states is the concept of ‘arousal’ of the central nervous system, the 'activation of behavior.' Going under different names - elementary , fundamental, global, or generalized arousal - all of these terms refer to the same primitive capacity of the vertebrate central nervous system. Arousal underlies all mammalian behaviors…Gene/behavior thinking, even in Drosophila, is moving beyond single gene effects (Robert Anholt, 2004).”
As to arousal in Drosophila, Bruno van Swinderen and Rozi Andretic (2003) have written: “Changes in levels of arousal, such as occur during sleep or attention, most likely accomplish adaptive functions common to most animals. Recent evidence demonstrating changing arousal states in Drosophila melanogaster complements other behavioral research in this model organism. Herein we review the methodology related to the study of circadian rhythms, sleep and anesthesia where arousal, or lack of it, plays an essential role…Wakefulness is a state of increased arousal compared to sleep.”
The Boss (Adler, p. 60 of 2011) is related to generalized arousal but The Boss goes further, see October 1, 2018, above.
Adler J ( 2011) My life with nature. Annu Rev Biochem. 80:42-70.
Anholt RRH (2004) Genetic nodules and networks for behavior: lessons from Drosophila. BioEssays 26:1299-1306.
Pfaff D, Westberg L, Kow L-M (2005) Generalized arousal of mammalian central nervous system. J Comp Neurol 493:1-22.
van Swinderen B, Andretic R (2003) Arousal in Drosophila. Behav Proc 64:133-144.
Vang LL, Adler J (October 1, 2018) Drosophila mutants that are motile but respond poorly to all stimuli tested: Mutants in RNA splicing and RNA helicase, mutants in The Boss. bioRxiv
On 2018-02-14 07:47:10, user Jason Kroll wrote:
Note the title change from version 1 to version 2. <br /> The older title was "Multiple sources of variability drive a stochastic cell fate decision in C. elegans"
On 2020-06-06 13:03:25, user OxImmuno Literature Initiative wrote:
On 2015-08-12 16:15:22, user Jeramiah Smith wrote:
This article has been published
The sea lamprey meiotic map improves resolution of ancient vertebrate genome duplications
Jeramiah J. Smith and<br /> Melissa C. Keinath Genome Res. August 2015 25: 1081-1090; Published in Advance June 5, 2015, doi:10.1101/gr.184135.114
On 2019-03-28 15:49:53, user bioRxiv wrote:
Dear Charles Warden, blog posts that refer to bioRxiv preprints are linked on the preprint page automatically. The author does not need to do anything to make this happen. Thank you for your question. If you have any further questions, please feel free to email us at biorxiv@cshl.edu. Warm regards, The bioRxiv Team
On 2023-10-20 14:53:27, user Avolyn Fisher wrote:
This work is really fascinating and might help explain why women have higher rates of depression, instances of headache and are more susceptible to various forms of dementia. I hope that's being considered in this area of research since the disparity between men/women rates of mental health and brain ailments is somewhat inexplicable. Perhaps this is a breakthrough in explaining the gender disparity.
On 2020-09-13 07:34:50, user OxImmuno Literature Initiative wrote:
On 2021-01-29 09:05:33, user Meghamsh Teja wrote:
In fig.3A, I need some clarification. First, size exclusion chromatography was performed, fractions were collected and then subjected for SDS-PAGE. Color codes represent the type of the sample and color coded box represents their respective gel analysis result. In gel3, according to color code, only nucleosomes were added, so how did Mer2 appear on gel. Can someone explain it ?
On 2018-10-19 14:14:24, user Tim Fessenden wrote:
Where are the video files?
On 2021-05-12 12:11:06, user George Perez wrote:
If I'm not mistaken the other biopharmaceuticals never had their vaccine data Peer-reviewed by scientific community either.<br /> Great news for Inovio Pharmaceuticals, AGAIN!
On 2022-02-26 22:52:48, user David Borhani wrote:
The one presumed deer-to-human transmission case is described without epidemiologically relevant details. Could the Authors please comment on the following?<br /> 1. Was the proband a deer hunter? If so, details of contact (e.g., casual contact at a farm or in the wild; dressing a killed deer; masked or not; any cuts and or mucous membrane exposure; aerosolization; etc)?<br /> 2. Was the proband instead related to a deer hunter (e.g., family member who was exposed once the dressed deer was brought home)?<br /> 3. Was the proband instead somehow else in close contact with a deer?<br /> 4. Any relevant food (venison) contact?<br /> 5. Timing of the known close contact relative to the proband obtaining a PCR test?<br /> 6. Prior health status of the proband?<br /> 7. Clinical course of the infected proband?<br /> 8. Regarding lack of further spread from the proband: Is it obvious why not (e.g., strict isolation measures put in place)? Or, conversely, was there opportunity for further spread, but nonetheless it did not occur?
Separately, could the Authors please post Ct values for the deer and human proband samples?
On 2024-05-09 15:38:48, user Yi-HsuanPan wrote:
This paper had been accepted by Nature Communications volume 15, Article number: 3746 (2024) <br /> https://doi.org/10.1038/s41...
On 2016-06-15 10:24:58, user Adam Kampff wrote:
Some of the supplementary videos are rather large. We have uploaded them to YouTube for easier viewing: http://www.kampff-lab.org/s...
All feedback much appreciated!
On 2022-10-31 15:53:29, user Daniel Lüdke wrote:
Figure 3e: It is not indicated what the different groups represent (assume control and DC300)
On 2016-07-15 09:44:17, user Gianluca Corno wrote:
The methodology suggested is simply impressive, and I see the hundreds of potential applications! I wonder if you need to specifically design a model for every enviornment (i.e. a small pond, coastal waters, streams, wwtps) and for every condition (punctual samplings, time series), or the model can apply directly to the different environments somehow implementing the diverse ecological conditions...
On 2022-06-30 18:01:00, user QuiPrimusAbOris wrote:
Interesting piece of work substantiating the role of CAFs in tumorigenesis with some specific mechanism. The authors emphasize here the obvious TRANS effect (Fibroblast --> Epithelium). But the key question alas remains not answered: What makes the BRCA1 mutated epithelial cell convert the normal fibroblasts into (pre)CAFs? Can wildtype fibroblasts also become, with same ease, tumor promoting CAFs in this model?<br /> it shold be reminded that with germline BRCA1 mutation we have the rater unusual but interesting setting of having an oncogenic mutation in both the epithelium and stroma. <br /> The more usual setting is to have the mutated epithelium be surrounded by genetically wildtype fibroblasts - which still are converted into CAFs.<br /> This aspect is not addressed, not even discussed. It is also not clear what the authors mean by "control" when they say it since they do not specify it (there are many questions in this regard): BRCA1 wildtype cells from cancer-free healthy individuals or from precancerous, non-BRCA1 tissues.. Would be nice to have both types f controls. etc. Also the genotype of the fibroblasts with respect to BRCA shold be specified in every experiment.
On 2019-07-28 21:51:35, user Donald R. Forsdyke wrote:
POSITIVE SELECTION OF THE IMMUNE REPERTOIRE<br /> This interesting new contribution to bioRxiv (1) may inspire physicists to address immunological problems – a splendid goal! However, it does not accurately describe positive selection of the immune repertoire.<br /> .<br /> Positive selection means precisely that – positive selection – i.e. cells are actively selected for some characteristic that is likely to contribute positively to immune function. This contrasts with negative selection which actively removes cells because they have a characteristic that does not contribute positively, and may actively impair, immune function. <br /> .<br /> This active selection for what is becoming recognized as “near-self” reactivity, has a long history that is recently outlined (2). There are three fundamental thymic processes, death (or inactivation) by neglect, death (or inactivation) by negative selection, and positive selection. The following statement (1) wrongly indicates that positive selection should be equated with death by neglect:
"If the receptor fails to bind any self-peptide, even weakly, it will probably fail to bind any protein and the cell carrying these receptors is discarded {a process called positive selection which removes 80% of immature cells}.”
.<br /> 1. Altan-Bonnet G, Mora T, Walczak AM. (2019) Quantitative immunology for physicists. Biorxiv Here.<br /> 2. Forsdyke D. R. (2019) Two signal half century: from negative selection of self-reactivity to positive selection of near-self <br /> reactivity. Scand. J. Immunol. 89: e12746 Here.
On 2021-06-09 02:14:15, user Daniel Cameron wrote:
Very catchy title and some quite interesting results. Xiaotong Yao suggested I raise my terminology concerns as a biorxiv comment:<br /> ‘Loose ends’ is a catchy title but I am concerned that the use of new terminology for existing concepts has the potential to confuse readers. The definition you are using for ‘loose ends’ is equivalent to concept of a single breakend variant defined in the VCFv4.1 specifications about a decade ago.
Whilst it is only recently that variant callers have actually explicitly reported VCF single breakends (GRIDSS2 from SR/OEA/breakend assemblies, PURPLE from unexplained CN transitions), the loose end/single breakend concept has a long history of implicit usage. For example, NovelSeq (Sahinalp, 2010) uses an orientation-constrained assembly approach (OEA+/OEA-) very similar to that of jabba to produce what this paper calls loose end assemblies (although what NovelSeq does afterwards is different). The assembly similarity can also be seen in GRIDSS1 which uses the terms ‘breakend assembly’ and ‘anchoring reads’ when discussing it’s separate assemblies of forward/- and backward/+ orientation reads/breaks. Viral integration detection software has a similar history of implicit usage of loose ends/single breakends without explicitly using either terminology (only the recently published VIRUSBreakend uses the term).
In summary, this paper would benefit from briefing mentioning the long history of the loose end concept, and how it has been incorporated into existing tools.
Some additional thoughts to consider:
One interesting benchmarking comparison you could incorporate is against WEAVER. It also generates junction-balanced breakpoint graphs using integer programming, but its conceptual model does not appear to include single breakends. In my benchmarking (https://doi.org/10.1101/781013, Figure 3), I found that weaver failed badly on some samples and I suspect this was due to missing high copy SVs causing all junction balancing solutions to be terrible (thus reporting unbelievable ASCNs). Comparing to WEAVER (or comparing jabba MIP results with/without single breakend support) would be a good demonstration of how essential single breakend support is to the junction balancing process itself.
we found that almost half (48%, 12,068 of 25,271) of loose ends arose from Type 0 junctions that were missed during genome-wide analysis”
Is this a consequence of waiting till after SV and CN calling is done before looking for single breakends or due to the choice of caller? Would another SV caller (e.g. manta) reduce this? How many of the single breakends found by jabba can be found by stand-alone single breakend SV calling (e.g. GRIDSS2)? I notice jabba allows a BND style VCF input. Does it support single breakend BND calls?
Single breakend from SV calling and single breakends unbalanced CN junctions seems very much like they are complementary approaches so combining them should improve the final jabba results. I suspect this will be especially true for complex events as they frequently containing many clustered SVs and accurate copy number determination becomes more difficult the shorter the CN segments become.
On 2018-01-14 18:03:54, user Esteban Vargas wrote:
Too little done by Neuraminidase inhibitors
On 2020-04-17 13:57:57, user Liz Miller wrote:
This paper was the subject of the Miller lab journal club and, following a discussion of the findings, we offer the following comments.
In this work the authors explore the departure of vacuolar cargo from the Golgi complex in coordination with cisternal maturation in S. cerevisiae. Building up on previous work (Casler et al. 2019), they have developed a regulatable vacuolar cargo which forms aggregates at the endoplasmic reticulum (ER) and can be solubilized upon the addition of a ligand. Once soluble in the ER lumen, the cargo enters in the secretory pathway, reaching the vacuole as the final destination in a Vps10 dependent manner. Transport dynamics can be followed using 4D microscopy, whereby co-expression with fluorescent protein markers allows them to follow the entry and departure of the vacuolar cargo coincident with the different stages of Golgi cisternal maturation, pre-vacuolar endosomes (PVE) and vacuole. Using this strategy the authors have followed the kinetics of transport and maturation with excellent time resolution. One of the main findings of this work is that the vacuolar cargo departs from the Golgi complex half-way through cisternal maturation. The departure of vacuolar cargo coincides with the arrival of GGA adaptors, required for Vps10 dependent traffic from the Golgi complex en route to the vacuole. Subsequent analysis of the traffic kinetics towards the PVE and vacuole revealed that the majority of the vacuolar cargo reaches the PVE less than 10 minutes after solubilization, whereas arrival to the vacuole takes between 30 to 60 minutes. Interestingly, the transfer of material between these organelles seem to follow two dynamics; a gradual transfer of cargo for several minutes or an abrupt transfer of material accompanied by decrease in coincident signal with the endosomal marker Vps8. Based on these observations, the authors propose that the traffic between PVE and vacuole occurs via a series of kiss-and-run interactions between organelles over a lengthy period of time. Overall, this regulatable vacuolar cargo represents an exciting new tool to scrutinize the secretory pathway in yeast, with an undoubtedly great potential when combined with genetic and EM techniques.
Following our group discussion, we have some brief comments:
We noticed that only a fraction of the cargo seems to reach the vacuole, if the fluorescence intensity is compared before the addition of the ligand and in later stages at the vacuole. This is to be expected since traffic of vacuolar cargo is receptor mediated, and can therefore be expected to saturate as a wave of cargo reaches the Golgi, resulting in excess vacuolar cargo lost through extracellular secretion. Of course, photobleaching will also account for some of the loss in fluorescence intensity. It would be interesting to see a quantification of the fraction of cargo reaching the vacuole, which would be indicative of the capacity of the Golgi cisterna to deal with incoming material from the ER.
We would have liked to see a figure directly comparing cargo and Apl2 kinetics, as shown for cargo and Gga2 in figure 5c. We appreciate that Apl2 itself is distinct from the GGAs, so this experiment would largely serve to emphasize the distinct route that vacuolar cargo would take from Apl2-dependent cargo.
The appearance of Sec7 seems accelerated when GGA adaptors are deleted, and we noticed that vps10? seems to have a similar effect. Is the dynamic arrival and disappearance of Apl adaptors affected by this change in maturation dynamics? It would be interesting to hear the authors speculation on this and its relevance to Golgi maturation dynamics.
It would be interesting to know the kinetics of AP-3 and AP-3 dependent cargos in the context of the cisternal maturation kinetics provided here. Perhaps this will be the next study!
On 2020-03-18 18:38:34, user piper m treuting wrote:
What were the sexes of the Rhesus?
On 2020-05-04 11:08:10, user Maciej Smialek wrote:
https://www.mdpi.com/1422-0...
Peer-reviewed full article visible withing this link
On 2025-09-24 07:06:09, user Fabrizio Vacca wrote:
Comments, suggestions and constructive criticism are welcome!
Fabrizio
On 2022-01-10 23:28:13, user Daniel Himmelstein wrote:
Thanks to the authors for continuing to develop this important resource, which I first became familiar with when incorporating it into Hetionet.
I recently reviewed the manuscript for a journal and am sharing my review here. Note that this review is for version 1 of the preprint posted on 2021-12-09.
On 2020-03-15 00:06:24, user Irene Kung wrote:
Have you immobilized it onto a substrate yet? If so, how much does that reduce the affinity of 47D11 for its target? Any flow cytometry data?
On 2024-11-05 13:53:35, user Damien Garcia wrote:
The article has been published please link the archive to the published manuscript.<br /> https://doi.org/10.1093/plphys/kiab529
On 2019-03-16 02:10:00, user Kim Park wrote:
The results will be more convincing in brain tissues combined with single cell RNA-seq and single cell ATAC-seq, Dr. Li-Huei Tsai's recent work on AD using single cell techniques reveal interesting findings.
On 2021-02-28 21:22:27, user Ana Christoff wrote:
Article now published<br /> Frontiers in Microbiology<br /> https://doi.org/10.3389/fmi...
On 2023-09-12 12:02:55, user Phillip Gordon-Weeks wrote:
Your very interesting experiments on HTT and drebrin in growth cones provide insights into the biology of the T-zone but I think your interpretation of the results could be developed further. HTT depletion clearly induces a re-location (not a mis-location-since drebrin can locate to filopodia) from the T-zone to filopodia. Unsurprisingly, given that the drebrin/EB3/Cdk5 pathway enables the capture of dynamic microtubules by filopodia, this is associated with a striking increase in microtubules in filopodia-actually the most significant change you measured in HTT-depleted growth cones. Drebrin in the T-zone is largely unphosphorylated at S142 and therefore in the folded conformation, which can bind anti-parallel F-actin through one of its two F-actin binding sites. In contrast, drebrin in filopodia is phosphorylated at S142 and therefore in the open conformation, enabling it to bind to parallel F-actin bundles using both F-actin binding sites. Drebrin can cross-link F-actin to dynamic microtubules by binding to EB3 in filopodia. Another manipulation that re-locates drebrin (our unpublished observations) and myosin IIB from the T-zone to filopodia is the inhibition of myosin II by blebbistatin, which essentially disassembles the F-actin in the T-zone removing an impediment to microtubule advance into the P-domain (Hur et al., 2011, P.N.A.S. 108, 5057-5062; Shin et al., 2014, PLoS ONE 9(4): e95212. doi:10.1371; Dupraz et al., 2019, Current Biology 29, 3874–3886). I wonder, therefore, whether HTT depletion also disrupts the T-zone thereby disabling drebrin binding and unhampering microtubule advance.
On 2022-03-30 10:17:29, user GP wrote:
don't miss on relevant literature:<br /> https://www.frontiersin.org...
On 2018-10-22 20:18:15, user Pavan wrote:
Please see Nature Communications (https://rdcu.be/9Nr8) "https://rdcu.be/9Nr8)") for the final manuscript. Note that there is an inversion in the bioRxiv pre-print whereby the relationship between A1 neural activity and turning behavior is flipped.
On 2021-09-30 10:32:19, user Tim Weil wrote:
This preprint has been accepted for publication in Developmental Cell. The published title will be: "Adaptable P body physical states differentially regulate mRNA storage during early Drosophila development". A link will be forthcoming shortly.
On 2021-01-05 19:12:54, user Johanna N. wrote:
Hi
I have two minor questions/comments regarding the method section:<br /> 1) For how long did you incubate the cells with EdU? I assume it's rather a short time.<br /> 2) You state that you normalize cell=level data by using median and MAD from "empty wells". Do you maybe rather mean from "untreated" wells?
On 2021-11-18 11:28:27, user Kresten Lindorff-Larsen wrote:
The manuscript by Bock & Grubmüller describes a detailed, multi-pronged computational study of the complex and important effects of cooling during sample preparation for cryo-EM. The paper is generally easy to read, appears technically sound and provides relatively clear results that will be of interest both to theoreticians and practitioners of cryo-EM.
Over the last 10 years cryo-EM has delivered increasingly high-resolution structures that in some cases now rival those of e.g. X-ray crystallography. In addition to examining the structures of macromolecules, cryo-EM may also provide more detailed insights into their energy landscapes because it in principle is a single-molecule technique that enables the visualization of the conformational distribution of molecules.
These advances leave two questions that have been difficult to answer. First, to what extent does the “average” structure under cryogenic conditions reflect the ambient temperature “average” structure [realizing that the term average here is somewhat misleading, the authors will understand what is meant]. Second, to what extent does the distribution of conformations (conformational ensemble) present in the cryo-EM sample reflect the distribution at ambient temperatures [leaving aside the technical difficulties of determining structural models of these ensembles from experiments]. While the first question can to a certain extent be answered by comparing structures solved at cryo-conditions with those at ambient temperature (by crystallography), the second question lies at the heart of the utility (and large potential) for cryo-EM to study conformational ensembles.
This study provides welcomed data in an area that has been lacking detailed and quantitative modelling, and where experiments are difficult. The results are promising in the sense that they support the idea that cryo-EM can to a large extent capture conformational ensembles at ambient temperatures. Importantly, the study provides a framework to think about these problems in a more quantitative manner that will hopefully spur additional experiments and analyses.
Specific comments:
Major<br /> p. 4:<br /> I must admit that I found the RMSF-based analysis somewhat difficult to follow in places. First, just to be sure could the authors confirm that in each case the RMSF is calculated “locally” that is using an average over the specific simulation as reference. Second, when I look at Fig. S1 it appears that there are still some changes in the RMSF curves even towards the end of the simulations that are of the same magnitude (but in the opposite direction) as those observed during cooling. Is that correct or am I looking at the figure in the wrong way?
Also, while I realize that it is difficult to boil down a complex ensemble to one or a few numbers that can be tracked, it would be useful with alternative ways of looking at the ensembles. Are there local differences that are not captured by RMSF? What about rotamer distributions. I will leave it up to the authors whether to explore these issues further in this paper.
p. 11:<br /> In terms of future experimental studies, what kinds of tests of the models could the authors envision? For example, the authors discuss work by Chen et al (Ref 24) on differences depending on the starting conditions. Do the authors’ analytical model capture such effects? Do the authors’ results lead to specific criteria for selecting good model systems to test the effects of cooling on conformational ensembles?
p. 11/12:<br /> Maybe the authors could also briefly discuss the relationship to other techniques that rely on (rapid) cooling including ssNMR and EPR. I realize that the cooling process is different, but it might still be worth speculating on how the approaches and models the authors present could be extended to other situations. In this context I’d also like to point out relevant work from Rob Tycko studying protein folding by ssNMR with rapid injection into a cold isopentane bath (https://dx.doi.org/10.1021%... "https://dx.doi.org/10.1021%2Fja908471n)").
Minor<br /> p. 1/2: In the discussion of molecules settling into the lowest free energy minima at slow cooling rates, it might be worth making it clear that these minima may well be different from the minima at ambient temperatures.
p. 4: In the T-quenching MD simulations I couldn’t easily find whether the simulations were performed using pressure control and if so how.
p. 6: “the atoms are subjected to harmonic potentials with a force constant c which are uniformly distributed in an interval from –d to d” makes it sound like it is the force constants that are between -d and d. Consider rephrasing.
p. 6 “Model3 is a combination of model2 and model3,” should be, I guess, “Model3 is a combination of model1 and model2,”
p. 6: It is not clear what value of the pre-exponential factor that the authors use. I did not go through the maths, but I would assume that the choice would affect the “effective” barrier heights e.g. in Fig. 4. It would be useful if the authors would clarify this, given that there has/is some discussion about what pre-exponential factors are relevant for conformational changes in biomolecules.
p. 11: The authors write “Biomolecules can thermodynamically access more conformations at room temperature than at the cryogenic temperature”. While that is probably mostly true, examples such as cold-denaturation suggest it isn’t universally true.
Kresten Lindorff-Larsen, University of Copenhagen
On 2020-07-05 16:20:10, user David Curtis wrote:
If you want to evaluate the performance of this method you should compare it with SCOREASSOC, which was pretty successful at identifying genes for Alzheimer's disease.
https://www.biorxiv.org/con...<br /> (It's published now in Annals of Human Genetics.)
On 2017-04-06 16:18:55, user Katherine DeLong wrote:
We have a number of concerns regarding Nieuwland et al.'s (2017) <br /> multi-lab effort. Here, we highlight features of their project that <br /> undermine confidence in their purported non-replication of the DeLong et<br /> al. (2005) findings: http://kutaslab.ucsd.edu/Fi...
On 2019-11-18 08:30:14, user Wolfgang Graier wrote:
Congratulation to the team, this a very nice and excellent work. May I ask to kindly consider our recent findings when discussing your excellent findings:
Klec, C; Madreiter-Sokolowski, CT; Stryeck, S; Sachdev, V; Duta-Mare, M; Gottschalk, B; Depaoli, MR; Rost, R; Hay, J; Waldeck-Weiermair, M; Kratky, D; Madl, T; Malli, R; Graier, WF. Glycogen Synthase Kinase 3 Beta Controls Presenilin-1-Mediated Endoplasmic Reticulum Ca²+ Leak Directed to Mitochondria in Pancreatic Islets and ?-Cells.<br /> Cell Physiol Biochem. 2019; 52(1): 57-75.
Klec, C; Madreiter-Sokolowski, CT; Ziomek, G; Stryeck, S; Sachdev, V; Duta-Mare, M; Gottschalk, B; Depaoli, MR; Rost, R; Hay, J; Waldeck-Weiermair, M; Kratky, D; Madl, T; Malli, R; Graier, WF. Presenilin-1 Established ER-Ca2+ Leak: a Follow Up on Its Importance for the Initial Insulin Secretion in Pancreatic Islets and ?-Cells upon Elevated Glucose.<br /> Cell Physiol Biochem. 2019; 53(3): 573-586.
I think your data truly are very important and, in my humbling opinion, together with our findings mentioned above add to the current correction on the principles of the mechanisms of insulin secretion.
Thank you very much and good luck in publishing.<br /> Best,<br /> Wolfgang Graier
On 2025-06-30 15:27:28, user John McBride wrote:
Cool work! Any plans to release data / code?<br /> Personally I'd have interpreted these results differently at times, but I understand the need to produce compelling narratives...
For example, if you put the reaction time difference (20 ms) in context, you could have a different interpretation of relevance. One such context is the limits of human perception, which is about 5 ms for very sort stimuli, and scales with stimuli over about 250 ms.
Another point up for discussion is effect sizes (rather than 'significance'). Normally if I do an analysis with >1000 samples/participants, and I get a p value of 0.048, that means the effect size is so small that even if it's not random, it's an extremely small effect size (I'm not quite as experienced with GLMMs, so I lack a bit of intuition here). Personally I would scramble the participant results across stimuli and see what the p-value turns out to be, just to be sure that it's not just overfitting (as far I can see, there's quite a few parameters in the model). I've definitely seen cases where this level of 'significance' can be generated by better than 1 in 20 odds, and I assume that this is related to overfitting and hidden non-randomness in any randomly-generated data. Whether it's still significant or not, I'd prefer to see effect sizes discussed in some sort of meaningful way. Like, how many times would I have the same preferences as another animal when selecting stimuli? Perhaps that's the horizontal bar with whiskers in Fig. 1A? It'd be nice to know the number (e.g. 55%?).
I'd also be interested to see a graph where the same (or equivalent) measure of agreement was plotted for human-human agreement, animal-animal agreement, and human-animal agreement.
And I don't think this statement is properly qualified, "Our global survey discovered that humans share acoustic preferences with other animals, spanning insects, frogs, birds, and non-human mammals." Technically you showed a group-level effect, which says nothing about the individual species. So one might wrongly take away from your statement that humans share acoustic preferences with each of these animals. When in actuality, it could be that a few species have a strong enough effect to bring up the average, and other species may share no acoustic preferences.
Still, cool paper. But I would be open to the possibility that the results are actually random, and perhaps not try to make such a strong claim with effects that are borderline random.
On 2023-07-09 15:21:21, user Charles Feigin wrote:
You might consider an alternative name for velvet to avoid confusion with the genome assembler Velvet, which is extremely well known and has >10,000 citations.
On 2019-07-15 15:14:39, user Artur Wlodarczyk wrote:
Correction: concentration of Na2CO3 in the 5xBG medium should be 0.472 mM. Both 5xBG and 5xBGM were sterile-filtered, not autoclaved.
On 2019-12-30 18:36:11, user Jonathan Weissman wrote:
We have critical concerns regarding this preprint by Dr. Reddy and co-workers. In particular we have previously communicated data that we feel directly refute the claims in this preprint to Dr. Reddy, yet they are ignored here. We therefore feel it is important to provide some context. A more complete description of our findings will be presented within 30 days following external review required by the terms by which we obtained some materials.
Although Dr. Reddy and co-workers suggest that the microtubule-destabilizing activity of rigosertib is mediated by a degradation product present in formulations obtained from commercial vendors, they are aware of our results demonstrating that pharmaceutical-grade rigosertib (>99.9% purity) and commercially obtained rigosertib elicit qualitatively indistinguishable phenotypes across multiple assays. The two compounds have indistinguishable chemical-genetic interactions with genes involved in modulating the microtubule network, both destabilize microtubules in cells and in vitro, and both show substantially reduced toxicity in cell lines expressing the L240F tubulin mutant, a rationally designed mutant in which the rigosertib binding site in tubulin is mutated. Our results demonstrate that pharmaceutical-grade rigosertib kills cancer cells by destabilizing microtubules, in agreement with our original findings and refuting the claims in this preprint.
Dr. Reddy and co-workers claim that the resistance conferred by our L240F tubulin mutant is non-specific without acknowledging that these claims are in direct conflict with a previously published report by an independent third party (Patterson et al. PMID: 31302152). In particular, Dr. Reddy and co-workers suggest a lack of specificity of the L240F tubulin mutant because in their hands expression appeared to confer mild resistance to the PLK1 inhibitor BI2536, but Patterson et al. conducted an essentially identical experiment and found that expression of the L240F tubulin mutant did not confer resistance to BI2536 (see Figure 7 in the manuscript by Patterson et al.). Their failure to cite or otherwise acknowledge Patterson et al. is all the more surprising, as we have previously brought this paper to their attention. Regardless, the combination of our previously published data and those presented by Patterson et al. firmly establish the specificity of the L240F tubulin mutant.
Dr. Reddy and co-workers claim that cells expressing the L240F tubulin mutant fail to proliferate in the presence of rigosertib, but we had demonstrated in our original manuscript that rigosertib-treated cells expressing the L240F tubulin mutant proliferated at the same rate as DMSO-treated cells over the course of multiple days (Fig. S6F in our original manuscript), and observe the same behavior with pharmaceutical-grade rigosertib. Thus, rigosertib-treated cells expressing L240F tubulin do not undergo senescence, as suggested by Reddy and co-workers, but actively proliferate.
Altogether, we conclude that there is no merit to the claims in this preprint and feel that any evaluation of the potential of Rigosertib as a clinical agent should be made in the context of these findings.
On 2019-08-07 13:15:48, user Masa Tsuchiya wrote:
This paper reveals that
Whole genome expression is dynamically self-organized through the emergence of a critical point (CP): co-existence of distinct expression response domains (critical states). this happens at both the cell-population and single-cell levels through the physical principle of Self-Organized Criticality (SOC).
Coherent-Stochastic Behavior (CSB): Coherent behavior emerges in stochastic expression in both critical states and whole-expression:<br /> i) In whole expression, the dynamics of the CM of stochastic expression converges to that of the whole expression (Genome-Attractor).<br /> ii) In the critical states, the dynamics of the CM of stochastic expression converges to that of the corresponding critical state (Critical-State Attractors).
The Genome-Attractor guides global coherent expression, whereas critical-state attractors guides local coherent expression emerged in critical states through heteroclinic critical transition.
Characteristics of the CP are given by<br /> i) A fixed point during a specific biological regulation such as reprogramming and cell differentiation,<br /> ii) The center of mass (CM) of whole expression according to temporal expression variance, which is the order parameter of the self-organization of the whole genome expression,<br /> iii) ON-OFF state: A specific transition of the higher-order structure of genomic DNA corresponding to the CP, which suggests that the CP competes between the active (swelled or coil: ON) and inactive (compact or globule: OFF) states,<br /> iv) The Genome-Attractor: A change in the CP such as ON-OFF switch induces a global impact on genome expression - the origin of the genome-wide coherent expression waves.
A potential Universal Mechanism of Self-Organization can be interpreted in terms of the Genome-Engine: An autonomous critical-control genomic system is developed by a highly coherent behavior of low-variance genes (sub-critical state) generating a dominant cyclic expression flux with high-variance genes (super-critical state) through the cell nuclear environment: the sub-critical state acts as the generator (source) of SOC control of the whole expression, whereas the super-critical state acts as the sink.
Cell-fate change occurs<br /> i) When: The timing of the erasure of the genome-attractor of the initial state,<br /> ii) How: Coherent perturbation on the genome engine through the activation of the CP.
On 2018-10-30 13:36:28, user Samantha Smith wrote:
I am getting 404s on some of the github links https://github.com/smangul1...
On 2025-07-16 03:30:08, user mcdull wrote:
the cited references do not support the stated role of hypoxanthine. hypoxanthine is actually decreased in serum of CRC patients
On 2020-07-29 14:07:25, user Gabriel Moagar-Poladian wrote:
Maybe other materials have better gamma attenuation properties but what I consider amazing are two facts: 1) the resilience of fungi to gamma radiation (sometimes gamma irradiation is used to disinfect/kill bio-pathogens); 2) the ability to use the harmful radiation for the benefit of life.
On 2023-12-05 07:33:10, user ghujka wrote:
-If the increase in the amounts of specific amino acids in the lumen is because of a lack of absorption from the lumen as a result of declined transporter expression, the aged lumen could better be mimicked by substracting the AAs instead of adding more. This should reveal systemic effects of inability to absorb. <br /> -It could be more informative to conduct the DUMP assay after the genetic intervention to confirm the change in AA transport.<br /> -The grayed out transporters could be validated with new qPCR primers.<br /> -Will the data about cholesterol and sugars added to this manuscript?<br /> -Slimfast antisense RNA might have off target effects and it is known to be toxic. Confirmation of the data using commercially available RNAi might increase the reliability of the results. <br /> -How does the undigested AAs change after feeding with aged AA medium?<br /> -Maybe you can combine AA measurements using hemolymph and DUMP using normal and smurf flies?
On 2020-07-06 21:42:49, user thomas_carroll wrote:
Congratulations on the interesting approach and findings here! I too would hope to see a more comprehensive set of hits (and their Z-scores/FDRs) make it into the public domain, especially as this paper moves towards publication. The findings described here will be especially powerful in integrative analyses with data from other approaches- and these integrative approaches would be best served by access to a more complete results list (or the raw data needed to recreate such a list).
Interested to see how our understanding of entry proteases like TMPRSS2/CTSL continues to evolve, perhaps with future screens in other cell lines!
On 2018-06-11 14:02:26, user Dan Bollinger wrote:
Not surprising that newborn circumcision is an ACE. The child perceives it as an assault, a sexual assault, a betrayal by caregivers, and there's no lower age limit on what constitutes an ACE.
On 2020-11-24 15:33:56, user Kvido Strisovsky wrote:
Now published peer-reviewed and revised as Biophys J 2020 Vol. 118 Issue 8 Pages 1861-1875
PMCID: PMC7175701
DOI: 10.1016/j.bpj.2020.03.011<br /> https://www.ncbi.nlm.nih.go...
On 2017-01-30 06:27:35, user Amit Singh wrote:
Manuscript provides new insights on how a transcription factor, WhiB4, links redox and Beta-lactams efficacy in Mtb.
On 2015-03-17 19:27:35, user CJ Battey wrote:
Really nice paper. It would be nice to see the actual number of loci used in the D-stat tests in the tables - ie how many total ABBA+BABA sites were there for each test. Might help readers and other researchers (me) assess the power of the test with RAD data relative to whole-genome data.
I have also found treemix to infer what seem like implausible introgression events from outgroup taxa. Not really sure what's going on there.
Fontaine et al's 2014 Science paper with the malaria mosquitos was particularly interesting for this stuff: in their case so much of the genome was identified as introgressed that any reduced-representation approach would infer an (incorrect, at least based on their analysis) highly-supported species tree. I don't think that's what happened with oaks, but it is at least an interesting problem - if introgression shifts the inference of the species tree enough that the topology input into the D test doesn't reflect historical branching order then the test doesn't work (at least, interpreting D as evidence of introgression doesn't work), so presumably even lower levels of introgression in the genome could cause the results to be unreliable. The partitioned approach helps with that issue, but I'm not sure it would have worked on a situation with as much admixture as Fontaine's mosquitos. Looking forward to seeing this published. Best,
CJ
On 2017-08-27 20:49:13, user J Ir wrote:
Surprising that they did not make a point of actually clarifying the IQ separation between the ancient and modern DNA. It would have helped if they included some of the POLY edu scores. In that way, one could easily compare the results from the paper and those in different modern populations.
On 2020-07-26 06:46:44, user Sahan Laboratory wrote:
Secondary Metabolites will be the future for Drug Design and Development. This paper provide the best explanation for top metabolites from natural compounds.
On 2020-01-15 00:54:57, user Dai Mitsushima wrote:
Now I fixed graphic errors in Figures 2 and 3. PDF version 5 or later is OK. I am sorry for the inconvenience.
On 2020-03-09 15:58:40, user Karel Boissinot wrote:
In the Confirmatory qRT-PCR in RdRp and N subsection of Material and methods, you describe 1 single condition for primers and probes concentration at 300 nM and a single cycling condition with an annealing temperature of 60 C. Did you use this for all assays or did you adjust based on each assay's preferences? Corman RdRp uses significantly higher concentrations for their primers and uses 58 C for annealing. These changes could potentially impact the assays performance.
On 2020-03-07 01:29:20, user AJP wrote:
Dear team,
You also mentioned that "10 uL of purified viral RNA" was amplified. In the results section you say that there were "15 copies/reaction".<br /> How did you quantify how many copies you had in the purified viral RNA? Was it through working back from the RT-PCR signal?<br /> Secondly I don't know if it's a mistake but under "N Assays" you said "15 copies/reaction" and then said "15 copies/ul". But previously you said there was 10ul of purified viral RNA...
Thank you,<br /> AJP
On 2022-03-09 16:25:10, user Maxime Bellefroid wrote:
The peer-reviewed manuscript has now been published and is available at the following link : https://doi.org/10.1093/nar...
On 2014-04-07 03:26:21, user Ivan wrote:
Thanks for the update.
Could the authors address South Asian genetic history, since it seems that after the Americas, ANE peaks in South Asia?
On 2022-10-28 09:08:18, user Fabienne Jabot-HANIN wrote:
Hello ,
The link towards the github repository doesn't exist ...
On 2018-02-23 22:31:21, user Bernd Pulverer wrote:
Great initiative and impressive this system seems to scale up so well to high throughput screening. A cross-literature screen is clearly something where automation is absolutely essential and much desired too (and your finding that almost half the reuses detected are between articles speaks to the importance of this level of screening).
What certainly sets your study apart is that you screen both within articles and against the whole OA literature, albeit limited to comparing articles by the same authors, and restricted to straight inter-/intra- article duplications. As you note, the screening of tables and graphs would be an important extension for the future.
It is remarkable that the numbers you find are consistent with other studies and what journals that do prepublication screening tend to see (e.g. EMBO Press ; Pulverer B. Volume 34, Issue 20, 14 October 2015, Pages: 2483–2485). A key application of this and other technology will be in screening processes before formal publication as part of institutional review and/or the editorial process at scientific journals.
What you do not address is advanced screening processes to detect image manipulations beyond duplications. This involves cloning, splicing and erasure of information and in particular the most complex, but also the most important issue: when is a duplication between images ‘too similar to be different’. We use human judgement for this at EMBO Press, but it is extremely difficult to make objective, statistically supported arguments in such cases.
Very laudable not to release the results. In my view this is to be favoured to wholesale posting of all the cases publically in the absence of full validation
On 2020-08-13 09:54:28, user Martin R. Smith wrote:
Sounds like a promising framework – great that your analyses seem to indicate strong performance.<br /> Have you also considered how the precision of CellPhy results compares to those of the other methods you examine? It would be worth ruling out the possibility that the increased accuracy that you observe is simply a result of reduced precision (see Smith 2019, Biology Letters).<br /> To avoid the various biases that plague the RF distance, I also wonder whether there's an argument for using a method that offers a more complete picture of tree distance (e.g. those reviewed in Smith 2020, Bioinformatics) – information theoretic distances are probably better suited to evaluating the performance of methods against a simulated reference tree, and can be normalized in a more natural way. Indeed, I couldn't see whether you normalize the RF distance with respect to the total number of splits in the "true" and reconstructed tree, or the reconstructed tree only, which would have a significant effect on the values recovered.
On 2017-01-21 06:16:40, user Shi Huang wrote:
Interesting read. Thanks for flagging it up. It always smells true when similar conclusions were reached from different perspectives!
On 2017-02-09 16:22:15, user German Dziebel wrote:
Haplogroups N and R are typically defined by the following combination of mutations: 8701A 9540T 10873T 10398A 12705C 16223C. I checked your Table 10. 8701A is in Table 10 as a slow mutating site. Phylotree assumes that evolution went G > A. But Denisova has A there. How did you resolve this phylogenetically? Sites 9540, 10873, 12705, 16223 are not in Table 10, which means you excluded them as too mutable to define a clade. But 10398 is in Table 10. And it looks like a valid site, with all of hominins and modern humans having G allele and haplogroup N-carriers having A allele. Your Fig. 3 assumes that evolution went from G10398 (N-carriers having an ancestral allele) to 10398A. Is 10398A on the denisovan sequence homoplastic with 10398A on the human M'L3'4'5'2'1'0 branch?
On 2023-01-10 13:07:55, user rdrighetto wrote:
I suspect the results presented in this manuscript are a consequence of overfitting or some other issue(s) in the data processing:
--The density presented in Fig 1 looks quite noisy. Compare for example with the cryo-EM densities of apoferritin at 1.22 Å resolution (EMD-11638), apoferritin at 1.15 Å (EMD-11668), or even the maps of Beta-gal at 1.9 Å (EMD-0153 and EMD-7770). The zoomed-in densities for aminoacids presented in Figures 2d and 3a,d seem to be missing features.
--While the half-map FSC extends all the way to Nyquist at a high correlation (~0.3), it starts decreasing quite early, which looks suspicious for an allegedly very high quality map. Furthermore the model-map FSC only correlates (0.5 criterion) to ~3 Å, which is quite a difference to the half-map FSC.
--All figures provided in the manuscript are of very low quality, making it difficult to read the graphs and assess the results in more detail.
--The author should clarify why were external templates used for particle picking and for initializing the 3D refinement. Such a dataset should easily warrant the use of a reference determined ab initio, as well as templates generated from the own data. It should also be stated to which resolution the reference 3D map was low-pass filtered to exactly. The use of external references could be the source of overfitting (model bias).
--The pixel size of the acquired micrographs, as well as the defocus range used during acquisition should be stated in the manuscript. The manuscript is missing a table summarizing the cryo-EM data collection parameters.
--The atomic modelling is clearly not on par with the claimed data quality. A clashscore of 42 is abnormally high, especially for a claimed resolution of 1.1 Å. The Molprobity score of 3.32 is also an indication of wrong or incomplete modelling and refinement (for a claimed resolution of 1.1 Å).
--Most importantly: the main map, half-maps and masks resulting from this study should be made available to the community on EMDB for proper assessment of the results. The raw data should be deposited to EMPIAR as well.
Kind regards,<br /> Ricardo D. Righetto
On 2020-05-12 23:15:58, user Pooja Rana wrote:
Hi sir, I would like to know which target for inhibiting SARS-CoV-2 would be of more importance. Either spike protein or ACE-2 itself.
On 2021-01-31 02:37:45, user Michael Hall wrote:
Great work Kristoffer. I enjoyed reading this.
I have two comments/questions:<br /> 1. The example randstrobe hash values in Fig. 1B seem to be incorrect for the first two sequences - or am I missing something?<br /> 2. (Forgive my ignorance) The hash function you describe for randstrobes in the text is quite different to that in the example. In the text you say that you just concatenate the previous strobes for the current strobemer. And then I assume you take the hash of this concatenated string? If so, I am struggling to see how this would produce different strobes to the minstrobe method. Given, for randstrobes, the concatenated string of the previous strobes is fixed and you are effectively only considering the impact of the addition of the strobes in the current window. Therefore, wouldn't you end up selecting the "classical" minimizer for that window as in minstrobes?
My apologies if I have not effectively communicated my questions. I'm very happy to discuss further.
On 2019-03-17 06:50:00, user Simen Sandve wrote:
Interesting paper! I found a tiny detail you should correct in the next version. You refer to a ‘rainbow trout WGD’, but there is no such thing. Rainbowtrout is a salmonid fish, and all salmonid species share an ancestral WGD about 80-100 MYA. Hence, you should refer to this WGD event as the ‘salmonid WGD’.
On 2024-01-31 17:51:34, user Hao Wu wrote:
The reprint has now been published in Proceedings of the National Academy of Sciences doi: 10.1073/pnas.2117198119
On 2017-06-23 05:20:43, user Myron Best wrote:
With interest we have read the manuscripts written by Dr. Chakraborty regarding our tumor-educated platelet liquid biopsy study, recently posted on bioRxiv (1-3). We observed serious shortcomings in the interpretation of our manuscript text by the author, the analyses conducted by the author, and the concomitant conclusions that have been erroneously drawn by the author. In all, this series of manuscripts relies on apparently wrong data interpretation, and once more highlights the critical need for peer-reviewing.
First, we observed that the author has criticized the fact that TEPs do not contain or have low numbers of MET, EGFR and HER2. Indeed, in our article (4) we wrote that the investigated tissue biomarkers have low expression values in the TEPs: "Although the platelet mRNA profiles contained undetectable or low levels of these mutant biomarkers, the TEP mRNA profiles did allow to distinguish patients with KRAS mutant tumors from KRAS wild-type tumors in PAAD, CRC, NSCLC, and HBC patients, and EGFR mutant tumors in NSCLC patients, using algorithms specifically trained on biomarker-specific input gene lists (all p < 0.01 versus random classifiers, Figures 3A– 3E; Table S4)." (4). Second, the surrogate gene expression signatures in TEPs, thus composed of transcripts other than MET, EGFR, and HER2, did allow us to distinguish between molecular subtypes in these cancer patients, regardless of the levels of the biomarkers in TEPs itself. We emphasized this in the final sentence of the paragraph: "Thus, TEP mRNA profiles can harness potential blood-based surrogate onco-signatures for tumor tissue biomarkers that enable cancer patient stratification and therapy selection." (4). In addition, we also wrote that such analyses were not independently validated and require more follow-up research: "Even though the number of samples analyzed is relatively low and the risk of algorithm overfitting needs to be taken into account, the TEP profiles distinguished patients with HER2-amplified, PIK3CA mutant or triple-negative BrCa, and NSCLC patients with MET overexpression". (4). Third, the possible explanation proposed for this erroneous MET gene expression does bypass the filtering step we perform in the data processing pipeline, i.e. selection of intron-spanning reads, as can be read in the main text ("After selection of intron-spanning (spliced) RNA reads and exclusion of genes with low coverage (see Supplemental Experimental Procedures), ...") and Supplemental Experimental Procedures (4). The RNA-seq analyses presented in our article are based on intron-spanning reads only and do not include the intron- or exon-mapped reads. In the same way, our reasoning does also corroborate the arguments raised by Dr. Chakraborty regarding the use of Kappa statistics for accuracy evaluation (2).
In all, the conclusions drawn from these analyses do not agree with the original data. We do appreciate the enthusiasm from others, including Dr. Chakraborty, to critically evaluate our study and provide feedback on the work, especially with the use of the publicly available RNA-sequencing profiles (GSE68086). Although, in this series of manuscripts (1-3) we noted that the author clearly has no understanding of the general principle of surrogate signatures. Surrogate signatures are composed of indirect biomarkers, and as stated in the publication the direct markers were not detected using thromboSeq (shallow sequencing), in contrast to amplicon sequencing (deep sequencing), which did allow for the detection of such direct markers in tumor-educated platelets. We object against Dr. Chakraborty approaches, all conclusions are based on wrong presumptions. In addition, we wish to point at the importance of peer review. We urge all readers to read our publications on the use of tumor-educated platelets (http://www.cell.com/cancer-... "http://www.cell.com/cancer-cell/fulltext/S1535-6108(15)00349-9)").
Myron G. Best 1,2,3,*<br /> Thomas Wurdinger 1,3,4,*
1Department of Neurosurgery, VU University Medical Center, Amsterdam, The Netherlands. <br /> 2Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands. <br /> 3Brain Tumor Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands. <br /> 4Department of Neurology, Massachusetts General Hospital and Neuroscience Program, Harvard Medical School, Boston, MA, USA. <br /> *Correspondence to m.best@vumc.nl or t.wurdinger@vumc.nl
References<br /> 1. Chakraborty S. A plausible explanation for in silico reporting of erroneous MET gene expression in tumor-educated platelets (TEP) intended for “liquid biopsy” of non-small cell lung carcinoma still refutes the TEP-study. bioRxiv. 2017.<br /> 2. Chakraborty S. Ambiguous specification of EGFR mutations compounded by nil or negligible fragmented gene counts and erroneous application of the Kappa statistic reiterates doubts on the veracity of the TEP-study. bioRxiv. 2017.<br /> 3. Chakraborty S. No evidence of MET and HER2 over-expression in non-small cell lung carcinoma and breast cancer, respectively, raises serious doubts on using RNA-seq profiles of tumor-educated platelets as a ‘liquid biopsy’ source. bioRxiv. 2017.<br /> 4. Best MG, Sol N, Kooi I, Tannous J, Westerman BA, Rustenburg F, et al. RNA-Seq of Tumor-Educated Platelets Enables Blood-Based Pan-Cancer, Multiclass, and Molecular Pathway Cancer Diagnostics. Cancer Cell. 2015;28(5):666-76.
On 2025-07-18 19:42:45, user Melissa Collier wrote:
This article is now published in Communications Biology https://doi.org/10.1038/s42003-025-08161-1
On 2022-05-31 00:43:00, user ???? Dr. Jennifer Glass ???? wrote:
Congratulations to the authors on the first report <br /> (to my knowledge)<br /> for metatranscriptomes from hydrate bearing sediment!
On 2018-12-04 17:26:29, user Theodor Savvidis wrote:
Fascinating work! I want to know more about the function of HD domain...
On 2020-02-05 11:13:24, user Pei-Hui Wang wrote:
Prof. Wang Pei-Hui Lab in Shandong Uni, China, all orf clones of 2019-nCoV / SARS in pcDNA6B-FLAG can be freely requested, please contact him. Email: wphlab@163.com covering the following orfs: nsp1-16, S, 3, E, M, orf6, orf7a/b, orf8, orf9, orf14, N; and ACE2
On 2020-05-10 18:25:09, user Alina wrote:
"We have already instigated a programme to determine if this deletion also occurs in human clinical isolates and are currently examining the evolution of the S glycoprotein deletant virus in human cell lines to determine whether the furin cleavage site is essential for infection of human cells." - Exciting! Looking forward to your findings.
On 2014-04-11 14:40:07, user Robin Friedman wrote:
The point that presence or absence of miRNAs is not ideal phylogenetic data seems irrefutable. However, I think there's a significant piece missing from the discussion, namely the number of conserved targets of the miRNA. The introduction states that newly acquired miRNAs are under "strong purifying selection" and therefore are not expected to be lost. This is certainly true for the most ancient miRNAs, which also tend to be expressed broadly and highly, and have many conserved targets. However, microRNAs that are very deeply conserved tend to be expressed at very low levels and have very few conserved targets (see http://www.ncbi.nlm.nih.gov... and http://www.ncbi.nlm.nih.gov... "http://www.ncbi.nlm.nih.gov/pubmed/18955434)"). So most miRNAs are probably subject to little purifying selection, excepting the 50-100 most highly expressed mIRNAs, having many conserved targets.
In other words, treating all miRNAs as equal seems to be an obvious oversimplification (that it seems others in the field have made). I wonder how the results would change if the authors limited themselves to, for example, the 87 "highly conserved" miRNA families we used in PMID 18955434.
Thanks for making your preprint public!<br /> -Robin Friedman
On 2025-11-03 07:59:20, user Zoya Yefremova wrote:
Dear colleagues,
I read with great interest your preprint describing Tamarixia citricola Hansson and Guerrieri sp. nov. (Hymenoptera: Eulophidae), a putative new parasitoid of Diaphorina citri discovered during a classical biological control program in Cyprus. Congratulations on this interesting contribution to the taxonomy and biological control of psyllid pests.<br /> If I may, I would like to respectfully draw your attention to a publication that may be relevant to your study: Burckhardt, D., Yefremova, Z.A., & Yegorenkova, E. (2015). Diaphorina teucrii sp. nov. and its parasitoid Tamarixia dorchinae sp. nov. from the Negev desert, Israel (Zootaxa 3920 (3): 463–473). I apologise for the self-reference, but given the biogeographical proximity and the relevance of the Israeli Tamarixia fauna to the region, it was somewhat surprising not to see it cited.
In Israel, we have documented five native species of Tamarixia, including T. dorchinae, which shares several morphological characters with what you describe as T. citricola, particularly in forewing and antennal structure across sexes. A comparative discussion of these taxa might offer further insights into whether the specimens from Cyprus are truly distinct species. A discussion comparing the putative new species with other taxa in the region is warranted anyway.<br /> Additionally, I think that host specificity in Tamarixia isgenerally more consistent with psyllid host genus rather than the associated plant. This ecological pattern may be worth emphasizing in your discussion.<br /> We are in the process of barcoding the Tamarixia species of Israel, and a comparison with your material would be most useful.<br /> Thank you again for sharing this work,
On 2024-06-19 07:46:07, user Guillermo del Angel wrote:
I was trying to see the actual list of variants referenced in Table S1 but there doesn't seem to be any link to view and download these?
On 2022-10-02 18:32:50, user Carrie Partch wrote:
The labs of Seth Rubin and Carrie Partch at UCSC jointly reviewed this preprint. This manuscript examines how the two transactivation domains (TADs) of ?-catenin interact with several domains of CBP/p300 to potentially control transcriptional activation. A combination of biochemistry, NMR, and ITC studies narrow down several binding sites for TAZ1 and TAZ2 domains. Overall, the manuscript is well organized and provides new details on these interactions that may play a role in ?-catenin function. We have some suggestions that might enhance the clarity of the work below. Thanks for an enjoyable read!<br /> Figure 1: <br /> • The schematics do not depict consistent widths/domain lengths and CBP/p300 is missing some domains, including one implicated in ?-catenin binding (Emami et al. 2004, PNAS).<br /> • It would be helpful if your schematic also illustrated all of the constructs used in the study and these names were used consistently.<br /> • If space allows, a simplified diagram of the pathway described in the text could be helpful.
Figure 2:<br /> • It would be helpful to add dashed line for predicted secondary structure cut-off at 0.3 in panel B.<br /> • It would enhance the rigor of the work to show expression levels for the constructs used in panel C.
Figure 3:<br /> • Labeling the MW markers, light and heavy IgG chains, and proteins (does the 666-781 fragment overlap with the light chain?) in panel A, along with the input, would make this figure easier to read.<br /> • The results of the pulldown seem pretty straightforward so quantification from n = 2 experiments seems unnecessary. If you do so, please define what ‘relative’ means in the quantification and make sure that your statistical methodologies are appropriate for this low n.
Figure 4:<br /> • There is some concern that the ITC data might be overfit to a two-site binding model without more information on the fits. Additional rationale or evidence that justifies use of the two-site model would also be welcome.
Figures 5 & 7:<br /> • It might be easier to interpret the binding interface if you used surface representation for the TAZ domains instead of ribbon.
Figure 8:<br /> • It was a bit confusing to show an analysis of the NMR data from the construct used in Fig. S3 in panel A, but then use data in panels B – E from a larger construct. We struggled a bit throughout the manuscript to match domain names and fragments (see Fig. 1 comment above) with the data.<br /> • It could be helpful to conclude with a cartoon or schematic that illustrates what was learned here.
Other:<br /> • Discussion text mentions a possible role for phosphorylation of serines; if citations for this exist, please add them or perhaps broaden this to a possible role for PTMs in general.<br /> • Consistent labeling of ITC data throughout the paper would help clarify which constructs were used in each experiment.
On 2021-10-25 19:58:44, user Joseph Binder wrote:
I think you had a great paper and it was quite insightful. These findings will help with conservation efforts in these regions. I know the focus of this study was in deserts. The extreme temperatures drive mammals to wetlands. Do you think that by going into a different environment such as a rocky mountainous region would provide similar results? Could this be a potential follow up study? For figure 1 I know that the x-axis was shared among each sub-figure. I think to make the figure more clear to the reader having the x-axis under each figure would have helped.
On 2023-03-24 18:52:53, user Joseph Bedont wrote:
Hello, kind readers! The final, peer-reviewed version of this article is now live on Current Biology, at https://doi.org/10.1016/j.c....
Cheers,
Joe Bedont
On 2016-02-19 08:56:15, user Rajesh Gunage wrote:
On 2022-11-28 17:16:49, user connor wrote:
A little quibble: mmseqs2 taxonomy is not a 'long read' classifiers per se. Rather it was made for contigs (presumably assembled short read). this in part reflects it worser performance for noisy long reads
On 2015-03-31 09:44:49, user Evangelos Pafilis wrote:
The correct URL for retrieving the ENVIRONMENTS-EOL annotation dataset (tab-delimited format) is: http://download.jensenlab.o....
(instead of http://downloads.jensenlab.... mentioned in this preprint)
This has been corrected in the published version of the manuscript.
On 2020-08-15 10:44:11, user kdrl nakle wrote:
It would be nice to have some data or at least some data in relation to other transmissible diseases in marine mammals but there is nothing like that. This is more of an article for science mag than for scientific journal.
On 2020-05-04 12:04:56, user silvoLab wrote:
Thanks for the full-fledged evolutionary analysis. We show RNA editing of the viral transcriptomes (with a much more limited evolutionary analysis) here: https://t.co/JEpyMayhTQ
A few comments:<br /> - we also found that APOBEC-mediated editing is more evident than ADAR-mediated one at the evolutionary scale. Yet, ADAR-mediated editing is much higher at the transcript level (quasispecies). We are still wondering the reason for this.
the only APOBECs that target RNA are APOBEC1 (plenty of evidence), APOBEC3A (good evidence, but its target specificities could be tight), and APOBEC3G (so far only in vitro evidence). No other AID/APOBEC has ever been linked to RNA editing.
re’ the sequence context of the mutations: while APOBEC3G sequence context preference is quite different (CCC) and APOBEC3A could be a possible match (TC), we think APOBEC1 has a sequence context preference that matches the mutations on the coronavirus transcripts/genomes almost perfectly ([AU]C).
we also observe the strand-bias in APOBEC-mediated editing at the transcript level. In our manuscript we come up with a model to explain it. Long story short.. we think most APOBEC-mediated editing comes quite late, after replication and before packaging.
On 2020-12-04 19:33:39, user Oleg Butovsky wrote:
This is an important methodology paper describing alteration of ‘exAM- ex vivo “Activated” Microglia’ signature during microglia isolation using enzymatic digestion in 37C. In this work, the authors mentioned that: “Previous work, also using enzymatic digestion and room temperature centrifugation...(61-64)”, identified microglial molecular alterations including Fos, Jun, and Egr1 transcription factors during microglia maturation or in disease. The authors found among the exAM genes nearly exclusively expressed following enzymatic digestion without inhibitors were transcription factors such as Fos, Jun, and Egr1. Thus, the authors concluded: “the need for a reevaluation of studies which have highlighted genes that are a part of our exAM signature as being of particular importance in microglia”.
However, none of these cited studies (61-64), by Butovsky and Amit groups, used enzymatic dissociation or incubation in 37C, keeping the samples during all microglia isolation processes on ice, except Percoll gradient centrifugation at RT for 20 mins. Importantly, these cited studies, showed dynamics of the molecular signature alteration in microglia during development and in disease including induction of Fos, Jun, and Egr1 transcription factors in adult homeostatic microglia as compared to embryonic or postnatal microglia and their loss in microglia associated with neurodegeneration/disease (aka MGnD/DAM) as compared to the homeostatic microglia. Thus, these results imply that using the previous published non-enzymatic protocol including mouse perfusion with ice-cold HBSS and keeping the samples on ice during all microglia preparation processes except Percoll-centrifugation at RT for 20 mins is valid to study microglial biology in health and disease (PMIDs: 24316888, 28930663).
I agree that the protocol described in this work is important for enzymatic dissociation of microglia isolation, if absolutely required for isolation of potential microglial subsets which may not be isolated without enzymatic dissociation. However, as also mentioned by the authors, this enzymatic-based protocol may induce microglia activation associated with confounding results. Thus, enzymatic-based microglia dissociation, must be taken with extra-caution referring to “reevaluation of previous studies”.
On 2016-09-30 16:24:35, user mchimenti wrote:
PyVar can be found at this github repo:
https://github.com/jwertz01...
The link in the PDF is incorrect. Thank you.
On 2025-05-27 19:17:07, user Shipra Agrawal wrote:
Published: Journal of the American Society of Nephrology<br /> https://journals.lww.com/jasn/abstract/9900/rna_alternative_splicing_and_polyadenylation_and.659.aspx <br /> May21, 2025
On 2020-06-14 19:19:07, user Pandora wrote:
If Sarscov2 can infect monkeys, which they have in US zoos, could monkeys infect humans. Could the virus originate from Monkeys like so many other viruses. HIV, Malburg, Herpes, Sv40.
On 2014-06-05 15:28:47, user Tony wrote:
N <- 100<br /> oo <- rexp(N)
Voo <- 2*.2*0.8*oo^2
spVoo <- sort(Voo/sum(Voo),decreasing=TRUE)<br /> round(100*spVoo,0)[1:5]<br /> [out] 26 8 7 5 4
spVoo2 <- sort(Voo/sum(Voo))<br /> sum(round(100*spVoo2,0)[1:50])
On 2018-05-31 16:40:35, user Nicole KING wrote:
A .pdf of this study has now been published under a new title at eLife: "Gene family innovation, conservation and loss on the animal stem lineage" https://elifesciences.org/a...
On 2019-12-25 21:33:59, user HonSing wrote:
Hi Authors,
As the first person to image the ultrastructure of EVs with AFM (tapping mode, non-liquid), I am trying to understand the value of this preprint. I adore manuscripts/works that use AFM because in my opinion, there is just no other way to understand their volumetrics. But there are always major assumptions when using AFM for volumetrics.
Firstly, the cantilevers used (SNL-10 - or "A" in this preprint) have a forward angle of 15degrees and a back angle of 25degrees. This means that while you state it is impossible, multiple traces/scans must be performed if you want to measure the radius properly. As I understand it, the radius and height is critical for your analysis. From that you derive the contact angle, and then you will arrive at your stiffness (k) metric.
I did not see any incorporation of the forward/back angle into your calculations. If not, I will share with you why it is so critical. I remember that when I was a PhD student, I was informed by AFM experts that the angle of the tip will lead to an artefact when imaging and trying to ascertain what an object may topographcially look like. The tip is not perfectly shaped like a needle, it is a triangle shape and it is substantially larger than the object being scanned. Hence, vesicles/non-vesicles will become larger/wider than they really are.
I think that when you look at your different vesicle preps (which is a very cool experiment), you will find that due to your masks, some liposome preps are larger than others. Ie., the DOPC has smaller diameters, 40-60nm; whereas the DSPC has a median diameter of 120nm. This is a major difference when your team makes comparisons between liposome preps. When accounting for the geometry of the cantilever tip, this means that you are unfortunately comparing apples versus oranges. You need to control for size before calculating your stiffness (K). I think you will understand what I mean when you compare the LUT scales of the DOPC versus POPC, DPPC, and DSPC (40nm, 90nm, 140nm, 190nm). These are progressively larger and larger. This is why your Figure 4 (contact angle) exhibits a near linear relationship. The vesicles are simply just larger and hence, the contact angle is greater because the cantilever tip is not a perfect vertical tip, but a big-ass triangle.
The title states that it is a high-throughput screening method. But lets be very honest with each other, anyone that does AFM on EVs is already doing this "high-throughput" imaging by simply zooming out to a 5-10um FOV. In that instance, they will be able to image tens or hundreds of EVs in a single FOV. I'm quite sure this is not novel if this is something that all AFMers do when they simply are trying to look for the mica coverslip (when the cantilever engages the object of interest).
I also would like to see images of actual microparticles/microvesicles/ectosomes etcetcetc and what your contact angles as calculated are and what they look like. I think you will quickly see that they are no longer spherical but really highly heterogenerous objects with an irregular radial geometry and "rough" topography. That is because they will contain things like actin filaments and other structural components. That in itself would make a max/min XY radial measurement (that this work asserts) to arrive at a contact angle and stiffness (k) measurement an inaccurate one.
I think what would be valuable is a calculation that accounts for the forward/back angle of a cantilever and its limits of imaging a quasi-3D object, such as an EV. I think that is the most important issue that faces AFMers - the fact that the tip itself produces an imaging artefact and that I have seen very little in terms of how we account for how big this error is when we image dome-shaped things smaller than 500nm. I did enjoy reading about this work and I hope that with more work, it will be something that I and many other AFMs can cite and refer to in the future. Good luck!
Cheers,<br /> Hon S. Leong
On 2015-10-29 05:13:47, user J Sietsma Penington wrote:
Interesting, thanks! At the moment I am using QIIME with 16S rRNA amplicons, so not directly relevant (yet). Table 1 seems to be missing, and the multiple versions of Supplemental/Supplementary material is confusing (although it is explained at the top that the last group is the latest)<br /> Jocelyn
On 2017-03-13 19:16:41, user Anonymous_Tommy wrote:
Very useful, thank you!<br /> Extremely minor things:<br /> Line 52 -- grammar<br /> Line 348 -- typo
On 2017-04-02 17:21:06, user Andrew Firth wrote:
I feel I should point out that (a) the CCCAAAU sequence noted in the manuscript is in the wrong reading frame for -1 frameshifting, (b) "[[[[............((((((((..((((........))))...))))))))..]]]]" is not a pseudoknot, and (c) the quoted peptide sequence "...GYRTQMK|ARGGNMWN..." inexplicably skips from nt 3282 to nt 3345 in the given accession KU321639 at the proposed frameshift site. This is not how ribosomal frameshifting works.
On 2019-09-10 16:30:16, user Alex Crits-Christoph wrote:
I'd like to thank the authors for sharing this meaningful and careful work. This is an interesting and novel approach to solve a difficult problem in metagenomics - identifying misassembled contigs in metagenomic settings. Currently this problem is mostly only approachable from a perspective of manual curation, so the authors' novel method is sorely needed in the field.
I have a few questions for the authors after a brief read: (apologies if some of these answers are available on the GitHub associated with the preprint)
What is the respective accuracy / precision / recall on the different types of misassemblies? The misassembly types are "inversion, translocation, relocation, and inter-genome translocation", but each of these have qualitatively different outcomes for researchers than others. Ideally statistics should be reported for each assembly error type, and the distribution of the types of misassemblies predicted in the real datasets should be shown. Unfortunately combining all of the above can cause readers who won't check each pileup manually to make erronous assumptions about the rates and frequencies of different error types.
In the training / test datasets there are a few genomes with > 95% ANI to each other. What percentage of the "inter-genome translocations" and all types of misassemblies are these genome pairs responsible for? How often do we see inter-genome translocations between genomes with ANI <95% and <90% in the simulated data?
The abstract states that close to a 5% contig misassembly rate was observed in real datasets - should this be statement be qualified with the 62% precision, 50% recall metrics?
What is the contig length distribution of each type of misassembly in both the simulated datasets and the real datasets? Is it possible to interpret from the MetaQUAST results where these misassemblies occur in the contigs? A misassemblied 3 kbp contig has significantly different implications from a misassemblied 51 kbp contig (with 50 kbp species A and 1 kbp species B) and a misassembled 50 kbp contig (with 25 kbp species A and 25 kbp species B)
In Figure 5, S5, and S6, can the authors list what they assume the misassembly type to be is from manual curation? Which error types are each of these?
Can the authors take contigs identified as inter-genome translocations from both simulated datasets and in the predicted real data and use BLASTP/BLASTN to demonstrate that these contigs are actually chimeric? Visually the degree and breadth of chimerism is critical to understanding how it affects our data analysis.
I think that both the tool and the work demonstrated have quite a bit of potential otherwise, thank you for this work.
On 2021-11-01 09:15:53, user Marius L wrote:
Congrats to releasing MARGARET. Please consider citing CellRank (see cellrank.org or the preprint), which has many conceptual similarities with MARGARET, e.g. CellRank automatically detects initial and terminal states, computes absorption probabilities on the Markov chain and charts gene expression trends using GAMs. Interesting to you might also be CellRank's efficient computation of absorption probabilities, which uses iterative linear solvers to exploit sparsity, circumventing the need to sample waypoint cells while being much more efficient than Palantirs implementation both in terms of time & memory (see preprint benchmarks). Recent releases generalize CellRank beyond RNA velocity, including e.g. a PseudotimeKernel to assign directionality based on any pseudotime (Palantir inspired) or a Real-time kernel to link cells across experimental time-points (Waddington OT inspired).
On 2020-06-01 14:29:26, user OxImmuno Literature Initiative wrote:
On 2018-10-31 07:26:38, user Alex Crits-Christoph wrote:
"providing evidence for this sequence representing the first genome of a jumbo phage (genome size > 200kbp)to be identified in the human gut microbiome" - however, earlier this year megaphages with genomes greater than 500 Kbp in size were reported in human microbiomes: https://www.biorxiv.org/con... - this error should be amended.
On 2017-02-17 23:27:50, user Brian P. Grone wrote:
In this manuscript, the authors ask whether vertical movements of larval zebrafish in response to light are wavelength dependent, and they seek to identify brain regions that are activated by such light and mediate the behavior. They provide evidence that the vertical movement behavior is wavelength-dependent, with stronger responses to blue and green light than to red light. They use calcium imaging and lesion data to support their hypothesis that thalamus and habenula are activated by blue light, and that lesions of the habenula can impair the vertical movements.
Despite some significant concerns about the statistics and interpretation, this is a valuable study and is written clearly with well-organized figures.
General points<br /> Although the paper (in the abstract and throughout) refers to the light as “masking” diel vertical migration, all of the experiments were conducted between 11AM and 6PM, during the light phase. “Masking” of the normal dark-phase behavior can presumably only happen at night. Therefore, it would make sense to eliminate or reduce the reliance on the idea of “masking”, which isn’t directly shown for all aspects of the behaviors studied here.
What is the statistical power for the nonparametric tests used? Underpowered statistical tests can lead to false positive and false negative findings, and tend to inflate the size of any detected effects.
Specific Points<br /> Fig. 1<br /> For the comparisons where no difference is reported (i.e. vertical speed under high vs. mid intensity light), a two-tailed Mann-Whitney test was used. For some other tests, where a statistically significant difference is reported (e.g. vertical speed under high vs. low intensity light), a one-tailed test was used. What explains these differences?<br /> ?For the Discussion section: what could explain why the high intensity light generated a less robust behavioral response than the mid intensity light?
I suggest including the spectra data for the LEDs, since data at website links can easily disappear or change.
Fig. 2<br /> Blue light led to much more vertical movement than UV light did at the same intensity. It would be informative to also have a control group that received no light, in order to determine if UV light had any significant effect on vertical movement behaviors.
Figs. 3-4<br /> On pgs. 5-6, the description of the analyses is not entirely clear. What does it mean that “temporal signals” were “averaged by weights of each pixel”? This sentence is also vague because it is not made clear what “real fluorescence signals might be different” from.
What regions or pixels or timepoints do the ICA signals represent? Do they include the initial “wavelength-independent phasic excitation” period?
Fig. 5<br /> Interestingly, it appears that there is a lateralization of the blue-light response in the habenula in Fig 5b. This would be good to explore further, especially since the authors go on to lesion the habenula unilaterally or bilaterally in Fig 7. These experiments seem to be somehow logically connected and could be discussed together to give some insight into their rationale.
Fig. 6<br /> The abstract claims that the habenula is “tuned to blue light”, but green light is not tested here. Therefore, it would be more accurate to claim that blue light elicits a greater response in the habenula specifically when compared to red light.
In both Fig. 5 and Fig. 6 it would be helpful to state clearly somewhere in the results as well as the methods that the imaging is done using blue light, so it is impossible to remove blue light from the imaging data conditions. Discussion of this caveat would also be useful.
‘d’, ‘e’, and ‘f’ are missing values for the y-axes.
Fig. 7<br /> The text (pgs. 6-7) refers to “neuropil” when it appears that it should be “habenula”.
Are the statistical tests mentioned here one-tailed or two-tailed?
The description for ‘e, j, o’ (“correlation between the movement of individual fish”) is not clear.
Discussion<br /> Given that no thalamo-habenular connection was studied or tested, the results at best “suggest”, rather than “indicate”, that a thalamo-habenula projection is involved in the effects observed. The idea that blue light influences vertical movements via release of neuromodulators by the habenula is, to say the least, speculative.
In the discussion, it is claimed that the thalamic nucleus “involved here is likely to be the nucleus rostrolateralis”. What is the basis for this claim? This nucleus does not seem to appear anywhere in the results.
On 2019-04-09 14:30:48, user Paul Macklin wrote:
Link to github repo: https://github.com/rheiland...
On 2017-05-10 14:38:05, user Bell Beaker wrote:
Congratulations to the Harvard Team and all the associated archaeologists! This is a truly an epic work. And thanks for making everything available.
On 2021-01-14 16:44:28, user kmorano wrote:
Hey all, excited to have this up but N.B. - small error in typesetting to be corrected shortly, SBD-gamma in abstract should be SBD-beta!
On 2017-10-12 03:50:40, user Eric Fauman wrote:
Kudos on an impressive and hugely valuable study. However I would recommend that prior to publication someone manually review the results of your supplementary table 8 (list of druggable genes), in particular the rows annotated as being BP drug targets. What's relevant is not simply whether a drug molecule interacts with a protein, but whether the protein is actually the efficacy target for the drug. The text implies that PKD2L1 and BCL2 are BP drug targets, but there's no evidence that amiloride and atenolol lower BP because of their activity at these proteins. The table implies 23 of the genes are known BP drug targets; my own quick survey suggests half of these may be erroneous.
On 2020-07-03 12:45:06, user Dieter Egli wrote:
Linking this to our previous study:<br /> Inter-homologue repair in fertilized human eggs?<br /> https://www.biorxiv.org/con...
On 2021-02-12 03:23:48, user Mario Malicki wrote:
Is the published paper doi wrong, it points to the abstract in Gastro, while the paper should be the one in The Journal of Allergy and Clinical Immunology?https://doi.org/10.1016/j.j...
On 2019-12-28 16:53:30, user MrPete wrote:
I'm a completely independent engineer and citizen scientist. This research was incredibly misleading.
They exposed rats to energy at 1.5 to 6.0 watts per kg of body mass.
Let’s take a worst-case human example…. a 14 year old girl. Average body weight about 60 kg. The exposure rate corresponds to a 75 to 300 watt light bulb being held close to your daughter’s body.
A real cell phone emits at MOST about 40 thousand times less energy. When idling (not in a call) it emits about 75 billion times less energy.
Ridiculous. (My calculations: http://bit.ly/2Q6zZQV
On 2023-07-21 08:17:14, user Charlie Cairns wrote:
Dear all, congratulations on this very nice work! I think an <br /> important citation is missing from the Aspergillus niger literature, <br /> which used a similar approach four years ago:
Nucleic Acids Research, Volume 47, Issue 2, 25 January 2019, Pages 559–569, https://doi.org/10.1093/nar...
On 2022-01-14 18:46:27, user Ruben L Gonzalez Jr wrote:
Note that this preprint has now been published in Methods in Enzymology https://doi.org/10.1016/bs....
On 2017-03-23 20:40:49, user Peter Rodgers wrote:
I don't see any merit in not using the word retraction. in my opinion, a system that treats, say, a change in author order and a paper that is totally based on fraudulent data as variations on a theme is effectively normalizing and condoning the latter.<br /> Peter Rodgers<br /> Features Editor, eLife
On 2017-07-28 17:25:15, user AdamMarblestone wrote:
-"Competition for a limited supply of synaptic building blocks<br /> predicts multiplicative synaptic normalization and<br /> heterosynaptic plasticity" http://www.biorxiv.org/cont...
On 2017-06-15 21:36:29, user AdamMarblestone wrote:
-"Teaching Compositionality to CNNs" https://arxiv.org/abs/1706....<br /> -"Schema Networks: Zero-shot Transfer with a Generative Causal Model of<br /> Intuitive Physics" https://www.vicarious.com/i...
On 2017-04-24 15:18:39, user AdamMarblestone wrote:
-"The role of efference copy in striatal learning" http://www.sciencedirect.co...<br /> -"A hypothesis for basal ganglia-dependent reinforcement learning in the songbird" https://www.ncbi.nlm.nih.go...<br /> -"Derivation and Analysis of Basic Computational Operations of Thalamocortical Circuits" http://www.mitpressjournals...
On 2019-01-24 07:41:35, user NealeLab wrote:
We are currently revising this manuscript following useful suggestions from referees. Our main goal is to further test the activity of TDP2 in meiotic cells. We will post an updated version including these experiments in due course. Original version posted here for practical reasons: A follow-up study coming this week!
On 2025-08-15 17:01:35, user Rudian Zhang wrote:
The access to the dataset (Figure 2C) requires credentials.
On 2017-06-20 10:54:17, user Jon Humphries wrote:
Dear Authors,<br /> Thanks for posting this very interesting collection of MS datasets and experimental observations that arose from your data. I have a number of suggestions and comments that I hope you feel will help with the analysis and presentation of the data. My comments mainly focus on the proteomics side of things.<br /> 1. The title is misleading as far as I can tell you have not defined the beta 3 integrin endothelial adhesome by MS-based proteomics. From the supplementary data files it is apparent that the adhesion complexes isolated comprise other integrins including a substantial amount of integrin a5b1. I understand that you provide additional evidence for a role of b3 integrin in MT stability but this equally could be due to the shift in balance between the proteins recruited to a5b1 and aVb3.<br /> 2. I think you are underselling the interest in what the FN-induced (a5b1 and aVb3) adhesome comprises in endothelial cells. You perform a nice subtractive proteomic approach to enable you to define the FN-enriched proteins (compared to PLL) but you don't tell us what these adhesome components are, or provide any comparison with other proteomic datasets or the Geiger literature-curated adhesome. This sort of analysis would help the field to understand the context-based composition of adhesion complexes (similarities and differences). I was left with the question 'what is the endothelial adhesome?'.<br /> 3. I was concerned by the approach you have taken to you proteomic analysis of the isolated complexes. Whilst on the face of it you have used good informatic tools (maxquant / perseus) I note that you performed the MS analysis from 3 pooled adhesion complex isolations. This will only permit a measure of the technical variability in the LC-MS/MS and not give you any idea of the variability in the biology i.e between adhesion complex isolations. Maybe there is a good statistical justification for this approach but it needs to be provided.<br /> 4. Also your MS dataset lists of proteins contain proteins from identifications with only 1 unique peptide. In my experience these '1-hit wonders' are a major source of variation in the quantitative values from MS outputs. Again please justify the inclusion of such identifications.<br /> 5. Please provide more details in the methods of the isolation of adhesion complexes and MS set up.<br /> 6. You have used one EC-derived cell type for all the experiments. Are your findings observed in other EC's such as non-transformed primary ECs?<br /> 7. In figure 1 it would be nice to see more IF staining of some cannonical adhesion proteins such as integrins (aV, a5, b3, b1) or paxillin / vinculin. You also note in the methods that you check the quality of the isolations by western blotting before MS analysis - could you provide any of this data to supplement the silver stains? It is reassuring to show blots for components you expect to see and don't expect to see in adhesion complexes before MS analysis.<br /> 8. Figure 2e why choose to blot for hspa1a? Are you saying this is an adhesion component? If the idea was to highlight equal recruitment to adhesion complexes why not blot for talin or vinculin?
I hope you find these comments useful. Thanks for sharing your data.<br /> Regards,<br /> Jon Humphries<br /> WTCCMR, The University of Manchester, UK
On 2020-06-22 00:27:02, user Jun Wang wrote:
This pre-print is now published in Cell Research. Check the published version here:<br /> https://www.nature.com/arti...
On 2020-09-10 08:43:03, user Martin Steen Mortensen wrote:
Hi,<br /> This pre-print nicely present the data.<br /> I cannot see at what level you have clustered your OTUs, is that at 97%? Also, have you considered redoing the analysis with QIIME2, so that you can have ASVs instead of OTUs? This should improve the resolution of the sequencing output, but not change overall conclussions.<br /> Lastly, I would like to shamelessly suggest that you include the article by Gupta et al 2019 (I'm a co-author) in the introduction and discussion as it compares cultivation and 16S rRNA gene sequencing for more than 3500 samples. While not directly comparable (fecal and airway samples from children), to my knowledge it is the largest study comparing the two methods.
Good luck getting this study published in a journal!
On 2021-02-28 13:41:37, user Jorge Fonseca Miguel wrote:
Added value was obtained for different biotechnological approaches in cucumber,<br /> such as for large-scale micropropagation and genetic transformation studies. Jorge Fonseca Miguel
On 2016-03-30 11:11:25, user Scott Rennie wrote:
In wondering what the explanation is for the difference in the opening in saline conditions for opening in chocolate and normal conditions?
On 2019-09-19 12:51:27, user Yo Yehudi wrote:
Hey all - really nice to see the use of HumanMine and MouseMine in your paper! I'm one of the development team for InterMine, who runs HumanMine. I was wondering if you'd be willing to help us out by citing our papers where you're quoting HumanMine? We have citation guidelines here: https://intermineorg.wordpr..., and MouseMine, which is run by MGI, asks for this paper to be cited if you use MouseMine https://www.ncbi.nlm.nih.go...
Thanks so much!! :)
On 2023-02-28 04:25:51, user Jesse wrote:
The sustained engraftment of KO cells here is remarkable. In our published work we also consistently observed >95% KO in CD34+ cells using RNPs with nucleofector program DZ-100, which worked better for us than DS-150. https://ashpublications.org.... Detailed protocols at www.plateletlab.com.
On 2018-01-26 17:36:58, user lmackenzie89 wrote:
This article has since been published in Scientific Reports (Open Access).
On 2018-06-07 10:29:57, user Ingo Marquardt wrote:
Dear Kendrick and co-authors,
Congratulations on this topical work. I would like to suggest a slight adjustment to the discussion section. You write:
“Finally, a ‘spatial GLM’ approach has been proposed in which a model is first constructed to characterize the mixing of signals from different cortical layers due to blood drainage towards the pial surface and then used to invert observed BOLD response profiles (Heinzle et al., 2016; Kok et al., 2016; Markuerkiaga et al., 2016; Marquardt et al., 2018).”
In the following, you critically discuss model-based approaches to account for effects of draining veins. I would suggest to rephrase that sentence, because Heinzle et al., 2016; Markuerkiaga et al., 2016; and Marquardt et al., 2018 did not use the spatial GLM, as far as I am aware.
Kok et al., 2016 employed the spatial GLM as a model-based approach for *unmixing* of the signal from different depth levels. In contrast, we simply use interpolation to obtain depth-level specific activation parameters, and use a model-based approach to remove the effect of draining veins. (I think one could even combine the two, i.e. use the spatial GLM for the depth-sampling, and then use a model like the one by Markuerkiaga et al., 2016 to remove the draining vein effect.)
I agree with your conclusion that venous effects are still a major challenge.
Best wishes,
Ingo Marquardt
On 2025-04-01 15:53:37, user Analabha Roy wrote:
Authors' Note: This preprint was thoroughly rewritten (including the title) after submission for review. The revised version was published in SciRep: Bagchi et. al. A multifaceted examination of the action of PDE4 inhibitor rolipram on MMP2/9 reveals therapeutic implications. Sci Rep 15, 10963 (2025). DOI: 10.1038/s41598-025-95549-y
On 2018-07-24 08:20:58, user Maren Büttner wrote:
Hey, it seems that you should compare your tool to scanpy (single-cell analysis in Python): https://scanpy.readthedocs.... https://genomebiology.biome...
On 2020-03-27 12:42:55, user UAB Journal Club wrote:
Review of Dhasmana et al. “Bacillus anthracis chain length, a virulence determinant, is regulated by a transmembrane Ser/Thr kinase”<br /> University of Alabama at Birmingham Bacterial Pathogenesis and Physiology Journal Club
Summary<br /> B. anthracis chain length is an important virulence determinant that is associated with blockage of lung capillaries, leading to hypoxia and lung tissue damage in a murine model of infection. The authors identify and characterize a serine/threonine kinase PrkC as a regulator of bacterial chain length and cell division. A prkC knockout was found to contain shorter chain lengths than wildtype B. anthracis. In this mutant, they observed upregulation of BslO, a murein hydrolase which catalyzes daughter cell formation, and S-layer protein Sap, which is needed for BslO localization at the septal region and its subsequent murein hydrolysis activity. A decrease in cell wall thickness and increase in multi-septa was also observed in the prkC mutant.
Overall, we have found this paper to be well-written, with the results and methods clearly described and the authors’ conclusions convincing. With that said, we have some comments that may be beneficial to address and some additional questions for the authors.
Major Comments<br /> 1. The authors make fairly strong conclusions about the differences in the growth curves between the wild-type and prkC mutant strain. However, chain length has a major impact on OD600 (see Stevenson et al. 2016 Sci Rep 6:38828), which is not taken into account here. It would be useful to calibrate the CFU / OD ratio for each of these strains. The Methods do not mention any dilution of the samples to measure OD, but I presume this must have been done to obtain accurate measurements of OD600 > 1. Please specify.
It is clear from the Introduction and Discussion that PrkC is a known virulence determinant and regulator in B. anthracis, but its known regulon and signaling roles are not thoroughly described. The authors should replace some of the introduction of virulence mechanisms in B. anthracis (which is not really relevant to this paper) with an introduction to what is currently known about the signals PrkC responds to and how it controls gene and protein expression.
Some explanations in the results section are speculative and would benefit from
experiments to substantiate the claims be shifted to the discussion section.<br /> A. “In the presence of PrkC, synthesis of these molecule(s) is probably downregulated to allow bacteria to grow as chains.”
B. “Altogether, these results indicate that during bacterial growth, PrkC maintains an optimum level of BslO, Sap, and EA1 to maintain the chaining phenotype.”
C. “Our initial results suggest that ftsZ is constantly upregulated in the prkC disruption strain. This probably formed the reason for an increase in multi-septa formation observed in the prkC disruption strain possibly due to the mislocalization of FtsZ.”
D. It is noted that there is chaining observed in the prkC deletion mutant in the lag phase and that this may be due to a lack of de-chaining proteins at this time, but this is not further investigated.
i. Is it possible that PrkC is not the only sensing machinery for the chaining phenotype? If PrkC is the only sensing protein regulating the chaining phenotype, one would expect to not see the chaining phenotype at all. The authors claim this could be due to reduced expression or localization of de-chaining proteins at the early timepoints. However, the cultures used for these images were started from an overnight culture where the proteins would theoretically have been expressed. To shift from one phenotype to another from the overnight to a fresh culture would require some form of environmental sensing not provided by PrkC. Which transcriptional regulators does PrkC ultimately regulate? Are one of those transcription factors allosterically regulated by environmental cues, like nutrient starvation, or are any of them regulators of sap?
The chain length is highlighted in Figure 1 but never objectively quantified. This is done later (Figure 3) but is arguably more important in these initial observations as they set the foundation for all future conclusions in the paper.
The authors note an inconsistency with PrkC growth curve (noted in results section pertaining to Figure 5) to those which are published. One publication cited is from the lab which sourced the mutant strain. This poses concern for strain genetic drift and should be addressed.
One of the major arguments is that PrkC is an environmental sensor (“Through this work, we propose that PrkC, a transmembrane kinase with a sensor domain, perceives growth permissive signals and maintains the levels of the primary proteins involved in de-chaining to regulate the chaining phenotype.”). While the results in this manuscript support the claim that PrkC is the most likely Ser/Thr kinase to be involved in this phenotype due to its localization, there is not enough evidence to support the claim that PrkC senses the environment.
At the 2-hour timepoint, long chains are observed in the WT and PrkC KO in Figure 6. It seems to be a main point of the manuscript that this should not be the case. It is unclear and not addressed why both are chained in this figure/timepoint.
Minor Comments<br /> 1. Comments regarding visual display of results<br /> a. Figures 2 & 5: bacterial growth curves should be plotted on a semi-log scale. See https://schaechter.asmblog....
b. Figure S1 has unreadably small text.
c. Change the colors on the graphs so that readers with red/green color blindness can distinguish between datasets.
d. Figure legends contain too much of the methods. Move methods to the method section and make legends more concise.
e. Supplemental tables of P values are not necessary.
b. Figure 4 needs statistics to indicate at which time points each protein level is different between the wild-type and prkC strains. (Similar to what is present in Figure 3.)
b. The authors refer to the ?prkC strain as a “disruption” strain. However, from the reference (Shah et al 2008), the strain is a resolved excision construct. It would be more accurate, therefore, to call it a deletion strain, as disruption suggests that there could be a fragment of the original gene present that could be producing a semi-functional peptide.
c. There are multiple instances where unnecessary information could be removed from the text. For instance, the last sentence of the third paragraph of the results section (“In a study on PknB, a membrane-localized PASTA kinase from Mycobacterium tuberculosis, depletion, or over-expression of the kinase was shown to have a significant effect on bacterial morphology leading to cell death.”) does not contribute, at least in the way it is currently written, to the argument at large and could be removed without detracting from the value of the manuscript.
d. Line 64: “Bacterial chaining has been shown to contribute significantly to virulence” would be a more succinct way to describe this.
e. The authors say that they measured expression levels of BslO, Sap, and EA1 at the “indicated time points” and reference Figure 2A and 2B. There are no indicators of what time points in those figures was used. Most of the time points are used but some are not (12 hours, for instance).
Future-Specific Comments<br /> Figure 1B: CV staining to see how many cells per chain would be insightful. Hard to tell if this is a chaining defect or division defect based on this figure.
Figure 1C: The resolution between the WT and ?prkC strain is different. The WT strain is much cleaner than the knockout strain, however, this is a minor issue and the size bar indicates that the same scale is used.
Figures 2A&B: These images should be overlaid on the same graph for comparison sake. In the section “prkC disruption results in decreased cell wall width and cell septa thickness and increased multi-septa formation”, they even observe that the two curves are not superimposable, meaning that they do not overlap. It would also be beneficial to see the graph of the average chain length in Figure 2 instead of as a separate figure.
Figure 4: If comparisons are going to be made between WT and KO strain expression of Sap and BslO, they should be run on the same gel. If the comparisons are focused on the time course across one strain, it makes more sense to run them as they are, but the focus is on the difference between strains
On 2024-01-17 08:02:00, user Rasmus Kirkegaard wrote:
Cool analysis. I would recommend that you consider upgrading your reference genomes from Unicycler to Trycycler https://github.com/rrwick/P...<br /> Unicycler was great for hybrid assembly when long reads were mostly for sorting the short read based contigs in the right order. But with newer data the quality of the long read assembly is much better and Ryan has made a nice guide for curating the remaining errors using illumina data to achieve a perfect genome.
On 2020-05-21 15:24:30, user Jinkai Wan wrote:
The version 2 of our paper (Human IgG cell neutralizing monoclonal antibodies block SARS-CoV-2 infection) is currently undergoing bioRxiv screening. We updated the neutralizing experiment data of authentic virus.
On 2023-11-21 09:46:00, user Frederic Roemschied wrote:
Hi biorxiv, this article has now been published,
On 2020-01-16 09:10:48, user Gabriele Giachin wrote:
This paper has been accepted now on Biophysical Journal: https://www.cell.com/biophy...
On 2021-06-30 21:20:03, user Odysseas Morgan wrote:
Hello! Very cool paper. I work for professor Thuronyi, and our lab has been using the Marburg Collection system for building our plasmids. We've been using this paper a lot for reference, I noticed there is minor typo in figure 2D. I believe the sequence of 3C5OSF should be "TCAG" instead of "GCAG". This same junction is printed correctly in 2E. Best of luck with publishing!
On 2021-04-15 04:28:46, user Ural Yunusbaev wrote:
Hey there! Greate paper. But I cant find the following excell files from the supplementary: <br /> Table S3. List of candidate susceptibility genes and orthogroups (excel file).<br /> Table S4. List of orthogroups containing two or more genes including gene IDs (excel file).
On 2017-10-30 11:24:28, user Gal Haimovich wrote:
I want to inform the readers that this paper was published a few days ago in PNAS (early edition). Here's the link if you wish to compare this version to the final version (at least until bioRxiv will update it):<br /> http://www.pnas.org/content...<br /> It is an open-access article.
On 2025-06-24 12:26:50, user Kristina wrote:
A peer-reviewed version of this article is now available. DOI: 10.1016/j.stem.2025.03.011
On 2020-03-28 15:12:41, user Neal Haddaway wrote:
Can you provide more details on which databases and indexes were searched in WoS, please? WoS is not a database (in fact, neither is Google Scholar, it is a search engine). WoS is a platform to access different databases, and the databases available will depend on subscriptions. Within WoS Core Collections, the indexes subscribed to differ across institutions as well. If you just searched WoS from different locations, the results would of course differ by design - different institutions have different sets of bibliographic information available.
On 2019-12-19 20:09:09, user Justin wrote:
Could you please take a look at the genotype quality of low frequency variants, particularly rs117913124 (MAF=0.03) which has the largest effect size?
On 2022-08-13 03:45:59, user Keshav Singh wrote:
Please note the term numtogenesis was coined by Singh et al 2017 and not by Lopez et al 1994.<br /> Thanks <br /> Keshav Singh
On 2017-02-12 20:48:54, user David Angeles wrote:
I would like to thank the authors for an extremely clear and interesting exposition on the benefits of phylogenies versus pairwise comparisons. The thoroughness of the analysis and the clarity of the writing make this paper a joy to read.
My only question regards the Levin reanalyses. Although the authors stated (correctly) that the KS test is sensitive to the shapes of distributions, they then go on to suggest that a Wilcoxon test should be used instead. However, my understanding was that the Wilcoxon test is a test that is also sensitive to the shape of distributions. I thought the strongest null hypothesis that can be tested using a Wilcoxon statistic was that 'the two samples come from identical distributions'. I thought that, given that the two distributions are identical, and one is shifted away from the other, only then the Wilcoxon test indicates whether the median is statistically different between the two distributions. Is this incorrect? If not, then it is necessary to perform an additional test before the Wilcoxon statistic to ensure that the two distributions are in fact the same, up to a shift.
It appears to me that the only method that is insensitive to the shape of a distribution is to perform a bootstrapped permutation test. Using a non-parametric bootstrap, we can test differences in the mean or the median, regardless of whether the samples are normally distributed or not.
On 2020-06-04 02:52:13, user Abhijit Chakraborty wrote:
Knock-down of the TIM/TIPIN complex promotes apoptosis in melanoma cells<br /> https://www.oncotarget.com/...
On 2023-08-28 16:01:11, user alfredo kirkwood wrote:
Despite our request, the editor assigned by eLife was in clear scientific conflict. That could explain some of the criticisms raised by reviewer 1.
On 2022-06-18 13:53:05, user Marc RobinsonRechavi wrote:
Dear Yamaguchi et al,
You write under Data accessibility:
All code necessary to repeat the analysis described in this study have been made available. SLiM source codes of our model for speciation dynamics will be hosted on Dryad Digital Repository upon acceptance. There are no data to be archived.
This is a publication, i.e. it is made public as part of the scientific record and is citable, thus I strongly invite you to make the corresponding source code available without delay.
On 2020-12-05 23:18:07, user Maria wrote:
Thank you for your work on this paper! I found it enjoyable, and its language and presentation is accessible to a variety of readers. For Figure 1, I am curious as to why the published version does not include the PD-L1 results or the Peritoneal Panc-02 model data. Additionally, labeling all the figures on the right of the images rather than the left made it a bit difficult for me to keep track of which figure was which. Figure 2e and 2i had very good resolution, which made the images easy to interpret. I noticed that in Figure 3, similar slice sections were used for Collagen I, ?SMA, and CD31 staining. This provided a lot of consistency in the data, which I appreciated. However, one way I thought the consistency in these images could be improved is by staining the same area of each slice when looking at Collagen I, ?SMA, and CD31. In Figure 3j, the spread for the Saline and NAM groups was large, which made the overall results a bit less convincing. For Figure 4b, it is difficult to see the staining clearly. I assume the image is zoomed out to demonstrate what is meant by “peritumoral”, which is helpful. I think the best way to present the data would be to replace the big image with a zoomed in version that allows the readers to analyze the staining, and to place the current zoomed out photo in the upper right hand corner of the image.
On 2021-06-15 12:38:10, user Vinicius Waldow wrote:
Have you already submitted your work? I'm looking forward to the publication.
On 2017-12-01 02:51:38, user Jianhua Xing wrote:
I would like to hear your experience on making the constructs and the knockin procedure in general. Many who have no first-hand experience on making them have hard time to believe the difficulties, esp. when you have to add drug-selection marker and remove with the Cre-LoxP system later. Most commercial companies won't take the job or charge a lot with no guarantee of success.
On 2020-07-18 22:36:48, user Martin Alberer wrote:
Dear Sirs,
many thanks for this very interesting article. I have two questions: Although differences in glycosylation of the antibodies as a cause for differing effect on the induction of inflammation is a very interesting idea, couldn't it be that the enhanced internalisation of the spike protein bound to antibodies via Fc?R?? is causing this effect? For SARS it is known that the spike protein can induce IL-6 and TNF-? via NF-?B pathway (Wang et al. Virus research 2007). On the other hand differences in glycosylation can improve the affinity to Fc receptors and thereby enhancing the internalisation.
When examining the effect of IgG COVA1-18 you used a higher concentration. What was the stoichiometry concerning this antibody and the spike protein compared to the experiments with the patient antibodies. If unbound antibodies in surplus would have blocked the Fc receptors this could explain the reduced production of inflammatory cytokines as less bound spike protein would be internalised.
In my opinion, these results affirm the demand that every vaccine against SARS-CoV-2 has to prove that is not able to induce possible harmful immunologic effects before going into larger trials and broad usage.
On 2025-06-18 15:11:05, user Ahmed wrote:
This preprint has now been published in a journal: https://doi.org/10.1016/j.molmet.2025.102182
On 2021-06-22 00:20:06, user Jingsong Zhang wrote:
The new version of this preprint is available at https://www.nature.com/arti...
On 2020-04-10 12:43:19, user Dave O'Connor wrote:
In our hands, swabs collected into VTM have an RT-LAMP sensitivity of between 10^3-10^4 copies / µL (https://openresearch.labkey... "https://openresearch.labkey.com/Coven/wiki-page.view?name=lamp-testing)") which operationally misses about 30% of clinical samples that have also tested by qRT-PCR. There simply isn't any RT-LAMP product generated below this threshold. The approach in this paper will be really useful if this sensitivity issue can be addressed, but is an important caveat until sensitivity is improved.
On 2020-06-27 15:19:05, user jie huang wrote:
Hi, guys:
This guide is really useful!
it would be nice to add "guide" to use LDpred-funct, and SbayesR (implemented in GCTB), which claims to perform better than LDpred.
Also, a lot of people are using millions of SNPs to generate PRS these days, and do that on the UK Biobank data (N ~ 500,000). I don't know if Ldpred actually works for that. There is a BioRxiv paper from Stanford University titled "A Fast and Scalable Framework for Large-scale and Ultrahigh-dimensional Sparse Regression with Application to the UK Biobank". It would be good to "guide" how to use tools like this one.
Thanks!<br /> Jie
On 2018-06-28 21:14:29, user Gavin Sherlock wrote:
Note, there is already a different piece of software called GenomeView for, well, viewing genomic data. See: http://genomeview.org/ and PMID 22102585.
On 2021-02-26 21:35:36, user Aamir Rana wrote:
What about IVM precipitation? IVM is water insoluble. When you dilute it from 1% IVM injectable solution to PBS, it may precipitate. So, what about the bioavailability of IVM?
On 2019-11-17 23:04:04, user Eran Halperin wrote:
We have to strongly disagree with the comment by Teschendorff. As in several cases in Jing et al., Teschendorff makes another false claim about the TCA paper in his comment below: We do provide in the TCA package an option to learn the tensor, which is of interest (and works well, as demonstrated in the TCA paper), however, TCA should be applied differently for the task of association testing (i.e., EWAS). Specifically, we used Equation (13) in the Methods of the TCA paper for association testing; we clarified this in the paragraph that follows Equation (13) in our paper: "In this paper, whenever association testing was conducted, we used this direct modeling of the phenotype given the observed methylation levels."
Importantly, in his commentary, Teschendorff does not acknowledge the fact that there are two innovative components in the TCA paper: (1) inferring a three-dimensional tensor of cell-type-specific levels from two-dimensional bulk data, and (2) direct modeling of phenotypes as having cell-type-specific effects, given the observed methylation levels, which allows to integrate over the hidden tensor information; as pointed out in the TCA paper (and instructed in the vignette and manual of the TCA package), this is the preferred way to perform EWAS using TCA. While the estimates of the tensor may also be used for EWAS (as performed by Jing et al.), this option is substantially less powerful, as it does not take into account the differences in variance between samples. For more details see Equation (13) in the TCA paper.
Also, we concur that there is value in the CellDMC paper as a benchmarking paper for previous methods. However, our argument is that CellDMC is not a new approach (although in their own words, in the CellDMC paper Tschendorff and his colleagues present it as a “novel statistical algorithm”), as the same method has been previously applied to gene expression (Westra et al., Plos Genetics 2015, Shen-Orr et al., Nature Methods 2011), while to the best of our knowledge, TCA is a new approach, with its advantages and disadvantages.
Finally, we would like to emphasize that we disagree with most of the claims made by Jing et al. in their paper, however, these claims are irrelevant as long as they present irrelevant results based on an irrelevant application of TCA. If any of the reviewers or editors of Jing et al. would be interested in a more detailed criticism of their claims, we will be happy to provide it, although we do not think that it is needed at this point.
On 2021-02-25 01:31:11, user Paul Wolf wrote:
If the variants from California, South Africa and Brazil escape the same antibodies, do they pose the same threats of reinfection and to vaccine efficiency? Are they more or less the same? Will these types of variants dominate over B117, or occupy a different niche?