On 2023-03-20 22:38:16, user Axel Nimmerjahn wrote:

The peer-reviewed version of this paper was published on March 6, 2023: https://www.nature.com/arti...

On 2018-05-30 09:58:59, user Guillaume Rousselet wrote:

Unfortunately, because of the lack of control of image properties, not much can be concluded from this experiment. The analyses and graphical representations are also outdated. These papers might help you improve your experimental designs and analyses in future studies:

https://www.frontiersin.org...

https://www.frontiersin.org...

On 2020-12-30 19:11:26, user Jon Moulton wrote:

This is the first clear exploration of the upper thermal limit of zebrafish culture that I have seen. At 37C the zebrafish-parasite system can be a useful model for human parasitic disease (doi:10.1111/jfd.12393); now we know how close to the temperature limit that culture system really is.

On 2018-11-30 11:52:13, user Mubassherul wrote:

You did a nice work which makes NER faster. I really appreciate it. All the recognized name are disambiguated by identifiers which sources are from different databases. I am pretty curious that sometime "Acronyms are often homonyms".So, how the tagger manage this problem? It would be great if the user can get return of the results instead of returning the result to central BeCalm server. Great job. Thumbs up.

On 2016-01-14 13:55:48, user Natalie Davidson wrote:

In the Methods section "Model for the fraction of sampling and fragmentation duplicates", I am guessing that this explains the method for calculating the expected fraction of duplicates with sampling only (in red on figure 3b), how do you calculate it for sampling+fragmentation (in blue on figure 3b)?

On 2021-02-23 15:13:18, user Jon Fisher wrote:

This is an interesting analysis, but I see two aspects that would make it hard for it to get traction. First, as noted in the abstract, poor enforcement is a major problem now, and rapidly expanding PAs into areas suitable for agriculture would make enforcement much harder (even if more resources were available). Second, it appears that this centers around the need of species to keep almost all of their range, but doesn't seem to consider the needs of people to grow enough food and have livelihoods. Starting solely with an ecosystem need without considering capacity for enforcement nor opportunity cost to human well-being makes this pretty non-convincing to anyone who isn't already strongly supportive of conservation as a top priority.

On 2019-07-11 17:42:25, user Vagner Benedito wrote:

Very nice work, folks!<br /> I'd like to offer a few remarks to improve the manuscript:<br /> It would be good though to describe which leaves exactly were used for extraction, how much plant material per unit of solvent was used in the leaf dipping (and confirm that the exuding trichomes were preserved after the dipping - so that you captured RNA from these structures in the analysis - I'd fear that most trichomes would break from the leaves during the ethanolic dipping).<br /> An additional supplemental table with locus ID correlations between Sopen and Solyc orthologs would be very helpful.<br /> In your model (Fig 8), I'd add an element for the import of metabolites from the subapical cell, since it is very possible that the apical glandular cells is not fully responsible for generating substrates for acylsugar biosynthesis!<br /> I really like the way this paper used a simple comparative RNA-Seq approach to identify novel candidate genes involved in acylsugar metabolism - great job!

On 2021-07-29 16:12:46, user Andrew Kropinski wrote:

I deeply regret that the 12 sequences which I analyzed using megablast are either Escherichia/Shigella phages or are essentially identical to Escherichia coli genome sequences.

On 2022-10-31 15:51:23, user Daniel Lüdke wrote:

We appreciate the schematic diagram of light conditions/treatments in Figure 5. Would it be possible to include similar diagrams for the other figures as this would make it easier to follow how plants have been treated. In the methods could it be described in more detail at what point after infection the light conditions were applied?

On 2024-12-27 03:11:02, user samuel Yi wrote:

Thank you very much for this remarkable work. While reading the article, I noticed a detail that warrants further discussion. The authors used Codex staining results from adjacent sections as the gold standard to evaluate the performance of different spatial omics technologies. However, Codex exhibited relatively strong edge staining effects in certain channels, such as CD20, which led to an abnormal accumulation of B cells at the periphery of the sections. This observation is inconsistent with the results obtained from hematoxylin and eosin (H&E) staining. Therefore, a more meticulous examination of the Codex data analysis may be necessary to address these discrepancies.

On 2025-10-29 12:16:24, user Teemu Turunen wrote:

Update (29 Oct 2025): This work has now been peer-reviewed and published in Nature Communications: https://doi.org/10.1038/s41467-025-64095-6

On 2017-08-17 19:19:17, user Blake Joyce wrote:

The BUSCO app is available on the CyVerse Discovery Environment: https://de.cyverse.org/de/?...

On 2019-10-17 15:43:29, user Adrienne wrote:

Really amazing resource! I have also found the T cell isolation bead kits (by negative selection) result in CD8 loss - probably due to NK receptors expressed on CD8 populations. CD8+ MAIT cells are particularly reduced following bead-based T cell isolation - this can inform what the composition of antibodies may be (which StemCell won't disclose).

On 2016-08-31 23:02:23, user Mike Gandal wrote:

Really interesting method. Very glad to see that you are correcting for variability in GC content and looks like 5'/3' bias is in the works. We find that technical factors related to RNA-sequencing (library preparation, read depth, 3' bias) contributes substantially to RNAseq expression variation and is poorly accounted for by current methods.

I liked your comparison to other methods using GEUVADIS and SEQC datasets. It would be helpful to see how a few other "standard" methods perform: tophat2 alignment with cufflinks quantification or STAR alignment and HTseq-counts quantification.

Also, most of the standard quality control packages for RNAseq data work at the .BAM/.SAM level (e.g., PicardTools or RSeqQC). It would be interesting/helpful for fast transcript abundance estimator methods like this to be able to interface with such quality control pipelines.

On 2021-09-05 15:00:48, user UAB BPJC wrote:

Review of Barrasso et al., “Impact of a human gut microbe on Vibrio cholerae host colonization through biofilm enhancement” by the University of Alabama at Birmingham Bacterial Pathogenesis and Physiology Journal Club

Summary

This lab previously showed that Paracoccus aminovorans could be found in higher abundance in Vibrio cholerae-infected individuals compared to non-infected individuals. This study demonstrates that V. cholerae colonization is increased by the presence of P. aminovorans in an infant murine intestinal model of infection. Through crystal violet staining and murine intestinal colonization with a ?vpsL mutant of V. cholerae, it is shown that Vibrio exopolysaccharide (VPS) is necessary for P. aminovorans-dependent enhancement of V. cholerae biofilm formation and intestinal colonization. Microscopy also reveals VPS enrichment in areas of the pellicle with higher P. aminovorans abundance. Lastly, with mutants in accessary matrix proteins RbmA, RbmC, and Bap1, the researchers show that the ability of V. cholerae to form a structurally intact biofilm is necessary for P. aminovorans-dependent enhancement of V. cholerae colonization.

Overall, this is a very interesting paper with insightful experiments that sparked a great discussion in our journal club group. This paper gives strong evidence that P. aminovorans promotes V. cholerae colonization in a VPS-dependent manner. With that said, we have some comments that may be beneficial for the authors to address.

General comments<br /> * May be beneficial to keep y-axis scales for CFUs the same among all figures for more consistency<br /> * One-way ANOVA may be a more appropriate test for figures in which multiple comparisons are being made; we advise you to consider consulting a statistician on the appropriate statistical tests to use<br /> * The Mann Whitney U test is not a t-test, but is referred to as such in some of the figure legends. <br /> * It is mentioned multiple times that crystal violet absorbance was measured at 570nm, although measurements were made at 550nm for all crystal violet figures in the paper. <br /> * Discuss possible limitations with Wheat Germ Agglutinin; could WGA also stain GlcNAc produced by P. aminovorans?<br /> * Would be beneficial to describe in more detail the purposes of matrix proteins RbmA, RbmC, and Bap1, and why these were chosen to be studied.<br /> * One statistical test is mentioned at the end of every figure legend; is the same statistical test being formed on all panels in a given figure? If not, this needs to be clarified.<br /> * More details about how CFUs are quantified from pellicles in the results section would be good to add; the steps taken are only briefly mentioned and are a bit difficult to understand.

Figure-specific comments<br /> * Story may flow better if in vivo data from Figure 2 is added to the end of the paper along with Figure 7 in vivo data; introduce enhanced Vc colonization phenotype with in vitro data first<br /> * Would be beneficial to show single and dual species P. aminovorans CFUs in Figure 2B and 2C and also single species P. aminovorans CFUs in Figure 3B to see whether P. aminovorans colonization also increases in the presence of V. cholerae <br /> * Address the purpose of the grid in Figure 4A<br /> * Figure 5A: Title claims that P. aminovorans increases V. cholerae biofilm formation according to data in Figure 5, but can only make this claim if CFUs are quantified in the in vitro model.<br /> * Figure 6 should have a Vc single species control to compare dual species pellicle to.

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

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

On 2024-11-21 16:07:10, user Phillip Gienapp wrote:

This manuscript has now been published as:

JJC Ramakers, TE Reed, MP Harris & P Gienapp (2023) Probing variation in reaction norms in wild populations: the importance of reliable environmental proxies. Oikos e09592 doi: 10.1111/oik.09592

On 2017-11-07 15:26:42, user guillemaud wrote:

PREPRINT PEER REVIEWED AND RECOMMENDED by PCI EVOL BIOL

This preprint by Chauve et al has been peer-reviewed by by Mukul Bansal, Alexandros Stamatakis and 2 anonymous reviewers and recommended by Tatiana Giraud and Toni Gabaldón for Peer Community in Evolutionary Biology. Peer-reviews, decisions, author's replies and the recommendation can be found here: https://evolbiol.peercommun...

On 2020-11-09 12:32:02, user Niko wrote:

Hello,

did you test different anitbodies for the dot blot and do you have a reason, why you chose the antibody from Abcam?

Thanks for your help!

On 2019-03-20 15:27:31, user Claudiu Bandea wrote:

Free-Living Chlamydiae?

The finding that new chlamydial lineages, identified as metagenome-assembled genomes (MAGs), dominate the microbial communities in sediment cores from a region surrounding Loki’s Castle hydrothermal vent field (1) is a remarkable discovery.

Based on estimates of genome replication rate using the iRep algorithm (2), and in context of results indicating the absence of putative eukaryotic host cells in the sediments, the authors proposed that their new MAGs are derived from actively dividing chlamydia not associated with eukaryotic hosts. If true, this represents the first examples of ‘free-living chlamydiae’, an extraordinary finding given that all previously studied chlamydial lineages, both pathogenic and environmental, are obligate intracellular organisms (3,4).

However, the authors’ interpretation of the iRep results and suggestion of actively dividing extracellular chlamydiae might be questionable. A more likely explanation is simply that the chlamydial EBs, the extracellular spore-like cells in the chlamydial life cycle, contain a chlamydial genome at various stages of replication.

It is highly conceivable that during the differentiation of the intracellular dividing chlamydial cells, the RBs, into EBs, the chlamydial genomes is in process of replicating, which is ‘arrested’ during the differentiation process. Addressing this hypothesis, which is relevant for the entire chlamydial field, is relatively straightforward: perform a MAG-like sequencing experiment and an iRep study on a population of purified EBs from chlamydial lineages that can be grown in culture.

One additional comment on authors’ interpretation of the results and their proposal of putative free-living chlamydiae. Apparently, the authors failed to consider that the genome size of the new chlamydial MAGs is intermediary between that of environmental chlamydiae and the pathogenic chlamydiae, all of which are obligate intracellular lineages. Given that thousands of intracellular parasitic or symbiotic lineages have a smaller genome/proteome compared to that of their free-living relatives (4), it is very likely that the newly discovered marine sediment chlamydiae followed a similar evolutionary pathway (see also Ref. 5).

References

1. Dharamshi J, Tamarit D, Eme L et al. 2019. Marine sediments illuminate Chlamydiae diversity and evolution. bioRxiv: doi: https://doi.org/10.1101/577767 ;

2. Brown CT, Olm MR, Thomas BC, Banfield JF. 2016. Measurement of bacterial replication rates in microbial communities. Nat Biotechnol. 34(12):1256-1263; https://www.ncbi.nlm.nih.go...

3. Subtil A, Collingro A, Horn M. 2014.Tracing the primordial Chlamydiae: extinct parasites of plants? Trends Plant Sci. 19(1):36-43; https://www.ncbi.nlm.nih.go...

4. Taylor-Brown A, Vaughan L, Greub G, Timms P, Polkinghorne A. 2015. Twenty years of research into Chlamydia-like organisms: a revolution in our understanding of the biology and pathogenicity of members of the phylum Chlamydiae. Pathog Dis.;73(1):1-15. https://www.ncbi.nlm.nih.go...

5. Bandea C. Evolution of giant viruses from larger ancestors. 2018. Comment in bioRxiv on “Virus genomes from deep sea sediments expand the ocean megavirome and support independent origins of viral gigantism”; doi: https://doi.org/10.1101/469... https://www.biorxiv.org/con...

On 2022-01-21 15:22:14, user Ritesh Aggarwal wrote:

This manuscript is now online at Biophysical Journal - Cell Press. Read the peer-reviewed version at https://doi.org/10.1016/j.b...

On 2021-10-08 19:43:51, user Natascia Marino wrote:

This is the first molecular landscape of the NORMAL breast

On 2018-11-06 02:55:07, user Joseph Kirschvink wrote:

We are open to any comments or criticisms.<br /> Joe K.

On 2020-08-24 11:27:31, user Roberto Albanese wrote:

Which GENCODE version was used for the annotation of genes in samples of Series 5 (from GSM4462336 to GSM4462341)? Thank you!

On 2022-05-18 22:14:12, user Davidski wrote:

Hello authors,

Unfortunately, there are some serious problems with the geographic concepts in your preprint:

• your Steppe region includes a large swath of Eastern Europe that is mostly forest and forest steppe. Only about a third or less of this region is actually a steppe (the Pontic-Caspian steppe). Calling this region Eastern Europe would be more useful and in tune with geographic conventions.

• what you call Eastern Europe is not generally, by itself, known as Eastern Europe, especially since the fall of the Iron Curtain. That is, Czechia, Hungary and Slovakia (often along with Poland) are nowadays more commonly described as East Central Europe.

• what you call SE Central Europe is actually much of the Balkans, and thus straight up Southeastern Europe.

Honestly, calling Bulgaria Central Europe, while, at the same time, calling Czechia (inc. Bohemia) Eastern Europe just doesn't look right.

On 2024-12-05 12:22:35, user xPeer wrote:

Courtesy review from xPeerd.com

Summary<br /> The study explores the development of human heart assembloids integrated with autologous tissue-resident macrophages to replicate physiological immuno-cardiac interactions. The research emphasizes the importance of this model for understanding heart development and disease. The authors provide a detailed methodology for generating the assembloids, coupled with multi-omic analyses and functional assays, demonstrating the model's capacity to emulate key cardiac and immune processes. However, while the study presents comprehensive data and a novel approach, certain areas require further clarification and detailed statistical analysis to strengthen the findings.

Major Revisions<br /> 1. Statistical Analysis: The study lacks detailed statistical information to support the presented data. Including p-values, confidence intervals, and statistical tests used for each dataset is crucial for validating the results (e.g., Figures and gene set enrichment plots).

1. Reproducibility of Methods: While the methods section is comprehensive, it would benefit from additional clarity on specific protocols to ensure reproducibility. Including precise details about reagents, equipment, and any variations in experimental conditions can aid reproducibility (e.g., generation of heart assembloids).

2. Integration of Findings: The discussion should integrate the findings more thoroughly with existing literature. Highlighting how the study advances the field and addressing possible discrepancies with previous studies will provide a stronger context for the research.

3. Limitations and Future Work: The discussion needs a more critical examination of the study's limitations, potential biases, and confounding factors. Proposals for future research directions should be specified to guide subsequent investigations.

Minor Revisions<br /> 1. Typographical and Formatting Errors:<br /> - Page 15, Figure 2 legend: The phrase "?ë ëë ë ëëë" is a clear formatting error that needs correction.<br /> - Verify consistent formatting of subheadings and figure legends throughout the manuscript.

1. Definition of Terms: Ensure all technical terms, abbreviations, and acronyms are defined upon their first appearance. This will enhance readability for a broader audience.

2. Reference Formatting: Ensure all references are correctly formatted according to the journal's guidelines. Cross-check for the latest updates in cited references.

3. AI Content Analysis: Based on language consistency and technical depth, the estimated percentage of AI-generated content seems minimal. No sections explicitly exhibit characteristics typical of AI-generated text.

Recommendations<br /> 1. Enhancing Data Presentation: Incorporate more statistical data into figures and tables, such as error bars and exact p-values, to reinforce the reliability of the results.

1. Detailed Protocols: Append a supplementary section with detailed step-by-step protocols for key procedures to facilitate reproducibility by other researchers.

2. Expanded Abstract: Enrich the abstract with specific quantitative results to provide a clearer snapshot of the study's impact and conclusions.

3. Broader Impact Discussion: Expand the discussion on the broader implications of the model for disease modeling, drug testing, and therapeutic applications, tying it back to the study's findings.

This autonomous review aims to provide a comprehensive evaluation of the study, pinpointing critical areas for improvement while acknowledging its scientific contributions.

On 2022-08-29 10:14:15, user David Curtis wrote:

I just wanted to point out that I performed a similar study, though on a smaller scale with fewer gene-trait combinations:<br /> Exploration of weighting schemes based on allele frequency and annotation for weighted burden association analysis of complex phenotypes.Curtis D. 2022. Gene. 809:146039. https://doi.org/10.1016/j.g...

I didn't test VARITY but in my analyses REVEL was not an obvious winner and some other predictors performed better on some genes.

On 2018-03-18 09:51:40, user Slumbery wrote:

The attached map is seriously bad. The article claims that the population split happened north of the Himalayas and emphasizes that it did not happen in India. Then we have the included map (b) that places the splitting south of the Himalayas and in the middle of India. It totally contradicts the text.

Also the other map (a) already shows the endpoint of the first expansion pretty much inside the mountain range that separates Central Asia and South Asia, instead of Central Asia as the text claims.

On 2021-03-25 04:15:19, user Aalok Varma wrote:

Our lab presented this preprint at our journal club, and we thought we could start an open discourse about this work. The characterisation of V1 connectivity was quite interesting, and recording from such a diverse set of cell types in the spinal cord and stimulating over so many segments while retaining recording quality is definitely impressive. Here are some questions we have for you:

1. Have you tried recording fictive swims, either spontaneous or evoked, with optogenetic stimulation of different segments? One would expect that V1 activation should increase swim frequencies (since knockouts reduce speed), but since some of them are Renshaw cells (in limbed animals) producing feedback inhibition, the result could be something else. Moreover, have you tried monitoring a behaviour in response to V1 stimulation? That could address what role these neurons play in motor control more directly and with more spatiotemporal control than knockout models provide.

2. What is the effect of V1 stimulation on motor neurons, without network blocking, i.e. what happens if you’re recording from motor neurons in current clamp mode, and you stimulate V1s?

3. One of the striking results is that almost none of rostral stimulations led to IPSCs. Is it possible that the caudal arm of V1s are actually not axons, but rather dendrites that receive input? Perhaps imaging the localisation of pre- and post-synaptic markers, such as PSD-95 and Glyt2, for instance, within the V1 arbour could address this.

4. In continuation with the previous question, what are the inputs to V1? Are there V1s that function as Renshaw cells and type Ia interneurons even in zebrafish?

5. Lastly, is there any definitive evidence of the order in which the various cell types get activated on the arrival of a motor command? The model, for instance, directly connects pacemaker neurons to V2a’s. which would excite motor neurons, which would then activate V1s for feedforward inhibition? Are we correct in understanding how the model is set up, and does that match what one would expect from real spinal cords?

This work is quite interesting and we sincerely look forward to your thoughts.

On 2020-05-13 17:16:47, user FELIPE MARASCHIN wrote:

I suppose that, in page 8, in line 139, the sentence "the gain-of-function pPIF4:PIF4-FLAG mutant line (PIF4OX; Gangappa and Kumar, 2017)" relates to an overexpression line? In the referenced paper is attributed to Nozue et al., 2007. Nozue however, describes 2 PIF4 OX lines, one with the native promoter, and another with 35S:PIF4-HA. Nozue reports: "In this PIF4 overexpression line, designated PIF4-OX in this paper, PIF4 is driven by its native promoter but is expressed approximately 25-fold higher than in the wild type, presumably owing to the insertion site of the transgene" which was originally developed by Khanna et al., 2004. Otherwise, the PIF4 line it is a complementation line, not a gain-of-function?

On 2022-05-29 22:25:18, user Andres Betancourt-Torres wrote:

Summary:

This work investigates the role of nerve growth factor (NGF) during bone repair. Previous work from these authors established that NGF is important for the reinnervation of injured bone tissue. Here, they explore a secondary effect of NGF on mesenchymal cell migration that appears to be mediated by NGF’s low-affinity receptor p75. Using a variety of genetic techniques, the authors demonstrate that NGF promotes stromal cell migration in vitro, and that knockout of p75 in vivo slows bone repair. Further, p75 appears to control the expression of genes associated with cell migration. Thus, in addition to its role in bone reinnervation, NGF may act via p75 to promote stromal cell migration during bone repair.

Major successes:

The major success of the paper is a clear and well established connection between p75-NGF signaling and an effect on mesenchymal cell migration in mice. Also established is the importance of p75 for other processes, such as NGF production in macrophages and ossification of stromal cells.

Major Weaknesses:

The authors do not establish whether the effects of p75 knockout are independent of other NGF signaling pathways, such as the Trk family of NGF receptors. Thus, it is difficult to determine the relative importance of p75 versus Trk signaling in many of their experiments.

Impact: This paper is the first to describe the dependence of calvarial bone repair on p75 signaling. This finding could have important clinical implications for treating bone injuries.

Major Points:

-A description of review committees overseeing the use of human samples for the research presented in this paper should be included.

-Much of the paper relies on understanding the genetic tools, particularly transgenic mouse lines. There is no explanation in the text of what many of these transgenic lines are or what the abbreviations/notation used for them mean (e.g. NgfLysM). Interpretation of the results would be made easier if the authors included one-sentence descriptions of each of these tools and the logic behind using them. The genetics should also be added to the figures when they are critical to the experiment (e.g. a diagram of how the Cre-ER system works in figure 2).

-In other contexts, p75 plays a modulatory role in neurotrophin signaling through its influence on other receptor pathways (e.g. Trks). The authors did not test here whether the effects of p75 knockout were via independent functional consequences of the p75 pathway, or relied on modulatory influences over other neurotrophin receptor pathways. For example, the results shown in figure 1 could be largely the result of TrkA signaling, with only partial dependence on p75. The additional finding that p75 knockout reduces overall NGF expression by macrophages (Fig 5) also further complicates the interpretation of many of the in vivo results. To address these complications, the authors could test whether p75 deletion from stromal cells influences the function of other neurotrophin receptors. This can be accomplished by using Trk inhibitors in conjunction with the p75 knockout/knockdown (these authors have demonstrated the ability to use such tools in their previous paper). This is an important experiment because it would determine whether the influence of NGF on p75 can be studied independently of its role in promoting reinnervation via Trk signaling, or if the two phenomena are better examined in relation to each other.

-The authors’ should acknowledge additional caveats of their data from human cells/tissue. An important role for p75 in human cell migration is demonstrated in vitro, but it is not fully established in vivo. The observation that p75 is expressed during human bone injury does not alone indicate its function in vivo. Further, the human samples used were taken from tibia and ribs, whereas the rest of the paper is focused on calvarial bone repair. The authors should address these caveats and adjust the sentence beginning on line 261 to better reflect the full range of possibilities.

-The paper emphasizes a role for p75 in cell migration. However, it is also clear that p75 likely influences a range of cellular functions beyond cell migration as well. For example, RNA-seq experiments revealed a wide range of genes whose expression changed following p75 knockouts. Functions relating to NGF translation in macrophages were also likely impaired. These findings suggest that p75 may be critical to a broad range of cellular processes during injury repair, rather than just migration. Devoting more text to the discussion of p75’s role beyond just coordinating cell migration may broaden interest in this paper beyond its current scope.

-The authors should include further details about calvarial defect procedures. Specifically, the authors should elaborate on why they choose this injury model over other available models that could recreate common fractures that skulls experience (e.g. of possible reasons: convenience for observations or less pain for animals). At a minimum, the authors should state a reason why they choose this injury model for their studies. This discussion could best fit in the Materials and Methods.

Minor Points:

-The number of observations appears to be underpowered for some experiments. Particularly, the results in Figs. 2K and 4C look to be trending towards significance, but include small sample sizes. A power analysis for these data, or increasing the number of observations per sample for these experiments, would strengthen the authors’ interpretations of the data.

-The authors state the macrophage populations show a minor shift in population distribution based on the single cell data, but their IF shows significant differences in the number of macrophages at the injury site in the p75fl/fl and p75PDGFRa mice. An explanation for this discrepancy should be included.

-The data in figure 5 demonstrate that p75 knockout depletes NGF expression in macrophages. This suggests a positive feedback loop between NGF expression and NGF signaling. The authors should explore the consequences of this finding in their discussion as it may be important for considering the interaction between p75 and Trk signaling during bone repair in vivo.

-The authors should consider testing tissue samples for the presence of osteoclasts, due to the hypothesis that these cells could modulate the activity of osteoblasts. Generating this data would reinforce Figure 2 and the argument that p75 deletion is driving the lack of bone repair.Alternatively, the authors could discuss how osteoclasts modulate the osteoblast activity they describe through the presented data. This topic could be addressed in the Discussion section, for example as either a study limitations or future projections of this project.

-The authors should consider generating new images for Figure 2 panel I, and see if they can observe osteoblasts in the fracture healing area, using a higher magnification. This could reinforce the comparison among the two conditions presented by demonstrating the presence and hence participation of osteoblasts in the fracture healing process.

Stylistic Points:

-The order of the figures is a bit confusing. The authors switch back and forth between in vitro and in vivo experiments. One possible order is: Fig 1, 4, 3, 5, 2, 6.

-Typo on line 77: “microdissection bone defect site”.

-Reorganize Figures 1 H and I as Figures 1 B and C; they validate the model, and it may help readers accept the model before reporting any further data.

-Each chart should have its own legend. Although the color coding is clear, this will help each graph to stand independently from each other and help readers interpret the data quickly.

-Remove “squares” from the test in the Materials and Method section, or substitute with any possible missing symbol.

Whitney Tamaki (Whitney.Tamaki@ucsf.edu)<br /> Scott Harris (Scott.Harris@ucsf.edu)<br /> Andrés Betancourt-Torres (Andres.Betancourt-Torres@ucsf.edu)

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

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

On 2019-01-17 12:48:50, user Michael Hiller wrote:

Great to see more well assembled lizard genomes, but it would have been nice to cite the more recent assemblies of Salvator and Lacerta (even if the N50 metrics of both are assemblies better)

On 2022-03-29 09:55:05, user Daniel Baldauf wrote:

Beautiful study! These are amazingly strong decoding results with 90+ percent accuracy. That's great. I wondered though whether this might have to do with the relatively easy auditory targets (yes/no)?Or is it that the two speech streams can be easily distinguished by low-level features such as the pitch? I would be curious what role you think high-level attentional processes might play in this task, particularly, for example, when it comes to parsing words in a even more noisy environment. For example, recently Marinato & Baldauf (2019, Sci.Rep.) used a speech signal mixed with an environmental 'sound-scene', and showed that top-down object-based attention has a strong effect on the parsing of the language stream. DeVries et al. (2021, JN) then also recorded MEG during such a task, showing that it is particularly the alpha band in a fronto-temporal network that mediates these functions of object-based attention to words, and that allows for successful trial-wise decoding of the locus of attention (but not on quite the same level as 90% accuracy). Best wishes!

On 2019-02-12 21:39:33, user systemsbiology wrote:

Now published in Bioinformatics, https://doi.org/10.1093/bio...

On 2023-08-07 14:09:23, user LUCIANO RODRIGO LOPES wrote:

Dear Professor Porter and colleagues,

I have read your scientific article with great attention and interest. The initiative to include new species of deer as an experimental model to verify susceptibility to SARS-CoV-2 is of remarkable importance. The susceptibility and virological surveillance analyses involving the white-tailed deer (WTD; Odocoileus virginianus) have demonstrated the potential spillover of SARS-CoV-2 into wildlife. However, this appears to be just the tip of the iceberg. By adopting new species of deer as a susceptibility model, similar to your approach, we can better predict new scenarios involving SARS-CoV-2.

According to your results, it is concerning to learn about the potential susceptibility of mule deer and their ability to transmit SARS-CoV-2 with real infection capacity, whereas we observed that elk have lower susceptibility to this virus.

In an analysis involving deer ACE2 protein sequences (https://doi.org/10.1007/s10... "https://doi.org/10.1007/s10393-023-01632-z)"), I compared the binding sites that SARS-CoV-2 uses to enter the host cell. The mule deer shares the same binding sites with the WTD, while the elk has a different site, in addition to the evolutionary distance with the deer of the genus Odocoileus. I argued that this variation in an ACE2 binding site could decrease elk susceptibility to SARS-CoV-2. It appears that our results corroborate each other.

I conclude my comment here by congratulating you on the interesting work and publication by your group.

Sincere regards

Luciano

On 2020-05-08 15:18:05, user Manish Kumar wrote:

Supp Fig. 6. Technical typo: f = 4 mm for 50x Nikon.

On 2020-07-21 07:40:53, user MCMF wrote:

Thanks for quoting Fischer et al. (2019). It should be easy to integrate our method into your workflow for evaluation. It is open source and also written in MATLAB: https://github.com/RWTHmediTEC/PelvicLandmarkIdentification. You can cite the program with the DOI 10.5281/zenodo.3384110.

On 2016-09-01 18:16:03, user Ami Tsuchida wrote:

Very interesting paper...! I think the main analyses are very sophisticated, but I wonder whether the final analysis truly captures the change in functional connectivity. It seems that the authors simply included the time course of anterior LH as a regressor to compare change in correlation with this seed before and after learning. Isn't it possible that the correlation change because of the patterns of activations change before and after learning (i.e. both anterior LH and PFC regions are recruited during the task at early learning phase, but this pattern changes after learning)? Why not use PPI, modelling both task regressors and the seed time course, as well as an interaction term to see whether the change in correlation is above and beyond the change in the pattern of co-activation? I think the results would be interesting regardless, but the precise interpretation of "functional connectivity" may be different depending on whether it's driven by the change in the responsiveness of these regions to the common task stimuli.

On 2016-01-17 18:40:56, user Anthony Olszewski wrote:

In this paper my impression is that they are counting bacteria suspended in the fluid of the colon. Are there bacteria on the surface of the large intestine? How many might be there?

The bacteria concentration in the small intestine contents is stated as 10 to the 3rd compared to the large intestine 10 to the 11th. The surface area of the small intestine is amplified 60 to 120 times. What is the number of bacteria living in this real estate?

Even if less than originally thought, the surface area of the gut is considerable:<br /> http://www.ncbi.nlm.nih.gov...

On 2024-09-17 08:21:43, user EDG wrote:

This article is now published in Nature Communications : https://doi.org/10.1038/s41467-024-51638-6

On 2020-08-03 13:48:39, user SUMAN MISHRA wrote:

Thanks for sharing your generous comment. Somehow we overlooked this point while submitting our preprint. Accordingly we will modify the competing interest section.

On 2016-09-09 22:47:56, user Afif Elghraoui wrote:

The link in the abstract redirects to <https: <a href="github.com" title="github.com">github.com="" gifford-lab="" cpgenie=""/>, where the jedi octocat tells us the page doesn't exist.

On 2017-08-25 02:34:46, user Keith Williams wrote:

Gefeliciteerd! This is a really wonderful result; I hope that it holds up.

On 2025-10-12 06:26:52, user Chenxi Sun wrote:

Overall, the paper makes a strong and interesting contribution by showing that macrophage mitochondria transferred to cancer cells don’t serve as energy sources but instead act as ROS-producing signals that activate ERK and drive proliferation. I’d rate its significance a 4 out of 5, since this finding changes how we think about mitochondrial transfer, giving it a new signaling role in tumor biology. The experiments are carefully designed — live-cell imaging, RNA-seq, and biosensors all support the conclusions — but most data are from in vitro models, so it’s still unclear how much this process matters in real tumors. The main limitation is the lack of in vivo validation, which would make the story more convincing. Overall, the methods are solid, the logic is clear, and the conclusions mostly follow from the data, even though more physiological testing would strengthen it.

On 2019-08-29 12:40:51, user JMS van der Schoot wrote:

Please find the published (and revised) version of this manuscript at Science Advances:<br /> https://advances.sciencemag...

On 2020-05-13 20:54:28, user Are Bayode wrote:

The article is now peer-reviewed and published in PLoS NTD: <br /> https://journals.plos.org/p...

On 2018-05-08 07:52:25, user Mike Fainzilber wrote:

Very interesting work. The discussion on how precisely MAPK-15 influences length might also consider the possibility that it is part of a motor-dependent length-sensing mechanism similar to that described by us for mammalian neurons (admittedly axon-centric in our case, but nobody's perfect...). See https://www.ncbi.nlm.nih.go... and https://www.ncbi.nlm.nih.go.... There is also earlier work from the Eickholt group on myosin and PTEN in control of somatodendritic compartment size in mammalian neurons that is likely highly relevant, see https://www.ncbi.nlm.nih.go....

On 2021-02-06 06:24:23, user Olabode Omotoso wrote:

This preprint has been peer-reviewed and thereby modified. The peer-reviewed version can be accessed open access via https://bjbas.springeropen....<br /> Thank you<br /> We also appreciate all healthcare workers and researchers at the forefront of finding a lasting solution to the COVID-19 pandemic

On 2018-03-17 10:29:04, user Lukasz Kozlowski wrote:

The web server has been moved to http://isoelectric.org

On 2020-03-04 10:28:04, user Nikol Reslová wrote:

Hello, I would like to ask, the database is not working for some time now, will it be available once again or did you close the websites for good? Thanks for your response in advance, NR.

On 2021-08-17 09:17:54, user passanger lost wrote:

Can not found the SUPPLEMENTARY INFORMATION files

On 2020-06-02 14:05:55, user Stefania Di Blasio wrote:

This is a pre-print of an article published in Nature Communications, on June 2nd, 2020. The final authenticated version is available online at: https://doi.org/10.1038/s41...

On 2019-10-14 09:02:13, user Habi wrote:

As the first author and 'producer' of the analysis code, I'm happy to answer any questions on the manuscript.

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

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

On 2023-06-04 22:27:01, user Dr Ros Jones wrote:

Thanks for this detailed study. You mentioned concerns about spike protein from infection and from vaccines. Do you have any way to distinguish which you are finding? Were the findings similar in postmortems of those dying post-infection who had also been vaccinated?

On 2025-08-24 15:25:49, user Tsvetoslav Ivanov wrote:

Now published: https://www.nature.com/articles/s42003-025-08297-0

On 2019-05-28 02:20:02, user Huiwang Ai wrote:

There is a relevant paper published on Nature Methods two years ago: https://www.nature.com/arti...

On 2018-02-09 23:24:19, user John wrote:

Forgive me - I only skimmed the paper, so this question may be answered therein. Is one able to compare the scikit-ribo translation efficiency estimates across conditions? (i.e. does the TE for a given transcript differ between conditions A and B?) Or can you only compare the TEs within a single condition? (i.e. does X have a higher TE than Y?)

On 2022-01-15 03:53:20, user pierre wrote:

global mapping with 2000 points is nonsense, but the geospatial modelling of this article is also nonsensical.

On 2020-03-19 18:43:36, user Michael Ward wrote:

Hi, I've searched the PDB for 6M71 and it is not available. Do you have an idea when it will become available

On 2020-03-23 23:29:20, user Anna Maria Niewiadomska wrote:

Regarding ORF10:CUL2. Interesting particularly since there's evidence of other coronaviruses interacting with proteins in the ubiquitination pathway. However, two groups performing direct sequencing of SARS-CoV-2 mRNA have not been able to detect ORF10 transcripts and no ORF10 homologues exist in other coronaviridae. Although you do mention there's no evidence for ORF10 expression in the supplementary material, you may want to consider that the ORF10 results may be an artifact of over-expression and mention in the main text.

On 2021-01-22 17:50:16, user Fraser Lab wrote:

This work reports two atomic models of the main protease (Mpro) from SARS-CoV-2 using serial femto-second crystallography (SFX). The goal of the paper was to use this structural information to assist drug repurposing efforts against SARS-CoV-2. To accomplish this, the authors used computational docking and molecular dynamics simulations to investigate the molecular basis for binding of three previously reported Mpro inhibitors. There is a substantial (and growing) body of work describing the structure, function and inhibition of the main protease from SARS-CoV-2, but the claims of how the current results will become part of that effort are overstated in the manuscript.

The major result of this paper is the efficient use of the new macromolecular femtosecond crystallography setup at LCLS-II: two high resolution structures were reported from <5 hours of instrument time (1.9 and 2.1 Å). The major weaknesses of this paper stem from both the disconnected nature of the primary results and the rigor of some of the analyses. Specifically: 1) the reported structures have little relevance to drug repurposing efforts (the stated goal of the paper), 2) the differences between prior conventional structures and the analysis of the molecular dynamics simulations lack rigour, by lack of comparisons of electron densities and estimations of convergence/significance 3) the implications of the new Mpro models and/or the molecular dynamics simulations for inhibitor design are not articulated, 4) the compounds highlighted have been reported as promiscuous covalent inhibitors.

Some of the results described in this manuscript may be of interest to the SFX community, particularly if revised to more solidly compare to existing data. However, to maximize the relevance to the wider structural biology and SARS-CoV-2 research communities, the manuscript should be revised significantly.

Elaborating on point 2 above:

1. The manuscript does not present a fair comparison of structural information obtained from SFX versus traditional crystallography. Electron density maps are only presented for the SFX structures (e.g. Fig. 3B). Claims of structural differences would be stronger if the structural comparison was performed using crystals grown from the same conditions and with data processed/refined/modeled in a consistent way. Isomorphous FO-FO electron density maps would be particularly helpful.

2. The authors restrict their comparison to a single previously reported structure of Mpro (e.g. 6WQF). They should extend this analysis to the ~240 previously reported structures of Mpro from SARS-CoV-2. The majority of these structures were determined using cryo-crystallography and have ligands bound, however the comparison would still be informative, and is required for their claims of novelty. In particular, active site flexibility upon ligand binding has previously been characterized (e.g. Figure 1C of https://www.nature.com/arti..., Figure 4 of https://www.nature.com/arti... "https://www.nature.com/articles/s41467-020-16954-7)") - it would be helpful to know whether the structural differences reported by Durdagi et al match those previously reported.

3. The structural differences involve alternative side conformations of non-catalytic residues in the active site (e.g. Figure 2B and D). Importantly, no specific link is articulated between the alternative conformations identified and Mpro function or inhibition. The authors suggest that the new structures will help modeling efforts (e.g. P5 L20, P11 L5). The authors present modeling efforts in this manuscript. How have the structural differences identified by the authors helped their modeling efforts (compared to the previously available structural information)? There are obvious computational analysis controls here that are missing.

4. The catalytic histidine is modeled with a flipped side chain in 7CWB compared to 7CWC, however, this difference is not mentioned in the manuscript. Are the authors confident in their modeling? Looking at previously reported apo coordinates (e.g. 6WQF, 6YB7), both conformations have been modeled. The conformation modeled in 7CWB seems more compelling, based on the ability of His41 to H-bond to Cys145, and the H-bond with Wat441. The authors claim that Wat441 (W5) plays a crucial role in catalysis (P5 L28). Flipping His41 disrupts the Wat441 H-bond network, however this is not mentioned. If the authors are confident in this difference, then they should summarize their reasons and the implications for Mpro function and inhibition.

5. A substantial and growing body of work (e.g. https://scripts.iucr.org/cg..., https://scripts.iucr.org/cg..., https://pubmed.ncbi.nlm.nih..., https://www.pnas.org/conten... "https://www.pnas.org/content/117/8/4142)") deals with radiation damage in X-ray crystallography. Carefully designed experiments, coupled with advances in detector technology, mean that problems associated with radiation damage can be mitigated, even at room temperature. Indeed, the authors of a recent paper reporting the room temperature model of Mpro (Ref 27 in Durdagi et al - https://www.nature.com/arti... "https://www.nature.com/articles/s41467-020-16954-7)") explicitly state their efforts to mitigate radiation damage: “We grew large crystals that could be used on a home source to ensure minimal radiation damage.” If Durdagi et al are suggesting that radiation damage was a problem with previously reported data, then they need to present evidence to support their claim. One option would be to collect cryogenic and room temperature data using identical crystallization conditions, then calculate isomorphous difference maps to test for radiation damage (clearly, careful experimental design is required, and problems of non-isomorphism may be encountered). Without this experiment, claims of issues with radiation damage at room temperature are not supported by evidence and should be removed.

6. Differences in dynamics were identified with simulations performed using 7CWB and 7CWC, despite the starting coordinates being almost identical. This strongly suggests that the simulations have not achieved equilibrium sampling. Perhaps the 7CWC model was simulated with the four residue addition at the N-terminus, and this can explain the differences? This should be mentioned in the main text.

7. Differences are also highlighted between the dynamics identified from simulations of 7CWB and a previously reported cryo model (6WQF) and room-temperature model (6Y2E). As with the previous point, these coordinates are almost identical, and if simulations of the same coordinates produce different results, how can the authors be confident that simulations with different compounds will produce useful results?

James Fraser and Galen Correy (UCSF)

On 2017-03-06 03:22:17, user ale wrote:

Everything I have read on this URL is so far from the truth it’s surreal. This boils down to history, science and statistics. It is the most plausable form of nailing down any logical assumption on accuracy of reducing the probability for your origin to a demographic locus. So first the brass tax: most of you are Italians or have a significant genotype that is from the Italo Peninsula origin. Sorry, it’s the truth. I have been told this story by too many orthodox Ashkenazi as well as paleoanthropologists whom specialize in gene characterization. Being told this was not enough, so I researched it. Being a decendant of a Hebrew Tribe on my mother’s Sephardic Side from a family whom immigrated to Naples from Spain, we were traced by the Nazi Germans during WW2. Thus my family resided in a potato cavern behind a wall of an old farmer for two years before the war ended and were allowed to come to America. I did my due diligence and upon returning to Abruzzo visited a cousin whom happened to be on the accounting staff of the Vatican. This allowed me some leaway to utilize the Vatican library during my summer visits to truly understand my origins as an Italian-Jew. And what I leaned was right in line with science and history. My family originated from Spain, they were merchants whom specialized in the fishoil business to light lanterns. They had lived in Spain for almost 700 yrs generation to generation. Many were knights, nobleman, schlolars, lawyers and artists. I had a sample of my blood DNA analyzed and asked my mother and a few of my cousins to do the same. We all had the id mtDNA mitochondrial of Ashkenazi. But I always thought they were eastern EU and Germanic so I was confused because we were expecting to confirm some Sephardic lineage. We were wrong and I ventured to the local temple in a town nearby my mom’s hometown for further elaboration and I heard the story from an orthodox rabbi whom then was 101 yrs old. It was something out of the movies, and I will never forget his hand on my shoulder and smile and words “we are all related, so be nice to all.” The Romans who finally destroyed the second Temple of Jerusalem during Exodus led many Jews to live amongst the Romans in Rome and surrounding towns. Many were men who took Italian wives as they garnered citizenship as members of Rome. Some of the Rabbi’s own family he could trace to Poland now and Lithiania. Many of these same Jewish lineages were Italian/Hebrew, of which a small population left Italy during the Gaulish Invasions and found refuge amongst Northern Europeans. The spread out as a gypsy flock of people to Germanic states to settle down and form sub demographic and distinct cultural diaspora. That aside, each and every one of them, including yourself if you are from the eastern EU will find N1b2, M1a1b, K1a9 and perhaps even the major K1a1b1 genotype in your makeup. Yes, all origins from the Ibernian peninsula…(aka Italy). It was Exodus, you see. And the first stop was Rome of course. Romans were efficient at using the spoils of war for work and where better than the big apple itself? So, “I am who I am”. But you and I and most other Jews share almost an 80% statistical probability that the remnant non middleastern 49% of our genetic makeup is of Italian origin. This is the truth, this is what I learned. We are all connected and we all originated from two locuses, Israel and Italy. It all makes sense, think about it. Really think about it.

On 2023-02-06 02:08:53, user Susan C Kandarian wrote:

1. page 13, line 6, "(as in Figure 3C)" please correct to "(shown in Figure 4J)"
2. please discuss implications of increased TLR4 and TLR5 in monocytes from patients. Are these gene expression changes responsible for the difference in the "DNA sensing pathway" indicated in the figures?
3. The "TNFalpha signaling via NF-kappaB" is plotted in 3 figures but it is not referred to anywhere in the text. Is this what is meant by the few text references to inflammatory signals? Please expand on which gene expression changes were strongest in calling out this pathway by the gene cluster analysis program.
4. Related to the above comments, it would be helpful (where possible) to state some major gene expression changes that likely caused the bioinformatics programs (KEGG or GSEA) to call out pathways such as TNFalpha signaling, IFNalpha response, etc)
5. If you search the dataset for individual genes, like other PRRs (besides the 2 TLRs found), are there others whose expression was changed in patients such as the RIG-I like receptors that can be found on monocytes?

I think this work on scRNAseq in different immune cell types is an important advance, but realize that scRNA data alone are difficult for making conclusions about biological processes. The monocyte data (normal vs diseased) are compelling, and they are consistent with other data indicating a problem in innate immune cell function (detection of PAMPs or DAMPS and/or an innate immune cell signaling loop error?). Perhaps it is triggered by exertion products since even ATP or ischemia-reperfusion are known activators of the PRR called NLRP3.

I have no ideas about how to understand the messaging to macrophages since they are mostly in tissues. Lymph node biopsies?

On 2019-03-19 18:10:04, user Pete wrote:

Glad it's finally out!

On 2022-12-18 11:06:58, user Scott Cameron ???????????????????????????????????????????????????????????? wrote:

Couple of corrections needed in your paper here folks. Reference 24 did conduct ADP stimulation of human AAA platelets (supplemental Figure 2 — no difference). Also GP2B3A activation was assessed in AAA (Figure 2C — increased).

On 2016-09-01 19:18:38, user Erik Lindahl wrote:

Hi Steven!

Thanks for the feedback. You are entirely right that we should have used newer CPU nodes - we just hadn't gotten our new machines delivered when we wrote the first version. However, we got a bunch of brand new dual Xeon E5-2690v4 nodes this summer (and also realized the GPU version needs two SSDs in RAID0 to really shine), so we have more up-to-date numbers now. Unfortunately, new x86 CPU cores are not significantly faster (in particular not if your code doesn't benefit hugely from FMA instructions in AVX2). You can get _more_ cores per socket (at higher price), but the performance per core will not be much faster, both because the architecture has not changed a lot, the clock frequency trends downwards, and most importantly - all those cores in a single socket share memory bandwidth (so the memory bandwidth per core is sadly dropping). When comparing ten such latest-generation x86 nodes with a total of 280 physical cores (560 threads with hyperthreading) against a single-socket workstation with 4 GTX1080 GPUs, the latter single machine is more than twice as fast as the ten nodes (see attachment). The cards should be in stock pretty much everywhere, and a colleague just got a quote for a complete workstation with GPUs for just over $6000, available now :-). In my (admittedly non-neutral) opinon the relative advantage when running actual refinement is remarkable, in particular in terms of price/performance.

Our goal is simply to achieve massive speedup for data processing, and we are not married to any particular architecture. Relion will support them all, long-term too, and of course we hope to extend the impact beyond GPUs when possible. All changes in the code that are applicable to the CPU side have been implemented there too; when it comes to floating-point models double precision turns out to be just-so-slightly faster on the CPU, so it seemed fair to use the fastest alternative (but the difference is small). We hope to look more into the CPU side too in the future (in particular to help older hardware), but to be honest: right now we don't see any scenario where CPUs can become anywhere close to competitive for new investments, no matter how much work we invest in CPU acceleration for image reconstruction.

On 2016-09-20 22:10:00, user Ross Walker wrote:

Given the discussion on hardware here I thought I'd add a link to the Exxact preconfigured Relion GPU systems for those that are interested: http://www.exxactcorp.com/r...

These can be fully customized and can include GeForce or Tesla GPUs. The machines (including GPUs) are warrantied for 3 years even if equipped with GeForce and the QA process includes validating all GeForce cards for numerical correctness which has been very successful in the MD and other Life Sciences fields.

On 2022-09-02 01:38:59, user Matthew Templeton wrote:

What similarities are there to the Myrtle rust (Austropuccinia psidii) genome, given that both organisms have broad host ranges and very large Tn-rich genomes?

On 2025-08-15 09:06:18, user Alex Zhavoronkov wrote:

Did you see this paper? <br /> Machine Learning on Human Muscle Transcriptomic Data for Biomarker Discovery and Tissue-Specific Drug Target Identification<br /> https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2018.00242/full

On 2025-01-23 21:00:35, user Nicole Blackstone wrote:

The published paper linked below and on the main page is a cultivated meat LCA, not a media LCA. Is this a mistake?

On 2015-11-06 15:30:45, user Benjamin Voight wrote:

FYI - not sure if this is due to the comment below, but The google spreadsheet link in the supplement (pg. 35, section 4.11)

to the 3230 pLI genes is broken (as of 11/06/2015, 10:26AM)

On 2022-01-28 14:10:47, user Riccardo wrote:

Also check our 3D videos in Supplementary Material

On 2020-08-10 09:32:41, user OxImmuno Literature Initiative wrote:

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

On 2019-03-18 16:33:25, user Stephen Floor wrote:

Very curious what diffusivity looks like using SMdM if you deplete ATP!

On 2020-04-23 16:06:54, user keyser soze wrote:

Supporting information file?

On 2019-12-02 04:42:29, user Andrew Brooks wrote:

Thanks for the update. How did you measure the distance to the membrane. There is no membrane in ref 35. Just asking if this would depend on the prediction of where the JAK2 surface is associating with the membrane.

On 2023-04-04 18:07:42, user mkarikom wrote:

the geo accession is wrong: GSE190004 is from a lung cancer study

On 2020-02-02 03:41:00, user jay wang wrote:

I BlastPed the region that spans the first 2 “insertions”, and found no so called insertions at all in the alignments with other bat-cov viruses. On the contrary the alignments showed very natural diversities around the two ”insertion” regions. The two “insertions” are obviously products of evolution,not engineered artifacts! Furthermore,even human has huge number of proteins that are homologous with those of vegetables,so why it is a surprise that there are short homologous regions between 2 viruses?

On 2020-02-01 07:48:14, user ngschen wrote:

This paper is fake. I aligned all 4 insertions. 3 of them share with BAT sars-like virus (GISAID no.: BetaCoV bat Yunnan-RaTG13 2013 EPI_ISL_402131). The fourth insertion (CTCCTCGGCGGG), which is the only one 2019-nCov specific insertion, has the best match to Marine virus AFVG_250M1136. Since the 2019-ncov outbreaks from the seafood market, the possibility of marine virus rcombination might be much more persuasive.

On 2022-10-15 09:35:56, user Iratxe Puebla wrote:

Review coordinated via ASAPbio’s crowd preprint review

This study has developed a tool to characterize small molecule modulators of RNA-protein binding events. Please see below a few points which may help strengthen the manuscript.

• The term “temporal” is used multiple times in the paper, to facilitate clarity for readers from different disciplines, it may be useful to provide some further explanation or context for the term.

• Introduction section, “independent datasets have failed to reach consensus”, please provide some brief explanation about those independent datasets mentioned.

• Introduction section, last paragraph “We apply TRIBE ID to profile cytoplasmic G3BP1-RNA interactions …” - further explanation of these three processes linked together would be helpful.

• Figure 1, please provide some further explanation for the difference between TRIBE and TRIBE-ID. Since the dimerization is forced by rapamycin, a control experiment to explain artifact binding would be helpful.

• In the section, “Rapamycin-mediated dimerization of G3BP1-FRB and FKBP-ADAR”, recommend adding some clarification about the goal of this experiment, which could be understanding either native processes or in a rapamycin-dependent manner.

• In section, “G3BP1 TRIBE analysis with human and Drosophila ADAR2 catalytic domains” - suggest commenting on the reasoning for ideal ADAR to possess characteristics like “high editing activity when dimerized or fused to G3BP1”. Are these characteristics important to increase signal/noise ratio in the assay? Also, an explanation of T375G mutation and control experiments with wild type ADAR for any inhibition effect for Figure 2 would be helpful.

• In the section, “Temporally controlled G3BP1-RNA interaction analysis with TRIBE-ID”, please clarify whether the experiment described in Figure 3 provides information about the time of interaction between RNA and G3BP1.

• A paragraph describing any limitations and other possible applications of this tool on other systems would add to the manuscript.

On 2020-04-07 11:29:26, user Romain Koszul wrote:

Thanks @GauthierJeremy and JM Drezen for the nice collaboration and congrats for the paper.

On 2018-04-09 19:56:36, user Marcus wrote:

Bhatia and Heisler (2018) has now been updated to include images for 40 replicate leaves and an additional source of mDII seed.

https://www.biorxiv.org/con...

On 2013-12-24 21:36:22, user B. Schwessinger wrote:

In the introduction you are suggesting the following: <br /> 'If true, conservation of important effector-target interactions<br /> may form another important means besides convergent evolution, by which targeting of<br /> important host proteins by divergent pathogen effectors, is achieved.'<br /> Do you mean that effector-target interactions might be evolutionary conserved between fungi, phytoplasma and oomycete.This seems rather unlikely given the evolutionary distance between these pathogens.

On 2023-05-22 22:45:16, user Fraser Lab wrote:

Summary:

In protein engineering projects, it is always desirable to screen as efficiently as possible. Screening a relatively small number of variants becomes especially important when enzyme activity cannot be coupled to a high throughput sequencing readout. The major goal of the paper is to provide a proof of concept scoring and filtering system for selecting among proteins generated using computational methods to meet this challenge of efficient screening. They consider proteins generated using 2 machine learning methods and one phylogenetic method (ancestral reconstruction).

The end result is a scoring filter combining the language model ESM-1v (which uses only sequence information) and the deep learning method ProteinMPNN (which is trained directly to find the most probable amino acid for a protein backbone predicted by AlphaFold2). After accounting for some simple idiosyncrasies of merging generative models with reality (ensuring starts with Met, removing repetitive sequences, accounting for localization signals) with heuristics, their filtering steps results in an enrichment of active sequences.

The major success of the paper is a pipeline that actually works for selecting active sequences both in the experiments they conduct and (to some extent) literature examples. The table of potential protein failure modes is particularly useful as a baseline approach and reference for people designing sequences with computational methods. It is especially insightful to see how few deliberate filtering steps in the training process can have a big change in the outcome.

We expect that a combination of sequence and structure-based filters will be used for prioritizing screening resources in the future. This paper lights the way of how to do that. The next steps will be to take into account structural features beyond stability (which is presumably covered by the AF2/ProteinMPNN), such as catalytic residue positioning, pocket size complementarity to substrate, etc. These are presumably implicitly captured by ESM-1. The next logical step (beyond this paper) is to go beyond statistical combination of these two scoring features to account for such features explicitly or with a new integrated deep learning approach.

Major points:

We are a bit confused about the exact value and sequencing of each part of the selection/filtering pipeline. We interpret experiment 3 as:<br /> Apply ESM-1v and Quality Filters and then apply a ProteinMPNN filter on top of that. <br /> Select Negative Controls by selecting sequences that fail the first filter (ESM-1v and Quality Filters) but are within 1% sequence identity to the closest natural sequence for some positive.<br /> The quality checks discussed in the supplementary information seem to have substantial impact. If the selected control sequences failed this quality check, it’s not clear whether the success of the pipeline is due to these heuristic quality checks or due to the computational filtering. These filters are biologically simple such as starting with a methionine, removing long repeats and not having a transmembrane domain - and it is kind of amusing to one of us (JF) that generative models have these pathologies so commonly. More discussion on why these filters were applied and what the distribution of effects were for the quality filters vs the insilico filters would help clarify the impact of each stage.

This confusion then extends to determining how each of the two computational methods affect the selection. The authors contend that “no single metric would be sufficiently generalizable to screen against multiple sequence failure modes” and hypothesize that ProteinMPNN and ESM-1v “may capture distinct features.” However, because negative controls were selected only after failing the initial ESM + Quality Filters, its impossible to know what effect adding ProteinMPNN on top of ESM had. This is even more relevant given that the structures used to obtain proteinMPNN scores are first generated with Alphafold. Alphafold can be computationally intensive (expensive to run) and therefore it is imperative that we understand how much this part of the pipeline contributes to the overall success of the selection process. The authors themselves contend that “Structure-supported metrics, including Rosetta-based scores, AlphaFold residue-confidence scores, and likelihoods computed by neural network inverse folding models, take into account protein atom coordinates potentially directly capturing protein functionality, however, they can be impractical to compute, especially when evaluating thousands of novel sequences.” This is something that can potentially be teased out. In the case of the paper only 200 proteins were selected using ProteinMPNN, however, if many sequences end up passing the ESM filter and budget allows it would be within reason to expand this random ESM selection.

In summary, it is a bit hard to tell (without some ablation studies) which different pipeline components and filters drive the results. Additionally, it would have helped if these same quality filters were applied in Round 2 but that doesn’t seem to be the case? A deeper discussion on the selection of quality filters would also point the way forward with combining more “functional” structural features as outlined above.

Minor points:

1) The author’s generalize the results with a few literature examples: “similar results were obtained by independently validating COMPSS on previously published datasets of six enzyme families generated by models not considered in the present study.” Looking at the results in more detail reveals that some of these (including one that we generated!) are very small samples and this caveat should be discussed. In 3 out of the 6 studies, only 1 sequence was selected by their pipeline. In another of the 6, 2 were selected. In all 4 of these studies, a number of actives were missed. The limited number of selected sequences makes it hard to know how effective the pipeline really is in these 4 studies. Further, with such a stringent filter is not practical especially when we consider the fact that the authors don’t discuss the level of activity across positive and negative active compounds. It’s entirely possible that you could miss very active sequences and select only moderately active sequences. In one retrospective, the results were truly similar, however in the last other study, the filters worked far from intended.

Even more, my team has observed in its own work that the sensitivity of machine learning models for scoring can be heavily dependent on the sequences the models have seen before. It would have been useful for the authors to consider how the tested enzymes overlap with the model training data to understand whether these scorers generalize outside the models training distribution.

2) The authors largely discount natural sequence identity as a metric:<br /> “Surprisingly, neither sequence identity to natural sequences nor AlphaFold2 residue-confidence scores were predictive of enzyme activity.”

I think it’s important to qualify this with the fact that we are looking at sequences in the 70 to 90% range with very little dynamic range here. In their first experiment they looked at sequences in the 70 to 80 range. in their second they look at sequences in the 80 to 90 range. In their third experiment they looked at sequences in the 50 to 80 range but their filters end up selecting for sequences in the 70-80 range anyways. So it’s possible that locally, identity might not select for select for activity but globally, it could be a first filtering step on its own (which maybe is obvious and hence why it’s not more qualified?). Also to note is that sequence identity seemed to fare as well as or better than other metrics in identifying functional GAN-generated sequences and could be its own generative method:

More problematic I think is figure 3f and figure 3g:

It seems like the inactive controls are largely in a separate part of the tree compared to the active sequences passing and control. Does this have anything to do with the fact that these features failed the sequence based quality filters. Second,it suggests an approach where if you have some idea of where to focus on in the tree you could use sequence identity to those natural sequences as a metric for selection . Of course this information may not be readily available but the authors should discuss whether we could have hypothesized that the failing controls would have failed beforehand by considering their phylogenetic origins.

Technical points:

1) There is some problem with this sentence:

“CuSOD training sequences had only a single Sod_Cu domain, while MDH had an Ldh_1_N followed by an Ldh_1_C domain and no other Pfam domains that generally only rarely occur in 6.3% and 1.7% of sequences in both families, respectively.”

It’s much better captured in the supplementary material:

“For CuSOD, 1,632 out of 25,701 proteins (6.3%) had aberrant architectures. For MDH, 1,127 out of 65,639 (1.7%) had aberrant architectures.”

2) It’s not clear where/how they selected the natural test sequences for rounds 1 and 2. We assume it’s from the curated set of data but that’s not necessarily a given, further it seems that in round2, sequences were selected to span the range of esm scores. Was this done for the test natural sequences as well?<br /> “Only 13 test natural sequences were selected, as we had already screened five similar natural sequences in the remediation for Round 1.”

“Besides the identity range, the experimentally tested sequences were selected to span the entire range of scores on each metric (Supplementary Table 4)”

3) The authors should be more explicit on the natural sequence identities in each round. If you check the supplement you can find this information if you pay attention to the figures or check the supplement but I think that it should be explicitly stated in the section “Round 2: Calibration data for COMPSS” that sequences are selected in the 80-90 range and in Round3 that the filters resulted in sequences with >69% identity.

4) The following section is confusing:

“To further test the hypothesis that poor truncation selection was responsible for the lack of observed activity in the Round 1 CuSOD natural test sequences, we assayed an additional 16 natural SOD proteins (pre-test group)…”

It should be stated at the beginning that 14 of the 16 test sequences are CuSOD sequences and 2 of the sequences are FeSOD sequences vs letting the reader figure that out later in the paragraph. Additionally, it would help the audience to say explicitly that 3/7 bacterial sequences with clipping also passed or include the table from the supplement up front. 3/7 doesn’t seem clearly distinct from 4/5.

5) What’s the reason for changing the esm-msa sampling method in round 2? Did they observe some benefit or was this purely a computational choice?

6) I think the text for a and b are switched in the figure 2 description. a is the AUC figure and b is the correlation figure. Further for figure a If the test sequences are natural sequences, is the identity score meaningless here?

7) From the supplement: “We skipped the 'starts with M' filter because very few of the sequences in these sets start with M, and did not subset by identity to closest training sequence.” This modification to the pipeline should be mentioned in the discussion of the external validation tests. Or they should speculate what would happen if they just added a M at the beginning of every sequence?

8) Looking at the figures in the supplement e.g. Fig 30 it seems like they had quantitative activity values. It would have been nice to discuss if there was any correlation between scores and activity for ranking purposes. Was this not included because of variance in the assay?

Joel Beazer (Profluent) and James Fraser (UCSF)

On 2016-11-08 16:45:38, user Kasper Hansen wrote:

Skimming this, I am surprised there is no discussion and comparison to Finucane (2015) Nature Genetics. From my understanding Finucane et al basically solves the same problem, accounting for the same factors as GARFIELD attempts to account for.

On 2020-06-03 16:25:39, user tomas.blisset wrote:

Hi, there is a mistake in equation (11), The steady-state decay time:

t_eff = Tm*t_a / (Tm + <h>)

instead the reported equation is:

t_eff = Tm*t_a / (Tm + t_a) ( which is also incorrect dimension-wise)

Thanks for the paper, very nicely written and meaningful.

On 2017-03-30 13:13:47, user Jamie Timmons wrote:

Hi Benjamin

Thanks for your response to the discussion on the incorrect assessment of our work by Jacob and Speed.

I wondered if you could expand on your posting and update it with the essential information?

Jacob original stated that he ranked our training data set for differential expression by age and sampled "randomly" from the top 25%. No code was presented and the R code itself doesn't need to reflect the input data matrix or indeed be what was actually used.

Jacob and Speed have refused to answer any questions or share code for the past year.

We did carry out 10,000 random samples from the U133+2 with and without our 'model probe sets' and beyond any doubt their claims are false for tissue age classification. No random 150 remotely matches our signature.

The remaining analysis by Jacob is scientifically invalid as it 1) combines clinical groups that can't be combined and 2) it relies on 'other' gene lists out with any into all model. Anyone can fit a unique list to a given data set. No one has taken 1 model and made it work nicely across 7 independent data sets....hence our paper.

I'd like to learn more about the route you took to establish your opinions on Disqus and if you can share any materials with my team?

Thanks<br /> Jamie

Professor James A Timmons

On 2020-12-20 02:54:01, user Jie Wang wrote:

This paper has been published in Science https://science.sciencemag....

On 2013-11-18 23:54:52, user Matt MacManes wrote:

Thanks Kristian for the comments. I can add P30. I didn't as it seems this is not a commonly used threshold.

Also, I could start y-axis at 0- I just worry that doing this makes the plots, especially the differences between the different sized datasets more obscure.

On 2018-05-18 12:05:15, user makytou wrote:

Here is actually a demonstration of that in the aDNA data. Shallow sequencing to profile microbiome: https://academic.oup.com/gi...

On 2016-03-10 18:25:20, user Peter Rogan wrote:

This paper has been accepted for publication in BMC Medical Genomics.

On 2021-04-20 15:22:21, user Alex Sobko, PhD wrote:

Dr. Alex Sobko, PhD. Fascinating article and innovative approach to targeting transcription factors! Congratulations! The only criticism that I have - ERG binding moiety used to design O’PROTAC – ACCGGAAAT within a 19-mer double-stranded oligonucleotide containing the sequence of ACGGACCGGAAATCCGGTT. This sequence represents Class I of ETS proteins, containing more than half of the ETS family (PEA3, TCF, ETS, ERF, and ERG subfamilies), displays a consensus sequence identical to the most common in vitro–derived sequence (ACCGGAAGT). <br /> Reference: Hollenhorst PC, McIntosh LP, Graves BJ. Genomic and biochemical insights into the specificity of ETS transcription factors. Annu Rev Biochem. 2011;80:437-471. doi:10.1146/annurev.biochem.79.081507.103945

This brings to mind a question of redundant and specific occupancy by ETS factors, including ERG. Is it possible that target sequence binds other TFs, in addition to ERG? If so, it is less specific than one would wish!

On 2021-11-17 22:50:42, user Mattia Deluigi wrote:

Congratulations on this major breakthrough. The new cryo-EM structures of inactive-state GPCRs are a great advancement and certainly overcome several inherent limitations of crystal structures, which so far have been the only way to solve these receptor conformations. However, we would like to point out two issues with the current version of the manuscript:

1) To compare the cryo-EM approach with crystallography, the new cryo-EM structure of the hNTSR1:SR48692 complex is compared with a crystal structure of rNTSR1 bound to the same ligand, which has recently been reported by our lab (ref. 16; DOI: 10.1126/sciadv.abe5504; PDB ID: 6ZIN). However, we have to note that an essential part of our work was not considered in this comparison of cryo-EM and crystallography, resulting in (i) a potentially misleading description of some differences and (ii) an unnecessary downplay of the crystallographic approach.

As mentioned by the authors, most crystal structures of GPCRs require prior protein stabilization. This has also been the case in our study, which resulted in a first structure of the rNTSR1:SR48692 complex (PDB ID: 6ZIN) using the NTSR1-H4<br /> mutant. However, we realized that four stabilizing mutations were likely to affect some of the receptor structural features. Thus, in the same study, we reverted those four mutations to the wild-type residues, giving rise to NTSR1-H4bm and a second structure of the rNTSR1:SR48692 complex (PDB ID: 6Z4S). The structure of this back-mutant represents a more native-like rNTSR1:SR48692<br /> complex, obtained already prior to the cryo-EM structure. Consequently, we believe that a fair comparison of the cryo-EM structure with a crystal structure requires the consideration of the back-mutant in the first place, which is not the case in the current manuscript. Comparison with the back-mutant would allow, e.g., a better discrimination of which differences naturally occur between human and rat NTSR1, which is crucial for drug design.

Thus, some structural aspects detailed in the current manuscript need to be reconsidered:

• The difference in the position of TM1. As stated in our study (ref. 16; DOI: 10.1126/sciadv.abe5504), we suspected that the position of TM1 was affected by crystal packing forces in the structure of NTSR1-H4. However, this is not the case in the back-mutated construct (NTSR1-H4bm), as has been nicely confirmed by the cryo-EM structure. The conformation of ECL2 and the intracellular half of TM7 are also more native-like in the back-mutant structure (the difference at Y7.53 between cryo-EM and crystal structure discussed by the authors is nonetheless present, and we agree that the DARPin fusion can influence the NPxxY region).

• The differences in the residues beneath the ligand’s carboxylate group (e.g., 6.51, 6.54, 6.55, 7.42). We reverted the mutations at positions 2.61, 3.33, and 7.42 to their wild-type counterparts explicitly to provide a more native environment in these regions. Accordingly, the differences between the back-mutant structure and the cryo-EM structure are smaller or absent. It should also be considered that the cryo-EM and crystal structures could have captured partially different inactive receptor conformations.

• In the structure of the back-mutant, we were able to model ECL3 and the sidechains of F344(7.28), Y347(7.31), and W339(ECL3) (although the electron density for the latter was weak). Nonetheless, as pointed out in the preprint, some differences in the sequence between human and rat NTSR1 probably induce a slightly different conformation of the extracellular tip of TM7 and ECL3. The description of these differences is of great relevance to drug design.

Crucially, the ligand-binding mode is nearly identical in the cryo-EM and crystal structures underlining the validity of both approaches. In addition, the observation that the inverse agonist SR48692 is accommodated in a substantially wider binding site compared to the agonist-bound structures, as pointed out in our study, has now been nicely confirmed. It is correct that the gain of knowledge from the crystal structures of our engineered NTSR1-DARPin fusion is mostly limited to the extracellular receptor portion — as explicitly stated in our study — and that the cryo-EM structure now overcomes this limitation (e.g., by describing the Na+ pocket) and confirms the validity of the ligand-binding site.

2) The second problem is related to the comparison between the density of the hNTSR1:SR48692 cryo-EM structure and the electron density of the rNTSR1:SR48692 crystal structure. While the quality of the density in the cryo-EM structure certainly allows<br /> one to overcome the limitations of the crystal structure (e.g., modeling of ECL1, Na+ and H2O), we believe that the current<br /> comparison is not entirely fair.

If the crystallographic 2Fo–Fc map for the ligand (Fig. 2b) is contoured at a typical sigma=1.0, it also clearly features the chlorine atom of the chloroquinoline ring of SR48692. This is also true for the structure of the back-mutant mentioned above (PDB ID: 6Z4S), see fig. S5D and fig. S11A in ref. 16 (DOI: 10.1126/sciadv.abe5504). Thus, we believe that a fair comparison must include the 2Fo–Fc map contoured at sigma=1.0 and not only at sigma=1.25. In the end, it should be acknowledged that the electron density maps allowed unambiguous modeling of the ligand, and indeed the ligand-binding mode is nearly identical in the cryo-EM and crystal structures. To reiterate point 1 above, a comparison with the back-mutant in the first place makes more sense, and it does not reduce the impact of the cryo-EM structure (although the differences are vanishingly small, the ligand also adopts a more native binding mode in the back-mutant). Compared to the non-backmutated structure, the resolution of the back-mutant is very similar (2.71 Å), and the quality of the electron density allowed unambiguous modeling of most key residues (see fig. S12, A–C, in ref. 16 (DOI: 10.1126/sciadv.abe5504)).

Minor suggestions:

• In the legend of Fig. 2d, “ECL2” should be corrected to “ECL3”.

• In the legend of Fig. 2e, “rNSTR-H4” should be corrected with “rNTSR1-H4”. Note that in the back-mutant (PDB ID: 6Z4S), the F7.42V mutation has been reverted to the wild-type Phe residue.

• “Fig. 2c” should be corrected with “Fig. 2d” at the end of the following sentence: “First, the remodeling of the TM7-ECL3 region allows W334 in ECL3 to be resolved in the hNTSR1 structure loosely capping the top of the hydrophobic chloro-naphthyl and dimethoxy-phenyl moieties of SR48692 (Fig. 2c).”

• In Extended Data Fig. 3, the legend of panel a actually describes panel c. The legend of panel b describes panel d. The legend of panel c describes panel b. The legend of panel d describes panel e. In the name of the crystallographic construct, a hyphen is missing between “NTSR1” and “H4”.

• In both the main text and Supplement, “NTSR” is sometimes written instead of “NTSR1”.

Best regards,

Mattia Deluigi, Christoph Klenk, and Andreas Plückthun

On 2023-06-09 18:05:22, user Stacy Clark wrote:

The authors state that 'disease resistant' trees have been developed through hybridization and genetic modification'. This is inaccurate and misleading. The hybrids are not yet fully blight resistant, according to TACF's own data and other publications. The GMO chestnut has been shown to be resistant in very short time periods and in very restricted environments. They do not exhibit durable resistance. I suggest they change the wording accordingly to better reflect the reality of the current situation or cite papers that show durable resistance.

On 2014-03-29 16:34:11, user Russell Dinnage wrote:

A typo in the Abstract, last sentence: 'improves eplicability' I assume should be 'improves replicability'. This is a great idea, thanks for posting as a preprint so we can start using it sooner (if it is as good as it sounds). Now to read the paper and find out!

On 2021-06-24 09:39:28, user Max Telford wrote:

Hope ok to point out that (I think) the first use of the method of aligning codons to amino acids was in our paper https://doi.org/10.1073/pna....

On 2025-10-09 07:53:55, user Prof. T. K. Wood wrote:

Yes, since serine recombinases are themselves an anti-phage system as disclosed earlier this year. Please cite https://www.biorxiv.org/content/10.1101/2025.03.28.646004v1 and the final version to be on-line in the next few days.

On 2021-03-16 19:16:38, user Anandi Krishnan wrote:

Posting here a unique context to this work that might be helpful for some:<br /> this preprint is the first and major culmination of a unique research re-entry award to A.K. by the NIH (specifically, the National Center for Advancing Translational Sciences, NCATS but also available from other institutes). The research re-entry awards are designed for those experiencing life-related interruptions to their careers – and for me, this work would not have been possible without this unique NIH mechanism (that has now facilitated a subsequent NIH/NHGRI career development award). Please visit this NIH link for details if interested in these diversity/re-entry supplement awards: https://ncats.nih.gov/ctsa/....<br /> More of the backstory here: https://twitter.com/anandi_...

On 2022-04-25 13:40:05, user Santiago Justo Arevalo wrote:

The authors declare that this manuscript is now published in Scientific reports: https://doi.org/10.1038/s41...

Please, when approppriate cite the published version of this article: https://doi.org/10.1038/s41...

On 2025-10-05 11:39:34, user Yufeng Wu wrote:

This paper has been published advanced online at Genome Research on September 3, 2025. doi: 10.1101/gr.280542.125 . URL: https://genome.cshlp.org/content/early/2025/09/03/gr.280542.125.abstract

On 2021-07-09 14:09:26, user Vladimir Chubanov wrote:

The molecular appearance of native TRPM7 channel complexes identified by high-resolution proteomics. doi: https://doi.org/10.1101/202...

On 2023-05-15 09:26:21, user Martin wrote:

Dear S Denagamage, M Morton and A Nandy,

It is a very interesting study you report here.<br /> I noted that you use a delayed saccade task such that the probe was presented either 450 to 950 ms before the saccade (with an average saccade latency of 150 ms).<br /> Using a design adapted to human non invasive behavioral measures we found (Szinte et al, eLife, 2018) two spotlights of increased performance with a similar timing (that might correspond to the CF and FF positions and the forward hypothesis) but no real FF when the probe was presented in the latest instant preceding the saccade execution (effect that might correspond to the convergence hypothesis). We speculated that remapping mechanism are orchestrated by spatial attention (Colby et al 1998) and thus takes time to develop. In this interpretation, convergent remapping reflect the response to the saccade target which is presented earlier in time. <br /> Did you analyzed your nice multi-layer results as a function of the probe onset relative to the saccade onset ? I would predict a less clear forward remapping for the shortest durations (consistent with Zirnsak et al study who had very short durations).

Best regards,<br /> Martin Szinte

On 2018-02-15 14:43:17, user Chris Balakrishnan wrote:

And my first preprint comment! And it is the worst type, request for self-citation. But I really i would just suggest resolving whether you mean redundancy or degeneracy. We talk about the distinction a bit here: http://rspb.royalsocietypub... If you disagree with distinction of course let use know! This is a very interesting study

On 2025-08-01 20:29:38, user Roel Nusse wrote:

Rebuttal to Claims Regarding Reproducibility of Our WntD and Edin Studies<br /> Michael D. Gordon1, Janelle S. Ayres2, David S Schneider3 and Roel Nusse3<br /> 1. University of British Columbia<br /> 2. The Salk Institute<br /> 3. Stanford University

We write in response to the preprint by Lemaitre and colleagues that challenges the reproducibility of our published findings on wntD (Nature, 2006) and Edin (PLOS Pathogens, 2008). While we welcome efforts to independently validate and extend prior findings, we believe that the conclusions drawn by the authors are not supported by rigorous replication and, in several key respects, reflect significant experimental and interpretive flaws.

Mischaracterization of the wntD Mutant Allele

The preprint by Lemaitre and colleagues centers on the observation that our original wntD allele reproduces the published phenotypes, but a second allele does not. The authors infer, based on quantitative PCR in early embryos, that our stock must have lost the mutation and that the observed effects are due to background variation. However:

• The qPCR assay used by Lemaitre and colleagues is technically invalid for assessing the integrity of our allele. As clearly described in our original work, our wntD mutant was generated by insertion of a w+ cassette into the wntD coding region—leaving the 5´ portion of the gene intact. The primers used by the authors target this unaffected region and are therefore expected to detect RNA transcripts, even though the resulting mRNA is non-functional. This is a fundamental flaw that undermines their central argument.<br /> • The authors did not attempt genomic PCR or sequencing to verify the presence or structure of the mutant allele in our stock—an essential step before invoking reversion or mislabeling.<br /> • The phenotypes associated with the original wntD allele were in fact replicated in their hands, contradicting their own assertion of irreproducibility. The failure of a second allele, generated in a different background and evaluated under different conditions, does not constitute a failure to reproduce but rather a failure to validate by independent means—a distinction that matters.

Strain Background and Controls

Lemaitre and colleagues raise concerns about genetic background effects while applying inconsistent standards in their own work. Our original wntD experiments were conducted in a y-w- background, and controls were matched accordingly. Their second allele appears to be in a wild-type background, yet their comparison is made to both y-w- and w1118 lines. It is well established that immune phenotypes in Drosophila can be highly sensitive to background variation. As such, a difference in phenotypes between two alleles in different backgrounds is neither surprising nor indicative of poor reproducibility.

Misinterpretation of wntD Expression Patterns<br /> The preprint by Lemaitre and colleagues suggests that wntD is only expressed at embryonic stages, questioning its relevance to immunity. However, the Fly Cell Atlas reveals specific clusters of cells expressing wntD in the whole body, gut, and Malpighian tubule samples. Moreover, additional studies have demonstrated that wntD is in fact inducible in adult flies upon infection with either L. monocytogenes or S. pneumoniae, supporting its role in immune modulation (Chambers et al., 2012, https://doi.org/10.1371/journal.ppat.1002970 ). The claim by Lemaitre and colleagues that the field has not followed up is inaccurate.

Edin: Distinction Between RNAi Knockdown and Knockout

In the case of Edin, Lemaitre and colleagues observe that a knockout mutant lacks the infection sensitivity we previously reported using fat-body-specific RNAi. They suggest off-target effects as a possible explanation. However:<br /> • We demonstrated the phenotype using two independent RNAi lines, reducing the likelihood of off-target artifacts.<br /> • The tissue specificity and temporal dynamics of RNAi versus germline knockout are fundamentally different. Loss of a gene during development may engage compensatory pathways that are not triggered in an acute knockdown context.<br /> • The conditions of microbial challenge differ between studies and were not standardized, further complicating direct comparison.

Edin Overexpression: Dose Matters

Lemaitre and colleagues report that Edin overexpression is not deleterious in their system, but this is entirely consistent with our findings. In our original study, mild overexpression had no phenotype; only very strong overexpression (>600-fold) triggered a measurable deleterious effect. The line used in their study achieves ~50-fold overexpression and would not be expected to phenocopy our results.

Conclusion<br /> While we support efforts to critically assess past findings, we reject the characterization of our studies as “non-reproducible” based on the experiments presented by Lemaitre and colleagues. The authors did not repeat our experiments as described, used different alleles, different controls, and in some cases, flawed assays. Differences in outcome under these circumstances are part of the normal course of scientific investigation and should not be conflated with a failure of reproducibility.<br /> We encourage future studies that further explore wntD and Edin biology, using rigorously matched conditions and appropriate validation techniques. Scientific progress depends on careful experimentation, not just reanalysis.

Sincerely,

Michael D. Gordon, email: mikedgordon@gmail.com<br /> Janelle S. Ayres, email: jayres@salk.edu<br /> David S Schneider, email: dschneid@stanford.edu<br /> Roel Nusse, email: rnusse@stanford.edu

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

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

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

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

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

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

On 2016-03-10 00:09:21, user Simon Martin wrote:

Thanks Peter. I think you're absolutely right. Unfortunately we don't really have better options to estimates error rates at this stage. Trios might offer an improvement in the near future. Many of the analyses in this study are based on four-fold degenerate third codon positions, and indels should not be too much of a problem in coding regions, so I think for this study the estimates might not be too misleading.

On 2022-07-15 04:40:36, user Björn Brücher wrote:

Insightful study and analysis. Thank you.

Question:

Are 3-to-8-months old mice comparable to human findings?

The mice ages compare to humans of 20-to40 years of age, but AD and chroinic neuronflamation are found in in older humans (more comparable to mice of 15-to-30 months of age).

Please see representative age ranges adapted from:

Flurkey K, Currer JM, Harrison DE (2007) The Mouse in Aging Research, In The Mouse in Biomedical Research 2nd Edition, Fox JG, et al. (eds), American College Laboratory Animal Medicine (Elsevier), Burlington, MA, 637–672.

On 2020-02-13 02:40:39, user Victor Corces wrote:

melanogaster, with lower case

On 2023-01-04 17:25:33, user S Kalkunte wrote:

Excellent piece of work!! Congratulation to the entire team!! Desiccation triggers frugal use of biochemical resources. Are the physical attributes including size affected by desiccation?

On 2020-02-15 03:40:50, user Kim wrote:

Note: Printing error in Fig4.<br /> ...amino acids 473-477 should read 433-437.....

On 2020-05-13 10:37:11, user Zaniar Ghazizadeh wrote:

Correction: troponin T cut-offs: normal (<0.01 ng/mL) and abnormal (>=0.01). The corrected version will be posted soon.

On 2021-09-26 00:59:36, user Raghu Parthasarathy wrote:

[I wrote this for the earlier version; it applies to this version also.] Interesting paper! If you're going to claim a power law (such as an inverse square), however, it would be good to see the data plotted on a log-log scale, so that the scaling exponent is obvious, and also to see a robust fitting of the exponent value. Also, I don't see that the datapoints are available to the reader -- is there a supplemental data link missing? Thanks!

On 2016-05-27 22:56:18, user Albert Erives wrote:

Note from corresponding author: We would like to cite all publications showing evidence for a direct Notch/Su(H) enhancer. If anyone knows of such a pub that is not already cited in the pre-print please contact me.

On 2016-06-29 22:31:30, user Thorseng Liew wrote:

Latest Peer-reviewed version published at Tropical Conservation Science:<br /> http://tropicalconservation...

On 2021-04-14 01:10:41, user stephens999 wrote:

A review of Chris Wallace's preprint "A more accurate method for colocalisation analysis allowing for multiple causal variants", by Matthew Stephens

Summary

This paper introduces an extension of the "coloc" method for colocalization<br /> to deal with multiple causal variants in a region. This extension exploits a<br /> recently-introduced method for fine mapping (SuSiE). The extension is<br /> attractive in its simplicity, and simulations show it to perform better than some<br /> alternative approaches. The paper also suggests a way to speed up computations<br /> by pre-filtering out "non-significant" SNPs.

The key idea of combining SuSiE and coloc is nice, and I think that with<br /> some improvements to the presentation will make a nice publishable contribution.

The idea of speeding up SuSiE by pre-filtering SNPs is also attractive from<br /> a practical point of view, but it has some potential downsides that I feel<br /> are not sufficiently emphasized and explored (even though the manuscript does end<br /> with a statement that trimming might be not beneficial in general final mapping).<br /> Specifically trimming out non-significant SNPs<br /> could increase the potential for false positive identifications,<br /> and indeed such a result has been previously reported in<br /> https://www.biorxiv.org/con...<br /> (their Figure S7). It's not clear to me how, if at all, this is reflected in the results<br /> shown here. Maybe it is simply the case that, as the paper suggests in the discussion,<br /> that "Coloc benefits from comparing posterior probabilities across... two traits".<br /> But the overall way that the manuscript deals with false positive (or indeed<br /> false negative) identifications<br /> is not clear. (Maybe methods are applied with some<br /> knowledge of the true number of causal effects? It isn't clear to me.)<br /> Since there are also other potential ways to speed up computation (see comments below)<br /> I am not really convinced that the pre-filtering approach is really the way to go,<br /> and would like to see at least a stronger assessment of the potential downsides.

Main Comments

1. The presentation of the method requires more details, including more precise<br /> equations showing how quantities computed by SuSiE are used/combined. For<br /> example you could introduce $\alpha_{lj}$ for the matrix of posterior probabilities output by susie<br /> and then give explicit expressions for the Bayes Factors being computed<br /> ($BF_{lj}$) in terms of $\alpha_{lj}$. I'm not sure what $P_0$ is (is it something output by SuSiE?)<br /> Is $\pi=1/p$ where p is the number of SNPs in the region, or something else? How<br /> do you set the maximum number of effects in SuSiE (L in the SuSiE paper)? Do you get SuSiE to<br /> estimate the number of effects by estimating the prior variance, or do fix the prior variance?<br /> If $L_g$ is the number of effects identified by SuSiE in the GWAS and $L_e$ the<br /> number identified by SuSiE in the eQTL study, do you end up running coloc $L_g * L_e$ times?<br /> (as suggested by "for every pair of regressions across traits" on p3).<br /> How do you combine/summarise the results from all these different runs of coloc?

2. Presentation of colocalization results also needs more details. Can you say explicitly<br /> what is an "AA" or "BB" comparison and an "AB-like signal"? From the description on p3 I<br /> thought the simulations would include settings where there were 2 causal variants in each trait,<br /> but no sharing. But Fig 3 seems to suggest<br /> only a small portion of potential configurations of up to 2 signals in each trait are actually<br /> included - is that right? (why?) And in Fig 3, what happens if SuSiE finds a signal in one trait<br /> and not in the other - what comparison do you make? (Or do you force SuSiE to find the right<br /> number of effects in each trait by fixing L to the true value? If so, is that cheating?)<br /> Is the smaller height of the AA bar for susie_0 compared with other methods -- and indeed<br /> the slightly smaller height of all bars -- something to be<br /> concerned about? Are all methods equally applicable if (as is always the case) you do not know<br /> the true number of causal signals in each trait?

3. Figure 1 compares only the PIPs at causal variants. Since in practice we don't know the<br /> causal variants, one should also care about PIPs at non-causal variants. Is there a tendency<br /> for SuSiE to inflate PIPs at non-causal variants when trimming?

4. It seems there are many potential ways to improve computation than<br /> filtering out non-significant SNPs, and many of them may ultimately be better choices<br /> (although filtering is obviously very simple to implement!) I don't think the discussion<br /> in the paper really adequately reflects the options available or the many<br /> issues involved.

Although I did not see it explicitly said anywhere, I believe the<br /> paper is using the susie_rss function for applying SuSiE to summary data.<br /> The details of this function are not included in the original SuSiE publication, but I believe<br /> that at the time this work was done susie_rss<br /> worked by performing an initial eigendecomposition of the reference LD matrix R, which<br /> makes it possible to convert the summary data into "transformed data" to which<br /> regular SuSiE can be applied. This approach is appealing from a software engineering<br /> point of view, but not necessarily the most efficient, computationally. The eigendecomposition<br /> of R is quite expensive, being O(p^3) where p is the number of SNPs.<br /> The subsequent application of SuSiE<br /> to the transformed data is O(p^2) per iteration.<br /> Thus if p is sufficiently large the eigendecomposition step will likely<br /> dominate the susie_rss computation (and Figure 2 does indeed suggest computation maybe<br /> increase something like p^3?)

One way to reduce computational complexity would therefore be to avoid the eigendecomposition<br /> step, and we are currently actively exploring these in our development of susie_rss. <br /> However, note that computing R itself is already<br /> an O(np^2) operation, where $n$ is the number of samples in the reference sample used to compute R. So<br /> if n is big then this computation (which is basically considered free<br /> in this paper since R is precomputed) could be the dominant computational cost. Alternatively<br /> if n<<p, then="" one="" should="" perhaps="" entirely="" avoid="" forming="" r="" --="" in="" the="" case="" n<<p="" an="" eigendecomposition="" of="" r="" can="" be="" obtained="" by="" doing="" an="" svd="" of="" the="" reference="" genotypes="" (o(n^2p))="" which="" will="" cheaper="" than="" forming="" r="" (o(np^2))="" when="" n<<p.="" in="" the="" future="" it="" seems="" quite="" likely="" that="" pre-computed="" r="" and="" eigen(r)="" could="" be="" made="" available="" for="" some="" large="" panels,="" avoiding="" the="" need="" for="" each="" user="" to="" compute="" them.="" once="" these="" pre-computations="" are="" done="" there="" may="" no="" longer="" be="" any="" need="" to="" filter="" snps.="" other="" comments="" details="" -="" p3="" although="" the="" number="" of="" potential="" models="" increases="" exponentially,="" susie="" computation="" does="" not="" increase="" exponentially.="" -="" p4:="" "we="" labelled="" each="" comparisons="" considered...."="" i="" did="" not="" understand="" this="" sentence.="" -="" p4:="" "...="" having="" strongest="" posterior="" support="" for="" h\_4"="" -="" this="" should="" be="" h\_3?="" -="" p8:="" "="" this="" does="" apply="" to="" single="" trait"="" -="" missing="" \*not\*?="" -="" in="" the="" second="" row-set="" of="" figure="" 3,="" is="" the="" figure="" on="" the="" lhs="" wrong?="" (the="" methods="" suggest="" colocalization="" but="" the="" figure="" shows="" no="" shared="" variant...)="" -="" on="" p7="" the="" r2="" threshold="" is="" 0.8="" but="" on="" p4="" it="" is="" 0.5.="" are="" there="" referring="" to="" different="" thresholds?="">

On 2017-05-17 14:29:35, user Mo Huang wrote:

Open-source software for SAVER can be downloaded from https://github.com/mohuangx...

On 2025-02-05 21:52:46, user Evan Saitta wrote:

This preprint has now been formally published:<br /> https://www.sciencedirect.com/science/article/pii/S0016703723005781

On 2018-03-15 20:50:41, user Nelson Spruston wrote:

The revised version is the same as the original. We just corrected some html formatting in the author info. - Nelson

On 2019-08-01 13:06:12, user Arne Jorgensen wrote:

Is the influx from southern Scandinavia and Germany caused by the plague which exterminated most of the population in the 400 and 500 centuries, and the fimbul winter, caused by volcanic activity?

On 2017-04-21 12:28:33, user Nick Schurch wrote:

Figure 5(b) the x-axis should be just Read Length(bp). #pickyIknowbut

On 2017-08-04 22:15:00, user Ailong Ke wrote:

Page 10, line 7 from the bottom: "As Csn2 binds to the same substrate as Ku, it could interfere with NHEJ repair [Deltcheva et al, Nature 471, 602-7 (2011)]". I could not find where in this reference "Ku", "NHEJ", or "Csn2 binds to the same substrate as Ku" were mentioned.

On 2020-02-05 11:13:37, user Pei-Hui Wang wrote:

Prof. Wang Pei-Hui Lab in Shandong University, China, all orf clones of 2019-nCoV / SARS in pcDNA6B-FLAG can be 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