On 2021-05-06 03:32:43, user Parashar Dhapola wrote:

Scarf is available on Github<br /> Find documentation here: scarf.readthedocs.io

On 2021-05-05 19:05:07, user Christopher Schultz wrote:

Ah, it's good to know there is another preventative agent out there, however, NAPROXEN remains the premier readily available preventative. https://www.semanticscholar...

On 2021-05-05 17:51:51, user Qilin Shangguan wrote:

Hi, with the ORF direction in Figure 4a, are the noted coding strand and template strand reversed?

On 2021-05-05 16:10:41, user Bruce Kohorn wrote:

Interesting new data on the adhesion ELMO family in Arabidopsis, will add nicely to the work recently published on the family https://doi.org/10.1242/dev...

On 2021-05-05 11:07:02, user Milka Kostić, PhD wrote:

Dear authors, <br /> Thank you for sharing this interesting preprint with the community. Below are some more detailed comments that ay have others appreciate your work better. I congratulate you on an excellent piece of work. <br /> Kind regards, <br /> Milka

COMMENTS ON THE PREPRINT BY Dölle, Adhikari et al. <br /> Targeted protein degradation is a very active field at the moment. Many efforts in this area are focusing on transforming known ligands (binders, inhibitors) of proteins with a clear disease relevance into bifunctional (PROTAC-based) degrader molecules. Unlike the traditional antagonist/inhibitor based compounds in preclinical and clinical use that diminish (inhibit) activity of the target, these degrader molecules induce selective degradation of the target. Thus, they remove the target from the proteome. This type of pharmacological activity could be a real benefit when the target in question plays significant scaffolding roles, by engaging multiple binding partners using different regions and binding sites. In such a case, inhibiting individual protein-protein interactions would be highly impractical. However, if the target is degraded, all these PPIs would disappear together with the target!

Dölle, Adhikari et al. select one such target - WDR5, a protein that performs different scaffolding roles (i.e. binds different partners) in the context of epigenetic regulation. Because of this, WDR5 has been implicated as a target for drug development and couple of compounds that inhibit WDR5 mediated PPIs have been described. These compounds served here as a starting point for WDR5 selective degrader development. The authors used existing structures of WDR5 bound to the PPI inhibitors to identify surface exposed areas of the molecules that could be modified for degrader molecule development. In brief, each PROTAC (bifunctional degrader) includes a ligand for the target and a ligand that recruits an E3 ubiquitin ligase, connected via a linker. The linker is known to have an impact on the performance of PROTACs and the authors use three chemically distinct types of linkers (PEG based, aliphatic and aromatic). The nature of the E3 ligase is also a major factor that affects degraders' activity, and the authors start by incorporating ligands for cereblon (CRBN), VHL and MDM2. Altogether, they generate number of PROTAC-based degraders featuring different linkers and different ligases.

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

Overall, the work is of high quality and includes appropriate steps for degrader validation. This gives high confidence that WDR5 degraders described in this work are useful as probes for WDR5 biology. For example, what happens to histone methylation once WDR5 is removed? Does removal of WDR5 lead to destabilization (or stabilization) of some of its binding partners (proteomics results suggest that this may not be the case, but would be interesting to dig deeper into this question)? What happens to transcription? What effect does this have on MYC activity (MYC family is known to engage with WDR5)? I am sure the authors and the community have these and many other questions in mind, and I look forward to seeing what new biology they and others can discover with this new generation of tool compounds in hand.

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

Dear authors,

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

Kind regards,

Milka

COMMENTS ON THE PREPRINT BY Dölle, Adhikari et al.

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

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

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

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

On 2021-05-05 08:03:42, user zbfz wrote:

Thanks for your pre-print version. However, I have one major concern regarding to your methodology: 4OHT treatment from 30hpf to 50hpf VS 54hpf to 74hpf in Figure2C, to distinguish (Progenitors+ ) VS (HSCs).

As is known that the endothelial to hematopoietic transition (EHT) process initiates from ~30hpf, it is possible that hemogenic endothelium(HE) derived HSCs differentiate to mutiple progenitors earlier than 54hpf. In this case, the 54hpf to 74hpf treatment may label HSCs plus the later HSC-derived lymphoid progenitors or myeloid progenitors. These progenitors will contribute to the late emergence of lymphopoiesis and myeloipoiesis reported in Figure3/4/5. Could the authour explain this possibility caused by HSCs-derived early progenitors or HSCs-dervied late progenitors?

Futhermore, to date, in zebrafish, the lymphopoiesis is reported restricted to definitive hematopoiesis arising from dorsal aorta, but not primitive hematopoiesis which only contribute to erythopoiesis and myelopoiesis. How do the LyP population appear at 1dpf shown in Figure1C and 2A? Are this population possibly represent the LyP-potential hemogenic endothelium?

Bests,<br /> zbfz

On 2021-05-05 03:51:11, user Mark Kittisopikul wrote:

What was the local file system used to compare the file formats? I've noticed significant differences in the performance of I/O with the formats between NTFS on Windows and ext4 on Linux especially when there are many files involved.

On 2021-05-05 03:35:16, user Jamie Cate wrote:

Looks like this hasn't been linked to the peer-reviewed paper yet. https://pubmed.ncbi.nlm.nih...

On 2021-05-05 02:19:58, user philiptzou wrote:

Table 6, what does the "GISAID" mean under the mAb column of S477N?

On 2021-05-05 01:53:26, user philiptzou wrote:

Questions:

• Table 2, should the IC90 of WA isolate be 0.017?
• Table 3B, what's the definition of B.1.351.v1, P.1.v1, P.1.v2, B.1.526.v2?
• Table 5: should the pseudovirus IC50 of Wuhan strain be 0.003? Because according to K444Q and V445A, the closest control IC50 is 0.0028 (0.25/90 ≈ 0.227/82 ≈ 0.0028).
• Table 6: The pseudovirus IC50 of Wuhan strain is inconsistence to the fold of K444Q and V445A. 0.25/20 is 0.0125 which is far from 0.003.

On 2021-05-04 17:48:43, user Science Girl wrote:

Very nice and significant work. I loved the creative experiments and the cool results. The results are clearly a significant leap for plant biology, especially the unexpected findings.

On 2021-04-29 16:28:33, user WebbsWonder wrote:

Really nice! A suggestion for a change in title. "UBP12 and UB13 stabilize COP1 to promote CRY2 degredation". Current title suggests a complete revision of Ub model, but your conclusion makes clear that is not what you are suggesting. If I have understood it all properly!

On 2021-05-04 17:27:31, user Ilyas Singec wrote:

This paper has been published in Nature Methods: https://www.nature.com/arti...

On 2021-05-04 16:32:32, user Lucas Graybuck wrote:

A peer reviewed and updated version of this article is available with Open Access in eLife: https://elifesciences.org/a...

On 2021-05-04 16:19:56, user jd6876383 wrote:

This is a seemingly interesting work on a new MS1 tracking quantification method, IceR. As proteomics researchers who are longing for excellent quantitative data quality, we were initially excited to see this work. However, after careful examination of the data, we are sorry to say that we have some severe concerns.

Firstly, several MS1-based quantification workflows were compared in this work. As industrial-level users of MaxQuant, IonStar, and other methods for years, we are quite familiar with the performances and pros/cons of each method. We found some apparent discrepancies in the paper: <br /> a) In the paper, it was found that the IonStar has a median CV% of around 10% using the data published in the original IonStar paper, compared to 5% in the same publication (we also were able to verify the 5% CV using the IonStar dataset following the procedure). It appears that the authors used the wrong table as the results for IonStar: the table used seems to be https://www.pnas.org/highwi..., which is the non-normalized data set, shown as a “bad example” by the authors of the IonStar paper. This explained the suboptimal performances. The correct table to use would be https://www.pnas.org/highwi....<br /> b) We like MaxQuant as it is a one-stop solution. However, it is very surprising that MQ showed almost 0% false-positive discovery of changed proteins in this IceR manuscript. We have used MQ with the same parameters and same raw dataset and it resulted in a substantial false-discovery rate (>7% in all cases). Could the authors describe how exactly they achieved a 0% false-positive rate in this data?

Secondly, many important technical parameters are missing, making it difficult to judge or follow the IceR method. <br /> a) For example, how did the authors calculate the TPR and FDR? What protein fold-change and statistical cutoff were used, especially when comparing different methods?<br /> b) More importantly, when comparing the performances of IceR and DIA label-free proteomics, the absolute fold-change cutoff was set to > 10%, which is similar to or lower than the median CV% of the methods. Even with adjusted statistical tests, it would be very difficult, if possible at all, to reliably quantify or determine 10% difference in protein level change due to technical variation. How was this threshold justified? <br /> c) What is the quantitative accuracy of the method? In our experience, MQ did over-estimate ratios quite badly. It is important to evaluate quantitative accuracy. Also, was there any ratio compression problem?

On 2021-05-04 15:06:24, user AAAAAAAAAA wrote:

I noticed that you did the high salt tagmentation (300mM NaCl) for PBMC mixing experiments, which I think is the "right" way to avoid the open chromatin bias but for other experiments, you did the tagmentation in 10X ATAC buffer (10mM NaCl). Is there a particular reason for this? I thought the low salt would have serious ATAC signals, which is demonstrated in the original CUT&Tag paper.....

On 2021-05-04 14:57:26, user GISAID EpiCoV curation team wrote:

The authors of this preprint allege that nucleotide sequences of pandemic coronavirus in GISAID are rejected due to automated quality controls when detecting the deletion of 35 nucleotides in ORF8. They insist on improvement of the submission system to allow automatic release of submissions with frameshifts such as the one they described, although they also mentioned the difficulties of genome consensus generation after sequencing, depending on the technology used.

As a group of many data curators for GISAID, we can attest that the GISAID-EpiCoV database does not reject any sequence that has a change in the open reading frame of the viral genes, including frameshifts. It is public knowledge that the only two exclusion criteria for a nucleotide sequence to enter in GISAID is that it has a length less than 100 nt or that it contains more than 50% of the length of the nucleotide sequence with Ns (nucleotides not informative due to low sequencing depth of coverage). Additional criteria such as %unique coding changes are used for quality labelling.

The Curation Team of GISAID performs a test of general characteristics of the sequences including frameshift detection and detection of early stop codons in the Spike protein causing protein truncations. These are characteristics of biological interest that can also be erroneously generated during the analysis of NGS data and consensus sequences generation.

When a sequence with a frameshift or truncation in the Spike protein is detected, the given sequence is not rejected, but a confirmation from the data submitter is requested to ensure that these characteristics have not been the result of some previously undetected bioinformatic error. We also consider if the same frameshift has been previously credibly reported and which Bioinformatics pipeline has been used if the information is available. As a result, numerous submissions have been corrected for rare erroneous frameshifts, which is highly appreciated by the data submitters as it avoids the need to update submissions in the future. At the same time, when the author confirms that the feature (frameshift or spike protein truncation) is correct, then the Curation Team adds a confirmation comment in the metadata section, which can be reviewed by all users. This small comment gives a higher level of confidence during the use of the data by all GISAID users.

In short, GISAID does not automatically reject any sequences of good quality larger than 100 nt. Instead, GISAID is assisted by a large team of expert curators around the globe to help data submitters and reduce errors. Considering times as sensitive as the one we live in today, informing the field correctly and with the right words is essential to avoid misconceptions.

The GISAID EpiCoV curation team

On 2021-05-04 14:53:07, user Brian Haugen wrote:

The SPIRES database and method is described in the article below, if you’d like to cite it.

Metrics associated with NIH funding: a high-level view<br /> https://www.ncbi.nlm.nih.go...

Boyack KW, Jordan P. Metrics associated with NIH funding: a high-level view. J Am Med Inform Assoc. 2011 Jul-Aug;18(4):423-31. doi: 10.1136/amiajnl-2011-000213. Epub 2011 Apr 27. PMID: 21527408; PMCID: PMC3128410.

On 2021-05-04 12:27:25, user Kadi Liis Saar wrote:

The paper has now bee published:<br /> https://www.pnas.org/conten...

On 2021-05-04 09:58:40, user Sebastian Fiedler wrote:

The peer-reviewed version of this manuscript can be found here: https://besjournals.onlinel...

On 2021-05-04 05:32:49, user Lavinia wrote:

Could you also expand on why you don’t think these “granules” are RNaseL dependent, as they still seem to form independent of PKR. Are these granules you observe as a response to dsRNA CHX sensitive? Emetine sensitive? What about ISRIB? (Key characteristics that differentiate canonical SGs from RLBs)

On 2021-05-03 19:15:13, user Syevda Tagirova wrote:

Dear Readers, our preprint has been published and a link will be forthcoming.

Authors.

On 2021-05-03 15:35:03, user Shawn Egri wrote:

Are the structures available?

On 2021-05-03 11:29:40, user jean-claude Perez wrote:

Another Biomathematical approche of variants and particularly INDIA variants:

Perez, J. SARS-CoV2 Variants and Vaccines mRNA Spikes Fibonacci Numerical UA/CG Metastructures. Preprints 2021, 2021040034 (doi: 10.20944/preprints202104.0034.v5). Perez, J. SARS-CoV2 Variants and Vaccines mRNA Spikes Fibonacci Numerical UA/CG Metastructures. Preprints 2021, 2021040034 (doi: 10.20944/preprints202104.0034.v5).

https://www.preprints.org/m...

V2 india 28 avril

Perez, J. The INDIA Mutations and B.1.617 Variant: Is There a Global "Strategy" for Mutations and Evolution of Variants of The SARS-CoV2 Genome?. Preprints 2021, 2021040689 (doi: 10.20944/preprints202104.0689.v2).

https://www.preprints.org/m...

On 2021-05-03 11:23:55, user Santeri Rytky wrote:

Dear Reader,

Please note that this study is now published in the Journal of Anatomy (https://doi.org/10.1111/joa...

Kind regards,<br /> Santeri Rytky, corresponding author

On 2021-05-03 08:52:40, user Umberto Lupo wrote:

I wonder if the authors could further clarify some aspects of the validation setup for the experiments conducted in Section 4.1.

I understand that each set of PFAM families (in a given clan) is partitioned into five buckets, and that in turn each bucket is artificially shortened in depth. However, despite my best efforts reading and interpreting Appendix C.3 and the main body of text, I still am not sure exactly what data the independent Potts model and the NPM is fitted on during each of the five rounds of "cross-validation".

To be completely explicit, suppose we are at round i so that bucket i is being reduced. Then, is the NPM fitted on the reduced version of bucket i alone, or on the latter plus the other four buckets in their entirety? Or on something else? Similarly, what are the independent Potts models trained on exactly during each round?

If both models are fitted on the reduced version of bucket i alone, then what exactly is the validation set? Is it the rest of bucket i, i.e. the part discarded from the training set? (Otherwise, one may worry about trivial overfitting.)

Thanks in advance for your reply!

On 2021-05-02 22:26:56, user Timmy Jo Given wrote:

It is always exciting, even as a layperson, to read such research details. I am quite confident in immune memory to SARS-CoV-2, thanks to the details presented here. Please share this information with policymakers who seem to have abandoned all basic principles of human immunology during their dangerous one-size-fits-all vaccine campaign. The Covid-recovered do not need to be injected; in fact, it is contraindicated to subject them to it.

On 2021-05-02 21:38:54, user Marina Duarte wrote:

Very interesting paper, congratulations. I suggest to delete "animal" of the title because soundscape is a term related to all the sound components of a landscape. There is also a new paper about the topic: https://www.sciencedirect.c...

On 2021-05-02 16:49:00, user george mcnamara wrote:

Authors FAILED to mention AausFP1, which is much brighter, https://journals.plos.org/p...

On 2021-05-01 20:18:03, user Alex Timoshenko wrote:

This is a comprehensive study, which supports a new model of galectin/O-GlcNAc regulation in cells https://www.mdpi.com/2073-4... .

On 2021-05-01 16:57:22, user Michael Zalik wrote:

I'd like to point out a reference that is directly related to this work:<br /> https://www.biorxiv.org/con...

On 2021-05-01 15:11:25, user Farshid Memariani wrote:

Published in Scientific Reports on 27/04/2021<br /> https://doi.org/10.1038/s41...

On 2021-05-01 14:25:56, user Mackenzie Mathis wrote:

Hello! Nice paper, but I would like to point out a (simple) error. You state in the methods you used "DeepLabCut’s pre-trained human pose model (v2.1.7)" and cite us, thanks! This is great, as you use the model within our framework, but This is NOT a DeepLabCut trained model.

As we have no preprint/paper yet about the model zoo, we have descriptions on the website (modelzoo.deeplabcut.org), which states that this model is *only* DeeperCut, embedded in our software, and we please ask you cite that paper if you use the model: https://link.springer.com/c....

As such, you are comparing OpenPose, AlphaPose, and DeeperCut performance. I hope this is helpful, as it's important to get this right. I would also add that you might want to report the benchmark performance of these algorithms, for example, the rankings/performance for these three algorithms has been systematically tested here: https://paperswithcode.com/... (OpenPose is ranked 25, DeeperCut is ranked 27 as of April 2021)

On 2021-05-01 13:29:51, user DerKevinRiehl wrote:

Source code and software manual can be found here:

https://github.com/DerKevin...

On 2021-04-30 13:44:00, user NYUPeerReview wrote:

NOTE: This paper was selected for discussion and critique in “Peer Review in the Life Sciences”, a course for PhD students at the New York University School of Medicine. This course aims to build skills in the critical reading of the scientific literature, and provide formal training in the process of peer review. Following discussion as a class, students wrote this peer review, and received responses from the authors.

Summary<br /> In their recent preprint Kiani et al. report observations of a contact-dependent killing mechanism in Proteus mirabilis unrelated to the only known contact-dependent killing system in P. mirabilis, the type VI secretion system (T6SS). These observations lay the groundwork for further investigation into a yet undiscovered mechanism of contact-dependent bacterial killing.

Initially, the authors of this study observed that fetal mice bred from parents hosting either P. mirabilis or E. coli were only found to host P. mirabilis, as opposed to hosting a combination of the two bacteria, suggesting a P. mirabilis survival advantage or killing mechanism to outcompete E. coli. Attempting to recapitulate this observation in vitro, the authors co-cultured P. mirabilis and E. coli and found that E. coli demonstrated reduced viability in liquid culture and cell death on solid surfaces. This finding was extended to other Gram-negative bacteria. To investigate the nature of the survival mechanism, the authors co-cultured cells and found that cell-cell contact is required for P. mirabilis-mediated killing. Using a P. mirabilis T6SS knockout strain, the authors found that the P. mirabilis-mediated killing persisted in the absence of T6SS effector molecules, supporting the authors’ hypothesis that this killing mechanism is separate from T6SS. P. mirabilis did not kill E. coli during its exponential growth phase, only during stationary phase; however, E. coli was susceptible to this killing at any stage of its growth. Fluorescent microscopy visualized interactions between P. mirabilis and E. coli during the course of their killing and found killing did not compromise membrane integrity while halting metabolic activity. Killing required protein synthesis and was enabled by a heat-sensitive component culture supernatant. Osmotic perturbations in swarm limiting media attenuated killing efficiency, suggesting environmental factors and osmolarity are agents in this P. mirabilis killing mechanism.

General Critiques<br /> In a clear and effective manner, the authors of this study present a series of compelling observations that suggest P. mirabilis employs a contact-dependent killing system other than T6SS to compete against Gram-negative bacteria. The various growth assays reported in this study support the discovery of a new mode of interbacterial killing; however, questions remain regarding the nature and mechanism of this new mode of killing. The authors’ conclusions were based on powerful, yet broad, experiments that we believe could be enhanced by some further studies into the mechanism of P. mirabilis killing.

Upon discussion of the manuscript, a few points were brought up that we highlight below:

Figure 1: The authors may want to consider adding a control that introduces fresh LB to the E. coli and P. mirabilis co-culture to rule out reduced E. coli viability was indeed caused by P. mirabilis and not due to nutrient deprivation and starvation.

Authors’ reply: Thank you very much for your comment. We were initially concerned about this as well. We will perform an experiment specifically addressing this in the context of Figure 1 by adding LB during stationary phase of the co-culture. However, based on results in Fig. 2 and 5, we are very confident that starvation is not the reason for E. coli death: We do not see loss of viability in stationary phase P. mirabilis supernatant (Fig. 2A+B), in split-well assays where E. coli cells are in the same medium as P. mirabilis (Fig. 2D), or in E. coli single cultures (Fig. S1). In Fig. 5, E. coli remained viable after supernatant was exchanged to heat-treated stationary phase P. mirabilis supernatant. Contrary to this, E. coli lost viability in untreated stationary phase P. mirabilis supernatant even when the supernatant was mixed 1:1 with fresh LB.

Figure 2E: The authors use wild-type P. mirabilis in their killing assays on swarming permissive LB agar to show that the same process occurs on plates as they describe in liquid media. To rule out the possibility that P. mirabilis is using T6SS killing in combination with the novel mechanism, perhaps the P. mirabilis ∆T6SS strain would be appropriate to include in this experiment.

Authors’ reply: We thank the reviewers for this interesting suggestion. We will perform a killing assay on solid surface with the T6SS mutant as suggested.

Figure 3A: The authors conclude that the formation of Dienes lines is indicative of P. mirabilis utilizing the T6SS machinery. Can the authors clarify the relationship between this observation and their data (Fig. 3B,C) that show no observable reduction viability in P. mirabilis co-cultures?

Authors’ reply: We will clarify this section in the manuscript. Fig. 3 panels A-C show that T6SS are inactive during conditions when we see E. coli killing and thus make a contribution unlikely, whereas panels D+E show directly that T6SS are not involved in the killing of E. coli. <br /> Panels A-C illustrate the known pattern of T6SS activity in P. mirabilis: killing of other P. mirabilis strains on solid surface, no killing of other P. mirabilis strains in liquid culture. These panels therefore confirm that T6SS are not active during conditions where we see killing of E. coli. Fig. 3A demonstrates that the three P. mirabilis strains all possess a functional T6SS that is different from each of the other strains. Dienes lines on agar plates are the direct result of an active T6SS. Contrary to agar, there is limited extended contact time between the cells in shaking liquid media (Fig 1). T6SS are generally thought to require this extended contact time to be effective. Fig. 3B confirms that the same two species that killed each other on the solid media, as seen with the Dienes lines, do not kill each other in liquid media. The killing of E. coli that we observe thus does not seem to be the result of T6SS. To directly show that the killing is T6SS-independent and not to merely infer, we used a T6SS-deficient strain and a co-culture of P. mirabilis and E. coli in panels D+E.

Figure 5 D-F: The authors conduct several initial investigatory assays to narrow down the potential component(s) of the P. mirabilis killing system. To help further triangulate the nature/ identity of the effector molecule(s), have the authors considered studies such as SDS PAGE, HPLC, or mass spectrometry analysis of the different P. mirabilis conditioned media (heat-inactivated, inducing vs non-inducing supernatant, etc)?

Authors’ reply: These are indeed future directions we are very interested in pursuing for a future manuscript. In order to identify the nature of the communicatory or potential quorum sensing molecule, we will fractionate the supernatant and test fractions in killing induction experiments. The fraction(s) with the highest potency in killing induction can then be analyzed via Mass Spectrometry to identify the molecule(s). <br /> Our data indicate that the killing is contact dependent. We therefore consider it likely that the components that are required for the killing process (e.g. a scaffold that binds effector molecules) are cell-bound and not in the supernatant. In order to identify the genes coding for these molecules, we plan on screening a P. mirabilis library to identify mutants unable to kill E. coli. This screen might also help us to identify the communicatory molecule. Additionally, we are performing RNAseq to identify genes whose transcription is upregulated during treatment with “inducing” supernatant (Fig. 5). As E. coli loses viability in the inducing supernatant, but not the heat-treated inducing supernatant, we expect to find transcription of genes coding for killing system genes upregulated in the inducing compared to the heat-treated inducing supernatant.

While these experiments may not elucidate the mechanism entirely, they would likely help describe what components in P. mirabilis regulate this novel system of contact-dependent interference against competing Enterobacteriaceae.

Authors’ reply: We thank you for carefully reviewing our manuscript and your helpful comments.

We also noted a few other minor points while discussing this manuscript:

Figure 2: The authors delineate conditions required for P. mirabilis-mediated killing of E. coli (contact and live cells). Have the authors considered how anaerobic conditions (such as the gut) affect P. mirabilis killing?<br /> • On a small note, Fig. 2F is referenced in the manuscript, but does not appear in the figure.

Authors’ reply: The reviewers raised a very important question. We are currently performing co-culture experiments in anaerobic and microaerophilic environments. The gut mucosa is known to contain more oxygen than the lumen, especially in the infant gut microbiota. Specifically, P. mirabilis is known to colonize the mucosa more than the lumen. We will address the questions under which conditions the system is active with future in vitro and in vivo experiments.<br /> Thank you for noticing the reference to the non-existing figure. This will be corrected in a future version of this manuscript.

Figure 4B: The observation that E. coli membrane is not compromised during killing raised the question—are we observing true cell death or metabolic inactivity more similar to senescence?<br /> • We would appreciate a comparison of biological loss of membrane integrity (as opposed to ethanol treatment) to gain a better sense of the morphology of compromised bacterial membranes

Authors’ reply: This is a very interesting comment and something that we had wondered as well. We indeed do not know without any doubt whether the E. coli cells are dead or metabolically inactive. However, bacteria generally resume metabolic activity and emerge from senescence once the stressor is removed. This is the case for bacteria that enter a persister state during antibiotic pressure and resume growth when the antibiotic is removed. As E. coli cells are unable to resume growth on rich media agar plates, this strongly suggests that they are indeed dead. There are enzymatic toxins, for example some colicins, that kill cells without affecting membrane integrity. The effector molecules cleave either DNA, rRNA, or tRNA, thus degrading the genome or arresting protein synthesis. The cells maintain membrane integrity but fail to grow as vital parts of the cell were destroyed by the toxin. <br /> Antimicrobial peptide LL-37 results in loss of membrane integrity and we can use this as a control to visualize loss of membrane integrity by an antimicrobial compound. In addition, we plan to use negatively-charged dyes like DiBAC4 (PMID: 31792213) that enter cells that have lost membrane potential but are excluded from metabolically active cells.

We congratulate the authors on their efforts and hope our reviews are helpful in any revisions to this manuscript.

Authors’ reply: We thank the reviewers for taking the time to review and for their positive evaluation of our manuscript.

On 2021-04-30 03:26:00, user StreuthCobber wrote:

Phytopthora Agathadicida is not a "recent" introduction. It was discovered by Dr Peter Gadgil in 1972. And then renamed due to morphology. Kauri are not going extinct and Gadgil's site is regenerating. Kauri are still thriving on Great Barrier Island. Gadgil found P. Hevae (now known as agathadicida) under both sick and healthy trees. This is the same as P. Cinnamomi which also causes kauri dieback and is found under sick and healthy trees but only causes diseas under certain climatic conditions (eg drought years).

On 2021-04-29 21:00:08, user Maochang Liu wrote:

How about protection immunity? If the protection immunity is affected by the SARS-CoV-2 variants, we do not know yet. The T cells reactivity can not forecast the disease course.

On 2021-04-29 17:51:43, user Jay wrote:

It seems like the difference in MPRA results may be due to the ATRA differentiating the progenitors to neurons--> the difference of MPRA results (ATRA MPRA vs DMSO MPRA) may be due to different cell types and not the added ATRA itself. How did yall account for this? It would be really great if yall could do this MPRA in differentiated neurons without adding ATRA to compare the two (ATRA progenitors vs neurons).

On 2021-04-21 20:58:51, user Jess wrote:

Heads up that in figure 4 description you say " rs4801117-A shows greater activity with ATRA treatment while the G allele is unaffected" but the image has a C allele

On 2021-04-21 20:47:21, user Jess wrote:

Hey heads up that in figure 4 legend you have two Fs (the G is accidently an F)

On 2021-04-29 17:37:35, user h wrote:

There might be an issue with comparing field dependent resolution if the same maximum t1 & t2 evolution times are used, since the line widths are being limited in units of Hz, which naturally favors higher magnetic fields. Unless the line widths for the samples are constant in Hz at all fields, which does not seem to be the case for nano-crystalline samples, the maximum t1 & t2 evolution times should be inversely proportional to the applied magnetic field to account for the inhomogeneous line widths.

On 2021-04-29 14:03:50, user Silas Kieser wrote:

I think this is publshed: https://www.nature.com/arti...

On 2021-04-29 04:00:09, user Flux wrote:

This paper has just the type of experiment I was looking for. It is great that the authors tested all these different conditions.<br /> I am new to the field and can't comment on the work in too much depth. But I would suggest to make certain figures more accessible: eg. highlight functional areas of the sequences in table S1-S3, like toehold regions and mutations. Also in some parts of the paper the term "gate" is used and in another part "substrate". I know that there is a slight difference in meaning, but they refer to the same strand it might be better to keep it consistent.

On 2021-04-29 03:04:51, user Doug Shepherd wrote:

Hi Bin and crew, awesome work and beautiful presentation!!

Regarding:

Previous OPM systems with full NA detection12,14 reported an imaging volume of 180um × 180um × 60um, whereas DaXi can image a volume about 400 times larger

. In the eLife paper we showed stage scanning without motion blur for >1 cm x 220 um x 60 um (with some loss in resolution outside the Snouty v1.0 sweet spot as you note in your discussion). We then stitched each FOV to generate up to 1 cm x 1 cm x 60 um images. See Figure 9 and "stage scanning" setup in the methods.

We've made some changes since publication. All of our current Pycro-manager based control and Python reconstruction code (as well as Micro-manager 2.0 control code used in eLife version of the instrument) is available at our GitHub repo: qi2lab OPM repo.

Doug

On 2021-04-28 16:40:41, user Rob Martienssen wrote:

If the clock is free running without environmental trigger, should the study refer to CT6 and CT17, for circadian rather than zeirgeber time?

On 2021-04-28 14:13:14, user Stefan Lutz wrote:

Nice work! We noted the same presence of the flavin in the cryo-EM structure of our engineered encapsulin from Thermotoga maritima (PDB: 6wkv) that was just released. Manuscript will follow shortly.

On 2021-04-28 13:44:33, user lily wrote:

You say it’s yet to be determined if the LNPs cross the BBB but it’s not- in Moderna’s European Medicines Agency assessment it clearly states bio distribution was found in all tissue tested including the brain (only excepting the kidney). What else could this mean for long term effects?

https://www.ema.europa.eu/e...

Under Pharmokinetics section.

On 2021-04-28 00:35:36, user Cheong Xin (CX) Chan wrote:

This preprint (#800482) and preprint #783902 (https://doi.org/10.1101/783... were combined in a published BMC Biology article available at https://doi.org/10.1186/s12....

On 2021-04-27 23:02:36, user Prasanthi Kunamaneni wrote:

I enjoyed reading your paper and appreciated that the experiments were thorough and well-designed to assess fascin’s role in lung cancer. Some general comments that I had for the figures presented in the paper:

1.) In Figures 1G-H, the data felt redundant when comparing between different human cell lines for lung cancer in both glycolysis and glycolytic capacity conditions as the data results were similar. It may be effective to specifically focus on comparing between one human cell line (H1650) versus a mouse cell line (LLC) to better assess the similarities and differences in both glycolysis and glycolytic capacity. This could also give room to expand on other data sets in Figure 1 and make them easier to analyze, as many graphs were too small to read.

2.) For Figure 4H, the data other than WT was hard to interpret and too small to read. It would be helpful if there was a different way to represent the y-axis for the graph so that the data could be better analyzed.

3.) For Figures 5C-D, it was difficult to analyze the waterfall plot data as it was packed with information and I was also confused with the shift from correlation (r) to log (p-value). It would be helpful if this data was expanded in the results or discussion section or if there was another format to represent the data that came from the RNA sequencing database to make it easier to interpret.

Overall, the paper was very interesting and enjoyable to read. Great work!

On 2021-04-26 05:23:48, user YU QIAO wrote:

A great and very interesting study. The writing is concise while clearly articulates the logic. It’s very effective to use both knockout and over/ectopic-expression of fascin throughout the paper; it makes the information more comprehensive and the reasoning more solid. Personally, I really like the in vivo study with PFKFB3 knockdown, especially the use of a fascin OE background. Additionally, it is very appealing to target fascin, but I believe that for the last section, more data from an in vivo study can make the paper stronger. Also, it might be worthwhile to test other fascin inhibitors, to note the potential side effects of each, and to put the inhibitors with combination with, for example, other chemo- or radio-therapy. A general comment on the figures: the varieity of colors makes it hard to comprehend some of the figures, and some colors (e.g. Fig1.G-H) are too light or too similar to each other and cause confusion. The color choice for control and OE is not red-green blind friendly. In Figure 5, the representation in C and D is a bit confusing; it could be more clear to show r and p values in a different way, or in different colors. For Figure 6, it will be more effective to show only one or two representative mice/samples per group, as there is some repeated information and the figures are too small. However, overall, it’s a really beautiful study, and I greatly appreciate your work.

On 2021-04-25 22:04:23, user MICHAEL MARTINYAK wrote:

Hi I really enjoyed reading this. Some great science here.

Just a few things that I wanted to point out though:

1) On page 11, the line “We also observed chest wall metastasis in 50% or 100% of control shRNA…” does not match with the figure. The figure implies 100% or 50% respectively.<br /> 2) I did not see NSCLC or OXPHOS defined anywhere.<br /> 3) It seems unnecessary to include both KO1 and KO2 in many of the figures since including one over the other won’t change the interpretation of the data.<br /> 4) Figure 1: OE (1B-1F) could be put into the supplemental to save space.<br /> 5) Figure 2A - metabolite peak intensity for 2PG/3PG, PEP, & lactate could go in the supplemental. It would also be nice to see the conversion of F6P to F-2,6-BP by PFK2 and an indication that F-2,6-BP positively regulates PFK1. This could replace the unnecessary metabolite peak intensities. Also, the red arrow from F-1,6-BP to PFKM2 made me initially think F-1,6-BP inhibits PFKM2, so I would suggest changing this.<br /> 6) Figure 3A/C - 4th lane says PFKL but in the results it’s called PFML, so there’s a mismatch.<br /> 7) Page 9: “... fascin overexpression in NSCLC activates YAP1…” This should be something like “upregulates levels of YAP1.” Activation is misleading in this context.<br /> 8) Figure 5C/D should be explained in more detail - it’s hard to know what I’m looking at.<br /> 9) Figure 6A/C/E - You could probably include 3-4 mice rather than 10 to save space.

Great work though. I really enjoyed this read.

On 2021-04-27 16:54:08, user Leighton Pritchard wrote:

This is very interesting work. Thank you for sharing it.

I have a point of confusion. I may be missing something, but I understood that bidirectional/divergent promoters have been recognised for some time in prokaryotes, if not catalogued in depth. For example Beck and Warren (1988, https://mmbr.asm.org/conten... note that "Over 20 [divergent promoters] have been found on the chromosome of E. coli alone," and Rhee et al (1999, https://www.ncbi.nlm.nih.go... describe the divergent ilvYC system. Does this contradict the introduction's statement that there is a consensus view that prokaryotic promoters are unidirectional, and the abstract's note that in all prokaryotes promoters are believed to drive transcription in a single direction?

On 2021-04-27 13:08:49, user Anny Slama Schwok wrote:

Linking to our full paper accepted for publication in Molecules (MDPI publisher)<br /> Olivier Terrier, Sébastien Dilly Andrés Pizzorno, Dominika Chalupska, Jana Humpolickova, Evzen Boura Francis Berenbaum, Stéphane Quideau,, Bruno Lina, Bruno Fève, Frédéric Adnet, Michèle Sabbah, Manuel Rosa- Calatrava, Vincent Maréchal, , Julien Henri, and Anny Slama Schwok*. <br /> Antiviral properties of the NSAID drug naproxen Targeting the nucleoprotein of SARS-CoV-2 Coronavirus

On 2021-04-27 08:35:55, user PCalsou wrote:

Dear authors

Interesting results. I think it would be fair to mention our paper on the key role of Ku in regulating the balance between C- and A-EJ;

Cheng, Q. et al. Ku counteracts mobilization of PARP1 and MRN in chromatin

damaged with DNA double-strand breaks. Nucleic

acids research 39, 9605-9619,

(2011)

Best wishes

PCalsou

On 2021-04-27 05:43:00, user Min Zhu wrote:

The full version of this manuscript is online in PNAS. https://www.pnas.org/content/117/9/4781.short

On 2021-04-27 04:32:43, user OSCAR MANUEL COBAR PINTO wrote:

Good work but needs to solve "conflicts of Interest". Where is the reference 13?

On 2021-04-26 16:58:24, user Alex Sack wrote:

How is India owning the ip, or a portion thereof, after having funded Covaxin's research and it's creation by Bharat Biotech, not a "competing interest"?

When your team are India state scientists, in collabortion with V. Krishna Mohan, Ph.D., who works for Bharat Biotech?

With all the money at play, globally, and a quite vested interest within India, it would seem that there is a whole lot of competing interest. Which begs particular questions on your research here, given your finding here that India's state (funded) vaccine is the only one effective against B.1.617 - now found throughout the world.

Particular questions, indeed, as you did not disclose this significant conflict of interest...

https://scroll.in/article/9...

On 2021-04-26 22:37:49, user Esmeralda R. wrote:

I was looking for Histone H4 variants with a role in different types of cancer, then I bumped into this publication, now at Nucleic Acid Research. <br /> Thanks for posting the link to the now published article too. <br /> It will make my paper on histone modifications, much better. <br /> Esmeralda from Real Gramas

On 2021-04-26 21:18:39, user Pavel Flegontov wrote:

I read your paper with interest. It documents a known but indeed underappreciated issue: D- or f4-statistics should be interpreted in the phylogenetic sense, i.e. for a statistic (O, A; B, C) a positive value means a gene flow in any direction either between O and B or between A and C (I use the sign convention common in human archaeogenetics). Moreover, the sources of the flow can share just a small amount of drift with the respective lineages, i.e. any lineage diverging on the O branch would show a signal. Thus, interpreting isolated D- or f4-statistics is dangerous. Fitting many f4-statistics in the admixture graph framework looks more promising, however admixture graphs have a large set of other issues.

I mentioned this problem with f4/D-statistics in a recent preprint:

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

SI, page 22 (+Fig. S23)<br /> "These results are not unexpected, and it is known that D- or f4-statistics cannot be interpreted unambiguously. Statistics of the type D(Reference, Target; Source, Outgroup) are often used to test for gene flows, and very often distant outgroups (e.g., Africans) are used. However, our results show that for the test to be interpretable unambiguously the ancestry components need to be perfectly balanced in the reference and target, which is hard to control in high-throughput analyses. Moreover, there is a higher chance of encountering gene flows into the reference group if genetically distant outgroups are used since many lineages that could contribute gene flows could diverge on the outgroup branch."

On 2021-04-26 19:19:47, user Samuel Katz wrote:

Update from the authors:<br /> Our work has been published here https://www.cell.com/cell-s...

And our method is under a new name: SIGNAL.

The current URL to access this tool is signal.niaid.nih.gov

The design and analysis of the method have not been changed.

On 2021-04-26 14:18:55, user Katsu Murakami wrote:

rRNA transcription occupies ~70% of total RNA synthesis in rapidly growing E. coli cells. So I'm wondering observed reduction of rRNA level after Rif treatment can be explained by simple reduction of rRNA synthesis instead of rRNA degradation.

On 2021-04-26 13:36:23, user Bogi wrote:

Hello, nice paper. It is not clear for me, why do you think that Dgkk is characteristic for mature cardiomyocytes. The referred publication did not mention Dgkk. Thank you for your answer!

On 2021-04-26 12:10:00, user Thomas D Alcock wrote:

Very nice work! Could I please ask if you would expect Si to be taken up through OsNIP2;1 as silica or as silicic acid? I am curious, as you state in the introduction that silicic acid is the naturally occurring bioavailable form of Si, but in later figures, it looks like silica is represented as transiting through NIP2;1. Is silicic acid transformed to silica prior to plant uptake/translocation? Many thanks, Tom

On 2021-04-26 08:56:07, user Ye wrote:

The primary B cell were labelled by BrdU for 30 mins, which might be too long to locate the position of replisome. In theroy, the elongating replication forks moves approximate 54 kb (30 nt/s * 30 min* 60 s = 54 kb) along the chromatin. According to this sequential enrichment strategy, RNA polymerase II within this 54-kb regions could be pulled-down and be recognised as replication-transcription collsion sites.

On 2021-04-26 03:55:48, user Vladimir Dinets wrote:

Lines 652-653: You say "here we suggest the following system of generic group taxa within the tribe Arvicolini sensu stricto", but nothing follows :-(

On 2021-04-25 16:05:57, user Tom Coenye wrote:

The statement about the original P. aeruginosa panel (especially that no AST was done on this set) is not correct. See https://www.microbiologyres....

On 2021-04-25 15:03:34, user rajesh bheda wrote:

In which trimester was the vaccine given?

On 2021-04-25 09:57:27, user George Elias wrote:

Some nice data here but I wonder if it is enough to have correlations to say that there is a guided coordination between vaccine-specific Th1 CD4 and CD8 T cell responses! I would keep this on the speculative part of the story.

On 2021-04-24 14:08:44, user Bashar Emon wrote:

This paper is now publishes in Science Advances.

A novel method for sensor-based quantification of single/multicellular force dynamics and stiffening in 3D matrices<br /> Bashar Emon, Zhengwei Li, Md Saddam H. Joy, Umnia Doha, Farhad Kosari and M. Taher A. Saif<br /> Science Advances 09 Apr 2021:<br /> Vol. 7, no. 15, eabf2629<br /> DOI: 10.1126/sciadv.abf2629

On 2021-04-23 23:37:14, user Glyco Boy wrote:

That's one massive paper. The results on differentially N-glycosylated <60 kDa secreted proteins as a result of targeted therapy will be of great interest especially for those of us who work on finding biomarkes for cancer recurrence after therapy.

On 2021-04-22 21:01:20, user Constanz Shun wrote:

Very interesting findings! Glycosylated PON1 has been a consistent strong biomarker in several advanced cancers, particularly those that recurred or progressed after therapy. This is a great basic study complementing those studies that clearly lack mechanistic foundation especially as to why normal PON1 levels are depleted in patient serum and glycosylated PON1 is very high. Congrats on a fascinating study.

On 2021-04-23 22:57:23, user Aartee Potnis wrote:

Do we have any information on susceptibility to blood clots for the CureVac vaccine ?

On 2021-04-12 12:48:38, user Pascal wrote:

CureVac’sCOVID-19 Vaccine Candidate, CVnCoV, Demonstrates Protection Against SARS-CoV-2 B.1.351 Variant (South African Variant) in Preclinical Challenge Study First challenge infection study in preclinical mouse model to provide evidence for protection against SARS-CoV-2 variantCVnCoV induces robustantibody titerswith virus variant neutralizing capacity in immunized animalsFull protection of immunized micefrom infection and mortality during variant challenge infectionTÜBINGEN, Germany/ BOSTON, USA –March23, 2021 –CureVac N.V. (Nasdaq: CVAC), a global biopharmaceutical company developing a new class of transformative medicines based on messenger ribonucleic acid (mRNA), today announced the publication of preclinical data demonstrating that their COVID-19 vaccine candidate, CVnCoV, protects against challenge infections with the SARS-CoV-2 Variant of Concern B.1.351 (also referred to as the “South African” variant) and a strain of the original SARS-CoV-2 B1 lineage (BavPat1) in a transgenic mouse model. Consistent withavailable variant studies,the neutralization capacity of robust antibody titers was shown to be impacted by the B.1.351 variant compared to the original strain. However, vaccinated animals were fullyprotected from lethal challenge infections with both strains.The full manuscript of the preclinical data is available on the bioRxivpreprint server.“Emergence of new SARS-CoV-2 strains, which exhibit the potential to escape an existing SARS-CoV-2 immunity, pose an increasing risk to the progress of current global immunization efforts,” said Igor Splawski, Ph.D., Chief Scientific Officer of CureVac. “To our knowledge, this is the first challenge study in a human ACE2 transgenic mouse model of severe disease that shows complete protection against one of the most threatening virus variants.”Within the study,transgenicmiceexpressing the human ACE2 receptor, the receptor through which SARS-CoV-2 enters human cells,were immunized with 8μg of CVnCoV per dose, following a two-dose vaccination schedule at day 0 and day 28. Vaccination resulted in robust antibody responses and complete protection (100% survival) againstthe original SARS-CoV-2 strain and also B.1.351(variant strain first identified in South Africa)challenge infections. CVnCoV vaccination efficiently blocked viral replication of B.1.351 in the lower respiratory tractandbrain,and reducedviral replication in the upper respiratory tract in vaccinated and challenged animals.About CVnCoVCureVac began development of its mRNA-based COVID-19 vaccine candidates in January 2020. The vaccine candidate chosen first for clinical development, CVnCoV, is an optimized, non-chemically modified mRNA, encoding the prefusion stabilized full-length spike protein of the SARS-CoV-2 virus, and formulated within Lipid Nanoparticles (LNPs). Phase 1 and 2a clinical trials of CVnCoV began in June and September 2020, respectively. Phase 1 interim data reported in November 2020 showed that CVnCoV was generally well tolerated across all tested doses and induced strong antibody responses in addition to first indication of T cell activation. The quality of immune response was comparable to recovered COVID-19 patients, closely mimicking the immune response after natural COVID-19 infection. In December 2020,CureVac initiated a pivotal Phase 2b/3, the HERALD study, with a 12μg dose of CVnCoV.InFebruary 2021,CureVac initiateda rolling submission with the European Medicines Agency (EMA) forCVnCoV.CureVac has entered into several strategic partnerships for the further development, production and commercialization of CVnCoV. The company entered into a collaboration agreement with Bayer in January 2021 with regards to CureVac ́s current vaccine candidate CVnCoV. In February2021,CureVac and the British pharmaceutical company GlaxoSmithKline (GSK) agreed to jointly develop next-generation multi-valent mRNA vaccines against COVID-19. The development of new vaccine candidates is strengthened by a partnership with theUK Government and its Vaccines Taskforce, which CureVac also entered in February 2021. GSK will also potentially contribute to this collaboration. Clinical trial and commercial material is provided by the company’s substantial production capacities for mRNA vaccines at its headquarters in Tübingen, supported by the current expansion of manufacturing capacities in Europe, allowing broad-scale manufacturing of CVnCoV for potential commercial supply preparedness. About CureVacCureVacis a global biopharmaceutical company in the field of messenger RNA (mRNA) technology,with more than 20 yearsof expertise in developing and optimizing theversatile biological molecule for medical purposes. The principle of CureVac's proprietary technology is the use of non-chemically modified mRNA as a data carrier to instruct the human body to produce its own proteins capable of fighting a broadrange of diseases. Based on its proprietary technology, the Company has built a deep clinical pipeline across the areasof prophylactic vaccines, cancer therapies, antibody therapies, andthe treatment of rare diseases.CureVac had its initial public offering on the New York Nasdaq in August 2020. It is headquartered in Tübingen, Germany,and employs more than 600people at its sites in Tübingen, Frankfurt,and Boston, USA.Further information can be found at www.curevac.com.CureVac Media ContactThorsten Schüller, Vice President CommunicationsCureVac, Tübingen, GermanyT: +49 7071 9883-1577thorsten.schueller@curevac.comCureVac Investor Relations ContactDr. Sarah Fakih, Vice President Investor RelationsCureVac, Tübingen, GermanyT: +49 7071 9883-1298M: +49 160 90 496949sarah.fakih@curevac.com

On 2021-04-23 13:22:31, user Erik Gylfe wrote:

Dear authors,<br /> I find your study provocative and interesting, but somewhat difficult to read due to erroneous reference to Figures. You point out that most previous studies have been performed by stimulating beta cells with unphysiologically high glucose and now demonstrate interesting Ca2+ responses at more reasonable concentrations of the sugar. From this point of view I think you provide important new data using an elegant experimental approach.

However, I am concerned about the experimental design and some conclusions drawn. The data are taken as argument to return to very old and nowadays mostly abandoned ideas that Ca2+ influx has only a minor role during the first minutes of beta cell activation. Some of these old ideas are based on unfortunate experimental design, which I also find in the present study. The common denominator is simultaneously changing two parameters without considering that the timing of<br /> the effects may be different.

I think that the isradipine experiments is a telling example. It is obvious that the effect of the used concentration of isradipine has a slow onset (Fig S3-1) and an even slower off effect (Fig 2) in your system. The timing definitely seems slower than that for the glucose response. Therefore, the most likely explanation of the results is that the Ca2+ channels are not initially blocked and glucose maintains some of its early effects on voltage-dependent Ca2+ influx. The same explanation likely applies to the diazoxide experiment (Fig. S3-2), particularly since the concentration is on the low side. In my experience glucose-induced intracellular Ca2+ release is unlikely when Ca2+ influx is blocked except after artificial elevation of cAMP (see ref 16, which by the way is not correctly cited in the reference list). Instead, the typical<br /> effect of glucose elevation under such conditions is a lowering of Ca2+<br /> due to ER sequestration. However, the latter effect would probably escape detection with the presently used low affinity indicator and is likely rather modest when raising glucose from 6-8 mM.

It is stated in lines 322- 323 “The prominent role for intracellular<br /> Ca2+ release had strong early support from 45Ca2+ flux studies”. 45Ca2+ flux data are often difficult to interpret, and I think you may mix up efflux of 45Ca2+ from islets with intracellular Ca2+ release. Most 45Ca2+ studies are instead consistent with a [Ca2+]i lowering<br /> effect of glucose when voltage-dependent entry is prevented. See for example Bergsten et al. Am. J. Physiol. 255: E422-E427, 1988 for the effect of glucose on both 45Ca2+ and [Ca2+]i in the presence of diazoxide.

The isradipine experiments reminds me about old data taken to indicate that first phase insulin release is independent of Ca2+ influx (Wollheim et al. J. Clin. Invest. 62: 451-458, 1978). In that study first phase insulin release was unaffected when glucose was elevated simultaneously with addition of 5 microM Verapamil. With more effective prevention of Ca2+ influx they might instead have discovered that glucose lowers basal insulin secretion under such conditions (Bergsten et al. 1988).

Although I agree that Ca2+ release from (but also uptake into) the ER are important for shaping the slow [Ca2+]i oscillations, I get the impression that you implicate a more fundamental role of intracellular Ca2+ release in their generation. I think this must be discussed in relation to experiments indicating that the glucose-induced slow oscillations are maintained (with different kinetics) when intracellular Ca2+ uptake and release by the ER is prevented by SERCA inhibition (Liu et al., J Physiol 508: 471-481, 1998; Gilon et al JBC 274: 20197–20205, 1999).

The effects of ryanodine and acetylcholineare interpreted only in terms of Ca2+ release from the ER. I think there is a striking difference in the responses. The effect of acetylcholine is rather similar to that of raising glucose from 6 to 8 mM. This is likely explained by the Na+-dependent depolarizing effect of acetylcholine being sufficient to trigger electrical activity in beta cells exposed to threshold glucose concentrations (see cited ref 22).

The ryanodine data looks rather compelling but are inconsistent with other cited observations. As you acknowledge, the functional significance of ryanodine receptors in beta cells is controversial. I would like to see the effect of repeated exposures to 100 nM ryanodine which is expected to induce reproducible responses since the inhibitory effect of 100 microM ryanodine was reversible.

In this context I lack information about how representative the<br /> observations are between experiments. As I understand it, the illustrations show results from individual experiments. It is stated in Methods “At least 3 slices/mice were used for each experimental condition” but nothing is stated about variation between experiments.

Best regards,<br /> Erik Gylfe

@font-face<br /> {font-family:"Cambria Math";<br /> panose-1:2 4 5 3 5 4 6 3 2 4;<br /> mso-font-charset:0;<br /> mso-generic-font-family:roman;<br /> mso-font-pitch:variable;<br /> mso-font-signature:-536870145 1107305727 0 0 415 0;}@font-face<br /> {font-family:Cambria;<br /> panose-1:2 4 5 3 5 4 6 3 2 4;<br /> mso-font-charset:0;<br /> mso-generic-font-family:roman;<br /> mso-font-pitch:variable;<br /> mso-font-signature:-536869121 1107305727 33554432 0 415 0;}p.MsoNormal, li.MsoNormal, div.MsoNormal<br /> {mso-style-unhide:no;<br /> mso-style-qformat:yes;<br /> mso-style-parent:"";<br /> margin:0cm;<br /> mso-pagination:widow-orphan;<br /> font-size:12.0pt;<br /> font-family:"Times New Roman",serif;<br /> mso-fareast-font-family:Cambria;<br /> mso-fareast-theme-font:minor-latin;<br /> mso-bidi-font-family:"Times New Roman";<br /> mso-bidi-theme-font:minor-bidi;<br /> mso-ansi-language:SV;}.MsoChpDefault<br /> {mso-style-type:export-only;<br /> mso-default-props:yes;<br /> mso-fareast-font-family:Cambria;<br /> mso-fareast-theme-font:minor-latin;<br /> mso-bidi-font-family:"Times New Roman";<br /> mso-bidi-theme-font:minor-bidi;<br /> mso-ansi-language:SV;}div.WordSection1<br /> {page:WordSection1;}

On 2021-04-23 08:20:01, user Kien Xuan Ngo wrote:

For the first time, we clarify the molecular mechanism of cytotoxicity of cassiicolin toxin on specific cytoplasmic membrane of plant cells and disclose the mechanism underlying the host-selective toxin interaction in corynespora leaf fall (CLF) disease in rubber trees.

On 2021-04-23 07:33:33, user Owen wrote:

Great to see the application of MetaPhlAn 3 on the published CRC datasets! Really promising that the increased sensitivity on detecting rare taxa leads to the discovery of new CRC biomarkers. <br /> In Supplementary_file_10_CRC_metaanalysis_datasets, the BMI of YachidaS_2019 samples is mixed with other information. I am wondering how I can get the correct information from curatedMetagenomicData?

On 2021-04-23 07:32:56, user Georgios Pavlopoulos wrote:

Published here: https://academic.oup.com/na...

On 2021-04-22 15:26:46, user Mike H wrote:

Dear those interested in this work:

This work is now published at eLife at this link: <br /> https://elifesciences.org/a...

During its revision, we added several new experiments. These include further analysis of how Hmg1 assembles into high molecular weight species during AGR. We also now identify a Nvj1 mutant which still forms an NVJ contact site, but does not recruit Hmg1 to the NVJ. This enabled us to demonstrate that loss of Hmg1 partitioning impacted mevalonate pathway flux in a background where the NVJ is intact.

Thank you for your interest in our work, and we invite you to read the final version of this work at eLife!

Sincerely,<br /> The Henne lab

On 2021-04-22 13:38:53, user Michael Hoffman wrote:

This preprint seems to be missing a Methods section referred to within. Methods are not described with the minimum level of detail one might expect for a scientific publication.

On 2021-04-14 16:19:46, user Christophe Leterrier wrote:

This preprint seems to be missing a Methods section that is referred to multiple times within the text, as well as supplementary tables. It would be good to upload a complete manuscript as a new version, and/or use the supplementary files uploading option. Thank you

On 2021-04-22 12:33:11, user adalijuanluo wrote:

Dear readers, our second revision has been uploaded which would be online soon. We have added an extra section about the presence of virulence genes. There are a few mistakes especially Fig 4 (CC1 and CC13 are labeled wrongly), which have been corrected. Please feel free to contact me if you have any questions. Thanks.

On 2021-04-22 12:32:41, user JohnPravinKing wrote:

'Plate Reader with 390-400 nm excitation and 580-510 nm emission wavelengths'. <br /> Is the emission wavelengths correct for the substrate used in FAP activity?

On 2021-04-16 09:16:34, user JohnPravinKing wrote:

Forgot to highlight one more errror.

In page 30, "FAP activity assay buffer (50 mM Tris-BCl, 1 M NaCl, 1 mg/mL BSA, pH 7.5)." I think it should be 50 mM Tris-HCl than "Tris-BCl"

Great paper. Thanks for the wonderful work.

On 2021-04-13 15:14:19, user JohnPravinKing wrote:

In page 18, "While the spheroid experiments shown in Figure 4 suggest that FAP inhibition reduces NK cell migration through a tumor-associated extracellular matrix, interpretation of these results was constrained due to the expression of FAP by pancreatic stellate cells (PSCs) present in the tumor spheroids". It is actually Figure 5.

Also in page 22, " However, our finding that FAP is expressed exclusively in human NK<br /> cells, and not in murine NK cells or other human immune cell types (Figure 1)". The figure is actually from Supplementary Figure 1.

Need to correct the above. Not a big deal but thought I will let people know.

On 2021-04-22 12:15:24, user Ludmila Prokunina-Olsson wrote:

Now published in Genes and Immunity<br /> https://www.nature.com/arti...

On 2021-04-22 08:17:59, user Eric Lombaert wrote:

The correct supplementary file name that pertains to version 6 is "Supplemental file[supplements/452326_file02.pdf]".<br /> The other two "Supplementary material" links should be ignored.

On 2021-04-22 05:38:32, user ASHIMA SHARI wrote:

Dear authors

One thing I need to point out that "However, it is far more unclear what’s the role of PRC2 <br /> complex during fungi-plant interactions. None of these studies used in planta fungal samples, instead by the mycelium samples, for testing, due to difficulty in collecting enough fungal samples. " in L358-360 is not accurtae. Please take a look at these papers 1)https://mbio.asm.org/conten... 2) https://journals.plos.org/p... 3) https://onlinelibrary.wiley... 4) https://www.biorxiv.org/con..., all of these above papers used in planta samples for their experiments, genome-wide study is lacked but that does not mean noboday used in planta samples.

Thanks

On 2021-04-21 21:32:49, user Manendra wrote:

That's an amazing work which will be really helpful... Is it possible to increase the number of approved drugs (not just 2016). Atleast 10 years of FDA approved drugs... But this is a very good start

On 2021-04-21 19:41:19, user Yunlong Liu wrote:

I am wondering whether the concentration of the antigen in the plasma after the mRNA shots is too high and maybe much higher than natural infections.

None of the research so far talks about the concentration of the antigen after the injection.

On 2021-04-21 16:09:56, user Dong An wrote:

It seems to me that the net charge of TAT-RasGAP_317-326 is 8 because the last residue D contains 1 negative charge other than that for C-terminus...

On 2021-04-21 14:30:44, user Kirti Bardhan wrote:

Dear Readers,<br /> The preprint has been peer reviewed and published on 25th March 2021 in Current Science journal. Please do follow the link for published version. <br /> Current Science, Vol 120 No.6 pp. 1050

Thanks <br /> https://www.currentscience....

On 2021-04-21 13:36:14, user yegerlotemlab wrote:

At last, we are happy to announce our publication in Nature Communications! https://www.nature.com/arti...

On 2021-04-20 20:12:02, user ThomasBeder wrote:

Great to see the comprehensive prediction of bacterial essential genes. Maybe we can come up with something similar for eukaryotes with our method (https://www.biorxiv.org/con....

On 2021-04-20 20:06:23, user ThomasBeder wrote:

Interesting. Nice to see the work on cross-eukaryote essential gene predictions. Our related work, predicting essentiality across six model eukaryotes and Tribollum castaneum might be also of interest (https://www.biorxiv.org/con....

On 2021-04-20 16:05:46, user Christopher Richie wrote:

thanks for sharing what looks like a convenient way to get ssDNA for CRISPR.<br /> can you provide updated reference information for line 133, Viera and Messing? It does not match citation 30.<br /> thanks

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 2021-04-19 14:22:39, user Milka Kostić, PhD wrote:

Dear authors,<br /> Thank you for sharing this preprint with the community. I read the preprint with interest, and I am sharing the comments below in hope they will be helpful to you as you go forward with publishing and sharing your new findings further.<br /> Kind regards,<br /> Milka

Comments to the authors:<br /> In this preprint by Shao, Yang, Ding et al. the authors describe an expansion of the PROTAC (Proteolysis Targeting Chimera) concept into a new direction: using chimeric molecules that combine a DNA sequence fragment (used here to recruit DNA binding proteins) and an E3 ubiquitin ligase binding warhead (used here to recruit either VHL or cereblon (CRBN)). The resulting hybrid molecules are referred to in this preprint as O’PROTAC, short for oligonucleotide PROTACs. The big motivation for this work is the lack of strategies to target majority of transcription factors (TFs). <br /> TFs represent a large and diverse class of proteins that are critical for many different aspects of biological regulation. They could also be viewed as essential targets for drug development; and yet, outside targeting nuclear receptors (NRs), which represent a subfamily of TFs that are endogenously regulated through small molecule binding and have therefore evolved to bind drug-like molecules, efforts to develop chemical tool compounds and/or drug leads that target TFs has been difficult. <br /> Couple of relatively recent breakthroughs in this are IMiD compounds (immunomodulatory imid drugs) - small molecules that serve not to inhibit protein-protein interactions, but rather to promote complex formation between an E3 ubiquitn ligase and different TFs (such as SALL4, IKZF1, IKZF3), resulting in fully functionally competent E3 complex that marks these TFs for proteasomal degradation. In many ways this is similar to the effect of PROTACs, another kind of small molecule degraders, that feature two warheads connected via a linker. One warhead binds an E3 ubiquitin ligase (usually VHL or CRBN) and the other is a ligand for a protein of interest. By now, PROTACs targeting many different targets have been developed, but when it comes to TFs finding the ligand that can be transformed into the PROTAC warhead remains a major bottleneck.<br /> Enter Shao, Yang, Ding et al. - these authors exploit the fact that TFs bind specific DNA sequences, usually short-ish oligonucleotide sequences. They design O’PROTACs to include double-stranded oligonucleotides, on one hand, and a VHL or a CRBN warhead on the other. The two proof of concept target TFs they focused on are ERG transcription factor and Lymphoid enhancer-binding factor 1 (LEF1), both clinically relevant.

Dealing with nucleic acid based reagents requires special delivery methods (which is the down side of this strategy), so the authors used lipid-mediated transfection. They were able to observe:<br /> - degradation of exogenously expressed HA-ERG in 293T cells as monitored by western blotting<br /> - CRBN-based O'PROTACs had a stronger effect than VHL-based ones<br /> - ERG degradation could be achieved in prostate cancer cell line VCaP that overexpresses ERG as well as its truncated form (TMPRSS2-ERG)<br /> - degradation of ERG has the expected downstream effect on its transcriptional targets<br /> - similar behavior was noted for LEF1, and LEF1 targeting O'PROTACs were able to inhibit prostate cancer cell line proliferation; however some of the validation steps done for ERG O'PROTACs (ERG pulldowns, and proteasome dependence) do not seem to be included for LEF1.

I think this is an important proof-of-concept work, albeit a bit preliminary. What authors could have done a bit differently is:<br /> - try to be more quantitative (it's unclear how large the observed effects are)<br /> - use a negative control (create O'PROTACs that don't bind to the ligase, or feature an oligonucleotide that has no target binding); negative controls are essential and some work around developing a high quality negative controls for O'PROTACs would be useful<br /> - have the authors tried to see if their O'PROTACs have an effect on cells where ERG (or LEF1) have been deleted? These experiments are important when validating new modalities.<br /> - provide some commentary about and/or evidence that they affect their target cleanly (selectively). Are there any other TFs that would potentially bind to the oligonucleotide motifs they used here?<br /> - provide a more useful discussion of the design consideration for O'PROTACs, potential limitations of this strategy, and how to get the most out of using them as a research tool; in the current form the Discussion is not necessarily all that useful for anyone interested in using this technology. <br /> - (in the future) it would be cool to see what happens in cells that don't overexpress ERG. Have the authors tried those experiments?

Congratulations on driving forward this interesting new concept of O'PROTACs and I hope my comments help you strengthen your technology further.

On 2021-04-20 14:04:43, user Vinayak Singh wrote:

Very interesting. The below article maybe helpful.

https://pubs.acs.org/doi/ab...

On 2021-04-20 12:55:09, user Nelii cheps kips wrote:

Our recent work on bacterial attachment in high salt environment #brunlab #Caulobacterales #holdfast #polysaccharides

On 2021-04-20 03:32:07, user Karthik Raman wrote:

Interesting. Nice to see network features being used as well. We found some specific network features (beyond the regular centrality measures) to be fairly powerful: https://journals.plos.org/p...

On 2021-04-19 20:26:30, user Bogan Lab wrote:

This article has now been published:<br /> Habtemichael, E.N., Li, D.T., Camporez, J.P. et al.<br /> Insulin-stimulated endoproteolytic TUG cleavage links energy expenditure with glucose uptake. Nat Metab 3, 378–393 (2021)<br /> https://doi.org/10.1038/s42...

On 2021-04-19 15:28:21, user Anchi Cheng wrote:

Thanks for responding to my comment previously. It is addressed appropriately. During a Journal club presentation of this in the group, I've noticed some points that will strengthen the paper, too. Please read https://doi.org/10.1016/j.j.... Two points here: 1. The defocus used in the low-mag should be included in the method section since this has impact on the calibration. 2. The logI0/I is not strictly linear once defocus is applied. The reason for this is phase contrast Fresnel fringes. This is why empty holes have different intensity from an empty square since the fringe extended in a fixed physical distance. The non-linearity behavior is also why C value the author presented here should be taken with caution when very small values are used. I have never derived a theoretical solution that will give a formula for this non-linearity. Hopefully someone will be inspired to do so.<br /> Anchi Cheng

On 2021-04-19 07:08:08, user Martin R. Smith wrote:

Great to see a thoughtful consideration of the impact of polytomies on tree similarity. Resolving polytomies at random is a decent approach, but of course it is possible that any specific tree thus produced might not be supported by the data. In case it's of interest, I suggested an alternative approach in Smith 2019, Biology Letters: https://doi.org/10.1098/rsb... . This approach can be extended to Generalized RF distances, which avoid many of the issues that afflict the plain RF (Smith 2020, Bioinformatics, https://doi.org/10.1093/bio... ).

On 2021-04-19 00:55:22, user Nate Dyer wrote:

One of the main reason animal testing is used in vaccine research is so that scientists can intentionally infect vaccinated animals to look for issues with pathogenic priming and ADE which have both prevented every coronavirus vaccine from progressing to human trials.

Are there any animal studies that did this?

Why are scientists ignoring the decades of research that has found that coronavirus vaccines lead to ADE?

On 2021-04-18 13:14:31, 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 – please visit this link if this is of interest: https://ncats.nih.gov/ctsa/....<br /> This work would not have been possible without this unique NIH mechanism (that then facilitated a subsequent NIH/NHGRI career development award to A.K.). More of the backstory here: https://twitter.com/anandi_...

On 2021-04-18 09:47:51, user Joshua Mylne wrote:

This is now published in Chem. Sci., see https://doi.org/10.1039/D1S...

On 2021-04-18 04:01:17, user ConservationBytes wrote:

This paper has now been peer-reviewed and published:

Bradshaw, CJA, CN Johnson, J Llewelyn, V Weisbecker, G Strona, F Saltré. 2021. Relative demographic susceptibility does not explain the extinction chronology of Sahul’s megafauna. eLife 10: e63870. doi:10.7554/eLife.63870 (https://elifesciences.org/a...

On 2021-04-17 08:50:58, user CJ wrote:

The study provides valuable clues for discovering the transmission chains of variant B.1.1.7 and understanding the evolutionary process of SARS-CoV-2.

On 2021-04-17 04:15:11, user neeta lohani wrote:

Now published in Plants: https://www.mdpi.com/2223-7...

On 2021-04-16 21:52:12, user Charles Warden wrote:

Hi,

Thank you very much for putting together this preprint.

When I ran GATK and DeepVariant with my WGS and WES samples (using the separate DeepVariant models), I believe that I came a different conclusion: concordance was better with the GATK filtered variants, and I thought the DeepVariant calls were kind of like the unfiltered GATK calls.

1) I believe that I used the following commands to run GATK and filter variants:

`/opt/gatk-4.0.1.1/gatk --java-options '-Xmx4g' HaplotypeCaller --reference $REF --input $BAM --output $VCF1 --dont-use-soft-clipped-bases true

/opt/gatk-4.0.1.1/gatk --java-options '-Xmx4g' VariantFiltration --variant $VCF1 --output $VCF2 -window 35 -cluster 3 -filter-name QD -filter \"QD < 2.0\" -filter-name FS -filter \"FS > 30.0\""`

Is this similar to the hard filtering in Table 2?

2) I wonder if it possible to assess the possibility of overfitting?

For example, is there a way to show you would get similar results with a sample that has not been characterized as much (but still has multiple genomic technologies for the same individual)? I don't remember exactly what was used for training DeepVariant, but these are samples that I would guess could have been used in earlier benchmarks and development (in general).

Or, on the flip side, can you use an overfit example to show the difference in these samples as being reasonably representative for new, independent samples (lower than in the overfit sample)?

Thanks Again,<br /> Charles

On 2021-04-16 19:49:16, user Alondra wrote:

Genome Biology and Evolution, Volume 5, Issue 1, January 2013, Pages 61–74

The Missing Link of Jewish European Ancestry: Contrasting the Rhineland and the Khazarian Hypotheses, 14 December 2012

Abstract<br /> Alternatively, the “Khazarian hypothesis” suggests that Eastern European Jews descended from the Khazars, an amalgam of Turkic clans that settled the Caucasus in the early centuries CE and converted to Judaism in the 8th century...

Following the collapse of their empire, the Judeo–Khazars fled to Eastern Europe...

Our findings support the Khazarian hypothesis and portray the European Jewish genome as a mosaic of Near Eastern-Caucasus, European, and Semitic ancestries, thereby consolidating previous contradictory reports of Jewish ancestry...

Our results have important implications for the demographic forces that shaped the genetic diversity in the Caucasus and for medical studies.<br /> https://academic.oup.com/gb...

On 2021-04-16 18:47:13, user Young Don Kwak wrote:

It is very nice work! As I know, treatment of Plad-B has been reported to increase R-loops in numerous literature. But, you observed opposite phenomena. Could you explain why you observed different result?

On 2021-04-16 13:17:06, user Ben King wrote:

Did you link the ages of the fossils within the same poorly dated site? I found this could improve estimates a bit.<br /> https://peerj.com/articles/...

On 2021-04-14 13:04:34, user Martin R. Smith wrote:

This is a very instructive and encouraging study (even if the quality of dating you have available is somewhat better than is normal for my stomping ground in the Cambrian...).

Your simulated datasets look useful too, and really handy that they're available through Zenodo – I hope you won't mind my using them? One thing I wasn't quite clear on was the relationship between the "trees" and "samp_trees" objects: am I right in thinking that a tree reconstructed from morph\_seqs[[i]] is compared with samp\_trees[[i]]?

One small bugbear: given the limitations and biases of the Robinson–Foulds distance, I wonder whether you might get clearer results with a more discriminating tree distance? I've reviewed some alternatives at https://doi.org/10.1093/bio... , and these are implemented in the R package TreeDist: http://ms609.github.io/Tree...

On 2021-04-16 12:38:11, user Mick Watson wrote:

Fossil fuels are the greatest source of methane, not agriculture: https://www.globalmethane.o...

One of the first "facts" they state and it is wrong

On 2021-04-16 11:31:32, user Anbu wrote:

Hi I have VCF compare file for two population sets. How do I calculate percentage similarity between two variant type data? Kindly refer me some paper.

On 2021-04-16 09:55:19, user Nathalie Balakina-Vikulova wrote:

The part "3.1.2 Simulation of the physiological mode of contraction-relaxation cycles" of the 1st Version of the article "Mechano-calcium and mechano-electric feedbacks in the human cardiomyocyte analyzed in a mathematical model" was not included in the final version of the article published in the The Journal of Physiological Sciences (doi: 10.1186/s12576-020-00741-6). This results were sent as a conference paper for «IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine (CSGB)» csgb.ieeesiberia.org in 2021.

On 2021-04-16 08:17:18, user Jakub Zahumensky wrote:

Energy dependence of the existence of gel-like sphingolipid-rich domains has been demonstrated previously. Specifically, Herman et al., 2015 showed that depolarization of the plasma membrane leads to melting of these domains and apparently a more homogeneous plasma membrane. It would be interesting to see if the void zones disappear also following inhibition of Pma1 or using an membrane potential uncoupler, such as FCCP. In presence of PS, there could be a lot of small gel-like domains that coalesce into a big void zone when PS is removed and temperature is elevated.

On 2021-04-15 13:38:35, user Jakub Zahumensky wrote:

In lines 300-302 you write "Transmembrane proteins are also excluded from ergosterol-enriched raft-like domains that are generated in the vacuolar membrane of yeast in stationary phase". However, in 2020 we have localized a transmembrane protein (Nce102, native to membrane compartment of Can1) into these domains (https://www.mdpi.com/2218-2.... Have you considered this?

On 2021-04-15 14:52:31, user Kevin Roche wrote:

as far as I can see this paper has not been able to get published, suggesting it has not been able to pass peer review. I also can't find any research that replicates the result. It is being widely used by anti-vaxxers to claim that the vaccines modify DNA, which is clearly false. Given the dangerous uses to which the paper is being put, shouldn't it either be published after peer review or withdrawn by the authors. Leaving it in limbo suggests some validity which is clearly unwarranted

On 2021-04-15 08:31:36, user Alexander Kastaniotis wrote:

In contrast to inactivation of the ACP1 gene encoding acyl carrier protein in yeast, a knockout of the PPT2 gene encoding phosphopantetheine transferase in Saccharomyces cerevisiae is viable (e.g. Merz S, Westermann B. 2009. Genome-wide deletion mutant analysis reveals genes required for respiratory growth, mitochondrial genome maintenance and mitochondrial protein synthesis in Saccharomyces cerevisiae. Genome Biol10:R95-2009-10-9-r95). This would argue for a non-essential role of the PPT group also in yeast. We discussed this in a recent review (Kastaniotis AJ, Autio KJ, R Nair R.Mitochondrial Fatty Acids and Neurodegenerative Disorders.Neuroscientist. 2021 Apr;27(2):143-158).

On 2021-04-15 04:28:46, user Ural Yunusbaev wrote:

Hey there! Greate paper. But I cant find the following excell files from the supplementary: <br /> Table S3. List of candidate susceptibility genes and orthogroups (excel file).<br /> Table S4. List of orthogroups containing two or more genes including gene IDs (excel file).

On 2021-04-14 23:37:24, user April Peterson wrote:

Now published in Genetics doi: 10.1093/genetics/iyaa019

On 2021-04-14 21:12:38, user Gustavo Bellini wrote:

Hi! This 2010 study showed that T cells to be activated correctly depend on sufficient levels of vitamin D.

• Vitamin D crucial to activating immune defenses<br /> https://www.sciencedaily.co...

Ref: von Essen et al. Vitamin D controls T cell antigen receptor signaling and activation of human T cells. Nature Immunology, 2010

Is there any way to test the virus in a medium with normal / high levels of vitamin D, to check if in this case it cannot escape?

Sufficient levels of vitamin D are also necessary for T cells to produce IFN-y:

• Vitamin D Is Required for IFN-γ – Mediated Antimicrobial Activity of Human Macrophages<br /> https://stm.sciencemag.org/...

I am not a virologist and I apologize in advance if I spoke silly. Tks

On 2021-04-14 18:32:55, user Paulovic wrote:

Interesting report! Thanks for sharing with the rest of the scientific community. I would have also thought that peroxisomes may have an important role in PKD. Indeed, a metanalysis pointed to downregulated peroxisomal function in PKD (https://pubmed.ncbi.nlm.nih.... Do the authors think that this could be due to differences between in vitro models and the in vivo situation in the PKD kidney? I wonder if the authors have been able to measure VLCFA beta-oxidation (C26 for example) in PKD kidneys.

Thanks,<br /> Pablo Ranea-Robles Ph.D.

On 2021-04-14 16:39:49, user Kai Ewert wrote:

This article is now published: Zhen, Y.; Ewert, K. K.; Fisher, W. S.; Steffes, V. M.; Li, Y.; Safinya, C. R.: Paclitaxel loading in cationic liposome vectors is enhanced by replacement of oleoyl with linoleoyl tails with distinct lipid shapes. Scientific Reports 2021, 11, 7311. DOI: 10.1038/s41598-021-86484-9; PMCID: 8012651.

On 2021-04-14 05:35:46, user Michael Ailion wrote:

In this manuscript, the authors build on a previous paper that characterized a post-developmental role of the immunoglobulin cell adhesion molecule L1CAM/SAX-7 in C. elegans neurons. In the previous paper (Opperman et al. 2015), it was found that sax-7 mutants show synthetic behavioral phenotypes in double mutants with the synaptic vesicle cycle mutants rab-3 and unc-13. The current manuscript builds on this finding, further characterizing the behavioral and synaptic phenotypes of these sax-7 double mutants, and performing a forward genetic screen for suppressors. The key findings of the manuscript are:

1. sax-7 mutants show a synthetic genetic interaction with synaptic vesicle mutants (rab-3, unc-13, and unc-10), but not with synaptogenesis mutants (rpm-1, syd-1). The rab-3; sax-7 double mutants affect several aspects of locomotion, including posture (coiling), and rate of movement while crawling or swimming.
2. Mutants in the ERK MAPK pathway genes ksr-1 and mpk-1 suppress the locomotion defects of rab-3; sax-7 and unc-13; sax-7 double mutants.
3. sax-7 acts in neurons to control locomotion, while ksr-1 acts specifically in head cholinergic neurons. The data suggest that sax-7 is needed in more neurons than just cholinergic neurons for normal locomotion.
4. sax-7 and ksr-1 do not affect synaptic transmission at the neuromuscular junction as measured by electrophysiology and electron microscopy.

Overall, the experiments are rigorous and well-performed and the data are solid. Sample sizes for experiments are sufficient and appropriate statistical tests are used to analyze the data. Genetic analyses and rescue approaches are rigorous. A particularly nice experiment is the experiment to show that sax-7 acts in neurons, using an elegant Cre-dependent tissue-specific restoration of a sax-7 knock-in allele. Though the mechanism of the genetic interactions and the effects on behavior is not pursued here, the manuscript makes an important contribution that further demonstrates that sax-7 and the ERK MAPK pathway affect neuronal function post-developmentally, setting the stage for more mechanistic studies. Our only substantive critique is that it seems possible that the rab-3; sax-7 double mutant does have an enhanced phenotype at the NMJ and some effect on SV release (Figs 6 & 7), in contrast to the conclusion made by the authors. The rest of our critiques are all considered to be minor.

Considerations:<br /> 1. It is concluded that sax-7 does not affect synaptic vesicle release based on electrophysiology of the NMJ. However, the Results section describing these experiments notably does not mention the effect on mini frequency, in which it appears that the rab-3; sax-7 double mutant (and rab-3; sax-7; ksr-1) may have an enhanced defect as compared to rab-3. Fig. 6A shows an ~30% reduction in mini frequency in rab-3; sax-7 as compared to rab-3, though statistics were not done for this comparison. The authors should comment on these data in the Results, especially given that locomotion phenotypes are often correlated better with mini frequency than with evoked release.<br /> 2. Likewise, in the electron microscopy experiment analyzing synapse ultrastructure (Fig. 7), it is concluded that rab-3; sax-7 mutants have no enhanced defect in synaptic vesicle docking within 100 nm of the presynaptic density as compared to rab-3 mutants. However, if only vesicles within 30 nm of the presynaptic density were analyzed, it appears that rab-3; sax-7 (and rab-3; sax-7; ksr-1) mutants would have fewer docked vesicles than rab-3 (~50-60% reduction, Fig 7C). Given that the vesicles closest to the presynaptic density might be more likely to be released, this could indicate that there is a defect in these mutants relevant to behavior.

Minor points:<br /> 1. Given that two sax-7 loss-of-function alleles were used in the study, the specific allele used in the assays shown in Figures 1, 2, 4, 5, and S4 should be specified, as well as the allele used in the rab-3; sax-7 suppressor screen. Likewise, the sax-7 and ksr-1 alleles used in Figures 6, 7, and 8A should be specified.<br /> 2. The abstract describes the story flow as having the electrophysiology and ultrastructural analysis preceding the suppressor screen, but these are presented in the reverse order in the Results section.<br /> 3. The abstract is rather descriptive and vague and does not provide the major conclusions of the paper -- that mutants in the ERK MAPK pathway suppress rab-3; sax-7, that there do not seem to be defects at the NMJ, and that ksr-1 and sax-7 have distinct neuronal sites of action.<br /> 4. The term "mitogen-activated protein kinase (MAPK) pathway" (page 2 lines 20-21 and page 5 lines 11-12) is not specific as there are many MAPK pathways. The specific MAPK pathway (ERK) should be defined.<br /> 5. The genotype of the animal shown in Figure 7A should be specified.<br /> 6. Figure S4 should give sample sizes and statistical methods.<br /> 7. The Punc-17::Cre and Punc-17H::Cre plasmids and worm strains (Fig 8B) should be described in the Methods.<br /> 8. Many plasmid injection concentrations are not given, but should be specified in the Methods.<br /> 9. The methods for making single-copy insertions should say that the injected strain has a unc-119 mutation and that this is rescued by the injected plasmids. Otherwise, it isn't clear why non-Unc animals would be selected.<br /> 10. The genotypes for sax-7(eq22) and sax-7(eq23) strains in the Strain List (page 6) should provide in brackets the construct that has been knocked in or recombined using standard C. elegans nomenclature for CRISPR alleles.<br /> 11. The reference Andreyeva et al. 2010 is missing from the Bibliography.<br /> 12. Possible typo page 9 line 5: tmIs1027. Elsewhere this seems to be tmIs1028.<br /> 13. Possible typo page 36 line 5: eqIs6. Elsewhere this seems to be eqIs4.

Reviewed (and signed) by Michael Ailion and Amy Clippinger

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 2021-04-13 21:22:06, user Tinashe Prince Maviza wrote:

Nice article! I’d like to ask if premature termination on IRPs is a spontaneously driven process, or rather facilitated by some rescue system?

On 2021-04-13 18:18:04, user Dave Roe wrote:

For the synthetic data set, were any simulated sequencing errors introduced when creating the simulated reads from the reference?

On 2021-04-13 13:40:03, user Dave Roe wrote:

The real-world data was generated from the target capture method (reference 40). Is that required? For example, can it work with WGS?

On 2021-04-13 04:32:59, user Dave Roe wrote:

"Performance of genotype determination was assessed at three-digit resolution (protein level) for the European and Khoisan cohorts, and at five-digit resolution (synonymous mutation level) for the synthetic dataset". This is not explained in the captions for Tables 2 and 3, and it might mislead when these values are presented together. Why weren't the European and Khoisan datasets evaluated at five-digit resolutions?

All data sets were composed of known alleles, if I understand correctly. If this is an inherent limitation, it would be nice if this was discussed. If it isn't, it would be nice to see some evaluation or discussion as to why those alleles were excluded.

On 2021-04-13 14:06:30, user N RF wrote:

The fact is that those researchers to whom I have referred conclude that in some patients sick with COVID there is a skeletal muscle injury and this is confirmed by the high levels of creatine kinase. Everything verifies (in theory) elevated levels of creatine kinase, especially in patients with severe infection, and is related to the excessive immune response that leads to extreme inflammation in many patients. Other studies say that COVID affects phosphorylation (a phosphate group attaches itself to a protein by kinase), that is, apparently many of the proteins that interact with the virus are important phosphorylated and many of the kinases, in addition, show changes in their phosphorylation activity. The most significantly "sequestered" kinases contain casein kinase II (CK2).<br /> It is also known that this virus uses ACE2 receptors to invade cells, and that at brain levels these receptors have less activity, neurons express fewer receptors for ACE2 (at best, it may be, that this is why there are fewer brain affectations than of other types when there is COVID infection).<br /> Neural plasticity and how long-term potentiation (PLP) increases synaptic strength with the rapid and repeated entry of Ca2 + binding with glutamate, activating the induction of PLP, and how all this is critical in terms of memory and learning, intervening in the consolidation of memory and contributing to neuronal plasticity. Here, one of the kinases activated when calcium enters is II (CaMKII) 3, fundamental in this whole process.<br /> So, it is shown that this virus directly affects the activity of kinases and II (CaMKII) 3 is a kinase directly related to memory consolidation. something to do with how COVID affects the memory of some patients?

On 2021-04-13 06:48:47, user Gary Moore wrote:

There is a paper, based on analysis using the Automated Similarity Judgment Program (ASJP), also suggesting that the Indo-European family has an affinity with Chukotko-Kamchatkan languages. Ancient DNA from Neolithic and Neolithic kurgan burials in the Pontic Region from cultures thought to represent the earliest Indo-European speakers were found to belong to mtDNA C4. In a way, this may not be surprising in view of Robert Beekes’ remarks on the parallels in phonology between IE and Salishan Shuswap, C. C. Uhlenbeck’s proposed link between Eskimo-Aleut and IE, or John Asher Dunn’s hypothesis that Tsimshian is a highly aberrant Indo-European language. It’s also consistent with Julien d’Huy’s analysis of the Cosmic Hunt myth in which he shows that the Greco-Roman versions of the myth are more closely aligned with versions from the Pacific Northwest of North America and the Iroquois. See "Support for linguistic macrofamilies from weighted sequence alignment” Gerhard Jäger, Department of Linguistics, University of Tübingen<br /> https://www.pnas.org/conten...

While mtDNA X2 which is also found in North America is concentrated mainly in the region around the Black Sea and the Middle East, subclades of C4 have also been found in ancient DNA from the Pontic Steppe region, including C4a in Kurgan burials and C4a2 among burials associated with the Dnieper-Donets culture in Neolithic Ukraine. (See "Ancient Mitochondrial DNA From Pre-historic Southeastern Europe: The Presence of East Eurasian Haplogroups Provides Evidence of Interactions with South Siberians Across the Central Asian Steppe Belt”) From the abstract:

"In this study, we analyzed the DNA sequence of the first hypervariable segment (HVSI) of the mtDNA control region, as well as a portion of the coding region, in 14 individuals from three collective burials from the Neolithic Dnieper-Donetz culture and three individuals from Bronze Age Kurgan burials, all located in modern-day Ukraine on the northern shores of the Black Sea (the North Pontic Region, or NPR). While most of our samples possessed mtDNA haplotypes that can be linked to European and Near Eastern populations, three Neolithic and all three Bronze Age individuals belonged to mtDNA haplogroup C, which is common in East Eurasian, particularly South Siberian, populations but exceedingly rare in Europe. Phylogeographic network analysis revealed that our samples are located at or near the ancestral node for haplogroup C and that derived lineages branching from the Neolithic samples were present in Bronze Age Kurgans. In light of the numerous examples of mtDNA admixture that can be found in both Europe and Siberia, it appears that the NPR and South Siberia are located at opposite ends of a genetic continuum established at some point prior to the Neolithic. This migration corridor may have been established during the Last Glacial Maximum due to extensive glaciation in northern Eurasia and a consequent aridization of western Asia. This implies the demographic history for the European gene pool is more complex than previously considered and also has significant implications regarding the origin of Kurgan populations."

ScholarWorks Citation: Newton, Jeremy R., "Ancient Mitochondrial DNA From Pre-historic Southeastern Europe: The Presence of East Eurasian Haplogroups Provides Evidence of Interactions with South Siberians Across the Central Asian Steppe Belt" (2011). Masters Theses.

On 2021-04-13 03:49:38, user Dave Roe wrote:

I don't see where approach was demonstrated on KIR. Did I miss it?