On 2020-08-24 12:11:22, user Sebastian Jaenicke wrote:

This is clearly focused on amplicon sequencing, i.e. metabarcoding, and should not make use of the term "metagenomics".

On 2017-10-23 02:48:59, user Gilles Vanwalleghem wrote:

Hi,<br /> great work and very impressive. I was wondering were we could find the tables ?

On 2020-06-27 02:05:24, user Alexander Novokhodko wrote:

Hello,

I believe that there's a need to make a correction here: In the text you say "When focusing on the sole RBD, from amino acids 319–541, 13 variants arise, all with a relative frequency less than 0.1% and 10–20 absolute occurrences."

When I looked at Supplementary Table 1, I found four mutations in that range of the spike protein with a frequency > 0.001. If I understand correctly, Percentage = 100*frequency. Thus, Asn438Lys, Ser477Asn, Thr478Ile, Val483Ala have frequencies > 0.1%. Also, why is Ser477Asn absent from Figure 2A?

Thank You,<br /> Sincerely,<br /> Alexander Novokhodko

On 2021-02-10 20:39:04, user Senay Beraki wrote:

Dear Dr. Kesh and colleagues,

I am an undergraduate student from UCLA and would like to share some of the comments/questions myself and other students had while analyzing your paper during a journal club session.

Figure 1: The schematic diagram of the experimental timeline in (A) was helpful and important to include in the first figure of the paper. Part (C), however, was confusing because I wasn’t sure what the green and red colors represented. Adding a legend next to the heatmap or in the caption could definitely help understand the heatmap better.

Figure 3: In part (G), it was puzzling and unclear how you were able to get the curve for the red line using those data points. Also, how is the effect of Oxaiplatin treatment in colon cancer cell line connected with the figure’s or the paper’s research question? We were not sure what the purpose of oxaiplatin was in relations to obesity and pancreatic cancer.

Figures 5, 6 and 7: There were a lot of experiments performed for these three figures including flow cytometry, immunohistochemistry, western blot, PCR array, and ELISA based analysis. However, the methodology for most of these techniques was not explained or mentioned in the methods section of the paper. It would be helpful for your readers if you could briefly explain how they were done or reference past papers on those techniques.

Overall, your paper was very interesting and exciting to read. The introduction was well written as it introduced the main components of the papers and made it easier for me to follow the overall research question. Thank you for doing such as incredible research and I hope this feedback helps in strengthening your paper.

On 2017-01-31 13:01:56, user SamGG wrote:

Thanks for sharing your work. Why the PCA in figure 2a does not use the same 100 genes that the tSNE used in figure 2b? This should build a more fair comparison IMHO.

On 2020-11-03 17:17:08, user Gerry Smith wrote:

On 8 September 2020, this paper was accepted for publication in Scientific Reports. The accepted version differs little from the version available here.

Gerry Smith

On 2019-11-05 14:49:49, user Erin Landry wrote:

We are students in BI598 at Boston University, and we reviewed this paper for an assignment for the course. We hope you find our comments helpful!

Boston University - BI598 - Group 2

Complement-dependent synapse loss and microgliosis in a mouse model of multiple sclerosis

Summary<br /> Hammond et. al. focus on the general pathology of multiple sclerosis (MS) in grey matter, a glaring gap in our understanding of the disease. Specifically, they use CA1-stratum radiatum (CA1-SR) of the hippocampus in both sham-treated and experimental autoimmune encephalomyelitis (EAE) mice as a model region to investigate complement system activity in MS-linked synapse loss. Using immunohistochemistry (IHC) and Western Blot (WB), they found increased expression and localized protein levels of C1q and C3 in the target region of EAE brains (Figure 1, 2). Using knockout mice, they then correlated loss of specifically C3 with improved subject symptoms over time (Figure 3). After using another round of IHC to target post-synaptic markers, researchers claimed that knocking out C3 or C1qa led to a decrease in EAE grey-matter synapse loss. (Figure 4) They then found that C3 knockout mice did not experience microglial activation to the same degree that WT or C1qa KO mice did, leading them to conclude that C3 is essential for EAE inflammation and concurrent synapse loss. (Figure 5) This research into early complement pathways and the dramatic effects of C1q and C3 in an immune-mediated disease like MS are particularly compelling as they lie outside of established treatment methods, which focus more on T-cell activation and inflammatory pathways. Therefore, this line of research opens up the possibility of a new class of drugs whose effectiveness could be competitive with canonical medications.

The title of the paper states a global effect of complement activity in synaptic loss; however, the experiment almost exclusively collected data in the CA1-SR. A more specific revision of the title or broader research into the effects of this pathway on the grey matter in additional brain regions is needed to support the current claim. Expansion of explanations of affected pathways with figure diagrams would help with understanding for unfamiliar readers. Additional experiments exploring different developmental time-points and alternative complement system routes are necessary to support claims of the efficacy of a complement-based treatment to decrease MS symptom severity. More quantitative analysis, including further synaptic density analysis, DAPI and GFP in cytosol to show morphology, and combined pre- and post-synaptic markers are needed. Electrophysiological data, fMRI imaging and/or confocal imagery of dendritic tufts could potentially be useful in this regard, as depicting synaptic density and strength would help to understand the foundations of synapse degeneration in MS. It could be an interesting take to look at in vivo imagery through miniscopes or viral load injections for possible KO or KD of genes at different developmental stages. Sex differences are only investigated during immunization status experiments and genotype, but separate analysis based on sex for the performed experiments may have proved interesting, as MS is more common in women than men. Expanded criticisms, merits, and possible future directions are discussed below.

Merits <br /> In this project, researchers centered grey matter degeneration in multiple sclerosis, a process that has been under-characterized until recently. Importantly, researchers determined that mRNA expression of complement chemokines increased along with protein localization in the brain, supporting the hypothesis that complement deposition in grey matter lesions is not exclusively due to blood brain barrier breakdown. Overall, researchers effectively present their finding that there is differential complement activity in their target brain region and that this differential activity correlates with loss of certain synaptic markers.

Major Criticisms<br /> Interrogating changes in the complement pathway in the hippocampus of EAE mice is a very reasonable scope for a research paper. Though this is the true scope of the paper, very large claims about the global effects of EAE on the complement pathway are made, which cannot be corroborated by these experiments. Even the title of the paper suggests global changes in the complement system were studied, which exaggerates the work that was done.

In general, the experiments performed are disconnected and lack a cohesive thought progression. It appears as though subsequent experiments were performed in only specific cell types or brain regions in order to observe the expected results, rather than performing those experiments in multiple cell types or multiple brain regions to yield a holistic picture of the changes caused by EAE (e.g. observing increased complement proteins in the hippocampus and then only performing Western blots with CD11b+ cells, and previous studies showing synapse elimination in SR and then only showing IHC images in SR).

The paper would have benefitted from more comprehensive examination of the different regions of the hippocampus rather than focusing on the SR. Making any claims about EAE affecting only the SR of the hippocampus would require the experiments to be performed in all regions of the hippocampus and results with only significant changes in the variable in question in the SR. Previous work can be used for guiding hypotheses on how EAE affects the hippocampus, but previous work does not justify limiting experiments to one particular region of the hippocampus.

More extensive IHC experiments in this region are necessary to provide a compelling argument for the contribution of complement-dependent synapse loss to grey matter degeneration in EAE. For example, it would be useful to look at cell distribution and morphology throughout the hippocampus. Simultaneously looking at morphology and the various markers used in the paper would provide stronger evidence for claims made about synaptic degeneration in EAE. Some suggestions for additional markers to stain for are 1) any presynaptic marker (to show colocalization with Homer1 or PSD95), 2) myelin (as synaptic degeneration was stated to be independent of myelination state of grey matter), 3) nuclei (DAPI stain to show distribution of cell nuclei throughout the hippocampus), 4) cytosol (GFP in the cytosol in addition to markers used in the paper would yield images that are easier to interpret), 5) blood vessels (Figure 2G would benefit from differential labeling of blood vessels), 6) markers for the alternative and lectin complement pathways, and 7) colocalization of microglia markers, C1q and C3, and synaptic markers (could suggest synapses were phagocytized by microglia).

With all of the IHC experiments already performed and the suggested IHC experiments, there is a great opportunity for an exhaustive quantitative analysis of changes in the complement pathway in the hippocampus of EAE mice. Figure 2C in which C1q intensity is quantified in each region of the hippocampus provides a very thorough description of the IHC results. Repeating this analysis for all other IHC experiments would help to more clearly convey results to the reader. In addition to looking at number of puncta of different markers present, it would be useful to look at the density of puncta in different regions and the distribution of puncta size. Figure 5B-E give a nice quantitative description of the IHC done in Figure 5A. In addition to these analyses, the complexity of the arborization of microglia could be quantified by a Sholl analysis.

Minor Criticisms<br /> While language and syntax were frequently clear, there were instances where more specific language would be useful to the reader. Generally, it should be possible for a reader to understand the context of a finding in the results section without needing to reference prior or subsequent sentences. Editing for both grammar and clarity is needed.

In Figure 2D, the researchers claim to show punctate and diffuse C1q expression in both Sham and EAE mice. While to the eye the EAE sample appears to have more staining, thresholding and quantification would make this finding more meaningful. Additionally, Figure 2E underpins your claim that there is colocalization of C1q to the synapse. However, in the EAE sample it appears that there is significant C1q expression both in the red-stained regions (near PSD95) and in dark tracts where there is no synaptic marker present. Better quantification of this co-localization is required. Additionally, presence of PSD95 alone is not enough to characterize a synapse. Further tests must be completed to establish cell-to-cell interface. Electron microscopy, time-based changes in fluorescence, or paired pre- and post-synaptic staining are options that could further support the finding that synapse density has decreased.

Throughout the set of experiments, there is some inconsistency in sizes of treatment groups, with n ranging from 7 to 24 in Figure 3. Consistency in these sizes will help with statistical strength. Providing a summary figure of the complement pathway described in this investigation would also be helpful for the reader and is consistent with wider trends in journal articles. Moving towards accessibility and clarity helps to both increase reader belief in the findings of the article and increases the chance of the article being useful to scientists who have less specific knowledge of the field.

Future Directions <br /> The novelty of focus on grey-matter MS symptoms provides a wide scope for follow up studies. While a lack of time-based results is a definite gap in testing described in the above criticism, it could easily be remedied by completing the experiment at different time points using the same setup. Additionally, it would be interesting to, as referenced in the paper, produce a C3 location- or time-dependent knockout. Given the complement system’s essential role in developmental synapse pruning, it is important to determine whether developmental complement maintenance and then later decrease in activity through conditional knockout sees the same protective result as described here. This is particularly crucial when talking about this pathway as an avenue for drug development, as human patients will have active complement systems through development. Additionally, it would be interesting to see whether subjects experiencing greater MS progression see similar rescue.

Another set of important follow-ups focuses on expanding the scope of the paper. This study used CA1-stratum radiatum due to its limited demyelination in EAE, but other regions with greater rates of demyelination should also be investigated. Knockout of complement proteins (C3) led to a decrease in microglial activation, leading researchers to conclude that this synapse-loss was dependent on C3 activating microglia to increase inflammation. Clodronate could be used at different timepoints to kill microglia, investigating whether decreases in inflammation alone without complement changes could produce the same protective effect. As described these criticisms, not looking at the effects of MS in different brain regions limits possible claims from observed data. The complement system has universal activity, but in order to make a universal claim, more than one region of the brain must be studied.

On 2019-11-05 16:43:34, user Cyrus Cheung wrote:

BI598 Group 6 (Alondra, Cecar, Cyrus, Safiya, and Can)

A review written by Boston University undergraduate students majoring in Neuroscience and Neurobiology as a requirement for the class, Neural Circuits (BI598).

Summary:<br /> Multiple Sclerosis (MS) is an inflammatory, neurodegenerative disease of the CNS characterized by both grey and white matter injury. Hammond et al used the established experimental autoimmune encephalomyelitis (EAE) animal model to recapitulate features of the disease. Their goal was to evaluate whether complement dependent synapse loss contributes to grey matter degeneration in EAE, which they highlight as being understudied. The complement system is part of the innate immune response and it signals microglia to prune synapses by phagocytosis. Therefore, their experiments revolved around studying the effects of knocking out C1qa and C3, which are both immune signaling proteins that signal microglia to phagocytose cells and cause neurodegeneration.

To determine if EAE hippocampus produces the complement proteins that could make synapses vulnerable to phagocytosis by glia, they analyzed C1q and C3 protein and mRNA expression by Western blot and qPCR. As shown in Figure 1A-C, they determined that the increase in protein expression of C1q and C3 in the EAE hippocampus was due to local gene expression, and not because of blood brain barrier breakdown. They also isolated CD11b+ microglia from the hippocampus and cortex, and found that CD11b+ cells in EAE mice overexpressed C3 28 days post immunization. However, they found no significant difference in the C1qa expression (Figure 1D). They concluded that microglia contribute to elevated C3 expression, and possibly C1q expression. They acknowledge, however, that the EAE elevated expression of complement proteins could be induced by other cells in the hippocampus.

They performed IHC using anti-C1q and anti-C3d antibodies to investigate the locations of elevated C1q and C3. Through IHC, they measured an increase in C1q fluorescence across the hippocampus in EAE vs sham mice (Figure 2B). At high (60-100x) magnification they observed that C1q was diffusely localized throughout the neuropil but also was localized at higher density in small punctate regions, some of which co-localized with synapses or along the dendrites in both sham and EAE brains (Figure 2D-E).

To answer whether the loss of C1q or C3 protects against EAE induced motor impairment caused by spinal cord damage, the authors compared the clinical score of WT EAE mice with the C1qa and C3 KO from 0 to 26 days post induction (Figure 3). The graph shows that only the C3 KO presented less severe motor deficits, C1qa KO did not alter the course of the EAE disease, and neither the C1qa KO nor the C3 KO changed the symptom onset.

The researchers proceeded to study how knocking out C1qa or C3 in the CA1-stratum radiatum layer of the hippocampus could have a protective role for synapse loss. They used IHC to stain for two postsynaptic markers: Homer1 and PSD95 in mice 28-30 days post immunization. As seen in Figure 4C-D, the knockouts played a slight protective role in synapse loss, with C3 KO playing a greater effect.

Hammond et al. took their results further by investigating whether C1qa or C3 KO alter the morphometric parameters of microglial activation induced by EAE. More specifically, they utilized IHC to stain for microglial proteins IBA1 in the CA1-SR of the hippocampus (Figure 5). They then tested for various parameters such as sum volume, sum intensity, surface area, and skeletal length as a way to test for change in cell morphology. The data from this figure suggests that the C3 KO prevented hippocampal microglial activation induced by EAE, however it also revealed that the C1qa KO had no effect on microglial morphology.

Merits: <br /> The paper identifies an interesting gap in knowledge, the introduction highlights their goal of studying the role of grey matter degeneration, a result of complement-dependent synapse loss. The complement-dependent synapse loss was studied effectively by use of C1q and C3 knockout mice.

Overall, they made a convincing argument that the C3 knockout has the most potential for any rescue of symptoms. This makes sense because C1q activates C3, which accumulates at synapses, hence microglia can detect it as a signal to phagocytize. It is intuitive to think that knocking out C3 would slow the engulfment by microglia and decrease the loss of synapses in EAE.

Specific Critiques:<br /> Overall, the paper made a connection between multiple sclerosis and the complement pathway with a focus on the motor cortex. However, multiple sclerosis has many different symptoms beyond just motor movements, therefore the paper would benefit from an investigation into other symptoms and circuits of MS, such as vision and sensation. In the introduction, the paper also discusses the use of the induced EAE mouse model as a model of MS grey matter injury; however, the results section does not indicate the data refers to grey matter. An explanation of how the complement dependent synapse loss affects grey matter would tie the paper together.

They reported that the inflammatory response was localized in the hippocampus through the qPCR analyses that showed local gene expression of C1q and C3. Overall in Figure 1 the error bars were large, the data would specifically benefit from a larger and more consistent sample size, since the N’s range from n= 5 to n=11. While Figure 1B-C quantifies levels of C1q and C3 expression in the hippocampus, Figure 1D shows data from CD11b+ cells isolated from the cortex and hippocampus, which was as stated, a way to “obtain sufficient cells”. In order to discern if the increased expression of C1qa occurred locally in the hippocampus or in both hippocampus and cortex, the author should include another set of graphs for the qPCR results of C1qa and C3 gene expression of just the hippocampal extracts separated from the cortex. Without this, it is inconclusive if microglia contribute to elevated C3 expression in EAE, as there is insufficient evidence of microglia directly elevating the levels of C1q and C3 in the hippocampus.

The method of evaluating the protein levels of C1q and C3d in the hippocampus of EAE mice was well thought out. However, in order to make a stronger claim a few elements could be added to Figure 2. In addition to the treated hippocampal areas, a normalized signal should have been presented such as a DAPI stain in order to show the living cells in the localized area, allowing for a helpful comparison. An alternate would be to stain for blood vessel markers, allowing for the reader to understand the layout of the image presented. On the other hand, a simple addition of insets on top of the current images would give the reader a greater sense of what exactly they are looking at. This would allow for quantification of the data presented as the colocalization is not very clear. The C3 knockout would also benefit from an increased sample size, since this might make the large error bars smaller. A discussion of a discrepancy between a significant increase of C1q, but not C3, in the EAE mice and it’s relation to its synapse loss significance that is discussed in later figures would help bridge the paper together and explain something that might be unexpected.

Figure 3 opens the possibility of targeting C1q and C3 to ameliorate the motor deficits caused by MS through investigation of the different complement pathways. However, the paper did not elaborate on why they concluded the alternative complement pathway as the most important in EAE. As stated in the introduction the complement system, classical, alternative, and lectin pathways all converge on the production of C3. Therefore, there is not enough evidence to conclude that the lectin and classical pathways are also not involved. An explanation on how they conceptualize these pathways would provide increased clarity to the conclusions made from the data.

Through the pictures and graphs shown in Figure 4, the authors are trying to show that knocking out C1qa and C3 decreases the synapse loss seen in EAE. The data would benefit from an increased sample size, especially for the C3 KO model, as there are large error bars. They use Homer1 and PSD95 as postsynaptic markers but they should confirm the presence of synapses by including EM images or using a presynaptic marker such as Synapsin, in addition to Homer 1/PSD95. Authors could also run a western blot for synaptic proteins like DLG4, Synaptophysin or Neuroligin to confirm the presence or absence of synapse through the different conditions. Again, the data does not provide enough evidence to conclude that the alternative pathway is the most important in EAE. A more elaborate explanation of how the data concludes it is the alternative pathway would add clarity.

Figure 5 displays the stained brain sections from the WT, C1qa KO, and C3 KO, sham and EAE immunized mice along with the quantification of various parameters to show that C3 KO mice with EAE have reduced microglial activation when compared to the WT EAE mice. To properly convey this, a few sentences on how C3 is specifically connected or related to microglia are needed. Considering the IBA1 stains can be expressed in other parts of the brain, it would be beneficial to check for other markers such as TMEM 119, a microglial cell surface protein that is not expressed in other neuronal macrophages or immune cell types. In addition to this, figure 5A, could potentially be combined with figure 1 considering they both look at morphology.

Minor Concerns: <br /> Introduction<br /> The introduction includes a few grammatical errors:<br /> In the second paragraph, “triggers” should be “to trigger”<br /> In the fifth paragraph, the authors should have used present tense while writing about the previous findings.

Methods<br /> They included background information about C1qa in the methods section. This belongs to the introduction section of the paper.

The clinical score was mentioned in the methods and it would be beneficial to name the section appropriately or create an entirely new section.

It was unclear why Homer1 and PSD95 was chosen to investigate synaptic loss in the CA1-stratum radiatum. Meanwhile, IHC with CamKII is more commonly used and could show synapses more clearly. More explanation could be provided.

In the fourth line of the EAE section, instead of “each of two sites”, it should be “each of the two sites”.

Figures<br /> In Figure 1D, the authors include data on the qPCR results of C1qa and C3 from CD11b+ microglia/myeloid cells and mention this in the results. However, they should clarify the role and importance of CD11b+ microglia/myeloid cells by adding background information in the introduction.

Figure 2 and Figure 4 could be visualized better with a larger and higher resolution image.<br /> Figures 4 A through B are stained in different colors and the green is harder to see than the grey. It is recommended that they stick to one color so that they both images are easy to see.

Results<br /> In the fifth line, “C1q and C3 protein” should be “C1q and C3 proteins”<br /> In the sixth line, the sentence should start as “By Western Blot, we found that...”<br /> Please review the paper for spelling, grammatical, and punctuation errors.

Future Directions:<br /> The researchers investigated a motor impairment in a mouse model with C1q and C3 knockout, affecting the hippocampus as well. Different diseases, such as Alzheimer’s Disease, are heavily associated with hippocampal damage, indicating the complement pathway could be extrapolated as a therapeutic target for neurodegenerative diseases as a whole. Behavioral paradigms that test other symptoms of multiple sclerosis, such as vision, and tasks that test for Alzheimer’s Disease, such as working memory, could show the impact of the complement pathway on common diseases.

As complement genes are important for neuronal development, confounding factors might have played a role in the experimental paradigm. Use of ASO to knock down genes and bypass development would help remove confounding variables in the experiment.

Lastly, accounting for more time points prior to 28-30 days post immunization would allow for a clear visualization of the EAE progression. Including more time points for figures 4 and 5 would allow for a comparison of how synapse loss progresses through time.

On 2016-04-14 16:51:23, user Kevin Geyer wrote:

This is an interesting study, and research addressing the controls on ‘relic DNA’ concentrations in soils is important! We provide the following comments in an effort to strengthen the manuscript for publication:

1. Clarification of the pH effect on relic DNA is important - conflicting information exists in the text (lower pH soils have more relic DNA; Lines 207-208, Table 1) and figure S7 (lower pH soils have more similar communities after PMA treatment). Given these relationships, it appears that your data suggest a negative relationship between the quantity of relic DNA and community dissimilarity. Is this the case? In our opinion the presentation of the data obscures this point. For example, samples are ordered differently in Fig. 1 and 2, and regression of community dissimilarity against pH is in the supplement. Why not directly regress community dissimilarity against relic DNA quantity?

2. Why was relic DNA quantity converted to a binary variable for regression with edaphic factors (Table 1, Fig. S6)? Why was 20% chosen as a cutoff for relic DNA presence/absence? If the assumption is that PMA treatment is not quantitative this should be discussed prominently in the manuscript, as this affects interpretation of results.

3. Sample storage should be discussed more explicitly as this could potentially affect the number of viable cells. How long were samples stored before PMA treatment? In addition, a test of the effect of soil moisture on relic DNA quantity would be useful for interpreting the data as this could be an important determinant of the number of viable cells.

4. A broader discussion of the significance of these findings for other biogeographical work on soil microbes would be valuable, as it calls into question the validity of earlier DNA-based approaches (e.g., how diversity relates to soil properties).

-- Kevin Geyer, Eric Morrison, Serita Frey (University of New Hampshire)

On 2014-11-24 09:55:39, user Helen wrote:

DOT, the Dresden Ovary Table is not online! Fully access all data at: http://tomancak-srv1.mpi-cb...

On 2016-08-31 14:55:44, user Jayarava wrote:

I'm betting that there is no hypersphere in the brain. Results which require a complete, from scratch rewrite of all the laws of physics seldom turn out to be correct.

On 2025-02-24 01:16:55, user Alex Monell wrote:

Hi bioRxiv staff,

The published version of this manuscript is out at https://www.nature.com/articles/s41586-024-08466-x

Thanks!

On 2018-11-19 20:53:04, user Diedrichsen_lab wrote:

This is a very interesting study investigating the spatial organization of hand movement representations in M1. We agree with the authors that the hand representation in M1 is likely complex and therefore requires advanced methods to probe. We would like to point out, however, that the authors’ reference to a previous paper from our lab (Ejaz et al., 2015, NatNeuro) contains a number of misunderstandings. Specifically, we take issue with the authors stating that 1) our work argues for a simple topographic arrangement of single finger representations in S1, and 2) that the overlap between finger activation patterns is “due to noise”.

In our work (Ejaz et al., 2015), we used BOLD fMRI to measure the activity patterns evoked by single- and multi-finger movements in M1 and S1. The spatial arrangements of these patterns in both regions were stable within each participant (compared across different scanning sessions), but highly variable across participants. These finger patterns are shown in figure 1 of our paper. Close visual inspection of the patterns reveals they do not follow a clear linear arrangement in either S1 or M1, and perhaps some evidence of digit “mirroring” can be observed – definitely there are parts of the cortex activated for the thumb at the dorsal end of the hand region.

We then calculate the dissimilarity between all pairs of finger patterns for M1 and S1, separately. Importantly, the relative dissimilarity between any pair of activity patterns (within a participant) was highly stable across participants. This is notable given the spatial arrangements of these patterns was highly variable across individuals. One stable characteristic was that the thumb pattern was more similar to the little finger than to the ring finger. This finding clearly shows – contrary to what our paper is cited for - that a simple linear somatotopic arrangement cannot account for the digit representations in M1 or S1.

We then show that the stable structure of overlap of finger representations in M1 and S1 can be accounted for by the statistics of everyday hand movement. Thus, we did not interpret the spatial variability of these patterns “noise due to inter-individual variability in every day hand movements”. On the contrary, the statistics of hand use is stable across individuals (Ingram et al., 2008, Exp. Brain Res.), as is the organizing principle underlying the spatial organization of activity patterns in M1 and S1.

Overall, both imaging and neurophysiological evidence clearly suggests that M1 is not so much concerned with the representation of fingers, but rather of complex hand movements. The use of a winner-take-all map for fingers is therefore a less effective way of gaining a deeper understanding of the organization of M1. We do agree with the authors that M1 organization is more complicated than a simple linear finger organization. Whether the organization really is best described by two discrete finger maps with phase reversal, however, really has to await a more rigorous experimental and statistical evaluation. Whatever the answer may be, however, we do think that the improved specificity of the VASO sequence may play an important role in uncovering such representations in the future, and we are excited to see these new developments.

On 2019-11-17 15:24:56, user Sebastian Aguiar Brunemeier wrote:

Very interesting. One wonders why CXCL9 would be elevated in the first place.

On 2025-11-05 00:47:29, user Jessica Lingad wrote:

This has now been published at 10.1016/j.neurobiolaging.2025.10.005

On 2021-04-12 20:52:59, user Alexis Germán Murillo Carrasco wrote:

Dear authors,

First of all, I would like to thank all of you for your invaluable effort to improve Peruvian scientific research. To continue this effort, I would like to adequate some points in your pre-print.

There is interesting the use of Syrian hamsters as a study model. It was announced by various articles mentioning similarities between Syrian hamsters and humans on COVID-19 disease. The response to SARS-CoV-2 infection of these animals is usually increased in aged (instead of young) individuals, as happens in humans. In the methods section, you described the use of 4-5 weeks-old Golden Syrian hamsters. Therefore I believe that the age of these animals could influence the interpretation of histopathological results. I would suggest your review published data (and discussion) on PMC7412213 and PMID32571934.

About your challenge experiment, I felt a lack of scientific rationale to determine the proper doses of vaccine candidates that were applied on animals. In Figure 9A, I would hope to see higher levels (above 80%) of viral isolate for all cases in 2 dpi. Can you explain a bit more possible reasons for this situation? Also, I think it would be interesting to see a statistical comparison between 2-5-10 dpi at least for the most important candidate in your proposal (rLS1-S1-F).

In the text, you wrote: "This is consistent with previous studies, which reported that viral load is reduced to undetectable levels by 8 days after infection in the hamster animal model". Today we know that viral load is detectable up to 14 days after infection in Syrian hamsters. I think different factors (as the age and sex of these animals) would intermediate this fluctuation. Probably, you should update this information on your preprint, especially on the discussion.

You also wrote: "Being lyophilized, this vaccine candidate is very stable and can be stored for several months at 4-80C". However, I think there is not sufficient evidence to say this by your western blot with products stored up to 50 days. You could attach results of the biological effect of previously-stored vaccine candidates. Also, you may consider testing candidate vaccines stored for more than 2 months. In a general view, I suggest showing more technical details, such as information about qPCR efficiency curves (or efficiency ranges) for all studied genes.

Finally, I kindly hope these comments can improve your high-quality work and stimulate further studies in Peru. I look forward to your next version (or published article). Please share it with me when it comes out.

Best regards,

Alexis M.

On 2018-03-13 16:32:48, user Pavel Payne wrote:

A peer-reviewd version of this manuscript has been published in eLife 2018;7:e32035 DOI: 10.7554/eLife.32035, https://elifesciences.org/a...

On 2019-12-12 04:09:50, user Alex Hall wrote:

I read your paper thoroughly and have some concerns.

In short, the link between methylation in dogs and canine aging is inferred too loosely. It's a correlation vs. causation issue. I would greatly appreciate if the authors could rephrase their abstract, results, and discussion to reflect that their study is on the topic of methylation in a population of dogs, rather than the cumulative effect of methylation in aging dogs.

I am concerned that the anchor author's conflict of interest jeopardizes the legitimacy of the strength of the conclusion. It would seem that there is financial incentive for this study to say a certain thing: dog age can be inferred using health data analytics.

95% of the dogs used in this study are Labradors. Though not intrinsically an issue, the generalizations made from such a homogeneous population is perplexing at a minimum.

It would be useful to look for an accumulation in methylation across the lifetimes of individual dogs, rather than to census a population. In the present study, you are unable (I think) to disentangle cohort effects from age on the amount and genomic region of methylation observed.

The section "Fitting the epigenetic age transfer function" is being widely interpreted by non-experts as "a new formula for aging in dogs," but it is not really based on new understanding of the biology of dogs. The recursive nature of how the variables are populated in the model also seems to yield a pre-determined conclusion that there is a ln-linear correlation between methylation in humans and dogs, and thus there is some kind of underlying relationship between aging in humans and dogs.

The authors have excluded almost all other literature on the topic of aging. I assume this is because they are aiming to submit this paper to a high-impact journal that will typically ask for a lower word count at the cost of a more complete argument. In the present form, it feels very incomplete and presents a picture that would lead an uninformed author to believe that methylation is essentially the BEST predictor (and cause) of aging in mammals.

It is a shame that there is so much loose language in a preprint that is being picked up by media outlets. The methods are largely high quality and this is an important contribution to the study of methylation and correlates of aging in mammals. The interpretation by the authors and media is really problematic though, and I hope the authors can address some of these concerns before and during peer review.

On 2022-02-10 16:22:54, user Jo Wolfe wrote:

The peer reviewed paper is now published: https://royalsocietypublish...

On 2016-10-11 17:52:24, user Simon Schultz wrote:

This paper will appear in a special issue of Proceedings of the IEEE in early 2017:<br /> SR Schultz, C Copeland, A Foust, P Quicke and R Schuck (2016). Advances in two photon scanning and scanless microscopic technologies for imaging neural circuits. Proceedings of the IEEE, in press, doi: 10.1109/JPROC.2016.2577380.

On 2018-08-30 00:47:42, user The Rational Hindu wrote:

La Genetique Scandale<br /> by Premendra Priyadarshi

A critique of the recent articles particularly the one by V Narasimhan et al (This will be in 4 parts)

PART 1 — Cracking the Narasimhan code

Please read at https://therationalhindu.co...

On 2017-03-15 16:18:30, user AdamMarblestone wrote:

-"A neuromorph's prospectus" http://web.stanford.edu/gro...

On 2016-11-17 05:06:57, user AdamMarblestone wrote:

-"Reinforcement Learning with Unsupervised Auxiliary Tasks" https://arxiv.org/abs/1611....<br /> -"Learning to Navigate in Complex Environments" https://arxiv.org/abs/1611....

On 2016-03-18 14:09:06, user stevepiccolo wrote:

Fabien Campagne Thanks for the comment! By mentioning Bioconductor in the paper, we did not mean to imply that it is without flaws. More so to illustrate the value of software ecosystems that help in managing dependencies. We state that "it is often useful to combine approaches." Indeed, the solution you describe is a good example of a way to do this for better reproducibility. One possible alternative solution is http://bioarchive.github.io.

On 2024-01-20 09:46:36, user professor esterdo. mikail wrote:

the structure with the hydrogel should have the hydrogel structure such as probe tensile, DSC, swelling behavior, and characterization for the hydrogel at first. and then for the microonedle. maybe it was composoite not hydrogel.

(Maybe electrochemical mesaurment was done without the surface .

on the hand the microonedle should be analyzed for MTT test as biodegradability

the antimicrobial test also not confirmed in the figure . it should be repeat

On 2023-09-14 19:40:53, user Stacey Deleria Finley wrote:

this paper has now been published:

"Genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer"

https://doi.org/10.1016/j.i...

On 2024-01-02 12:48:19, user Anita Bandrowski wrote:

Hi I am looking for the mice that you deposited in the MMRRC. Would you be able to share their identifiers like RRIDs?? I see that you made them available, but not which ones. <br /> Thank you in advance for your help, <br /> anita

On 2021-06-06 15:55:16, user Xingyu Liao wrote:

SRC is publicly available at https://github.com/BioinformaticsCSU/SRC

On 2022-08-22 12:32:11, user Ana C. A. Melo wrote:

Amazing work! They were able to show elegantly that the change of an unique aminoacid can modify the OR specificity into recognize a ligand. Congrats to all authors.

On 2018-08-29 08:57:07, user Wiep Klaas Smits wrote:

This is an interesting paper, collecting methylation patterns of clinical isolates of C. difficile from PacBio sequencing. However, the authors do not cite two highly relevant previous papers that described m6A methylation in this organism: Herbert et al FEMS Microbiol Lett. 2003 Dec 5;229(1):103-10 and Van Eijk et al BMC Genomics. 2015 Jan 31;16:31. doi: 10.1186/s12864-015-1252-7.Though the title suggests a broad survey, only Fig 1 shows the landscape, and the rest is actually a characterization of the (effects of) m6A methylase, that appears the only one to be conserved across most if not all C. difficile species.

On 2020-03-31 15:19:29, user Azmeraw T. Amare wrote:

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

On 2020-05-25 01:12:51, user François Boucher wrote:

For the theory to hold, that D839Y/N/E mutation they are calling a "European" variation should track to the UK & USA, where most cases of MIS-C were observed…<br /> This following database has only a few examples, limited to Holland, Portugal, Italy and Georgia… maybe they're looking at a different collection of sequences? <br /> https://nextstrain.org/ncov... <br /> https://nextstrain.org/ncov...

On 2020-06-14 01:03:59, user Alison Chaves wrote:

Hi guys, congrats for the work in the first place. This is a mechanistically quite interesting study. It makes a lot of sense. But while reading this paper I was wondering, we know that the influenza virus induces IFN and causes acute respiratory distress syndrome, but the therapy with corticosteroids does not offer clinical benefit for the patients (Here is the evidence http://tiny.cc/32gsqz) "http://tiny.cc/32gsqz)"). Of course, we know that the influenza virus does not use the ACE2 receptor in order to enter the cell. Anyway, It would be great to know how the pathology caused by the SARS-Cov-2 differs from that by the influenza virus in order to justify the counterintuitive therapy with corticoid.

On 2023-05-23 11:48:26, user Fanny Cavigliasso wrote:

A peer reviewed version of this manuscript is available in Evolution Letters: https://doi.org/10.1093/evl...

On 2023-09-17 06:30:29, user Diego del Alamo wrote:

This is a comment on version 1 of this manuscript.

The authors present compelling evidence that fine-tuning sequence-based machine learning models (protein language models) on in-house experimental data can accelerate the discovery of high-affinity binders, in this case against CD40. However, the entire manuscript is focused on single-chain nanobodies, not antibodies as the text suggests, and the authors only mention this in second and third paragraphs of Results as well as the caption of Figure 2.

This is an extremely important distinction and I think the authors need to revise their language throughout the document to make this clear; i.e., use the term nanobody, not antibody. Nanobodies differ from antibodies in several key respects, such as loop lengths, which are discussed here: doi.org/10.3389/fimmu.2023..... Relevant to this manuscript is the fact that they comprise a single chain, and are thus amenable to out-of-the-box masked and/or autoregressive protein language models. Standard antibodies consist of two chains; to my knowledge, only one method, which has not been peer reviewed, has been trained on paired antibody sequences: arxiv.org/abs/2308.14300. Thus, several obstacles still exist that prevent the methods described here from being directly translated to standard monoclonal antibodies. The manuscript does not discuss or acknowledge these obstacles.

On 2023-05-03 13:45:00, user UTK Micr603 wrote:

Hello. Below is a review compiled by the MICR603 "Journal club in immunology" at the University of Tennessee Knoxville:

UTK MICR603 “Journal club in immunology” review of the paper by Gül et al. “Intraluminal neutrophils limit epithelium damage by reducing pathogen assault on intestinal epithelial cells during Slamonella gut infection”

Summary:

The work of Gül et al. investigates the role of neutrophil recruitment and activity on epithelial cell damage during Salmonella infection. They investigate this using several techniques including several in vivo models and microscopy. The authors investigate this from different angles by utilizing germ free mice that lack a resident gut microbiota and by investigating epithelial integrity and shedding during normal and neutrophil-depleted infections.

Positive feedback:

The authors provide an in detail review of what is understood during Salmonella infection and what is not during the different stages of infection in several different models. This provides solid reasoning for their use of several different model systems in this paper. Additionally, they are commended for their use of not only different Salmonella strains (wild-type vs mutants) but their use of different host models and antibiotic treatments to strengthen their claims and understanding of Salmonella infection and the role of neutrophils during infection. The authors use several different controls/treatments/previous studies to further back up their results and claims seen in this paper. For example, they further confirm their neutrophil depletion results by comparing against their controls as well as previous study results with neutrophil and monocyte depletions which takes away uncertainties that their results could be due to monocyte presence in the neutrophil depleted mice. Additionally, the use of experimental diagrams/design in figures is very useful when referencing other data in the figure. The authors are also commended on their use of microscopy to back up their data quantification and their flow and organization of figure panels.

Major Concerns:

• 4a- Antibiotic pre-treatment can greatly influence Salmonella invasion… every other figure/model uses streptomycin model and this one is using ampicillin (inconsistency with pre-treatment). Could you compare streptomycin and ampicillin results? Can they comment on what happens with ampicillin+WT treated mice?<br /> • Were mice being placed in clean cages after gentamicin treatment? <br /> • Gentamicin treatment: clarify if it was just in drinking water or during cecal tissue plating. A few sentences clarifying this is needed. <br /> • Germ free mice: they interpret these as no bacteria in the gut, but they also have weird immune systems because of this. Would it be better to pretreat WT mice with antibiotic cocktail to deplete the residential microbiota without perturbing the immune system? <br /> • How do you know the pad4 drug is working? Some confirmation here is needed.

Minor concerns:

• The authors use the abbreviation “mLN” in multiple figures and their writing without defining what this is. It may not be clear to some readers what this is referring to. <br /> • When referencing P values in the figure captions, it would be beneficial to state the actual P value and not just >/< in order to add more impact to the statistics. <br /> • 5e- different microscopy planes : control is a cross section and neutrophil depletion is from a top plane of view <br /> • 5d - levels spelled incorrectly <br /> • Why use pad4 inhibitor instead of pad4 deficient mice <br /> • Pad4 inhibitor IP injection vs oral administration – reasoning for the use of one over the other could be better described. <br /> • Controls in Fig1 are incomplete - no uninfected group and no isotype control group <br /> • What is the dashed line in 1B? – Some further clarification is needed <br /> • Conclusion: limitations of study needed <br /> • 1e: instead of quantifying with a 63X field of view they could use area metrics instead (more quantifiable) <br /> • 4 - B&C y axis - connect these units to whole organ so you can compare the bacterial load in lumen vs epithelial tissue <br /> • Mention division time of salmonella in vivo / in vitro <br /> • Speculate mechanism of expulsion? <br /> • Do we know that these are intact cells in imaging

On 2025-05-19 15:35:29, user Minwoo Kang wrote:

This preprint has been published in a journal.<br /> https://pubs.aip.org/aip/apb/article/9/2/026116/3346984

On 2018-07-02 05:29:38, user Enrico Bucci wrote:

I would be very interested into comparing my results with those obtained by the authors. Beside what I already published (https://www.nature.com/arti... "https://www.nature.com/articles/s41419-018-0430-3)"), I have also extensive data on 5 different Elsevier journals (manuscripts screened at submission). As far as I can see, the author's results are very much in line with my own findings.

On 2023-11-22 16:25:24, user Mark Houston Plitt wrote:

After adding extensive new experiments and analyses this preprint was split into two manuscripts. <br /> We posted a new manuscript (https://doi.org/10.1101/202... "https://doi.org/10.1101/2023.11.20.567978)") focused on the in vivo two photon calcium imaging and virtual reality behavior.<br /> A revised manuscript focused on the in vitro physiology and and freely moving behavior will be posted at this DOI (https://doi.org/10.1101/202... "https://doi.org/10.1101/2022.01.04.474865)") in the coming months.

On 2018-08-21 06:21:37, user BenjaminSchwessinger wrote:

This is now published as Harnessing the MinION: An example of how to <br /> establish long-read sequencing in a laboratory using challenging plant <br /> tissue from Eucalyptus pauciflora - Schalamun - - Molecular Ecology <br /> Resources - Wiley Online Library <br /> https://onlinelibrary.wiley...

On 2019-07-22 13:38:35, user Elias Grieninger wrote:

If this works out the way I think it will, you can easily call it the next cognitive revolution of humankind. I am very excited to see what kinds of new things people will create, as soon as this technology is accessible.

On 2018-09-05 08:55:11, user daniele marinazzo wrote:

Dear Benedikt and Olaf

I am posting this here since academickarma.org cannot find the preprint, but looks like the reviews are automatically ported there.

thanks a lot for this really interesting work.

Here some suggestions I collected:

general impression: the paper is very well written, and clearly specifies why the toolbox is needed, and the theory behind it.<br /> The limitations are also properly described.

general questions:<br /> 1. would it be feasible to design an experiment in which stimuli are isolated as much as possible, and use these shapes as prior for the estimation of overlapping responses?<br /> 2. This paper "A Statistical Framework for Neuroimaging Data Analysis Based on Mutual Information Estimated via a Gaussian Copula"(https://onlinelibrary.wiley... "https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.23471)") combines neuroelectrical data with stimuli (in the form of discrete data), and quantifies the interaction not only between different stimuli, but also, within the same ERP, between different ERP peaks (fig 13). Maybe you could use this info to estimate to which extent different stimuli overlap in the ERP, prior to your analysis?<br /> 3. would it be possible to extend the ANOVA to a MANOVA for multivariate analysis, as in the LIMO toolbox, within your deconvolution framework?<br /> 4. Do you consider effects of the combination of two responses, i.e. the fact that a "face" response could be different when combined to a color versus another? Here I don't talk about the overlap, which is the main motivation of your study, but to the fact that the separated responses could still be interacting? I refer to an interaction at the psychological/physiological level, not to the actual linear interaction, which you do model, so not sure whether this makes sense as a practical question.<br /> 5. How would the deconvolution look if we considered response-locked instead of stimulus-locked ERP?

technical details:

1. maybe you could describe the effect of the number of available events and the sampling frequency. I tried the extreme case of a single "car" event, and all the rest "faces", and of course the estimation is much noisier, but still present.
2. how would the estimate change if using reconstructed sources instead of sensor space?
3. This question comes from my experience with our blind deconvolution toolbox for fMRI (https://www.nitrc.org/proje... "https://www.nitrc.org/projects/rshrf)"), so maybe it's completely not relevant here: would it make sense to apply the toolbox when the timing of the stimuli are unknown, spotting a signature of a possible neural event in the data, and using the lag between the event and its signature as a parameter in the GLM? This would probably very short for EEG data.
4. related question: qould it be possible to deconvolve also unknown (i.e. spontaneous) events?
5. figure 6: why the data are padded with zeros before and after?
6. You mention that truncating the spline has a filtering effect, it makes sense. Would this conflict with a filtering previously applied by the user?

other small things:

1. maybe you could add a sample of real EEG data to the code
2. you could uniform the color scheme (sometimes you have parula, other times the cbrewer one).
3. there are a few formatting issues in the figures.
4. page 18, there a strange symbol replacing "pi" in "from 0 to 2X"

code issues (more for github, but I put them here for consistency too):

1. in the tutorial you use init_deconvolution, that does not seem to exist in the github version
2. there is a typo "warning onW"
3. the dependency on cbrewer could be made explicit, or you could check whether it's in the path, otherwise use a default color scheme
4. simulate_data.m is called simulate_data2 in the function name

thanks again, and looking forward to see more applications of this tool!

On 2018-04-04 19:55:03, user Matthew Gillum wrote:

The authors may find this somewhat colorful description of an accidental melanocortin agonist overdose in a human of interest given MC4R/SIM1 overlap: https://www.sciencedirect.c...

On 2017-09-08 04:47:02, user ppgardne wrote:

What does "7 - 90 daily dilutions" in Figure 1 mean? Do you dilute 1:100 between 7 and 90 times a day, or did you run the experiment for between 7 and 90 days?

On 2024-11-27 23:21:01, user Monica Berger wrote:

Great preprint and I agree about the two basic types of hoaxes.

I keenly followed the Conceptual Penis hoax (Boghossian and Lindsay) as it unfolded in real time as I was writing my book on predatory publishing. Although it initially correctly stated that the Conceptual Penis hoax as exposing gender studies, he later says they published in a predatory journal. This is incorrect. There were peer review problems but no predatory publishing.

They submitted the article to a prestigious gender studies journal, NORMA: International Journal for Masculinity Studies, from Taylor and Francis. The journal rejected the article and transferred the article down to another T & F lower-tier open access journal, Cogent Social Sciences. This editorial process is called “cascading.” The less prestigious journal peer-reviewed it and, when it was accepted, requested an APC. After publication, the authors revealed the hoax, and the article was retracted; the journal explained that the peer reviewers for the lower tier publication lacked experience. See: https://www.skeptic.com/reading_room/conceptual-penis-social-contruct-sokal-style-hoax-on-gender-studies/

On 2022-10-24 14:55:29, user Alexander wrote:

the paper has been accepted by Antiviral Research

On 2017-09-20 04:32:30, user Davidski wrote:

Hello authors,

I feel that this comment in the paper is somewhat misleading: "Unexpectedly, one Neolithic individual from Dereivka (I3719), which we directly date to 4949-4799 BCE, has entirely NW Anatolian Neolithic-related ancestry."

This individual actually clusters with Balkan Chalcolithic and Neolithic samples, so in reality he's only distantly Anatolian Neolithic-related. His ancestors were probably in Europe for a couple thousand years.

So I don't think that there's anything unexpected about the result, because apparently at the time Balkan farmers were pushing into the steppe looking for new farm land.

Your comment almost suggests as if there was a direct migration from Anatolia to the Pontic steppe. But there's no evidence for such a thing.

On 2021-01-10 10:20:45, user Stefano Campanaro wrote:

Dear Francisco Zorrilla, Kiran R. Patil and Aleksej Zelezniak,<br /> I read your preprint and I really appreciated it. However, I would like to mention that we have recently demonstrated the feasibility of reconstructing the GEMS starting from Metagenome Assembled Genomes for hundreds of species and a series of microbial communities associated with the anaerobic digestion environment. Our paper was recently published in "Metabolic Engineering" with the title "Revealing metabolic mechanisms of interaction in the anaerobic digestion microbiome by flux balance analysis" (DOI: 10.1016/j.ymben.2020.08.013). The procedure used is similar to the one you reported and based on assembly with Megahit, binning with Metabat2, quality evaluation with checkM, GEMs reconstruction using CarveMe, evaluation of the GEMs using Memote. Additionally, we performed an additional series of analyses and verifications using other software. Without diminishing the importance of your study, on behalf of my co-authors, I think it could be interesting for you to compare the procedure reported in your preprint and the results obtained with our one.<br /> Thanks a lot.<br /> Sincerely<br /> Stefano Campanaro<br /> Associate Professor<br /> Department of Biology University of Padova

On 2019-04-24 07:15:16, user ST Sebastian wrote:

The topic “theranostic potential” is very misleading to reviewers and readers. Theranostics means therapeutics plus diagnostics. The studies did not show any diagnostic function. Using MRI to monitor drug release from cornea implant is not practical.

On 2019-12-16 21:36:52, user Ross Jones wrote:

Updated reference for DiAndreth et al: related work from our lab developing a library of endoRNases, of which CasE is one: https://www.biorxiv.org/con...

On 2020-05-06 15:19:16, user Sinai Immunol Review Project wrote:

Summary: Using publicly available scRNA-seq data of healthy human samples from 31 organs, the authors compare the gene expression levels of ACE2 and TMPRSS2 of each organ. The authors categorized each organ as susceptible to SARS-CoV infection based on its expression of ACE2 and further stratified 11 susceptible organs into three levels of risk for infection based on each organ’s TMPRSS2 expression (i.e. TMPRSS2 expression ratio >20% was defined as level 1). The authors claimed that for the first time, their scRNA-seq analysis showed the brain, gall bladder and fallopian tube as vulnerable to COVID-19 infection in addition to confirming previous molecular and clinical data implicating other organs susceptible to SARS-CoV2 (i.e. nose, heart, intestines, etc.).

Limitations: The article advances its risk stratification strategy based on a couple of naïve assumptions that are being actively contested in literature: a) ACE2/TMPRSS2-mediated viral entry is the only route used by SARS-CoV2 to infect cells and b) ACE2/TMPRSS2 expression is stable in systemic inflammatory contexts such as COVID-19. Furthermore, their risk stratification does not consider the immune contexture of each organ. For instance, while several papers have suggested that reproductive organs (i.e. testes) are also susceptible to SARS-CoV2 based on their ACE2/TMPRSS2 expression, there are no reported clinical manifestations of COVID-19 in those organs. Lastly, even though they were using scRNA-seq data available from 13 different publications, their paper does not discuss how they accounted for variations in scRNA-seq protocols as well as how they normalized each data set for comparative analysis.

Significance of the finding: Mostly confirmatory. While it is nice to see that there were clinical cases describing the adverse effect of COVID-19 for most of the eleven organs identified as SARS-CoV2 susceptible in this study, previous studies have already shown most of these organs to be susceptible to SARS-CoV2 using various approaches (including scRNA-seq analysis). Furthermore, their risk stratification strategy is simplistic as it doesn’t seem to take account of recent findings on SARS-CoV2’s alternative mechanisms of entry as well as reported clinical manifestations of COVID-19.

Review by Chang Moon as part of a project by students, postdocs and faculty at the<br /> Immunology Institute of the Icahn school of medicine, Mount Sinai.

On 2020-04-30 16:08:25, user Janet Smith wrote:

Seems like there was a very short (2-4 weeks?) time between recovery from the Original infection and second challenge. Has there been follow up of serum antibody titre in both original (non-challenged) cohort and the challenged cohort? And/or has a later challenge been attempted?

On 2021-07-12 01:32:59, user Robert George wrote:

Great paper & new appraoch

One minor issue regarding:<br /> ''The YHG H (H-L901) is thought to have formed in South Asia approximately ~48 kya (Sengupta et al. 2006).''

As a modern aDNA paper, it should not rely on older, modern DNA based inferences. Instead, aDNA points to western Asia (Lazaridis; Nature 2016)

On 2022-04-19 22:49:23, user Joseph Wade wrote:

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

This paper addresses the mechanism by which SARS-CoV-2 infection causes inflammation. The authors argue that SARS-CoV-2 spike protein interaction with the ACE2 receptor results in a reduction in CFTR protein levels, which in turn leads to increased inflammation in the COVID-19 airway. This is impactful because the authors identify a mechanism of inflammation caused by SARS-CoV-2 infection that was not previously appreciated. Moreover, these findings link the pathophysiology of COVID-19 and cystic fibrosis.

The paper is well written and easy to follow; the list of goals at the end of the Introduction, and the italicized conclusions at the end of each results section were particularly helpful and contributed to overall clarity. Overall, the data support the major conclusion that there is less cell-surface CFTR following Spike protein binding, leading to inflammation. Nonetheless, we felt that the title of the paper is overstated, and western blot experiments should be replicated, and quantified where appropriate.

Major comments:<br /> The paper title is overstated. Specifically, the paper does not look directly in the “COVID-19 airway”, and the paper does not determine the extent to which CFTR-mediated inflammation contributes to inflammation during infection by SARS-CoV-2. We recommend rewording the title to something like “Inhibition of CFTR signaling by the SARS-CoV-2 Spike protein leads to inflammation”.<br /> Experiments involving western blots should be repeated. Where the differences in protein levels are modest, e.g. TRADD levels in Figure 1, the authors should quantify the band intensity normalized to the control. The authors could either include replicate blots as supplementary data, or quantify all western blot data from replicates, showing variability.

Additional comments:<br /> It would be helpful if the authors could briefly describe the differences between the original Spike protein and the Beta variant in relation to why the Beta variant binds ACE2 more strongly.<br /> Figure 5 could be moved to the supplement since it reanalyzes data shown in other figures.<br /> The conclusion from Figure 6 is understated: “Thus the possibility of ACE2 being a physical bridge, direct or indirect, between Spike protein and CFTR cannot be excluded.” The data in Figure 6 make a more compelling case for an ACE2-CFTR interaction than the text suggests.<br /> The description of data for ENaC in Figure 8 should be expanded. First, it is not clear from panel A that ENaC gamma cleavage is higher in the presence of Spike protein, rather than overall higher ENaC protein levels with the same degree of cleavage. Second, there is no explanation for the lower band seen in panel B (first lane after the ladder).<br /> What is the significance of the pairs of lanes in Figure 6B? Are these replicates? Different elutions?<br /> What is the “lysate” lane shown in Figure 6B? Please also explain in the figure legend what “NRS” is.<br /> Figure 3 – what concentrations of Spike protein are used in glycoside-treated samples? The legend appears to have an error.<br /> The conclusion from Figure 7 is overstated. It does appear that the levels of CFTR at the cell surface are reduced more than total CFTR levels as a result of adding Spike protein, but the mechanism for this cannot be inferred from these data.<br /> Suggested future experiments:<br /> Test the Beta-1.315 Spike protein variant alongside the original S1S2 Spike protein for experiments shown in Figures 1, 3, and 7.<br /> Measure levels of additional cytokines/chemokines in Figure 1B.

On 2024-07-10 18:05:43, user Matthias Samwald wrote:

Final article has been published in Computers in Biology and Medicine:

GPT-4 as a biomedical simulator https://doi.org/10.1016/j.compbiomed.2024.108796

On 2017-11-13 09:13:20, user guillemaud wrote:

PREPRINT PEER REVIEWED AND RECOMMENDED by PCI EVOL BIOL

This preprint by Suffert et al. has been peer-reviewed by Benoit Moury and one anonymous referee and recommended by Benoit Moury for Peer Community in Evolutionary Biology. Peer-reviews, decisions, author's replies and the recommendation can be found here: https://evolbiol.peercommun...

On 2022-11-23 12:54:23, user David Roe wrote:

PING isn't the only algorithm for KIR genotyping. It would be a benefit to everyone if you could compare your results with that from this[1] and maybe other algorithms.

1. Sakaue S, Hosomichi K, Hirata J, Nakaoka H, Yamazaki K, Yawata M, et al. Decoding the diversity of killer immunoglobulin-like receptors by deep sequencing and a high-resolution imputation method. Cell Genomics. 2022 Mar;2(3):100101.

On 2019-07-26 09:35:13, user Carlos Lopez-Vazquez wrote:

Interesting work... just wondering how far were the SRT applied in each plant from the minimum SRT required for nitrification? <br /> And how can they relate and influence the maximum growth rates of nitrifiers and their decay rates (e.g. due to predation and other endogenous processes)?

On 2019-03-12 02:57:25, user Huiguang Yi wrote:

Have you compare it to other metagenomics reads classification tools ? what is its superiority over Kraken?

On 2020-04-28 09:15:02, user Me Too wrote:

Interesting study, lot of work done. Could the authors motivate why they only chose 1 distance, ie 10cm? It would've been great to see several different distances and the likely inverse relation with infection rate. <br /> Eg., would it not be interesting to see that infection would always occur no matter the distance as long as you're in the same room?

On 2024-03-15 14:38:32, user Jesse Bloom wrote:

I have written a response to this pre-print that is available at https://docs.google.com/document/d/e/2PACX-1vR8PQ3CCGiKB7thJF3E4H0b18BU_uTw8actdeYN-wyQmloB3IPMLH0ixw6mZL8kgpGtiXwNry-HN6R8/pub

On 2017-03-18 19:52:24, user nobel barua wrote:

great work for human kind

On 2022-09-30 22:29:26, user MIT Microbiome Club wrote:

Figure 2D displays nearly bimodal distribution of effect on focal by pairs of affecting species. Could be nice to explore if this difference is consistently (across RP, BI, CF) due to the same groups of affecting species.

On 2021-03-16 22:14:35, user Team Thomma wrote:

This manuscript has been published as: https://www.nature.com/arti...

On 2013-11-15 03:16:18, user Animesh Ray wrote:

Upon a reading of this paper, it remains unclear to me what the 2-state model of water accounts for in the context of protein folding/denaturation, which the continuous distribution (of kinetic energy) of water fails to do. Can the authors come up with a precise and quantitative test that would provide one answer for 2-state water model and another for continuous distribution model? I think that will be the only way to proceed, otherwise the 2-state model becomes just another way the same phenomenon can be explained. On a slightly different note, I seem to recall that the deuterium exchange studies with proteins in the 1960s and early 1970s (mostly by A Pullman and B Pullman in Sweden or their collaborators) had shown that the "cage" model of water around proteins is adequate to explain their exchange data.

On 2025-05-15 12:36:30, user Egle Jakubaviciute wrote:

This is now published in https://doi.org/10.1016/j.ecss.2024.108801

On 2020-06-06 08:58:50, user Luis Toledo wrote:

KU60019 at 10microM also inhibits ATR...

On 2018-12-22 17:39:08, user Ferrel Christensen wrote:

"We assigned gender"--arbitrarily, regardless of the author's actual gender? How could this throw light on the question of whether quality of content rather than some sort of bias affects acceptance for publication? (And only one double-blind journal was included in the test?) The brief description above raises serious questions.

On 2021-01-20 18:24:44, user Maxwell Neal wrote:

A peer-reviewed, revised version of this paper has been published by Nucleic Acids Research at https://doi.org/10.1093/nar....

On 2020-07-19 07:03:14, user Jen Skuban wrote:

Rotavirus needs trypsin to enter intestinal cells. It's why rotavirus vaccinated kids are immune to Covid 19

On 2017-05-29 18:50:13, user Richard Stevens wrote:

Any plans to do anything with the genome of the Amesbury Archer, like a separate paper maybe?

On 2017-06-06 05:24:08, user Nicky Rosenblatt wrote:

The dating of the split roughly corresponds to the divergence between A00 and other Y-haplogroups, but somehow A00 isn't mentioned in the paper.

On 2021-08-25 15:49:58, user Julie Hanson Ostrander wrote:

An Open Access, read-only version of the peer-reviewed article published in Oncogene can be accessed through the following link: https://rdcu.be/cv1jC . The Open Access, published article can also be accessed through the J. Willard Marriott Digital Library at the University of Utah: https://collections.lib.uta...

Additional disclosures found in the published manuscript.

This work was supported by NIH grants R01 CA236948 (JHO, CAL), R01 CA229697 (CAL), F32 CA210340 (THT), T32 HL007741 (THT), U54 CA224076 (BEW), R01 CA248158-01 (CODS), and R01 AG069727-01 (CODS). ACS Institutional Research Grant #124166-IRG-58-001-52-IRG5 (JHO), University of Minnesota Masonic Cancer Center (CAL, JHO), the Tickle Family Land Grant Endowed Chair in Breast Cancer Research (CAL), National Center for Advancing Translational Sciences of the NIH Award UL1TR000114 (JHO), and Department of Defense W81XWH14-1-0417 (BEW). We thank Bruce Lindgren for biostatistics support, and the Masonic Cancer Center Biostatistics and Bioinformatics, Analytical Biochemistry, University Imaging Core (UIC), and Flow Cytometry cores. We also thank Zohar Sachs and Michael Franklin for critical reading of this manuscript.

We disclose that CAL is a Scientific Advisory Board Member for Context Therapeutics, Inc. BEW, EC-S, KPG, and C-HY may receive financial compensation from intellectual property and tangible property licenses managed by the University of Utah. The remaining authors have no COI to disclose.

On 2019-10-24 06:40:02, user Mark Rubin wrote:

Interesting for us the confirmation that SPOP mutations are depleted in CRPC as compared to Primary Naive PCA. I am also interested in the potential pathology associations. Do you see Neuroendocrine/Small cell cancers in this population...hope RNAseq data comes soon

Great work

MarkI

On 2021-09-27 18:29:26, user anna moroni wrote:

Dear Authors, very interesting results. I noticed that in C-type inactivated Shaker channels, the selectivity filter is impressively similar to that of HCN4 channels in their non-conductive form (Saponaro et al, Mol Cell 2021,DOI:10.1016/j.molcel.2021.05.033). The comparison between WT and W434F mutant in Shaker highlights the large movement of Y445 and D447 sidechains, similar to those of Y482 and R484 observed by comparing conductive and non-conductive HCN4 SF. Further, C-type inactivated Shaker channels show two ion binding sites only and low conductance, two typical features of HCN, as well as reduced selectivity for K over Na (Kiss et al, 1999, DOI:10.1016/S0006-3495(99)77194-8). So, really striking similarities!

On 2022-10-31 15:54:21, user Daniel Lüdke wrote:

Figure 6: Is the qifq mutant affected SA production/accumulation as ICS1 expression appears not to be strongly affected?

On 2024-01-22 20:24:56, user Michael wrote:

Please link the published version of this article to the preprint. See below for details:<br /> J Tissue Eng. 2023 Jan-Dec; 14: 20417314231197282.<br /> Published online 2023 Nov 17. doi: 10.1177/20417314231197282<br /> https://journals.sagepub.co...

On 2019-09-20 22:50:25, user Steve Rozen wrote:

This is a very important paper. It shows for the first time that AA I causes liver cancer in mice. This is important because many East Asian liver cancers have been been exposed to AA (DOI: 10.1126/scitranslmed.aan6446). In light of Lu and colleagues' mouse study, then, there is very strong evidence that AA contributed to the development of many of these human liver cancers.

On 2020-10-30 20:44:13, user Adrian Flierl ???????? wrote:

Just came across this and having spent several years working on this, can confirm some of these data, but not all the conclusions.

When one is talking about ANT4 (germ cells), one has to consider that the liver cells used for this study are transformed into a stem-cell like regenerative state. Hence one would expect to see ANT4 expression (similar to germ/stem cells).<br /> However, this may not be the case in adult, differentiated tissues, and it would be interesting to see if ANT4 is actually expressed in muscle cells aka myocytes or other finally differentiated cell types devoid of ANT1 and ANT2. <br /> Nevertheless, my data in myoblasts certainly support your findings in this study, as is the developmental aspect of ANT4 expression masking effects of ANT1/2 in stem cells.

Regards,<br /> Adrian

On 2023-09-25 16:32:21, user Leonardo Couto wrote:

Dear Authors,

I am Leonardo Couto, a master's student at the Federal University of Minas Gerais (UFMG), located in Belo Horizonte, Minas Gerais, Brazil. I am currently being supervised by Professor Juliane Karine Ishida. Throughout my academic career, I have been studying the interaction between plants and microorganisms, and your preprint titled "Disease Resistance correlates with Core Microbiome Diversity in Cotton" caught my attention. Recently, our research group received an invitation to evaluate preprints in order to gain experience and contribute to the advancement of science. Therefore, I have chosen your work for discussion in one of our meetings. In collaboration with other colleagues, we have brought forth some suggestions to assist in improving your article, and we hope they may prove useful to you.<br /> We noticed a lack of introductory content in his introduction, which would be essential to better understand his study.<br /> (Lines 95-100) - The explanation of cotton leaf roll disease (CLCuD) and its economic implications for the country is provided only at the conclusion of the preprint. We believe these topics could be explored in more depth in your introduction.<br /> We also note that the text is not structured into categories such as introduction, methodology, results, and conclusion. We understand that this may be in line with the journal's guidelines.<br /> In your methodology section (lines 62 - 68), we missed a more detailed description of your samples. For example, we are curious about the distribution of samples among susceptible, partially tolerant, and fully tolerant varieties. Furthermore, it would be useful to know how many samples are attributed to epiphytic leaves, endophytic leaves, rhizosphere, and endophytic roots. We suggest that compiling this data into a supplementary table could be beneficial.<br /> These are some small contributions we offer to help you further improve and refine your work. We hope these suggestions are valuable to you in advancing your research.

Yours sincerely,

Leonardo Couto<br /> Master's Student, Federal University of Minas Gerais (UFMG)

On 2025-04-28 09:52:40, user Anne Hoffmann wrote:

Review of “Feedback and feedforward control are differentially delayed in cerebellar ataxia” by Di Cao, Michael GT Wilkinson, Amy J Bastian, Noah J Cowan

This review was done as part of the SfN Reviewer Mentor Program (Mentor: Dr. Andre Cravo, Mentee: Dr. Anne Hoffmann, https://www.jneurosci.org/rmp ).

Summary:

This study develops a computational model based on system identification to describe cerebellar contributions to feedforward and feedback control pathways as well as to the coordination between these two pathways. By comparing healthy controls and cerebellar ataxia patients in a visual tracking task with perturbations, the authors observed that both control pathways are preserved in patients, but that delays are higher for patients in both feedforward and feedback control loops. Additionally, patients exhibited a reduced feedback gain, which the authors interpret as a compensation strategy applied by patients to respond to the temporal incoordination between feedforward and feedback pathways rather than a consequence of the cerebellar damage itself. Finally, the authors demonstrate that the intact feedforward control mechanism can be leveraged in patients to improve tracking performance by presenting a 500ms preview of the future target trajectory. Although patients overall exhibited smaller performance improvements in the preview condition compared to controls, the authors suggest that the significant improvements seen in patients demonstrate that visual preview may serve as a direction for future therapeutic approaches to alleviate motor coordination problems in cerebellar ataxia patients.

Major comments:

• Page 4/Figure 2C: At the bottom of page 4 you cite that the mean (or is it the median?) feedforward time delay was 245ms in the patient group. This does not line up with the mean shown by the two error bars in figure 2C. Could you please check if this is a typo or if not mention which moments are described in the text and the figure (mean/median)? The same question applies to the value 144ms (patients) cited for the feedback pathway on page 6.
• Page 10 (Section “Target preview improves …”): As shown in figure 5E you mention that there was no significant relationship between ataxia score and error reduction. Based on this non-significant result you suggest that path preview leads to performance improvements independent of ataxia severity. However, have you considered if the absence of significance here might be because of the relatively small sample size of 17 patients? You could consider performing a Bayesian analysis to provide evidence for the absence of an effect? Or maybe have you tried performing a median split of the patient data and compare the means for patients with a high vs. low score? I am asking because figure 5E gives the impression that there is at least a linear trend between error reduction and ataxia score.
• Page 12 (end of paragraph): how did you conclude that the improvements in smoothness and phase lead where stronger for the control group compared to the patient group? Did you perform a quantitative analysis of the data presented in figure 6D?
• Page 18, Table 1: please provide a more detailed description of the different diagnoses of your patient group. Do you have any information about the approximate onset of symptoms (age at onset) in these patients?
• Page 21 & Figure 10: You mention that you compared “various” model candidates for the transfer functions of the feedback and feedforward pathways but in figure 10 you only mention the top two candidates for each pathway. Could you provide more details about the different models that were compared and how you selected which possible transfer functions could describe the feedforward and the feedback controller?
• Page 24/section 5.6: Could you please provide more details about your statistical analyses. For example, did you test if the assumptions for ANOVA and t-test were satisfied? Which correction method did you apply to compute the degrees of freedom for your unpaired t-tests (DoF are decimal numbers here)? Could you provide F-values, p-values, and effect size measures for all the ANOVA results (for some analyses you only mention p-values)?

Minor comments:

• Figure 1D: how does the phase in the lower panel relate to average in figure 2B? Specifically, the range of phase shifts are very different in these two figures and I don’t understand why from the text. Why is there a phase lead shown in figure 1D, while there is a phase lag in figure 2B? Why does the gain increase in figure 1D and decrease in figure 2B? Please explain Figure 1C/D better in the text.
• Page 4, 7th line from the bottom: You refer to figure 2B but it should be 2C
• Figure 5 B/C: x-labels are missing
• Figure 6A/B: For clarity maybe rename the y-axis labels to gain ratio and phase ratio?
• Figure 8: Could you please specify somewhere in your methods what the total duration of one trial was? This was not clear to me from the various figures in the method and result sections.
• Page 21/22 (section 5.4) and page 29 (section 6.1): Could you please improve the consistency in your notation of the transfer functions? Specifically, in the method section you use j? to denote the Fourier transform but in figure 10A you use s. Further, you use sometimes k, sometimes a, and sometimes b to refer to the gain and the pole of the filters.
• This might be field/journal specific, but I would find it easier to follow as a reader if the statistical results would be added to the main text of the results instead of the figure legends

On 2019-10-15 17:47:58, user TheNaturalist wrote:

How do I find the videos? None of the mentions to "see videos" are links?

On 2018-12-29 22:18:36, user Alex Zhavoronkov wrote:

We are looking for collaborators with the WS microbiome sequencing data, annotated with age, sex and disease/lifestyle choice/drug, etc. to test the clock. Please forward this paper to your friends interested in the microbiome. We are interested in testing the clocks in a variety of applications.

Constructive criticism, comments, and edits are very welcome. This paper needs to be polished.

On 2017-04-12 17:34:48, user Ludmila Prokunina-Olsson wrote:

Yep, the ref 4 was provided for the very generic statement "This mutational pattern is predominant pattern in bladder cancer and is also frequently found in breast, cervical, head and neck, and lung cancer". But not for the findings actually reported in the paper and now rediscovered by you :).

On 2021-09-27 18:24:26, user L. T. Fang wrote:

A 2021 MAQC/SEQC2 presentation describing the method presented in this preprint:<br /> https://youtu.be/nn0BOAONRe8

On 2015-06-03 15:06:20, user Daniel S. Standage wrote:

I caught a couple of minor typos in section IV: 2nd paragraph, "The rational" should be "The rationale"; 4th paragraph, "casual <br /> relationships" should be "causal <br /> relationships". I saw a couple of others, but couldn't find them again. :(

Overall, excellent insight into current and impending challenges. I've discussed (or heard discussed) bits and pieces of what's in here before, but never have I seen this material been so completely, clearly, and concisely presented. Great work!

On 2022-09-05 08:53:44, user Kristian Unger wrote:

Congratulations to this great work! Have you made the software already available somewhere?

On 2019-11-10 15:50:52, user David Curtis wrote:

It might be worth also citing these other papers which used similar approaches to analyse most of these exomes:

https://link.springer.com/a...<br /> https://www.nature.com/arti...

On 2025-03-14 21:15:12, user Rajendra K C wrote:

We discussed this paper in our RNA journal club. We found the discovery of this novel splicing mechanism quite interesting, which seems like a last-resort strategy for cells to remove transposons that have already integrated into exons while still retaining some level of functional protein. The mutagenesis experiment to identify SOS splicing factors was particularly interesting. One question that came up in our discussion: What happens to the excised transposon post-SOS splicing? Is there any evidence that it gets degraded or reintegrated elsewhere in the genome?

On 2022-11-03 05:01:46, user Anubhav Prakash wrote:

Dear Author, <br /> Congratulations for this very Interesting paper. I want to further understand two things<br /> 1. Does the down regulation of sox2 expression in the segregating patch, also triggers the expression of some different kind of adhesion molecule to facilitate the segregation? <br /> 2. Probably a little tangential to the paper, does the size of segregating sensory patches are similar in different individuals ? If it is similar, then how do u think that might be regulated. Can also throught as how the segregating patches being (Sox 2 down regulation/ lmx1 expression) positioned in the common sensory regions?

This paper is very informative. Thank you very much.

Anubhav Prakash <br /> Graduate student, NCBS (India)

On 2022-02-08 00:04:42, user Yang Xu wrote:

Nice Job!<br /> I wonder whether you block these flippases causing the death of bacteria due to the abundant undecaprenyl-phosphate inside bacterial cells.

On 2023-06-30 09:31:40, user Chanel Thomas wrote:

Dear authors<br /> We read your paper as part of our Genomes Journal Club at the Forestry and Agricultural Biotechnology Institute (FABI) at the University of Pretoria. We’d like to share a few of our thoughts and comments with you.

We thought this was a really wonderful paper. One of the things that we found really exciting and novel was that you were not able to identify one characteristic that confers pathogenicity to banana (in contrast to e.g. tomato pathogenicity that can be traced back to chromosome 14 of Fol4287). This suggests either that a unifying characteristic does not exist for banana pathogens or that it is perhaps broken up into different components of the disease - e.g. a specific thing that causes browning, another that causes softening, etc which may be controlled by different genes, potentially harboured within different ARs. We thought that this was a message that you could state more clearly in the paper and in your abstract.

Given the title of your manuscript, we interpreted that the message you chose as your selling point for the paper was the issue of segmental duplications and their role in evolution. We felt that we lacked the necessary background information to understand the significance of this. It would be really helpful to have some information on gene duplications in the introduction. From Fig. 6e, we could tell that segmental duplications were something unique to Fol4827, but it was unclear on how significant this is as opposed to having another type of duplication. For example: why are segmental duplications particularly important in evolution and/or why was it a surprise that they were involved. Fig. 6a gave a good explanation of the basic differences between the duplication types but (1) we felt that information would be useful earlier in the paper and (2) a written explanation would complement the figure nicely.

Another query that came up was related to the RNA data from 8 days post inoculation. It is not clear in the methods which strains were used to infect the Cavendish bananas or which strains were inoculated onto the PDA medium prior to RNA extraction. Given that different races are distinguished by their pathogenicity on different subsets of banana varieties, we wondered if there are any implications for the RNA-seq results? I.e. if strains were used that are not usually found infecting the type of Cavendish used for inoculation in this study, would the RNA expression results possibly be impacted? <br /> The figures in the paper were informative and conveyed the data well. However some of the multi-panel figures were not always intuitive to read. For example, Figure 6 c,d and e are intended to be read together, which you can figure out by reading the legend, but a visual cue such as some background shading or a box would improve the readability. Similarly, the background shading in Fig. 1 that links panel d to the earlier panels wasn’t noticed by most of us (only 1 member picked it up) so it may be worth making that clearer.<br /> Overall we found this to be a really well executed study and we all thoroughly enjoyed reading and discussing it.

On 2016-01-17 18:14:02, user Jesse Bloom wrote:

This paper does a great job of putting together a dataset of curated ddG / dTm values for testing structure-based predictions.

However, I don't think the conclusions about the distribution of stability changes are justified. The mutations here are pulled from the literature from a variety of studies, each of which only examined a small fraction of the many possible mutations that could be made to a protein. In general, these mutations were NOT chosen randomly, but were rather selected by the investigator because the mutation was of enough interest to make it worth careful characterization. Therefore, the distribution of stability effects (for instance, the dTm values shown in Figure 2 or Figure 4) reflects the aggregation of the personalized choice of many researchers about which mutations to study. These distributions are almost certainly different than the distribution of stability effects that would be observed if you instead systematically made all point mutations to any given protein.

For instance, I would strongly suspect that if you made all mutations to any given protein, the distribution in Figure 2 would have many fewer stabilizing mutations. The reason that there are so many stabilizing mutations is almost certainly because there is an investigator bias towards characterizing stabilizing mutations.

On 2021-03-02 20:22:19, user Black Wang wrote:

So even in the ATG5 KD cells, unlipidated LC3C band is still not detected? How and why?

On 2025-01-07 11:09:38, user Hossein Shirali wrote:

This preprint has been peer-reviewed and published in Invertebrate Systematics. Please see the final version here: 10.1071/IS24011.

On 2017-11-02 11:22:40, user theempiricalmage wrote:

Interesting how it tends to avoid that Steppe ancestry was largely Iranian Chalolithic, if not Iranian Neolithic.

On 2021-07-22 08:24:50, user Alizée Malnoë wrote:

Nawrocki et al. with this manuscript make an important step forward towards understanding the molecular origin of nonphotochemical quenching (NPQ) qI using the microalga Chlamydomonas as their study system. Indeed upon high light stress, chlorophyll fluorescence quenching is observed attributed to PSII photoinactivation, and this work demonstrates that it stems from PSII reaction center and that degradation of D1 by FtsH is required to relax to an unquenched state. The authors further show that qI formation is more rapid in presence of oxygen but independent of PSII activity (in DCMU) and propose that qI is due to oxidative modification to chlorophyll molecules of PSII reaction center (RC). Accordingly a minimal model was built with qI-ON RC and qI-OFF FtsH-processed/broken RC which successfully fits the experimental data indicating that photoinactivation mechanisms at donor and acceptor sides co-occur.

Here are some suggestions/comments. Looking forward to discussion!

The first point pertains to semantics, I would suggest using the word “photoinactivation” of PSII instead of “photoinhibition” as much as possible. I was convinced of this idea by Barbara Demmig-Adams when I worked on a review about qH, reserving the term photoinhibition for decrease in CO2 fixation. We proposed a possible new definition for qI that would be quenching due to photoinactivation of D1 rather than due to photoinhibition, as qZ and qH are also photoinhibitory (in that they decrease CO2 fixation). Take a look here, see intro, section 1.1.2. and 4.: https://doi.org/10.1016/j.envexpbot.2018.05.005.

Title: I’d suggest a more descriptive title of findings stating where qI stems from, e.g. with oxygen sensitization of D1 as the origin of qI. As is, one could understand the title as qI doesn’t exist (dogma rose and fell kind of idea) but you mean instead molecular origin/mechanism of qI induction and relaxation, right?

Throughout text, I’d suggest to use the word “relaxation” instead of “quenching loss”. The word loss is used elsewhere to describe fluorescence decrease i.e. quenching and it can be confusing to have the word “loss” used for both quenching induction and relaxation (e.g. line 94 loss= relaxation; line 96 loss= decrease).

I’d also suggest for clarity to use “new synthesis” instead of “repair” (e.g. line 101), as repair encompasses both degradation and new synthesis, it seems confusing to read that qI relaxation is independent of repair but relies on degradation.<br /> e.g. Figure 2 title becomes: qI is transient and its relaxation is independent of new D1 synthesis<br /> and Figure 3 titles becomes: qI relaxation is due to PSII core proteolysis by FtsH

Also for clarity, in title of Figure 1 add - is a quenching “due to energy dissipation”-<br /> And line 452 - Using quenching -add “of Fm” - might be beneficial for (f)uture studies

Line 202 FtsH-mediated (name of protein uppercase here; not mutant italic lowercase)<br /> Line 211 side -> sites

There’s a lot of crucially ;-)

To go further in the discussion, here are some points that could be interesting to raise:<br /> - Addition of lincomycin blocks synthesis of all chloroplast-encoded proteins, impact on qI formation/relaxation.<br /> - qI relaxation in presence of nuclear gene synthesis inhibitor.<br /> - Slower relaxation of qI in the dark compared to low light (at least after 30min HL).<br /> - qI transient: explanation for differences between strains (e.g. 1009 vs. 124).<br /> - ftsh complemented line (in ftsh1-1), comment whether less qI compared to control because more repair enabled; due to higher level of FtsH in that complemented line compared to one in ftsh1-3?<br /> - formation of qI site precedes cleavage (line 242) would need deg mutant to definitely say that. Might be better to say that it precedes D1 degradation (at timepoint 0 there are some D1 fragments, so there has been cleavage already before HL starts).<br /> - damage at acceptor side triggers cleavage in the lumen? (1995 Plant Phys D1 qI https://www.jstor.org/stable/4276408. After HL stress, decrease in D1 but DCMU-binding sites 2x higher vs. D1 detection by antibody; propose preferential cleavage in the lumen).<br /> - if oxygen sensitization proceeds by PSII charge recombination (line 404), then should qI be enhanced in DCMU? Compare with hydroxylamine (HA)+DCMU to test it.

• These publications about Arabidopsis mutants seem consistent with your findings, what do you think (although hard to say for sure without Fm quenching data):<br /> 2014 Plant J Kato & Sakamoto, https://onlinelibrary.wiley.com/doi/full/10.1111/tpj.12562<br /> Fig5b,c: similar levels of D1 as control (fug1) in fug1 var2 stn8 (~ equivalent Chlamy would be ftsh stl1) but with lower Fv/Fm so D1 is removed in fug1 var2 stn8 but would be quenching more than in fug1 due to more ROS in stn8 background; fug1 var2 has most damaged D1 so most quencher (OR alternative hypothesis phosphorylated RC are quenchers?)

2011 Plant Cell LQY1, www.plantcell.org/cgi/doi/10.1105/tpc.111.085456 <br /> 2014 Plant Cell HHL1 LQY1, www.plantcell.org/cgi/doi/10.1105/tpc.113.122424 <br /> lqy1, hhl1 have faster rate of degradation and low Fv/Fm after HL due to higher Fo but Fm stays the same; that would be consistent with faster degradation, less quenching of Fm.

2017 PNAS MPH2 www.pnas.org/cgi/doi/10.1073/pnas.1712206114 <br /> Impaired degradation of D1, low Fv/Fm, more quenching of Fm.

Schematic model: the different shades of grey in the wheel do not represent light/dark, correct? maybe would be clearer to make it according to light treatment. The dash-line is not described: is it the alternative hypothesis to cleavage, that full degradation is required to relax qI? I like the purple scribble on Nter of D1 to signify ‘hey, degrade me!” ;-)

Alizée Malnoë (Umeå University) – not prompted by a journal; I’m an assistant professor, my research group investigates the molecular mechanisms of plant photoprotection. Catherine de Vitry was my PhD studies advisor.

On 2016-08-15 14:54:21, user Maikel Peppelenbosch wrote:

I am Maikel Peppelenbosch, the apparent corresponding author. I posted this comment earlier, but it seems not to have been logged. This manuscript was not approved by me or most of the other authors and may be premature. Hence I would urge the scientific community to ignore this version.

On 2016-07-31 15:32:39, user David Suter wrote:

Here the description from the 2003 Pallier et al. study:

"As early as 1 min after paraformaldehyde addition, HMGB1- and HMGB2-EGFP mostly diffused away from chromosomes of mitotic cells. This resulted in the absence of any detectable signal on the chromosomes after a 10-min incubation (Figure 8)."

A mechanistic model was also proposed:

"Rather, PFA or FA may alter the accessibility of HMGB to their target(s) by modifying the overall structure of the mitotic chromosomes, and/or inactivate the free form of HMGB1/2. HMGBs contain ?40 lysine residues, and a lot of these are expected to interact directly with the charged phosphate backbone of DNA. PFA reacts with the amino group of lysines, and the reaction product is a Schiff base. This is still charged, but both hydrogen bonding and van der Waals contacts of the lysine residue will be disrupted."

Also as illustrated in Fig.4 of Kumar et al. 2008 cited above, neither formaldehyde nor methanol (both tested in the present study) seem to work for the mitotically-bound TF they looked at …but methanol/acetic acid does (not tested in the present study) - these earlier studies were not cited.

Thus I am not totally clear about the extent of the novelty of the findings reported here - could the authors of the present study comment on this ? Is there any fundamental difference I missed between what was shown here as compared to earlier work ?

On 2019-07-08 12:45:18, user Dave Lahr wrote:

Hello - I'm wondering if you have / can provide the data (or location of the data) that indicates for each cell line whether or not it is MSI?

On 2020-06-02 11:38:58, user Tobias Broger wrote:

Dear Study team. Thanks for writing this up. The study is well-conducted and interesting. <br /> I have a question/comment: did you test antibody response against other antigens? I believe it is a major limitation of the assay you used that it only detects antibodies against S1 as there appear to be a number of patients including with mild disease that mount an immune-response against S2 or N proteins that the assay from this study would miss. I think this should be discussed (and I suggest you test your serum samples with 1-2 alternative assays that cover several virus proteins, which is quick). This could change your conclusion quite a bit. Happy to chat on the phone and provide some more background if there is interest. Tobias Broger.

On 2018-03-26 07:57:48, user Tygrysek wrote:

I'm not a matematician, so I'd like to ask: 7 groups 90 animals each is just 630. How can we get 1:14000 chances?

On 2016-05-29 22:59:46, user Dubiakw wrote:

There is no aura and Kirlian photography is a scam. Did you drop out of grade school?

On 2023-02-24 02:32:18, user markyz wrote:

Hello I like your article anything that makes data analysis more straightforward is going to be useful to many! In line with PMID:36750393 and the literature cited there, the tool should accept a background list, otherwise the enrichment test results could be invalid. Moreover the enrichment p-values should be corrected with FDR or similar, otherwise there could be many false positive results.

On 2019-10-22 09:28:17, user Rebecca Gladstone wrote:

Really nice, I had a brief stab at this a while back when exploring GPS, you've done a much better job, great to see GPS data in use! We were interested to see serotype 1 and 38 rank so highly as we didn't see this in GPS. Geek that I am I just mined pubMLST and saw serotypes 1 and 38 in four different CCs (SLVs), the minor serotype was always n=1 observation. I know this is a problem with bias in pubMLST and people only submitting the first occurrence, but it does make it difficult to rule out mistyping. I'd be interested to see which of the other serotype pairs were both observed multiple times within lineages as an extra layer of confidence in their co-occurrence?

On 2018-05-21 14:27:33, user Ricard Argelaguet wrote:

Is the data available somewhere?

On 2020-08-21 20:13:28, user Andrew wrote:

if cells that produce antibodies are multiplied, then there will be a vaccine against covid-19

On 2017-01-03 19:41:50, user Graham Coop wrote:

I note that I have not sent this paper to any journal, nor do I plan to. If you want to cite the paper, please cite the preprint. If you have comments leave them here, or shoot me an email. I will revise the preprint as needed.

On 2025-11-10 16:12:23, user Ben Auxier wrote:

The work presented in Tan et al. is provocative, suggesting that during asexual spore dispersal Neurospora crassa segregates its chromosomes across multiple nuclei, instead of the nuclei being mitotic copies of each other as previously assumed. While similar to the results that some of these authors have presented in Botrytis and Sclerotinia, showing this in a genetic model organism would provide the genetic tools to dissect this phenomenon.

However, the results presented here lack definitive proof. The evidence presented can be summarised as follows:

1) relatively low measured DNA per spore, based on DAPI fluorescence, compared to yeast

2) chromosome specific probes show patchy distribution across nuclei, compared to probes that target all chromosomes.

The first line of evidence suffers from an apples-to-oranges comparison, because the comparison is across species. The compound DAPI binds to the AT regions of DNA, and so differences between species in both AT% as well as those that affect general fluorescence, will influence this measurement. For instance, yeast has 62% AT genome, while Neurospora has 46%. Highlighting the futility of such comparisons, the data in Figure 1C shows clearly that while N. crassa has a haploid genome that is 4 times as large as yeast (compare 1st and 3rd columns), the fluorescence signal is equal (compare 6th and 4th column). Even assuming the authors’ hypothesis, there is still "too little" fluorescence given the genome size of N. crassa. Clearly, while within a species there is a strong correlation between genome size and fluorescence, when compared across species such correlation disappears.<br /> The second evidence is from fluorescent hybridisation. Here the authors show that a FISH probe specific to chromosome 1 is never found in more than one nuclei, while a telomere probe that targets all chromosomes is more often found in multiple nuclei. The main issue is that the claims rely on negative evidence, that is to say the absence of signal. However, the absence of signal is not a strong signal of absence. FISH is a very sensitive process, and differences in probe design and washing steps can greatly affect the process. Highlighting this, in 14% of spores, the authors' own chromosome 1 specific probe does not bind to either nucleus. If this data was to be taken at face value, this would indicate 14% of spores lack Chr1, which would be inviable. Extrapolating across all chromosomes, we would only expect 36% of spores to have all chromosomes and be viable ((1-0.14)^7). Such low viability is inconsistent with observed spore viability of N. crassa, which generally exceeds 95%. An alternative explanation is that the probe binding is not very efficient. This is supported by the telomere probe, where it remained undetected in 10% of conidia , despite this probe targeting 14 different chromosomal regions! Again, taken at face value this would indicate that a significant fraction of spore nuclei lack chromosomes entirely. Notably, only 64% of spores had all nuclei with signal from this telomere probe.

Aside from inconclusive data, the claims here are also inconsistent with the basic empirical genetics of this fungus. It has been known for decades that when one wishes to isolate loss-of-function mutants in N. crassa, like the auxotrophic mutants used to demonstrate the "one gene - one enzyme" principle, regular macroconidia do not work. Instead, microconidia prove useful, allowing for the easy isolation of such mutants (Catcheside, 1954). This provides powerful evidence that macroconidia have redundant nuclei, which compensate for loss-of-function mutations (See Gross and Lester 1958 for further discussion). One only needs to read the experimental methods of Beadle and Tatum to see the effort in isolating auxotrophic mutants in this organism. Instead of simply mutagenizing conidia to obtain auxotrophs, they needed to use the extremely labour intensive purification process of individually crossing mutagenized conidia to a wild-type background, to be able to isolate homokaryotic ascospores (Beadle and Tatum; 1954). This is also true of genetic transformations, which are performed on macroconidia. Transformants always need to be purified, as the original colony that grows on selective medium generally contains a mix of transformed and untransformed nuclei. If the macroconidia would contain a single haploid genome, divided over multiple nuclei, as suggested by Tan et al, there would be no need to purify induced mutations.

Further, the claims here are also inconsistent with the evolutionary dynamics of this fungus. This fungus is the foundation of the studies in heterokaryosis, the presence of multiple distinct genotypes within a single mycelia. Arising from either de novo mutations, or from fusion of two mycelial hyphae, such heterokaryons in Neurospora are quite stable. It is well-described that conidia can be heterokaryotic, meaning a single conidium inherits two distinct genotypes. This has been studied in detail for the soft mutation, where up to 40% of conidia are heterokaryotic, containing both the wild type and mutant alleles of the soft gene (Figure 2A; Grum-Grizmaylo et al. 2021). This is not due to specific dynamics of the soft mutation, as similar ratios of homokaryotic and heterokaryotic macroconidia have been observed in auxotrophs (Atwood and Mukai, 1955), which were used to form the current model of random segregation into multiple mitotic nuclei in Neurospora. A recent example of such evidence has been shown by Mela and Glass, who inserted either green or red fluorescence into the his3 locus in different genotypes, and using fluorescent microscopy they readily recover ±40% of conidia with both colors (Figure 1f; Mela and Glass, 2023).

The claims made by Tan et al. are strong and in my opinion the evidence does not rise to the level needed. Measures like fluorescence intensity or hybridisation can never be definitive and therefore are at most a start of further experimentation. However, the predictions of incomplete chromosome sets per nuclei can be definitively tested through single nucleus sequencing. The technology has advanced to the level that single nuclei can be reliably processed for whole genome sequencing, which is the most reliable way to determine if these claims ultimately reflect reality.

References:<br /> Beadle G.W. & Tatum E.L. 1945. American Journal of Botany. https://doi.org/10.2307/2437625 <br /> Mela A.P. & Glass N.L. 2023. Genetics. https://doi.org/10.1093/genetics/iyad112 <br /> Grum-Grzhimaylo, et al. 2021. Nature Communications. https://doi.org/10.1038/s41467-021-21050-5 <br /> Lester H.E. & Gross S.R. 1959. Science. https://doi.org/10.1126/science.129.3348.572 <br /> Catcheside D.G. 1954. Microbiology. https://doi.org/10.1099/00221287-11-1-34 <br /> Atwood K.C. & Mukai F. Genetics. https://doi.org/10.1093/genetics/40.4.438

On 2017-02-03 19:45:16, user anon reviewer wrote:

Thank you for your response. Unfortunately, size-exclusion chromatography is susceptible to contaminants of highly variable sizes (for example, non-specific protein-protein interactions can cause contaminants to co-elute with the protein of interest). This is clearly evidenced on the gel shown in Supplemental Figure 1b. There are visible bands toward the bottom of the gel, indicating that purified NgAgo contains smaller contaminants following SEC. These are most obvious for D704A and D863A, but can be seen in all lanes if the contrast is adjusted for the image. Therefore, I do not think you can rule out the possibility that this purification procedure eliminates RNase H contaminants.

The reported activity differs from previously characterized catalytic Agos in several ways: 1. No 5'-end preference for guide strands; 2. Aspartate residues that align with catalytic DEDX tetrad of TtAgo are not required for observed activity; 3. RNA target is cleaved in multiple locations rather than at a single site. It is certainly possible that NgAgo activity is highly non-canonical, but there is an alternative explanation that has not been sufficiently ruled out. The manuscript would greatly benefit if you included more controls demonstrating that the activity is not due to a contaminant. The experiments that you mention (alternate purification procedure, side-by-side comparison with RNase H activity) are a good start, and I encourage you to add these data to the manuscript. The most convincing evidence would be to identify a catalytically dead mutant, although that will of course be challenging if NgAgo contains a non-canonical active site.

On 2022-02-14 13:24:42, user Jessica Polka wrote:

The following feedback has been provided by members of the ASAPbio Preprint Reviewer Recruitment Network

Summary: Romero-Becerra et al. show in this work the consequences of the loss of the p38 kinase activator MKK6 in mice. Their data presented in this manuscript include a reduction in lifespan and a cardiac phenotype that starts in young mice and is characterized by cardiac hypertrophy that ends up in cardiac dilatation and fibrosis. The cardiac phenotype is also present in two cardiac-specific MKK6 KO models, which is consistent with an important role of MKK6 in the heart. Importantly, they present mechanistic data to propose a model in which MKK6 deficiency leads to hyperphosphorylation of MKK3-p38?/? and increased mTOR signaling, a well-known cause of cardiac hypertrophy. The paper is well structured, the data is clearly explained and the results are relevant in the sense that they identify a novel pathway in the development of cardiac hypertrophy. After reviewing this preprint, these are our questions and comments to the authors that we think may help to improve this manuscript.

Major points:

The image that is represented in Fig. 2D may not be the best to represent the size effect observed for cardiac fibrosis. Most of the mice seem to show a similar degree of fibrosis. Also these mice are 23-24 month-old, long after the first mice start to die due to cardiac dysfunction, so this specific population of Mkk6 KO mice may show some kind of resistance to develop cardiac dysfunction when compared to others that died before. If cardiac dysfunction is the cause of the death, it should be present earlier in life. Thus, measurement of cardiac fibrosis at an earlier time point would be important to sustain authors’ claims.

The investigation of the two cardiac-specific KO is very relevant for the conclusions of this study. It would be important to know whether those cardiac-specific KO develop similar phenotype compared to that developed by the global KO, besides the cardiac hypertrophy already described.

There is no mention of the n of animals used for the data shown in Fig. 5. For example, Fig. 1A has panels with n=2 and n=1, whereas Fig. 1B and Fig. 1C has n=4. This should be better described, and mentioned in figure legend.

Minor points:

Data shown on Fig.1 re MKK6 KO animals is shown at different ages. It would be beneficial for the message of the paper to clarify the progression of symptoms in these mice. For example, Fig. 1C shows phenotype at 20 months of age, but then kyphosis is shown on Fig.1D at 19 weeks of age. It could be a typo but anyway paper would improve by clarifying this aspect.

Fig. 1D does not have a scale bar but taking into account that MKK6 KO animals are smaller, if images have been enlarged to show better the kyphosis, a scale bar is needed. Authors may discuss this possibility if they do not have the data available.

Authors claim that MKK6 KO mice have decreased BW due to increased browning of the adipose tissue and increased energy expenditure. However, it seems they have not considered the possibility that they have a reduced food intake, which could be the case due to the ataxia present in these animals.

On Fig. 2, the observed size effect on different cardiac parameters may be better visualized if authors include the baseline value 0 for each parameter measured.

Scale bar units in Fig. 2D does not match with what is stated in Fig legends.

After the mention of puromycin, the source of the remaining antibodies is not mentioned.

Why did the authors use a global p38gamma but a MCK-driven p38delta-specific KO to study the role of these 2 kinases in the phenotype of MKK6 KO mice? We found no reasoning behind that explained in the manuscript and doing so would help the readership of this paper.

On 2019-10-25 17:36:12, user Oh wrote:

Hi, I thought your research was very interesting, so I would like to share some of my thoughts.

In order to make a clearer connection between nuclear mechanotransduction, I think it is worth studying what other types of mechanical stress/forces (i.e. tension, compression, shear, static vs dynamic, ECM stiffness) are transduced through Vrkl/BAF pathway to regulate the downstream effectors. Also, is it just the disruption of myonucleus-sarcomere connections? To what extent do the myonuclei have to be detached to observe the changes in the BAF nuclear membrane accumulation? What happens when myonucleus-sarcomere connections are fully detached. When the nuclear membrane ruptures, does the BAF localization changes? The paper mentions that BAF is associated with repair of mechanically induced membrane rupture. When the nuclear membrane ruptures, does the BAF localization in the nuclear membrane changes? Also, the LINC complex plays a central role when cells migrate in 3D (2). Does the BAF expression change during migration? One study used a 3D collagen matrix and studied nesprin-3 activity in migrating fibroblast (2). Another study applied mechanical stress by using magnetic tweezer to analyze the consequence of nuclear strain on the LINC complex related gene expressions (1). Besides disrupting connection between myonuclei and sarcomeres through D-Titin knockdown to mimic mechanical stress, applying mechanical stress by magnetic tweezer or by exposing to different levels of ECM stiffness to induce changes in the BAF accumulation in the nucleus can possibly strengthen the data.

Does this mechanotransduction pathway via Vrkl/BAF also play a central role in other cell types besides muscle cells? What about immune cells, metastasizing cancer cells, fibroblasts, and other migrating cell types that are regularly exposed to different mechanical forces?

The Baf RNAi efficiency is confirmed with qPCR, but can the efficiency of the gene knock downs of the others (i.e., D-Titin/sls RNAi in Figure 3 or Vrik1/ball RNAi in Figure 6) be also verified with qPCR or western blot?

For the experiment that showed that the Vrik1/Ball BAF kinase is required for the BAF localization at the nuclear membrane, is the Vrik1/Ball sufficient for BAF localization? Does adding Vrik1/Ball in muscles expressing Vrk1/ball RNAi recovers BAF localization near the nuclear membrane?

The increase in the DNA content in the myonuclei in D-Titin/sls RNAi was quantified by DAPI fluorescence in IF, but can it possibly be verified with FACS to see changes in the cell cycle and to observe an increase in DNA content induced by elevated endoreplication with reduced BAF levels at the nuclear membrane. I think this will help confirm the observation that reduced BAF levels at the nuclear membrane is associated with endoreplication.

On 2017-05-01 18:38:15, user Paul Carini wrote:

The main broad conclusion of this work (“relic DNA contributes minimally to the characterization of microbial community structure”) is not well-supported by the data presented.

First, the efficiency of relic DNA removal with the DNAse-based approach used in this manuscript does not appear to have been tested empirically. That is, there were no experiments reported to conclusively validate whether the DNase treatment effectively removed extracellular DNA or DNA from dead cells. The lack of such experimental controls makes it difficult to interpret their results, as there is extensive literature showing that DNA bound to soil/sediment components (minerals or organic matter) can be highly resistant to DNAse digestion (e.g. Romanowski, et al., 1991, Paget, et al.,1992, Cai, et al., 2006, Khanna and Stotzky, 1992, Lorenz and Wackernagel 1987, Nielsen et al., 2006). If DNAse treatment is not effectively removing relic DNA pools, it would reduce the apparent amounts of relic DNA and reduce the apparent influence of relic DNA on estimates of community structure.

Second, the authors analyzed 6 unique samples from each ecosystem type (e.g. 6 unique soil samples and 6 unique lake water samples, etc.). They then assessed whether there were consistent increases or decreases in richness across all 6 samples from a given ecosystem. The problem with this approach is that this effectively obscures any effects that removal of relic DNA might have on estimated richness within individual samples. This is important to account for as we know from our work (Carini et al. 2016. Nature Micro.) that in 20% of our soil samples, removal of relic DNA had no significant effect on richness estimates. Thus, by running the analyses across all 6 samples combined, instead of quantifying relic DNA effects on a per-sample basis, it likely obscures any effects of relic DNA. This is apparent from their Figure 3, where there appears to be a subset of samples where removal of relic DNA reduced richness as the mean richness ratio is >1 for three of the sample types. Thus, instead of concluding that “relic DNA contributes minimally to estimates of microbial diversity”, it would be more accurate if the authors had concluded that the effects of relic DNA are variable and relic DNA does not always introduce biases.

Just to be clear: we do not advocate that relic DNA is always important to consider when conducting DNA-based analyses of environmental samples. As we detailed previously (Carini et al. 2016), relic DNA is unlikely to obscure the ability to say that two distinct communities are indeed distinct with respect to both richness and community composition. Likewise, relic DNA effects are not going to be equally important across all samples from a given ecosystem type. Depending on the temporal variability, community turnover rates and the residence time of relic DNA, failure to remove relic DNA could have no effects or it could introduce significant biases. We still maintain that there are common situations where failure to remove relic DNA could obscure patterns in community structure (e.g. trying to detect temporal changes in communities or the short-term effects of environmental perturbations on microbial communities). While removal of relic DNA can complicate laboratory analyses and the presence of relic DNA can alter how we interpret our estimates of microbial community structure, the data presented here do not provide enough evidence to justify ignoring the potential importance of relic DNA.

On 2020-01-28 14:02:01, user Bowen Zhang wrote:

Nice work! but I think there is some Dec. 1st patient, would it be great if you can find those sample's sequence? as they may help to locate a more ancestral ancestor.

On 2024-02-14 01:34:50, user QRB&D biophysics_cup wrote:

Compelling study! It seems it could fit in the following Special Collection, "Frontiers in Computational Biophysics":

https://twitter.com/Biophys...

On 2022-04-02 16:22:59, user Aditya Singh wrote:

Manuscript reviewed for Reviewer Mentor Program, SfN working with Mike X. Cohen as my mentor in Fall 2021:

In this study, authors used high-density probes to perform large-scale neural recordings for identifying and characterizing the coordinated activity across MEC grid-cell modules in the presence and absence of visual cues. Authors aim to show that the inter-module activity in MEC is coordinated even in visual-sensory deprived conditions. The study is well-designed to test the hypothesis that local networks within MEC can calibrate the inter-module activity even in absence of visual feedback of animals' spatial location. With the cutting edge experimental and statistical tools, this study provides novel insights into the role of MEC activity in maintaining a coherent cognitive map.

There are a few concerns about module classification, and application and validity of dimensionality reduction technique.

Major concerns:

The exact definition of the term "module" is unclear, and a more precise definition will help readers understand the impact of the findings. For example, is a module defined by a specific volume of tissue, or number of cells in a volume, or a number of cells within a network showing toroidal topology manifolds, or functional connectivity?

Relatedly, what are the estimated physical dimensions of the modules in EC? Are different cell types part of each module across layers?

How different are the probabilities that two cells with correlated activity could belong either to the same or different module (line 9-13 on page 5)?<br /> It is an interesting observation that grid cell modules retain the coordination even when mapping between grid cell activity and position deteriorates (line 23-24 on page 5). What does the coordination in such a case reflect, if not position? Are there any scenarios where position decoding is possible even when grid cell modules do not retain coordination?<br /> Are there any sessions when light recording is followed by dark session to compare the change in grid cell coordination across light-then-dark sessions? (line 37 on page 5)<br /> In line 37 on page 5, light recording sessions have a broader duration range of 30-60min. Is it possible that the difference in the duration of dark vs light sessions led to a systematic bias? It may be helpful for readers to compare the ratio of time in dark vs. light for all the individual animals or comparing the total time for each animal in dark (all dark session durations combined) and light (all light session durations combined).<br /> Similarly to prevent the doubts about systematic bias in spiking data (line 1 on page 6), it would be helpful to compare the ratio of spikes in light vs dark for each of the 842 grid cells identified in the experiments. Including the information about temporal distribution of spikes would also be helpful to address whether all the grid cells were active for full session or they varied their activity within different epochs of a session i.e were all 500+ spikes spread homogeneously over full session?<br /> McInnes et al mentions that UMAP is more reliable for large datasets and UMAP assumes there is manifold structure in the data. Considering this along with the fact that the number of datapoints processed through UMAP in this study are very close to the lower limit for UMAP, are there other alternate dimensionality reduction techniques (e.g. PHATE which may be more suitable for the size of datasets for this study) that lead to similar conclusions in this study? Could authors quantify the false positives that could occur due to limitations of UMAP in clustering of grid cells into different modules? <br /> Also, could authors demonstrate the reliability of module classification e.g. through cross-validation or split-half reliability?<br /> According to Roy et al 2000 (Neural Computation), low firing rates (<10spikes/s) often lead to spurious correlations. Hence, it may be helpful to compare the likelihood for actual, not down-sampled, mean-firing rate between light and dark. Also, considering the issue with correlations for low-rate spike trains, are there any alternative methods to best estimate inter-module coordination other than pairwise correlations? <br /> What are the approximate dimensions of the brain region around the tip of a tetrode vs a recording site on the neuropixel probes used in this study? If they are considerably different, should tetrode data have comparable statistical interdependence across modules as we expect for neuropixel probes? If not, it brings to question the validity of applying the statistical methods used for tetrode data to neuropixel data.<br /> Figure 2 and Supplementary Fig.2 - module classification and clustering issue: Supp Fig 2 - Grid M3 in ‘a’ and M2 in ‘c’ appear too close to the respective non-grid clusters, do the results hold if such grids are excluded from the data?<br /> Minor concerns:

Introduction - Line 6-9: “the recurrent ...sensory inputs.” - a reference for this statement would be helpful.

It may be useful to mention in discussion about which spatial encoding mechanism would render grid-cell coordination sustain in darkness? Odor cues and Path integration? (Fischler-Ruiz et al 2021 Cell), Step-count? Proprioception?

Page 4 – line 1-2: “The grid cells within a module encode together a two dimensional quantity which, in some conditions – could be dissociated from the true position of the animal” It is not clear why authors mention this fact. It may be helpful to relate this to the findings.

Page 4 - Line 14-16 – “Since population activity of an individual module lies on a two-dimensional manifold, the joint activity of M modules spans, at least in principle, a 2M dimensional space” Reference for Mathematical principle on which this statement is based.? References for this statement supporting whether the joint activity of M modules spans at least 2M dimensional space or different?

Please add references for the statement in Line 8-9 on page 5.<br /> Line 16-18 – “However, during continuous motion…..the state of each module is faithfully mapped to the location of the animal in two dimensional space”. What is the definition of continuous motion (3cm/s?)? How consistent is it across the studies on spatial encoding of Ent cortex.? If there is tactile/olfactory feedback in the dark, would the modules represent increased accuracy of encoding spatial location?<br /> Line 26: For clarity, ‘rate adjusted likelihood’ could be referred to in the manuscript as ‘likelihoodRA’ instead of just ‘likelihood’.<br /> Page 15 – Methods: Open field foraging task in light: Were the odor-cues (excerements) removed from the arena during light-on recording session?<br /> Page 17 – Methods: Rate map analysis- What is the rationale behind using the bin size of (1/120)s for spike trains and tracking data? Isn’t this bin size too small?<br /> Page 18 – Methods: Markov decoder- How is the normalizing factor Z(t) defined?

On 2016-07-08 16:31:31, user Aleksey Belikov wrote:

Well, it has been clear since ages that JIFs cannot represent the impact of individual papers and scientists. Why not to move finally to individual research metrics such as this:<br /> http://www.biorxiv.org/cont...<br /> or this:<br /> http://f1000research.com/ar...<br /> ?!!

On 2023-06-09 21:42:09, user Lonki wrote:

Could the authors please clarify the parameters by which they determined mice to have “pronounced pathology” and hence, inclusion in the data set? Could you please expand on the rational for selecting this subgroup for analysis?

What happens when you test for correlation between changes in Iba+ and Neun+ counts with the weight loss observed at day 4-5 for individual mice?

On 2022-09-27 23:04:37, user anonymous wrote:

Throughout: it is confusing to shift between common and scientific names. Both Marine Iguana and Amblyrhynchus are fine but switching between them in the discussion is confusing.

On 2022-01-26 15:39:59, user aswarren wrote:

Figure 1 caption groups (A,C) (B,D) but the axis labels and values disagree with the caption.

On 2016-12-01 17:54:59, user Gene wrote:

On step 10 I've found is that you always need fresh eyes on the final review. If you have one person repeatedly review something they often start accidentally assuming previous knowledge.

On 2019-06-27 20:29:47, user George Davey Smith wrote:

This is a really interesting paper, with lots of good things, including a clear discussion of the latent causal variable approach and its relationship with that. A few comments:<br /> (1) A new term, "correlated pleiotropy" is introduced (see figure 1), which is covered by the widely used term in Mendelian randomization of "vertical pleiotropy", but it is vertical pleiotropy when you have mis-specified the primary phenotype (in this case, unknown). see https://www.ncbi.nlm.nih.go... for vertical pleiotropy with mis-specified primary phenotype <br /> (2) This issue is generally addressed in MR by bi-directional MR applying Steiger filtering between M and Y in fig 1 (see https://journals.plos.org/p... "https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1007081)"). It would be very interesting to know how this approach and CAUSE compare<br /> (3) The approach to HDL cholesterol would be through multivariable MR (which gets the right answer) - currently this is not implementable in CAUSE. It would be interesting to know if in principle if MVMR could be implemented in CAUSE<br /> (4) The SBP to BMI association in figure 5 is interesting. It says conventional MR shows SBP lowers BMI . The SBP GWAS used is adjusted for BMI, so collider bias is introduced and you should get this effect. This is the right answer to the wrong question. CAUSE gets the wrong answer to the wrong question

On 2021-05-17 20:59:10, user Virginia Bain wrote:

Hello~ I have a question for the authors. It looks like there is nuclear DHFR by immunofluorescence in A549 cells in figure 2C but in 2F you nicely show that DHFR is only in the cytoplasm. What do you make of the nuclear DHFR in figure 2C? Thanks!

On 2021-06-15 23:54:35, user Mohammad Dehghani Ashkezari wrote:

This article is now published on Limnology and Oceanography Methods:

https://doi.org/10.1002/lom...

On 2020-01-27 10:38:47, user Wiep Klaas Smits wrote:

May I suggest changing the Peptoclostridium to the (nowadays commonly accepted) Clostridioides?