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    1. On 2024-02-01 02:33:33, user Tania Gonzalez wrote:

      This pre-print is now peer-reviewed, edited, and published at Biology of Reproduction. Main data stays the same so these pre-print supplemental spreadsheets are safe to use. For the final version, we added details on the specific genes used to identify decidua "contamination" during quality control, combined numbers of protein coding and long noncoding genes (the pre-print mostly focused on protein coding only), included a comparison to our single cell RNA-seq (Sun et al 2020), added immunofluorescence for select genes, and added more about the biological significance of our results. [PMID: 38271627] https://doi.org/10.1093/bio...

    1. On 2024-01-30 21:38:19, user Vitaly V. Ganusov wrote:

      [NOTE: This preprint was discussed in the Journal club of Texas Biomed in Jan 2024. The listed comments are the result of that discussion]

      Summary

      It has been implicitly assumed that all mosquitoes, carrying Plasmodium sporozoites (SPZs) in their salivary glands (SGs), are infectious to their mammalian hosts. One recent study (Aleshnick et al. 2020) challenged this assumption by showing that the probability of mouse infection per a single mosquito bite increases with the number of SPZs the mosquito carries in the SGs. Here, authors extend this previous work to investigate how the number of SPZs in the salivary glands relates to the number of oocysts in the mosquito's gut and to the number of SPZs that mosquitoes expelled during probing on artificial skin. The authors went through many experimental steps to rigorously quantify the SPZ number (in mosquito and expelled) and the number of intact and ruptured oocysts. Authors also used two different ways to generate SPZs in mosquitoes - by feeding the mosquitoes blood culture of gametocytes and by feeding mosquitoes on blood from Plasmodium-infected individuals. While previous work focused on murine Plasmodium species, this study looked at human parasite (Plasmodium falciparum) that is likely to be more relevant to human malaria. The results are in line with several previous findings: the number of SG SPZs correlates with the number of oocysts and with the number of SPZs expelled into the skin, and mosquitoes with more oocysts have SPZs in SG earlier.

      Positive feedback

      There are many things to like about this paper. Although the question is not new, the authors approached it with high rigor of experimental design and performed many troubleshooting analyses (many of the latter are shown in Supplement, e.g., Fig 8). The experimental procedures are well described and problems associated with the data are mentioned. The analyses performed are mostly straightforward. A combination of different techniques (dissection, microscopy, PCR) provides a more comprehensive understanding of system. Studying human malaria parasites is important, and measurements done for mosquitoes fed on cultured gametocytes and on blood from P. Falciparum-infected volunteers are very interesting.

      Major comments

      1. The authors performed some basic statistical analyses but, in some cases, the choices of the models used were not clearly justified. For example, for looking at relationships between oocyst density and infection, authors used logistic regression. In correlation analyses, spearman rank correlation was used. Why were those the right choices? Using linear regression (Pearson correlation) could be more sensitive to trends, Deming regression is more appropriate when there is an error in both variables, and using alternative models to look at relationships between oocyst density and SG SPZ number, or SPZ number and expelled SPZs could reveal useful information (e.g., for different alternative models see PMID: 32453765).

      2. Additional points regarding statistical rigor/analyses. What is exactly oocyst density and infection prevalence (Fig 1B)? How can oocyst density be <1? Is that an average per several mosquitoes? This is not well explained. At 100 oocyst density there are still not 100% infections - how is that possible? Is that because time of sampling is too early and SPZs did not yet develop? Perhaps structuring the data on time since feeding could reveal interesting patterns. Also, analysis of binned data (e.g., Fig 2A and others) is not a good way to analyze data because answer may depend on binning choices (e.g., 17960243). Finally, presenting results when excluding mosquitoes that did not deposit SPZ as main result is incorrect as this likely introduces bias.

      3. Generating calibration curve and determining threshold for estimating the number of SPZs in the sample is important. How did you generate the dilutions of the SPZ numbers and how did you make sure that you had 100k vs. 5 in other samples? Did you count the 5 SPZs or was that estimated? If estimated, did you take into account errors with the dilution? I wonder if plotting the data using a log-log plot and doing a linear regression analysis would be useful for Fig 1A. Also, for Suppl Fig 2A, how many samples are the curves based on? Should that be plotted for every sample analyzed?

      4. While I appreciate the generate a robust artificial skin model, I wonder how realistic is using artificial skin with blood. In real skin, mosquitoes must probe to find the blood vessel while with the artificial skin, mosquito may find blood every time of probing - could that bias the results in some ways?

      5. Previous work established the importance of probing time and unimportance of taking the blood meal for infection (32453765). What were these parameters in your experiments and could they explain difference in how SPZ numbers in SG relate to number of expelled parasites?

      6. The finding that there is not "threshold" in the number of expelled parasites with SG SPZ number is interesting. What if you assume that infection occurs only if the number of deposited SPZs is above some critical number - will then you able to "match" the threshold model found previously (32453765)?

      7. One interesting mosquito had 9 ruptured oocysts but only about 2k SPZs - how is this possible? Where are the other SPZs? Could it be that they are in the “missing” parts of the salivary glands? That seems unlikely.

      8. The authors have very rich dataset but did not make conclusions that could be also interesting. For example, how many SPZs are made per oocyst given these data? What is the summary of maturation process from these data? Having a cartoon with steps and quantifying them (e.g., what is the efficacy of transfer of SPZs from gut to SG?) could be very useful (e.g., as Figure 6 or as graphical abstract).

      9. In their sporozoite quantification assay, the authors used the COX1 mitochondrial gene target. This gene is typically present in multiple copies in eukariots. However, it is not mentioned anywhere in the manuscript how many copies of the gene are present in the P. falciparum mitochondria. It is also not mentioned nor tested if all the different populations of P. falciparum used in this study have the exact same COX1 copy number. If they share the same copy number, then the sporozoite quantification assay presented is indeed robust. However, if this number of copies varies between P. falciparum populations, and moreover, within a population, then the quantification assay will lack robustness. In such cases, it would have been more valuable to use a single-copy nuclear gene. Could the authors provide more information regarding the COX1 copy number in P. falciparum and check that this number of copies is consistent across all populations?

      10. When looking at the Methods section, I realized that the authors have used two different populations of P. falciparum to infect their An. stephensi: NF54 from West Africa and NF135 from Cambodia. When measuring the duration of the EIP, the authors stated that '…mosquitoes were fed with P. falciparum NF54 and NF135 gametocytes…'. Did the authors infect their An. stephensi mosquitoes with a mix of these NF54 and NF135 gametocytes, or did they infect each batch of mosquitoes with one parasite population (i.e. either NF54 or NF135)? <br /> If they infected each batch of mosquitoes with one parasite population, why is this not reflected in the text and on the figures? Did one population produce more oocysts and sporozoites than the other? <br /> If the authors mixed NF54 and NF135, what is the rationale for doing this? I suspect these two populations also vary in their ability to produce gametocytes. Did the authors observe recombination between the two populations? <br /> Also do An. stephensi mosquitoes exhibit the same host-parasite compatibility/co-adaptation with both populations of parasites? If not, this could be a possible explanation for the weak correlation found between the total sporozoite load and the sporozoite expelled. I suggest that the authors carefully clarify these points, as it is quite confusing at the moment in the present manuscript.

      Minor comments

      1. Because of very large difference in SPZ numbers, plotting the data on log (or log-log) scale would help to see the data scatter better. Also, perhaps in some cases highlighting ranges of oocysts and/or SPZ number in SG that are "realistic for the field" (e.g., by using gray boxes) could be useful.

      2. The authors could calculate the total number of SPZs in the mosquito and the skin which is better than that done in previous study that calculated residual SPZ number in SGs (32453765). However, would any conclusions that look at correlating SG SPZ number with other parameters change if you consider the remaining SPZ number rather than total per mosquito (e.g., Fig 2A, 3A/C, 4B, etc)?

      3. In Fig. 1D: What is the rationale for comparing mercurochrome staining of oocysts at day 8 with anti-CSP immunostaining at day 18? Why not compare both stainings at both time points (i.e., mercurochrome at day 8 and immunostaining at day 8, as well as mercurochrome at day 18 and immunostaining at day 18)? Otherwise, it's challenging to compare different staining methods at different time points and draw robust conclusions about the number of oocysts present.

      4. In their discussion, the authors never mentioned that potential co-evolution between hosts and parasites could explain why they observed a tighter correlation between total sporozoite load and sporozoites expelled in the context of the infection conducted in Burkina Faso. Indeed, for this experiment, naturally circulating P. falciparum parasites were used to infect their sympatric host, An. coluzzii. However, this is not the case for the lab experiment, where two geographically and genetically distant parasites (i.e., NF54 from West Africa and NF135 from Cambodia) were used to infect An. stephensi, Nijmengen Sind-Kasur strain, which originates from Pakistan and has therefore not coevolved with either of these parasite strains.

      5. In Fig. 2B: When examining the EIP, authors chose to assess the number of sporozoites per mosquito at days 9, 10, and 11 post-feeding with gametocytes. They also looked at the number of sporozoites in a single oocyst and compared these numbers at days 9 and 10. Why not also investigate day 11, as they did for the EIP? Do the authors know the dynamics of sporozoite growth in an oocyst? Does it exponentially increase until the oocysts burst? Does it reach a plateau at some point and then burst? It would be very interesting to explore these aspects.

      6. I did not see that the data are made available. Per FAIR principles (https://direct.mit.edu/dint..., the data should be shared with the community so they can be further analyzed.

    1. On 2024-01-29 20:25:30, user Ekaterina Voronina wrote:

      After revisions, this manuscript was accepted to Genetics, with minor changes. The link will be provided when available.

    1. On 2024-01-28 16:58:08, user William Foley wrote:

      Interested to see your manipualtive experiment with PEG blocks and herbivore diet. I think that the use of PEG as an adjunct to herbivore diets has outstripped any evidence of what it really does. Which tannins are bound by PEG? All tannin groups or only some?. Why is the emphasis on condensed tannins and not on elagitannins? Were elagitannins absent from your savvanah site? I think its important to acknowledge that we don't really understand what PEG does! Windley et al (2016) made some useful comments on this point but data is sparse. The interaction between tannins and herbivore nutrition is not simple with both positive and negative effects and I think your article would be stronger if this was acknowledged.! Finally the studies by Foley and Hume and Marsh did not take place in penned domestic ruminants as you state nor did they focus on diet selection as claimed.

    1. On 2024-01-26 22:14:47, user Julia Trevorrow wrote:

      Summary: <br /> In a field where conservative treatment options are limited for chronic spinal pain, it is inspiring to see molecular research on another potential pharmacological option.

      Strengths: <br /> (1) As opposed to opioids, SSRIs and NSAIDs, the usage of targeted senolytics to clear senescent osteoclasts addresses chronic pain at the cellular level. This provides a new modality among the available treatment regimens.

      (2) This is one of those articles where we see the promise of molecular medicine. The RNA primer selection was highly relevant and specific for neuronal development, differentiation and metabolism. These served to accurately assess the relationship of spinal hypersensitivity and the mechanism of action of senolytics. Additionally, this research helped to further tease apart the signaling pathways associated with senescence and senolytics.

      Weaknesses: <br /> (1) With regards to the sham group, there is a possibility that detachment of the posterior paravertebral muscles from L3-L5 possibly induced a level of mechanical instability. To mimic the stress of surgery in the sham group, saline injections into the paravertebral muscles could mimic the muscular disruption of surgery without inducing spinal instability.

      (2) Inclusion of a non-surgical "sham" group, female mice, and surgically altered, aged mice, in the control set would be helpful in identifying the mechanism of drug action.

      (3) Although mice are not bipedal, it may be helpful in preparation for the use in humans to evaluate the L5 and S1 endplates as well since the L5/S1 endplates are more likely to be degenerative in adults than L4/5.

      (4) It was difficult to assess the behavioral effects since there was no baseline prior to surgery, after surgery and before the administration of navitoclax.

      (5) Additionally, in studies moving forward, the risks and benefits of the systemic side effects of navitoclax should be evaluated.

    1. On 2024-01-24 09:55:31, user Francois Paquet-Durand wrote:

      Dear Readers of this pre-print,

      Please note that the TRIP reviews posted alongside the pre-print manuscript relate to the first version of the pre-print. As of 24. January 2024 the revised version is online, which addresses many of the earlier reviewer comments.

      Nevertheless, there are always points and questions that are still open and as the authors of this manuscript we would very much like to hear your opinions.

      François Paquet-Durand

    1. On 2024-01-23 15:37:22, user Nick Bauer wrote:

      The approach described herein is quite nice and simple, but it is missing some key details and discussion to understand the benefits of the approach and its potential limitations.

      The color glass filter used is not specified, and its performance is only characterized within a small range of wavelengths, which limits the fluorophores that can be used and the total number that could be used in this system, unlike some of the previous methods.

      It can of course be a strength that fluorophores in a small spectral range can be used for 2(to-4?)-plex imaging instead of having to use well-separated fluorophores which have wildly different photophysics, so that potential limitation is not in any way fatal. The paper would benefit from more discussion of how the present work fits into the current landscape, both positives and negatives.

    1. On 2024-01-23 09:00:36, user pedro estralin wrote:

      Raman and Tensile Probe images indicate manipulation of repetitive images and writing in the photos and are not reliable.

    2. 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

    1. On 2024-01-22 20:56:44, user Anonymous wrote:

      Version 1 of this manuscript could be improved by extending the time axes in figure 8 (antigen 6 sensorgrams) to match the times shown in figures 3-7 (antigens 1-5) and figures 9-10 (antigens 7-8). Currently, the time is cut off at about 400 seconds, shortly after the beginning of the dissociation phase of the measurement, whereas the other figures all extend out to around 1300 seconds.

      Usually, the reason for including sensorgrams in a published article is to convince the reader that the regression fit curves (red) approximately overlay the data curves (blue-green). If the shapes of the curves match, then it's a visual confirmation that the model fits the data, and that the -log10(KD) values obtained from the regression are therefore trustworthy. In figure 8, with the latter ~900 seconds of data missing, it's more difficult for the reader to make that determination.

      This is important because it's later shown in figure 24 that antigen 6 apparently gets some of the best results of the entire study. Compared to figures 19-23 and 25-26, the antibodies designed against antigen 6 have both the largest total number of binding affinities extending beyond the reference (i.e. largest number of dots above the line), and the highest affinity -log10(kD) values appear to extend farthest past the reference as well (i.e., the dot values along the vertical axis go farthest past the line).

      A key conclusion stated in the abstract (that the IgDesign tool can produce "improved affinities over clinically validated reference antibodies") rests on the premise that the tails of the affinity distributions plotted in figures 19-26 can sometimes extend past the reference line. However, if it turns out the -log10(KD) values aren't reliable to begin with, due to a poor regression fit result, then this conclusion is weakened or possibly even invalidated.

    2. On 2024-01-03 18:29:59, user anonymous wrote:

      Section D.3 of version 1 of the manuscript states that SPR binding affinity measurements on individual DNA variants are repeated four times: two duplicate measurements per experimental run, multiplied by two technical replicates of each run.

      In section F.3, version 1, figures 19-26 show SPR binding affinity measurements for several dozen DNA variants per antibody. The values plotted for -log10(KD)(M) appear to represent the sample mean of each set of four SPR measurements. These figures could be improved by also including error bars representing the sample standard deviation of each set of four measurements.

      Including error bars is important because the precision and accuracy of SPR experiments varies depending upon experimental context. Although some authors report standard deviations of +/-10% or better (see, for example, Table 1 in Brown, M.E. et al., (2020) "Assessing the binding properties of the anti-PD-1 antibody landscape using label-free biosensors" PLoS ONE 15(3) doi: 10.1371/journal.pone.0229206), the repeatability of SPR experiments can be impaired by many experimental artefacts: baseline drift, bulk shift discontinuities, mass transport effects, non-specific binding, manufacturing batch variation of sensor chips or other consumables, fitting to an incorrect kinetic model, etc.

      If the measurement uncertainties are large enough, then the observations of stronger binding affinities than the reference antibodies may not be statistically significant. Affinity values supposedly above the references could be explained away as random statistical noise around a true binding affinity which is actually left unchanged relative to the reference (presumably because the sequence mutations for those DNA variants ultimately had a neutral impact on the final shape of the folded antibody).

      This is a critical point, because if IgDesign does not actually produce mutant sequences with binding affinities signficantly above the reference, then it would weaken a key conclusion of the paper: in such a scenario, IgDesign would not be useful for affinity maturation.

    1. On 2024-01-22 17:35:49, user Mohieddin Jafari wrote:

      After reviewing your paper, I found it to be a little bit confusing. In the first Figure, you reference Fargpipe and DIA-NN, but the case studies appear to be centered around Spectronut. Moreover, it's unclear in the "Mass Spectrophotometry Methods" section which specific case study you are addressing. Do you have any updated version of this manuscript?

    1. On 2024-01-22 05:41:51, user Fraser Lab wrote:

      https://www.biorxiv.org/con...<br /> The paper aims to advance structure-based drug design by using serial crystallography to study room-temperature ligand-bound structures of the soluble epoxide hydrolase protein and discussing the implications for drug discovery. The major success of the paper lies in its innovative approach to microcrystallization and the use of serial crystallography, which allows for detailed room-temperature structural analysis. This technique provides a more dynamic picture of protein-ligand interactions, as evidenced by the observed potential temperature-dependent differences in ligand-binding modes and the improved resolution of flexible loops. The primary limitation of the method is the extended data collection time (1-2 hours).The paper does not fully explore the potential for reducing data collection time for these highly redundant datasets.

      Major Points

      1 - Data Collection Time: The data collection time of 1-2 hours is unusually long. The discussion could focus more on the prospects for reducing this time. An example is in the high redundancy in the “Table 1”. It would be beneficial to see an analysis of data subsets or suggestions for online processing to determine the minimal data collection time needed for viable interpretation. The paper could elaborate on the potential for automation in this method, especially given the lack of "looping" in the process that would help trade off for some of this concern.

      2 - Indexing Rate Variability: The paper should address the variability in indexing rates between different compounds. Understanding and explaining this variability is crucial for the broader application of this method.

      3 - Comparative Literature: Other papers discussing RT ligand binding differences could be cited, including recent works: <br /> - https://pubmed.ncbi.nlm.nih...<br /> - https://elifesciences.org/a...

      4 - Visualization Improvements in Figure 7: A correlation plot or residue plot in Figure 7 would provide a clearer visualization of the differences and regions of increased flexibility at room temperature.

      5 - The improved resolution of certain regions is not entirely unexpected (Fraser 2011 has other examples). The authors may be interested in reading Halle's study (PNAS 2004) on cryocooling for reasoning why the loops may be more ordered.

      6 - Interpretation of Compound Five: The conclusion about the change in pose for Compound Five requires further clarification. Detail the different refinements that led to this conclusion, considering alternative explanations like reduced occupancy should be presented.

    1. On 2024-01-20 17:19:23, user Lihua Song wrote:

      A revised draft will be submitted. I extend my deepest gratitude to the reviewers and editors for their invaluable suggestions and insightful comments, which have significantly contributed to the improvement of this work.

    2. On 2024-01-18 07:57:40, user Lihua Song wrote:

      There have been some folks trying to misinterpret our work as gain-of-function research. Let me be clear – that is not the case. What we've done is simply tested a passaged virus mutant, nothing more. The ACE2 humanized mice used in our experiments are unique and do not exist in nature. The outcomes from these tests cannot be applicable to humans.

    3. On 2024-01-17 12:14:46, user Lihua Song wrote:

      This preprint paper is being misinterpreted on social media. I would like to state the following facts:

      1. The GX_P2V virus has been published in Nature in 2020 (doi: 10.1038/s41586-020-2169-0). It is not a brand-new virus.

      2. The GX_P2V(short_3UTR) mutant was published in Emerging Microbes & Infections in 2022 (doi: 10.1080/22221751.2022.2151383). This cell-adapted mutant is the actual isolate published in the Nature paper. So, the original GX_P2V virus was not isolated. Clearly the original GX_P2V virus in the pangolin sample has severe growth deficiency in Vero cells.

      3. The GX_P2V virus is not a human pathogen, although, based on molecular and animal infection experiments, it can infect a broad spectrum of host species, like human, cat, pig, golden hamster, mouse, rat et al. There is no evidence of the original GX_P2V virus circulating in these animals, not even consider this GX_P2V(short-3UTR) mutant. Please refer to publications: EMBO J, doi: 10.15252/embj.2021109962 and J Virol, doi: 10.1128/jvi.01719-22.

      4. The GX_P2V(short_3UTR) isolate is highly attenuated in in vitro and in vivo models. In Vero, BGM, and Calu-3 cell lines, the virus induced only mild cytopathic effects, notably failing to produce viral plaques even on the human lung cell line Calu-3. In golden hamster and BALB/c mouse models, the virus can infect the animals' respiratory tracts but did not result in any observable disease symptoms. The attenuated nature of GX_P2V(short_3UTR) was also validated in two distinct human ACE2-transgenic mouse models. Please refer to publications: Emerging Microbes & Infections, doi: 10.1080/22221751.2022.2151383 and J Virol, doi: 10.1128/jvi.01719-22.

      The attenuation of GX_P2V(short_3UTR) was also hinted in the Nature paper on the GX_P2V(short_3UTR) isolate (doi: 10.1038/s41586-020-2169-0). In Extended Data Figure 1, after infecting Vero cells for five days, GX_P2V caused noticeable cytopathic effects, but which were limited to cell rounding and mild cytolysis, which starkly contrasted with the severe cytopathic effects reported in SARS-CoV-2.

      1. The public has developed a high level of population immunity against GX_P2V due to SARS-CoV-2 immunizations and infections. Collectively, the biological safety risk posed by GX_P2V(short_3UTR) is extremely low. I don’t think there is any immediate risk of spillover into the human population. Please refer to publication: J Med Virol, doi: 10.1002/jmv.29031.

      2. Based on previous reports on ACE2 humanized mouse models with SARS-CoV-1 and SARS-CoV-2, there is significant variability in the outcomes of infection in these models, a topic extensively documented in the literature. A single ACE2 humanized mouse model does not constitute a reliable paradigm for evaluating viral pathogenicity. While GX_P2V(short_3UTR) proved lethal in our mouse model, it's important to consider that it did not cause disease upon infecting two other distinct ACE2 humanized mouse strains. The findings reported in this paper do not alter the fundamental nature of GX_P2V(short_3UTR) as being highly attenuated.

      3. Several other research groups have repeatedly reported the spillover risk of this virus based on its spike protein binding to human ACE2. Those reports have not caught much attention. In our study, using a unique lethal model, we inadvertently reinforced the perception that this virus has a strong tropism for human brains and causes 100% mortality. We need to revise this in the subsequent revision of the paper and provide additional clarification on the intrinsic attenuated nature of the virus.

      4. The GX_P2V(short_3UTR) mutant is a promising live attenuated vaccine against pan-SARS-CoV-2. Partial results can be found in this preprint paper: https://www.researchsquare.....

    4. On 2024-01-14 05:29:31, user Lihua Song wrote:

      We realize this manuscript misleads readers to believe that the attenuated pangolin coronavirus GX_P2V(short_3UTR) posed a spillover risk to human brains, resulting in a 100% mortality rate, which sparked panic among the public. This virus has no pathogenicity in normal animals. This manuscript necessitates revision to accurately state the abnormal nature of this mouse model, and the fact that these animal outcomes cannot be applicable to humans.

    1. On 2024-01-20 00:06:45, user Pamela Bjorkman wrote:

      This paper was published as: Cohen, AA, Gnanapragasam, PNP, Lee, YE, Hoffman, PR, Ou, S, Kakutani, LM, Keeffe, JR, Wu, H-J, Howarth, M, West, AP, Barnes, CO, Nussenzweig, MC, Bjorkman, PJ (2021) Mosaic nanoparticles elicit cross-reactive immune responses to zoonotic coronaviruses in mice. Science 371: 735-741. PMCID: PMC7928838 doi:10.1126/science.abf6840

    2. On 2024-01-19 03:55:33, user Pamela Bjorkman wrote:

      This paper was published as: Cohen, AA, Gnanapragasam, PNP, Lee, YE, Hoffman, PR, Ou, S, Kakutani, LM, Keeffe, JR, Wu, H-J, Howarth, M, West, AP, Barnes, CO, Nussenzweig, MC, Bjorkman, PJ (2021) Mosaic nanoparticles elicit cross-reactive immune responses to zoonotic coronaviruses in mice. Science 371: 735-741. PMCID: PMC7928838 doi:10.1126/science.abf6840

    1. On 2024-01-19 22:44:36, user Guest wrote:

      I think this is great work ! Have you ever checked this work, seems relevant: "Characterizing chromatin landscape from aggregate and single-cell genomic assays using flexible duration modeling" ?

    1. On 2024-01-19 12:36:25, user Rob wrote:

      Is there a link to the "A references file of 500kb of subtelomere sequences was<br /> assembled from each of the genomes" to reproduce the work?

    1. On 2024-01-19 04:01:11, user Pamela Bjorkman wrote:

      This paper was published as: Barnes, CO, Jette, CA, Abernathy, ME, Dam, K-M A, Esswein, SR, Gristick, HB, Malyutin, AG, Sharaf, NG, Huey-Tubman, KE, Lee, YE, Robbiani, DF, Nussenzweig, MC, West, AP, Bjorkman, PJ (2020) SARS-CoV-2 neutralizing antibody structures inform therapeutic strategies. Nature 588: 682-687. PMCID PMC8092461 doi:10.1038/s41586-020-2852-1

    1. On 2024-01-19 03:58:39, user Pamela Bjorkman wrote:

      This paper was published as: Barnes CO, Schoofs T, Gnanapragasam PNP, Golijanin J, Huey-Tubman KE, Gruell H, Schommers P, Suh-Toma N, Lee YE, Cetrulo Lorenzi JC, Piechocka-Trocha A, Scheid JF, West AP Jr, Walker BD, Seaman MS, Klein F, Nussenzweig MC, Bjorkman PJ. (2022) A naturally arising broad and potent CD4-binding site antibody with low somatic mutation. Sci Adv. 8(32): eabp8155.

    1. On 2024-01-19 03:57:14, user Pamela Bjorkman wrote:

      This paper was published as: Gristick, HB*, Hartweger, H*, Loewe, M, van Schooten, J, Ramos, V, Oliviera, TY, Nishimura, Y, Koranda, NS, Wall, A, Yao, K-H, Poston, D, Gazumyan, A, Wiatr, M, Horning, M, Keeffe, JR, Hoffmann, MAG, Yang, Z, Abernathy, ME, Dam, KA, Gao, H, Gnanapragasam, PNP, Kakutani, LM, Pavlovitch-Bedzyk, AJ, Seaman, MS, Howarth, M, McGuire, AT, Stamatatos, L, Martin, MA, West, AP, Nussenzweig, MC, Bjorkman, PJ (2023) CD4 binding site immunogens elicit heterologous anti-HIV-1 neutralizing antibodies in transgenic and wild-type animals. Sci Immunol doi:10.1126/sciimmunol.ade6364 *Co-first authors.

    1. On 2024-01-17 14:11:41, user Boris Hedtke wrote:

      This paper was published in February 2023 in Plant Physiology with the modified title <br /> "Two isoforms of Arabidopsis protoporphyrinogen oxidase localize in different plastidal membranes" <br /> Plant Physiology, Volume 192, Issue 2, June 2023, Pages 871–885

      https://doi.org/10.1093/plp...

    1. On 2024-01-17 08:02:00, user Rasmus Kirkegaard wrote:

      Cool analysis. I would recommend that you consider upgrading your reference genomes from Unicycler to Trycycler https://github.com/rrwick/P...<br /> Unicycler was great for hybrid assembly when long reads were mostly for sorting the short read based contigs in the right order. But with newer data the quality of the long read assembly is much better and Ryan has made a nice guide for curating the remaining errors using illumina data to achieve a perfect genome.

    1. On 2024-01-16 21:38:49, user JongYoon Jeon wrote:

      Hi, just encountered this manuscript looking for a mutation rate and thanks to this I could find a source reference, Smeds et al 2016. However, you might want to double-check the paper since Smeds et al. reported 2.3 × 10^-9 as mutations per site per year, not generation, if I read correctly. Thanks,

    1. On 2024-01-16 17:21:01, user Letarov Andrey wrote:

      Important UPD to my previous comment.<br /> Today, 17 January 2024, I had a Zoom meeting with Mr. Berryhill, Ms. Gill, Dr. Smith and Prof. Levin. We discussed the issues regarding my negative reaction to this preprint and I was convinced that there was no intentional disregard of our work or of the work of the others. Although some of my criticisms remain, I no longer have any doubts about the integrity of the authors. I apologise for my hasty judgement in the original post.

    2. On 2023-12-03 11:56:12, user Letarov Andrey wrote:

      I saw in Google Scholar that this manuscript cites our 2021 paper (that from my point of view is somewhat under-appreciated by the community) and came here to have a look. Unfortunately, I'm disappointed and even irritated.

      Berryhill et al. ignore (may be deliberately since the key info and refs present in the source which they do cite) almost the whole body of knowledge about the O antigen-mediated shielding of E. coli the bulk of which (over the last 20 years) has been created by my lab with several important contributions form other teams, e.g. Martin Loessner's group (PMID 34784345). Among our papers this one and the refs therein covers the essence of the question (PMID: 30814597). <br /> The systemic analysis of the current state-of-the-art in this field can be found in my review which I hope to see published shortly and now it is posted as a preprint (https://www.preprints.org/m....

      The phenomenon of narrow spectra of sensitivity of gut E. coli isolates to co-existing phages was also discovered by us (PMID: 17704275) and later studied in-depth and linked to the O-antigen mediated anti-phage protection (multiple papers are easy to find using my name as the keyword).

      The essential thing is that despite highly effective non-specific protection from of phage infection provided by E. coli O antigen, many phages have various mechanisms to penetrate the barrier. So the fact of this protection does not preclude phage infection in the community. The fact that humans seldom excrete virulent coliphages is long known (see our old review PMID: 19239553), and most probably is due to environmental constraints. The gut coliphage ecology is different in some other species such as horses or pigs despite the omnipresent O antigens.

      The isolation of the phages by enrichment on a rough (lacking the O antigen) lab strain pre-programs the poor infectivity against most co-existing O antigen-proteceted isolates. By itself this result means nothing.<br /> There are also other methodological problems in this paper.

    1. On 2024-01-16 14:43:15, user Reviewer1 wrote:

      This study investigates the distribution of food source partitioning, across major groups of the animal kingdom. The overarching aim is to create a global trophic pyramid of biomass, partitioned by food source. The authors collected a large dataset on diet composition from the literature and other sources, ensuring a broad taxonomic spread. They then estimate diet partitioning for major taxonomic groups (~class) by averaging species-level data, and further estimate partitioned food source biomass by multiplying with class-level biomass estimates. This is taken to be provide a representation of a trophic pyramid, and the findings are discussed in the light of this concept. The major claim of this study is that they find a middle-heavy trophic pyramid, with invertivory more prominent (by biomass) than herbivory.

      The study pursues a very interesting question in studying the trophic pyramid on a global level. The authors have invested a lot of effort in compiling a large dataset on species-level diet partitioning, and such a dataset would certainly be very valuable for species-level comparisons and analyses, such as the taxonomic distribution of feeding styles or the evolutionary history of feeding specialisations. However, such questions are not the focus of the present study. Rather, an attempt is made to convert this species-level dataset into a trophic pyramid of food source biomass. In the process, the authors make several sweeping assumptions and generalisations, resulting in analyses that are not at all well supported by the underlying data.

      First, the conversion of species-level data to class-level partitioning of food sources, by averaging the data from available species, assumes that the compiled species are representative of the group (class) as a whole, and that a simple species average would provide a meaningful group average. Both are highly doubtful and not supported by any data.

      Second, the assumption is made that the class-level partitioning of food sources can be transformed into a partitioning of diet biomass by a multiplication with that group’s estimated biomass value. However, this will yield the biomass of that specific partition (e.g., the combined bodymass of all vertebrate herbivores) and not the biomass of their diet.

      Third, species groups (and their biomass) are assigned to a trophic level by their food source type, which leads to the three categories “herbivores” (= primary consumers), “invertivores” (= secondary consumers) and “vertivores” (presumably considered as predators including apex predators as they are placed at the top of the pyramid in Fig. 2). This is a strong oversimplification and does not represent a trophic pyramid. Most worryingly, the category “invertivores” will lump many higher-level consumers (third-level, fourth-level…) into the secondary consumer category, which as a result has by far the highest proportion (= biomass in this analysis). Thus, one of the key claims of the study, that the global trophic pyramid is middle-heavy, is likely due to a methodological artifact.

      In summary, the study attempts a methodological shortcut for deriving a trophic biomass dataset from species-level data, without verifying the assumptions. At the current time, there appears to be no ready substitute for species-level abundance or biomass data. Until such data are available for the majority of organisms, analyses of trophic pyramids on a global level may be premature.

      Recommendations for the authors:

      As mentioned in my public review, I commend the authors on compiling such a large and potentially very valuable dataset on species-level diet partitioning. I believe such a dataset can be very informative for species-level analyses, or possible investigations into the evolution of such partitioning. However, such a dataset cannot be transformed into a trophic dataset without corresponding data on species abundances and/or biomass. Your attempts to perform this transformation without such data unfortunately fall short, as it requires a series of sweeping assumptions that are almost entirely unfounded by real-world data.

      I will attempt to explain my views in the sections below:

      Title<br /> The title is misleading: in the current form, the manuscript deals with many more analyses than the number of herbivore and predatory species in each class. Though as I mentioned, this species-level analysis is actually the most relevant (and valid) analysis in your study while the trophic pyramid aspect is not.

      Introduction<br /> You provide a very nice overview of the different concepts of trophic pyramids and their development over time. As you point out, all these variants of the pyramid include a measure of scale for each level, such as ‘abundance’, ‘biomass’, or ‘energy’. It is also implicit in this introduction that this concept considers multiple levels (L42: “…food chains…”, L45: “…and so on up to…”) and not just three as in your following analysis.

      Materials and Methods<br /> The success of the method hinges on the representativeness of selected species. This is highly unlikely, as data on diet composition will be much more readily available for large or well-studied organisms, which are not necessarily the ones that are the most important (by number or biomass) members of their class. The authors themselves acknowledge that for many groups, even with a minimum of ~500 species per group, still only ~0.3 to 1.3% of described species are covered for insecta, arachnida, mollusca and crustacea (L265-267). In addition, I would strongly argue that even with good taxonomic coverage, as is achieved for birds and mammals, calculation of the group average has to consider the highly differing abundance and/or biomass of separate species. To illustrate these points, I would like to highlight the study’s data on the arachnida (Figs. 1 and 4). About 20% of their diet is considered as “parasite vertebrate”, with a considerable biomass. Without knowing the details of the species that were considered, I would assume that the majority of these are ticks, as these feed on (mostly) vertebrate blood. Roughly speaking, we know of maybe 60 000 species of arachnida, of which perhaps 1000 are ticks. On the species level, ticks therefore seem to be highly overrepresented in the dataset, possibly because it is straightforward to infer their food source from their specialized morphology. On the other hand, the group arachnida does not seem to consider very many oribatid mites, of which there are around 12 000 known species that are almost exclusively detritivore. In addition, oribatid mites are known to be extremely abundant in soils, so their biomass is likely many times that of ticks. A similarly obvious over-representation in terms of diet and biomass occurs in the marine dataset with “vertivore crustacea”. Please note that I only picked some obvious examples here, but that the same issues will be prevalent in all animal groups.

      Indeed, I believe that your method “validation” using bird species data shows that your estimate can be very unreliable, even for a well-covered group such as birds. Your Results (L345-347) show that “the respective contribution of invertebrates and vertebrates switched from 56% and 8% in the estimate to 23% and 45% in the species-weighted partitioning”. These are very large differences.

      A further point I would like to raise: using an animal group’s biomass to gauge the biomass of the separate diet partitions seems to oversimplify matters. You are assuming that the body biomass equals the diet biomass. However, foods have very different nutritional content (e.g., carbohydrates/protein/fiber). A Panda and a Polar Bear may have fairly similar body weights, but the panda needs to eat much more plant matter biomass due to the poor nutritional content.

      Overall, the Methods section is a little disjointed, and is difficult to match to the Results section. Also, some of the chosen methods are not well justified or explained. E.g., <br /> - How were Wikipedia sources selected and “confirmed” (L130), or how was the literature searched (L132)? <br /> - How did you incorporate a diet category that only exists for a single class (“plant-derived, L150”)? <br /> - How did you deal with separate diet data for juveniles and adults (L157)?<br /> - L184ff: It remains unclear why you compare your global dataset to two location-specific datasets. What did you aim to achieve? A validation of the global dataset in this manner appears dubious, as local datasets may always remain location-specific.<br /> - What is your justification for collecting a further dataset on dinosaur diet? You mention that you aim “to test if herbivory is related to higher body mass and lower metabolic rate” (L206), but then compile only diet data for these dinosaurs (inferred from dental morphology, adding a further level of uncertainty), and no data on body mass or metabolic rate. In addition, I would think that your dataset on mammal diet composition would be much more suitable for this purpose, as it appears to be quite comprehensive and would include many species with “high” body mass. Also, in extant mammal the diet composition has presumably been directly quantified, and not just inferred from dental morphology.<br /> - L214ff: Why have a specific method for assessing human diet? We are just one more species in your dataset.<br /> - L223ff: The use of reptile biomass data for amphibians is not justified. Your assessment that the differences in average body mass and population density ‘cancel each other out’ cannot be verified. If you do not have a good biomass estimate for amphibians, you cannot include this group in the analysis.<br /> - L265ff: Your statistical “validation” of achieving representative data from poor species coverage is inappropriate. By sampling 0.3% of bird species 10 00 times and calculating an average, you merely verify that you can calculate a good average from ~300 000 (~30 species x 10 000), overall randomly sampled, data points. To “validate” your approach, you need to investigate the variance of your 10 000 repeat samples, which presumably is extremely large.<br /> - L265ff: The Methods appear to be incomplete here, as the Results section describes an analysis that was weighted by bird species biomass and abundance (L340).

      Results<br /> Throughout the manuscript, but particularly noticeable in the Results section, you are using misleading terms to refer to your data and results. I believe this stems from your multiple assumption to derive trophic pyramid data from a species-level dataset. E.g.<br /> - Fig.1: “species in most animal groups”; this figure shows the group average diet composition, not the species proportions.<br /> - L355: “partitioning of diets… expressed as biomass (Fig. 2)”; this figure actually shows the biomass of the trophic group, not their diet.<br /> - Etc.

      L333: “we assumed a homogenous distribution of biomass across trophic levels in each group” – a further example of an unfounded assumption that weakens your analyses and conclusions considerably.

      The data on dinosaur diet is missing from the Results.

      Discussion<br /> As outlined above, I believe that your main conclusion of a middle-heavy global trophic pyramid is not supported by your analyses, as are other conclusions on the trophic pyramid. Your study does not support the conclusion of a “paradigm shift” (cf. L407).

      Finally, some further minor comments:<br /> L173: what is the category “Food I”, and why is it relevant to mention these categories here?<br /> L311: Conservation areas might include some “important species” that are missing elsewhere, but that should not distract from the fact that species lists remain highly biased and incomplete there, as everywhere. Most obviously, Kruger NP is bound to have more than 13 species of insect (Fig. S4). And certainly such species list do not consider the microfauna to a meaningful degree.<br /> L402: It seems very unfair to disparage previous efforts as biased, when your own study is based on highly incomplete datasets and unfounded assumptions.<br /> L476f: I find the definition of a carnivore from Román-Palacios et al. in this context highly misleading. Heterotrophs include fungi, which does not make a fungivore a carnivore.<br /> L494: There might have been larger insects in the prehistoric past (at least we know of one large dragonfly), but that hardly makes them “megafauna”.<br /> L523: “a world without insect would potentially mark the end of complex life on Earth” – there is certainly complex life in marine environments, where insects are not prevalent and their potential decline might not have large impacts.<br /> L676: “more abundant” – you are not considering abundance here.<br /> Fig. S8: Here you are literally comparing a species group with a single species (humans). I presume that your reasoning is that the diet of humans has important impacts on the global food web. This is a nice case in point that you absolutely need species-level information on abundance/biomass to construct trophic pyramids and food webs.

    1. On 2024-01-14 22:54:39, user Keji Zhao wrote:

      Very interesting study --- providing insights into how MutSb and CNG cooperate to drive the expansion of trinucleotide repeats in Huntington's and other relevant diseases.

      Do the authors know how well these trinucleotide repeats form nucleosome structure in cells?

    1. On 2024-01-12 11:13:24, user Ines Hellmann wrote:

      Finally, we completed the story and are proud to share it with the world.<br /> We started this nearly 10 years ago. The counterintuitive observation that the sequence of CREs that are active in more tissues were less conserved than the sequence of tissue-specific ones needed a solid explanation. We started with an avalanche of sanity checks.<br /> Remapped everything, re-evaluated peak-calling, included dinucleotide aware divergence measures, re-checked alignments. After we were convinced that the signal would not go away, we needed to find an explanation.<br /> We started to look for an unbiased measure of TFBS binding potential and add RNA-seq and ATAC-seq data from comparing macaques and humans. Finally, the fog cleared up showing that pleiotropic CREs show a higer functional conservation.<br /> It is just that this functional conservation of these large pleiotropic CREs is achieved by redundancy, thus TFBS are moving around and different locations got fixed in different species: i.e. compensatory evolution within the same CRE.<br /> Compensatory evolution has been suggested before as a common mode of evolution for CREs, it is only that for less pleiotropic elements this usually happens not within the same element, but between different CREs, i.e. the entire CRE jumps around.

    1. On 2024-01-11 10:11:41, user Beth wrote:

      Fetal sex is not mentioned in this study. Could you look at sex differences? Was fetal sex controlled for when looking at the effect of gravidity? Was there an equal balance of male/female placentas in the primi and multigravida groups? If not, this could confound the identified differences between these two groups.

    1. On 2024-01-09 12:47:09, user Andrea Page-McCaw wrote:

      A revised version of this article has been accepted for publication in Matrix Biology and is available online from their website. The title has been revised: "Peroxidasin is required for full viability in development and for maintenance of tissue mechanics in adults".<br /> Matrix Biol. 2023 Nov 22:S0945-053X(23)00117-8. doi: 10.1016/j.matbio.2023.11.005. Online ahead of print.<br /> K Elkie Peebles, Kimberly S LaFever, Patrick S Page-McCaw, Selene Colon, Dan Wang, Aubrie M Stricker, Nicholas Ferrell, Gautam Bhave, Andrea Page-McCaw<br /> PMID: 38000777 DOI: 10.1016/j.matbio.2023.11.005

    1. On 2024-01-08 17:48:05, user Jonathan wrote:

      The role of Creb3l2 and XBP1 in professional secretory cell has already been established in 2019 by Khetchoumian et al. Any reason why this paper wasn't mentioned in reference?

    1. On 2024-01-04 16:19:04, user Manuel Théry wrote:

      This manuscript has not yet been published in a peer-reviewed journal yet because we noticed that our engineered epithelial cell line, expressing ZEB1 under the control of doxycyclin, was contaminated with mycoplasma. We currently don't have the human ressources to make a new cell line, and repeat the key experiments in order to validate (at least) the main conclusions.

    1. On 2024-01-04 14:03:40, user Caroline wrote:

      Congratulations on the paper. It's very important that we start adding pieces to this immunity evolutionary puzzle.

      I just need to highlight that the work developed at the Baker Lab on the evolution of Argonautes, bringing archaeal sequences into debate and positioning them in the evolutionary context, must be cited here.<br /> (https://www.biorxiv.org/con...

      It's very important that both papers are linked so we can have a more comprehensive understanding of such a complex topic.<br /> Your paper basically

    1. On 2024-01-03 01:25:21, user Claudiu Bandea wrote:

      New evidence supports the hypothesis that Borgs are incipient viral lineages <br /> (Claudiu Bandea, Dec 28, 2023)

      The discovery of Borgs as giant extrachromosomal elements, presumably inhabiting Methanoperedens archaea, was first published in 2021, in bioRxiv [1]. More than a year later, the study was also published in Nature under a slightly different title and content [2]. The study, which reported the sequencing and analysis of more than a dozen Borg genomes (661,708 to 918,293 kb in length), including four genomes that were fully curated and analyzed, found no evidence of viral characteristics.

      On the basis of these results, the authors asserted the following: “We can neither prove that they are archaeal viruses or plasmids or minichromosomes, nor prove that they are not. Although they may ultimately be classified as megaplasmids, they are clearly different from anything that has been previously reported” (all quotes in Italics) [2]. This statement raises a critical question: what kind of evidence would warrant the classification of Borgs as viruses, megaplasmids, or minichromosomes? Surprisingly, the authors did not address this essential issue.

      Despite the Borgs’ apparent lack of viral characteristics, in a commentary entitled “Will Borgs Illuminate the Evolutionary Origin of Ancestral Viral Lineages?” [3], I suggested that Borgs are incipient viral lineages and, thus, illuminate one of the biggest mysteries in biology – the origin of viruses.

      Remarkably, in a new article published in bioRxiv by the same group [4], we learn that, after all, Borgs do encode numerous putative viral proteins, including several capsid proteins, as well as proteins implicated in the replication, recombination, and spread of Borgs to new host cells. The new study presents additional evidence, including a high ratio between the number of Borgs and their presumed Methanoperedens hosts and a distinct methylation pattern of their genomes, which point to an extracellular stage in the Borgs’ life cycle and to their viral nature.

      As I outlined in my previous commentary [3], the rationale for proposing that Borgs might be incipient viral lineages, even in the absence of the conventional physical, biochemical, and biological features historically used to define viruses (see below), was rooted in the Fusion Hypothesis regarding the evolutionary origin of viruses [5-7].

      According to this hypothesis, the ancestral or incipient viral lineages originated from ecto- or endo-symbiotic or parasitic cellular lineages that fused with their host cells. By fusing with their host cells and discarding their cellular membrane, these lineages transitioned to new type of biological organization and structure (see below), which gave them full access to the host cell resources, including the host’s ribosomes and other components of the translation machinery. After synthesizing their specific molecules and replicating their genome using the resources found in their special environmental niche (i.e., the host cell), this new type of organisms induced the assembly and morphogenesis of reproductive, transmissible forms, which started a new life cycle by fusing with other host cells.

      The absence of a cellular membrane within the host cell presented the incipient viral lineages with unique reductive evolutionary opportunities, not readily available for parasitic or symbiotic cellular lineages, which led to a myriad of new viruses with diverse lifestyles and biochemical composition. As outlined below, the fusion model completely changes the conventional views regarding the nature of viruses, their evolutionary origin, and their role in shaping the evolution of cellular lineages.

      The nature of viruses

      Ever since viruses were identified more than a century ago as infectious agents that passed through filters thought at that time to retain all microorganisms, they have been conceptually identified with the virus particles, or virions - the transmissible infectious forms in the viral life cycle. Accordingly, viruses have been defined based on the physical, biochemical, and biological properties of these particles, as illustrated in virtually all scientific literature and textbooks to date.

      For example, in his seminal book, The Molecular Biology of the Gene, James Watson, who was highly familiar with nucleic acids, as well as with viruses [8], wrote: “All viruses differ fundamentally from cells, which have both DNA and RNA, in that viruses contain only one type of nucleic acid, which may be either DNA or RNA” [9]. A decade later, in A Dictionary of Virology, viruses were defined as “Infectious units consisting of either RNA or DNA enclosed in a protective coat” [10], and in the 1990s, a classic microbiology textbook, Zinsser Microbiology, stated that viruses “consist of a genome, either RNA or DNA, that is surrounded by a protective protein shell” [11].

      Surely, the authors of these scientific publications were fully aware that, during the intracellular stage of their life cycle, many viruses, such as the “DNA viruses” and retroviruses, have both type of nuclei acids, DNA as well as RNA, and that many viruses are much more complex than a nucleic acid wrapped in a protein coat. Yet, all these renowned scientists fell victim to the concept of viruses as virus particles and used the physical, biochemical, and biological properties of these particles to define viruses. This is a strong example of the power of concepts in science. A concept that clearly misrepresents the experimental findings and observations can persist for decades, or, as in the case of viruses, for more than a century.

      Forty years ago, in 1983, I proposed that, like many parasitic cellular lineages, viruses pass in their life cycle through two phenotypically distinct stages: the extracellular, reproductive forms represented by the virus particles, and the intracellular forms in which the viral molecules and components are “free” or dispersed within their host cell [5].

      The viral particles are highly specialized structures that are used by some viruses for their transmission to new host cells. This role of viral particles in the viral life cycle explains their properties, including their apparent inert status and the presence of only one type of nucleic acid - DNA or RNA. Many viruses, however, do not produce viral particles, using instead alternative modes of transmission [12]. This fact alone indicates that identifying viruses with the virus particles misrepresents their nature. Nevertheless, the fundamental biological properties of viruses, whether they do or do not produce virions, are expressed during the intracellular stage of the viral life cycle, when viruses replicate their genome and synthesize their specific molecules, many of which are not components of the viral particles.

      To identify viruses phenotypically during the intracellular stage of their life cycle with the integrative sum of all their molecules, and to differentiate them conceptually from the parasitic lineages that maintain a cellular membrane within the host cell, I proposed the concept of molecular structure and labeled viruses as molecular organisms [5, 6].

      Although the concepts of molecular organisms and molecular structure (which, by analogy with the host cell’s cytoplasm, can be called viroplasm) are more suggestively envisioned within the framework of the fusion hypothesis, these concepts are also applicable in context of the other hypotheses regarding the origin and evolution of viruses (see below). Significantly, these concepts set the foundation for including other biological entities, such as plasmids, endogenous viruses, and viroids, within the same domain of biological organization - the viral domain.

      In a commentary entitled “What makes a virus a virus?” [13], Roland Wolkowicz and Moselio Schaechter wrote that the identity of viruses as historically conceptualized and defined (i.e., as virus particles) is missing “the most fundamental aspect of what makes a virus a virus: it breaks up and loses its bodily integrity, with its progeny becoming reconstituted after replication from newly synthesized parts” and that “We are surprised from our own experience that the world of virology has not fully embraced this outlook” .

      After the discovery of giant viruses, Jean-Michel Claverie asked, “What if we have totally missed the true nature of (at least some) viruses?” [14], and in a series of publications Patrick Forterre and his colleagues have discussed extensively the limitations of the concept of viruses as virus particles and suggested alternative ways to define viruses and to identify them during the intracellular stage of their life cycle [15-18].

      As I discussed in the original publication [5], referring to the intracellular stage of viruses as an “eclipse phase,” denoting the “disappearance” of viruses, was confusing. Likewise, identifying viruses with their genome, thereby ignoring the other viral molecules and components, misrepresents their nature. An alternative approach was to no longer refer to a virus as an individual biological entity, but as an integrated virus-host cell system (i.e., the infected cell0. Recently, Patrick Forterre labeled this integrated system with the term “virocell” [15, 17, 18].

      This approach was sharply criticized by Purificación López-García and David Moreira on both scientific and epistemological grounds [19, 20], and recently the virocell term was redefined by DeLong et al., [21], but Forterre rebutted the criticism [18].

      Nevertheless, these highly relevant discussions bring forward the acute problems with the dogma of viruses as virus particles and stress the need for a new scientific and academic perspective on viruses, which can productively integrate the extraordinary amount of knowledge about viruses and their role in shaping the life and evolution of their hosts and of the ecosystem in which their live [15, 22-30].

      The scientific limitations and academic confusion associated with the concept of viruses as virus particles in virology and related biomedical fields [31-33] remain to be fully addressed. However, questioning the validity of this dogma, which has guided several generations of researchers to extraordinary discoveries and progress in virology, is challenging.

      The origin and evolution of viruses

      As it would be expected, in the context of the dogma of viruses as virus particles, the hypotheses regarding their evolutionary origin focused on the virions and their structure: (i) thePre-cellular or Virus-first Theory suggested that viruses originated from precellular, self-replicating nucleic acids, or replicons, encoding for capsid proteins; (ii) the Endogenous or Escape Hypothesis suggested that viruses originated from cellular genomic sequences, or replicons, encoding for capsid proteins; (iii) and the historical Regressive or Reductive Hypothesis proposed a reductive transition of parasitic cellular lineages, such as bacteria, into nucleocapsid-like structures.

      Within the concept of viruses as virus particles, the validity of the regressive hypothesis was questionable as Salvador Luria and James Darnell pointed out more than half a century ago: “The strongest argument against the regressive origin of viruses from cellular parasites is the non-cellular organization of viruses. The viral capsids are morphogenetically analogous to cellular organelles made up of protein subunits, such as bacterial flagella, actin filaments, and the like, and not to cellular membranes.” [34].

      Indeed, many parasitic and symbiotic bacteria have a fraction of the genomic and proteomic repertoire of some viruses. For example, several endosymbionts, such as Carsonella, Hodgkinia, and Tremblaya, have a genome that is less than 200 kb and encode less than 200 proteins [35]. Yet, no symbiotic or parasitic bacteria with highly reduced genomes and metabolic capability resemble virus particles.

      As predicted by the fusion hypothesis, only symbiotic or parasitic lineages that have a genetic and metabolic system compatible with that of their host cells would be able to fuse with them and transition to a viral type of biological organization. Accordingly, only bacterial, archaeal, and eukaryotic lineages, hosted by bacterial, archaeal, and eukaryotic host cells, respectively, could evolve into viral lineages [6, 7, 36].

      Interestingly, numerous symbiotic and parasitic lineages that inhabit their kin and have reduced genomes and metabolic capabilities have been recently discovered, including highly diverse groups of DPANN archaea and CPR bacteria [37-42]. Hypothetically, some of these archaeal and bacterial lineages are in the process of transitioning into incipient viral lineages [6, 36], similar to the putative cellular ancestors of Borgs [3]. Nevertheless, one of the major appeals of the fusion hypothesis is that, unlike the other hypotheses, it can be addressed experimentally, as some members of these groups archaea and bacteria could be developed as fusion model organisms.

      Surprisingly, though, the strongest evidence for the fusion hypothesis is found among more complex organisms - the eukaryotes. According to the fusion model, the nucleomorphs, some of which have a very small genome (<1 Mb) [43], originated from algal endosymbionts that fused with their host cells. Although, currently conceptualized as organelle-like entities, the nucleomorphs are genuine molecular organisms that have maintained their nucleus.

      Even more surprising is the fact that numerous parasitic algal and fungal lineages have a life cycle and biological organization that, as I previously pointed out [6], represent overwhelming evidence for the fusion hypothesis. Indeed, several obligate parasitic species of red algae fuse with their host cells and use the host resources, including, in my assessment, the host ribosomes and other components of the translational machinery, to synthesize their molecules, replicate their genome, and induce the morphogenesis of spore-like progenies [44-50].

      I cannot overemphasize the significance of these discoveries which support the fusion hypothesis and should be considered breakthrough discoveries not only in the field of parasitology, but also in evolutionary science, and biology.

      Many viruses have been discovered serendipitously, including the recent finding in Chaetognaths, a small phylum of marine invertebrates, of two complex viruses, which have yet to be characterized at the molecular level [51, 52]. As more investigators become familiar with the fusion hypothesis and its predictions, it is likely that new types of viruses, as well as of new cellular lineages that are transitioning into incipient viral lineage, will be discovered.

      Although, similar to tens of thousands of symbiotic and parasitic cellular lineages, the viral lineages have evolved towards reduced genomes and proteomes, there is clear evidence of frequent exchanges of genetic material with their hosts and other coinfecting organisms [6, 7, 53]. Considering also their high mutational rates, the deep phylogenetic analysis of viruses is inherently difficult [54-58]. Therefore, trying to establish deep phylogenetic relationships among viruses, reaching the origin and early evolution of life, is likely to be a futile effort.

      The origin of incipient viral lineages from symbiotic or parasitic cellular lineages by a fusion mechanism is consistent with the current sequence-based phylogenetic analysis indicating orthologous relationships between the genes of some complex viruses and those of their hosts. The fusion hypothesis is also consistent with the complex biology and the life cycle of many viruses [59-62]. Also, unlike the virus-first, and the escape hypotheses, which dominate the current scientific literature [57, 63-65], the fusion hypothesis is consistent with the reductive evolution of thousands of endosymbiotic/parasitic microorganisms, which prompts the critical question: Why would viruses evolve in the opposite way?

      Unlike the other two hypotheses on the evolutionary origin of viral lineages, the fusion hypothesis also unambiguously addresses one of the most intriguing scientific and philosophical questions: Are viruses alive? If the viral lineages originated from cellular microorganisms as proposed in the fusion model, then, there are few remaining arguments, if any, against their living status and their rightful place on the Tree of Life [5-7, 66-68].

      Finally, it is relevant to mention that the fusion model on the origin of viral lineages is an integral part of a broader perspective - the fusion/anti-fusion theory - regarding the origin and evolution of pre-cellular and cellular lineages, including the archaeal, bacterial, and eukaryotic cellular domains and some of their defining characteristics [7]. Many aspects of this unifying theory, which addresses the major transitions in the history of life, including its origin, can be found as discrete published ideas and hypotheses [69-74].

      Luria’s Credo: There is an intrinsic simplicity of nature and the ultimate contribution of science resides in the discovery of unifying and simplifying generalization, rather than in the description of isolated situation - in the visualization of simple, overall patterns rather than in the analysis of patchworks [75].

      References

      1. Al-Shayeb, B., et al., Borgs are giant extrachromosomal elements with the potential to augment methane oxidation. bioRxiv, 2021: p. 2021.07.10.451761.

      2. Al-Shayeb, B., et al., Borgs are giant genetic elements with potential to expand metabolic capacity. Nature, 2022. 610(7933): p. 731-736.

      3. Bandea, C., Will Borgs Illuminate the Evolutionary Origin of Ancestral Viral Lineages? bioRxiv, 2021: p. https://www.biorxiv.org/con....

      4. Schoelmerich, M.C., et al., Borg extrachromosomal elements of methane-oxidizing archaea have conserved and expressed genetic repertoires. bioRxiv, 2023: p. 2023.08.01.549754.

      5. Bandea, C.I., A new theory on the origin and the nature of viruses. J Theor Biol, 1983. 105(4): p. 591-602.

      6. Bandea, C.I., The origin and evolution of viruses as molecular organisms. Nature Precedings, 2009; https://doi.org/10.1038/npr....

      7. Bandea, C.I., A unifying scenario on the origin and evolution of cellular and viral domains. Nature Precedings, 2009; https://doi.org/10.1038/npr....

      8. Crick, F.H. and J.D. Watson, Structure of small viruses. Nature, 1956. 177(4506): p. 473-5.

      9. Watson, J.D., Molecular Biology of the Gene. 1976, Menlo Park: Benjamin-Cummings.

      10. Rowson, K.E.K., Rees, TAL and Mahy, BWJ, A Dictionary of Virology. 1981, Oxford Blackwell Scientific.

      11. Joklik, W.K., Willett, HP, Amos, BD and Wifert CM, Zinsser Microbiology. 1992, Norwalk: Appleton and Lange.

      12. Hough, B., et al., Fungal Viruses Unveiled: A Comprehensive Review of Mycoviruses. Viruses, 2023. 15(5).

      13. Wolkowicz, R. and M. Schaechter, What makes a virus a virus? Nat Rev Microbiol, 2008. 6(8): p. 643; author reply 643.

      14. Claverie, J.M., Viruses take center stage in cellular evolution. Genome Biol, 2006. 7(6): p. 110.

      15. Forterre, P., Giant viruses: conflicts in revisiting the virus concept. Intervirology, 2010. 53(5): p. 362-78.

      16. Raoult, D. and P. Forterre, Redefining viruses: lessons from Mimivirus. Nat Rev Microbiol, 2008. 6(4): p. 315-9.

      17. Forterre, P., Manipulation of cellular syntheses and the nature of viruses: The virocell concept. Comptes rendus. Chimie, 2011. 14(4): p. 392-399.

      18. Forterre, P., The Virocell Concept. Encyclopedia of Virology. 2021.

      19. López-García, P., The place of viruses in biology in light of the metabolism- versus-replication-first debate. Hist Philos Life Sci, 2012. 34(3): p. 391-406.

      20. Lopez-Garcia, P., Moreira, D., Viruses in biology. . Evolution: Education & Outreach, 2012. 5: p. 389–398.

      21. DeLong, J.P., et al., Towards an integrative view of virus phenotypes. Nat Rev Microbiol, 2022. 20(2): p. 83-94.

      22. Claverie, J.M. and C. Abergel, Giant viruses: The difficult breaking of multiple epistemological barriers. Stud Hist Philos Biol Biomed Sci, 2016. 59: p. 89-99.

      23. Aylward, F.O. and M. Moniruzzaman, Viral Complexity. Biomolecules, 2022. 12(8).

      24. Correa, A.M.S., et al., Revisiting the rules of life for viruses of microorganisms. Nat Rev Microbiol, 2021. 19(8): p. 501-513.

      25. Moniruzzaman, M., et al., Virologs, viral mimicry, and virocell metabolism: the expanding scale of cellular functions encoded in the complex genomes of giant viruses. FEMS Microbiol Rev, 2023. 47(5).

      26. Moniruzzaman, M., et al., Dynamic genome evolution and complex virocell metabolism of globally-distributed giant viruses. Nat Commun, 2020. 11(1): p. 1710.

      27. Rosenwasser, S., et al., Virocell Metabolism: Metabolic Innovations During Host-Virus Interactions in the Ocean. Trends Microbiol, 2016. 24(10): p. 821-832.

      28. Howard-Varona, C., et al., Phage-specific metabolic reprogramming of virocells. Isme j, 2020. 14(4): p. 881-895.

      29. Braga, L.P.P., et al., Novel virocell metabolic potential revealed in agricultural soils by virus-enriched soil metagenome analysis. Environ Microbiol Rep, 2021. 13(3): p. 348-354.

      30. Alizon, S., et al., Towards a multi-level and a multi-disciplinary approach to DNA oncovirus virulence. Philos Trans R Soc Lond B Biol Sci, 2019. 374(1773): p. 20190041.

      31. Depuydt, C.E., et al., Human Papillomavirus (HPV) virion induced cancer and subfertility, two sides of the same coin. Facts Views Vis Obgyn, 2016. 8(4): p. 211-222.

      32. Bandea, C.I., The Prion Hypothesis at Forty: Enlightening or Deceptive? J Alzheimers Dis, 2022; https://www.j-alz.com/edito...

      33. Enquist, L.W. and V. Racaniello, Virology: From Contagium Fluidum to Virome, in Fields Virology, Vol. 4. Fundamentals, 7th Edition P.M. Howley and D.M. Knipe, Editors. 2024, Wolters Kluwer.

      34. Luria, S. and J. Darnell, General Virology 1965, New-York: Wiley.

      35. McCutcheon, J.P. and N.A. Moran, Extreme genome reduction in symbiotic bacteria. Nat Rev Microbiol, 2011. 10(1): p. 13-26.

      36. Bandea, C.I., Are Antarctic Nanohaloarchaeota emerging viral lineages? Preprints 2019; https://doi.org/10.20944/pr...

      37. Moreira, D., et al., Reductive evolution and unique predatory mode in the CPR bacterium Vampirococcus lugosii. Nat Commun, 2021. 12(1): p. 2454.

      38. Dombrowski, N., et al., Genomic diversity, lifestyles and evolutionary origins of DPANN archaea. FEMS Microbiol Lett, 2019. 366(2).

      39. Naor, A. and U. Gophna, Cell fusion and hybrids in Archaea: prospects for genome shuffling and accelerated strain development for biotechnology. Bioengineered, 2013. 4(3): p. 126-9.

      40. Naor, A., et al., Low species barriers in halophilic archaea and the formation of recombinant hybrids. Curr Biol, 2012. 22(15): p. 1444-8.

      41. López-García, P. and D. Moreira, Physical connections: prokaryotes parasitizing their kin. Environ Microbiol Rep, 2021. 13(1): p. 54-61.

      42. Bokhari, R.H., et al., Bacterial Origin and Reductive Evolution of the CPR Group. Genome Biol Evol, 2020. 12(3): p. 103-121.

      43. Archibald, J.M. and C.E. Lane, Going, going, not quite gone: nucleomorphs as a case study in nuclear genome reduction. J Hered, 2009. 100(5): p. 582-90.

      44. Goff, L.J. and A.W. Coleman, Transfer of nuclei from a parasite to its host. Proc Natl Acad Sci U S A, 1984. 81(17): p. 5420-4.

      45. Goff, L.J., J. Ashen, and D. Moon, THE EVOLUTION OF PARASITES FROM THEIR HOSTS: A CASE STUDY IN THE PARASITIC RED ALGAE. Evolution, 1997. 51(4): p. 1068-1078.

      46. Freese, J.M. and C.E. Lane, Parasitism finds many solutions to the same problems in red algae (Florideophyceae, Rhodophyta). Mol Biochem Parasitol, 2017. 214: p. 105-111.

      47. Blouin, N.A. and C.E. Lane, Red algal parasites: models for a life history evolution that leaves photosynthesis behind again and again. Bioessays, 2012. 34(3): p. 226-35.

      48. Kellner, M., et al., Transfer of genetic information from the mycoparasite Parasitella parasitica to its host Absidia glauca. Curr Genet, 1993. 23(4): p. 334-7.

      49. Schultze, K., et al., Sexuality and parasitism share common regulatory pathways in the fungus Parasitella parasitica. Gene, 2005. 348: p. 33-44.

      50. Ellenberger, S., A. Burmester, and J. Wöstemeyer, The fate of mitochondria after infection of the Mucoralean fungus Absidia glauca by the fusion parasite Parasitella parasitica: comparison of mitochondrial genomes in zygomycetes. Mitochondrial DNA A DNA Mapp Seq Anal, 2018. 29(1): p. 113-120.

      51. Shinn, G.L. and B.L. Bullard, Ultrastructure of Meelsvirus: A nuclear virus of arrow worms (phylum Chaetognatha) producing giant "tailed" virions. PLoS One, 2018. 13(9): p. e0203282.

      52. Barthelemy, R.-M., T. Goto, and E. Faure, Serendipitous discovery in a marine invertebrate (Phylum Chaetognatha) of the longest giant viruses reported till date. Virology: Current Research, 2019. 3(1).

      53. Filée, J., Genomic comparison of closely related Giant Viruses supports an accordion-like model of evolution. Front Microbiol, 2015. 6: p. 593.

      54. Holmes, E.C. and S. Duchêne, Can Sequence Phylogenies Safely Infer the Origin of the Global Virome? mBio, 2019. 10(2).

      55. Claverie, J.M., Fundamental Difficulties Prevent the Reconstruction of the Deep Phylogeny of Viruses. Viruses, 2020. 12(10).

      56. Caetano-Anollés, G., J.M. Claverie, and A. Nasir, A critical analysis of the current state of virus taxonomy. Front Microbiol, 2023. 14: p. 1240993.

      57. Koonin, E.V., et al., Viruses Defined by the Position of the Virosphere within the Replicator Space. Microbiol Mol Biol Rev, 2021. 85(4): p. e0019320.

      58. Barreat, J.G.N. and A. Katzourakis, A billion years arms-race between viruses, virophages, and eukaryotes. Elife, 2023. 12.

      59. Claverie, J.M. and C. Abergel, Open questions about giant viruses. Adv Virus Res, 2013. 85: p. 25-56.

      60. Nasir, A., et al., Untangling the origin of viruses and their impact on cellular evolution. Ann N Y Acad Sci, 2015. 1341: p. 61-74.

      61. Nasir, A., E. Romero-Severson, and J.M. Claverie, Investigating the Concept and Origin of Viruses. Trends Microbiol, 2020. 28(12): p. 959-967.

      62. Seligmann, H., Syntenies Between Cohosted Mitochondrial, Chloroplast, and Phycodnavirus Genomes: Functional Mimicry and/or Common Ancestry? DNA Cell Biol, 2019. 38(11): p. 1257-1268.

      63. Krupovic, M., V.V. Dolja, and E.V. Koonin, Origin of viruses: primordial replicators recruiting capsids from hosts. Nat Rev Microbiol, 2019. 17(7): p. 449-458.

      64. Krupovic, M. and E.V. Koonin, Cellular origin of the viral capsid-like bacterial microcompartments. Biol Direct, 2017. 12(1): p. 25.

      65. Koonin, E.V., T.G. Senkevich, and V.V. Dolja, The ancient Virus World and evolution of cells. Biol Direct, 2006. 1: p. 29.

      66. Forterre, P., Defining life: the virus viewpoint. Orig Life Evol Biosph, 2010. 40(2): p. 151-60.

      67. Kostyrka, G., La place des virus dans le monde vivant. PhD Thesis, Université Panthéon-Sorbonne-Paris I; https://tel.archives-ouvert..., 2018.

      68. Mindell, D.P., The tree of life: metaphor, model, and heuristic device. Syst Biol, 2013. 62(3): p. 479-89.

      69. Tang, S., The Origin(s) of Cell(s): Pre-Darwinian Evolution from FUCAs to LUCA : To Carl Woese (1928-2012), for his Conceptual Breakthrough of Cellular Evolution. J Mol Evol, 2021. 89(7): p. 427-447.

      70. Sinai, S., et al., Primordial sex facilitates the emergence of evolution. J R Soc Interface, 2018. 15(139).

      71. Vig-Milkovics, Z., et al., Moderate sex between protocells can balance between a decrease in assortment load and an increase in parasite spread. J Theor Biol, 2019. 462: p. 304-310.

      72. Babajanyan, S.G., et al., Coevolution of reproducers and replicators at the origin of life and the conditions for the origin of genomes. Proc Natl Acad Sci U S A, 2023. 120(14): p. e2301522120.

      73. Garg, S.G. and W.F. Martin, Mitochondria, the Cell Cycle, and the Origin of Sex via a Syncytial Eukaryote Common Ancestor. Genome Biol Evol, 2016. 8(6): p. 1950-70.

      74. Joyce, G.F. and J.W. Szostak, Protocells and RNA Self-Replication. Cold Spring Harb Perspect Biol, 2018. 10(9).

      75. Luria, S., General Virology. 1953, Hoboken, NJ: J. Wiley and Sons.

    1. On 2024-01-02 19:52:05, user Adam Zeno wrote:

      Incredibly interesting work in elucidating the molecular pathology of SMDCF ! I hope to see this work used in future investigations and treatments of skeletal disorders

    1. On 2024-01-02 11:29:19, user Anita Bandrowski wrote:

      Hi I am trying to track down this mouse "APP/PS1 mice (B6;C3-Tg(APPswe,PSEN1dE9)85Dbo/Mmjax from Jackson Labs, MMRRC Stock No. 34928, maintained as C57BL/6 x C3H strain)"

      The MMRRC mouse stock #34928 is Pleiades Promoter Project ES cell line mEMS592; That is not the right thing. The full name also does not come up except at MGI.

      Can you please check your records and provide the RRID for this mouse?

    1. On 2024-01-02 10:08:48, user Anita Bandrowski wrote:

      I am looking for the mouse that you got from MMRRC, but you state that you got the PG00171_Y_4_H09–Nfkbia vector from them. I don't think that is possible because they don't sell vectors. Can you check your lab records? Usually Addgene sells vectors, MMRRC sells mice. This is really odd.

    1. On 2024-01-02 04:27:32, user Jadora Ambrosia wrote:

      This article contradicts itself, stating in one instance that testosterone doesn't affect female "alpha" status, and in another instance that it does.

    1. On 2024-01-01 14:00:13, user Robert Arlinghaus wrote:

      This is a very interesting paper. I would like to draw attention to previous evolutionary selection experiments in other model species, especially zebrafish, that the authors either do not cite or I think misrepresent. The authors for example refer to Uusi-Heikkilä et al. (2015) saying that the positive size selected zebrafish became shyer. But if you look carefully at the paper, the positive size selection treatment did not differ from the controls (no effect on personality), while it was the negative size selection that became bolder. A number of follow up studies on the behavioural response were completed, several papers (not cited by the authors) first authored by Valerio Sbragaglia and later by Tamal Roy. Imporantly, examining boldness effects was found to be strongly context-dependent for the positive size selection line. In some cases there were strong trends for it to become bolder, consistent with the fast life history, but in the presence of predation threat, either no differences to control or shyer behaviour was found (e.g., Sbragaglia et al. 2022, Am Nat). Very consistently, the negative size selection line was found to be always bolder. So, in short, there was an asymetric selection response and a strong context dependency of the behaviorual effects in the positive size selection line. Importantly, the results did not disagree with the expectation (e.g., fast life history should be bolder) as claimed in this manuscript, but ecological context in which the experiment was completed moderated the response. Very consistent was the finding that the negative size selected line was consistently bolder. Imporantly, theory as shown in Andersen et al. (2018) showed that positive size selection with our without additional behavioural selection may bring about either bolder or shyer behaviour, depending on the size at which selection acts and which traits are under selection. I raise this to perhaps more critically evaluate past research and to compare your outcomes with our experimental evolutionary experiments to provide the full picture.

    1. On 2023-12-29 20:00:57, user Matthew Berg wrote:

      This manuscript has now been published in RNA Biology. https://doi.org/10.1080/154...

      Ecaterina Cozma, Megha Rao, Madison Dusick, Julie Genereaux, Ricard A. Rodriguez-Mias, Judit Villén, Christopher J. Brandl & Matthew D. Berg (2023) Anticodon sequence determines the impact of mistranslating tRNAAla variants, RNA Biology, 20:1, 791-804, DOI: 10.1080/15476286.2023.2257471

    1. On 2023-12-27 20:54:07, user Yury Goltsev wrote:

      Wonderful study! One small detail was not quite clear. What is the difference between the values computed in (two replicates) and (three replicates) type measurements and why the Q-values are better in (two replicates). Did one of your replicate transformations have a technical issue?

    1. On 2023-12-23 16:13:07, user Quinn Sievers wrote:

      Hello Andreotti lab!

      Quinn Sievers here, postdoc in the Abdel-Wahab lab at MSKCC. I enjoyed reading your paper and found it very informative.

      One comment I had was regarding line 255 of the manuscript where you assess kinase activity of the recombinant T474I and L528W mutants by monitoring Y551 phosphorylation; my understanding was that this site is typically phosphorylated by upstream kinases and that Y223 is an autophosphorylation site and therefore a better measure of kinase activity. I suppose since it was an in vitro assay it was not confounded by the presence of other kinases but I wonder if the Y223 would have shown discordant activity with Y551, particularly for the T474I mutant.

      Best,

      Quinn

    1. On 2023-12-22 15:43:41, user Curious Biophysicist wrote:

      As the title indicates that is the structure-informed language model that enables unsupervised antibody evolution, I would be curious if the authors could add the model predicted log-likelihoods to figure 3. This would help distinguish the contribution of the model from that of the experimental filtering applied at the end of the first round and strengthen the claim that model has learned and it's not just randomly sampling. Additionally, I would be curious what fraction, if any, of the beneficial model-recommended mutations are germline reversions. If the model is enabling evolution, one would expect non-reverting mutations.

    1. On 2023-12-22 13:24:14, user Hao Lu wrote:

      Thanks for your excellent work. I recently got fastq files from a CITEseq experiment and I am now trying to process it. I am new to sc analysis and generally not very experienced with bioinformatic tools upstream of R, apologies if my question sounds trivial. What is "feature-indexing function of Cellranger" for CITEseq ADT reads counting?

    1. On 2023-12-22 08:27:19, user Juri Rappsilber wrote:

      Dear Boris, <br /> thank you for inviting us to share our thoughts on this manuscript. We think keeping target and decoy proteins as joined entities is an important aspect of sound error estimation in crosslinking MS. Consequently, our open-source software for error estimation, xiFDR is doing this since version 1.0 (openly released on GitHub Sep 9, 2016, https://github.com/Rappsilb.... It is great to see that your lab is also now coming to this conclusion. There are a few details regarding the implementation that make a difference, but that are not clear to us from reading your manuscript. You do not mention how you report protein-protein interactions in the fused approach. Additionally, the level of crosslinks (CSM, peptides, residues) is not specified. Crucially, although the manuscript demonstrates a clear effect, it lacks a mechanistic explanation for the observed outcomes - both the lack of decoys in the non-fused and the recovery of decoys in the fused case. Further questions include: How are N-terminal errors accounted for? Why did you change your previous FDR formula to the one employed by us and others (and will you revise previous work accordingly...)? Finally, you are not comparing your results to those obtained by FDR boosting. This would be valuable, as FDR boosting can offset at least the gains attributed to context-sensitive subgrouping by the (corrected version of) mi-filter. <br /> Best wishes, Juri

    1. On 2023-12-21 23:58:19, user Rosalyn Fey wrote:

      Now published!<br /> "Discovery and Visualization of Age-Dependent Patterns in the Diurnal Transcriptome of Drosophila"<br /> Sebastian B, Fey RM, Morar P, Lasher B, Giebultowicz JM, Hendrix DA. Discovery and Visualization of Age-Dependent Patterns in the Diurnal Transcriptome of Drosophila. J Circadian Rhythms. 2022 Dec 8;20:1. doi: 10.5334/jcr.218. <br /> PMID: 36561348; PMCID: PMC9733130.

      https://doi.org/10.5334/jcr... doi.org="" 10.5334="" jcr.218="">

    1. On 2023-12-21 07:08:05, user 聂嘉俊 wrote:

      This is really a beautiful work. The authours provided a powerful tool for analyzing plant RLKs, which could be a great help for researchers.

    1. On 2023-12-20 20:44:07, user Gregory Way wrote:

      Ong et al. 2023 present an image analysis pipeline for 3D cell culture data. They use this pipeline to segment single cells and extract both single-cell and 3D-based features. The authors also pursue three application experiments, in which they expose cells to osmotic stress, topology modifications, and different gravity forces.

      We read this paper as part of a journal club, and have decided to compile a collective review and publicly share it with the authors. This was inspired by the Arcadia Science Preprint Review Pizza Party Initiative, and this represents our third preprint review. Overall, our review focuses on the need for additional clarity, strengthening methodological justifications, and an increased discussion about potential implications of the findings and techniques used. We provide several specific comments below:

      • The authors discuss “database architecture” (line 99, and throughout) but they do not provide any additional details on the architecture nor the technology used. Without these details, it is difficult to understand the role and functionality of the database in relation to the pipeline.
      • The authors describe their pipeline effectively, but they do not disclose any potential limitations or challenges. How user-friendly is the pipeline? Is it reproducible and interpretable? Will the pipeline work with other spheroid or organoid types?
      • The pipeline tool describes the power of flexibility. Users can enrich the database with additional features of their choice, but what benefit does this flexibility provide? Does having more features improve results? Does the software provide guidance to a user on how to decide these parameters?
      • The authors discuss that previous tools do not disclose compute time, but then they use a publicly available tool (StarDist) and report compute time. It is unclear how the authors improve compute time independently of these other tools. The authors also do not benchmark the full run time and resource usage of their framework.
      • The authors extensively discuss quality control, but they do not describe any impact of uneven illumination on the cell images. Do the images suffer from uneven illumination? Would illumination correction improve segmentation and feature extraction?
      • It appears that the authors are actually using spheroids and not organoids, but the terms are used interchangeably, which could be misleading.
      • The authors should consider making OrganoProfiler open source to increase community impact, and the authors should properly cite other open source software like VTAE and KNIME. Many other software tools are properly cited, thank you!
      • In line 546, the authors state: “...realistic simulated 3D image data…” but details on how they created the images and indicators if the simulation worked are lacking. Similarly, because the model was trained on these simulated images, how do the authors understand and trust accuracy metrics?
      • In line 610 the authors state: “In each image, an expert counted the nuclei that were missing and those with a significant precision issue in their contours, which exceeded 30% of the total nucleus area.” It is unclear why the authors chose precision over other metrics (e.g., IoU). Also, what were the expected error rates of the expert annotator and how might this impact performance interpretation?
      • We had several comments about clarity and claims that the paper makes in the discussion. The paper states: “Overall, our approach demonstrates a powerful and efficient methodology for imaging, analyzing, and extracting biological information from 3D cellular microsystems.” These are a lot of different focus areas, and it is not clear in which area the tool specifically innovates. Furthermore, the authors state that “large amounts of data can be analyzed with their approach”, but there are no benchmarking or resource usage details. Additionally, the authors state that they have developed a “groundbreaking method” but it is not clear what the authors are doing differently than what is already known/standard practice. Is it the collection of tools in a framework? Lastly, the authors declare no conflict of interests, but it is unclear how QuantaCell might factor in. For example, is QuantaCell a private company selling the software presented here?
      • We have several specific comments on select figures:<br /> Figure 1: The authors segment cytoplasm with Actin, but this may not be the best stain for all cell types. How would a user determine which cytoplasm stain to use if Actin is not a good option or is unavailable?<br /> Figure 2d is not mentioned in the text, and there is a typo in the y axis “median z-plan”<br /> Figure 3: The authors need to state how many organoids they used for the roundness analysis in Figure 3a - if this shows a single organoid then this approach may not be generalizable. Figure 3b says “2 distinct organoids per condition”, but there are only two curves. Showing one curve per organoid would help determine generalizability. Figure 3c depicts 10 organoids but the authors do not specify how they were selected<br /> Figure 4: Could the changes in cellular topology and morphology be due to the changes in oxygen exchange in the molds?<br /> Figure 5: Methodological details on measurements would help us understand what was transformed via PCA? Additionally, more details on controls are needed. We also have concerns about extensive user modification of data. We suggest making these modifications transparent and public. Without access to software it will be difficult to determine how the software works given that the data are modified.Without access to software it will be difficult to determine how the software discourages this practice. Also, the authors show only PC1; what is the expected explained variance of PC1? What signals are left in the other components?<br /> We could not find Supplementary Video 1, which is referenced in the text

      Collective review performed by:

      Gregory P. Way, PhD, University of Colorado, Department of Biomedical Informatics <br /> Erik Serrano <br /> Jenna Tomkinson <br /> Dave Bunten <br /> Michael J. Lippincott<br /> Cameron Mattson

    1. On 2023-12-20 18:45:20, user George Chistol wrote:

      our lab works on DNA replication initiation and we looked into the "neighbor network graph" for several key proteins in this process and I am sad to say that those graphs do not capture the well known protein-protein interaction networks. I am curious if there is a way to "filter" the results for the interactions with the highest significance/confidence. I am happy to talk about it with the authors if they're interested.<br /> Gheorghe Chistol, Assistant Prof.<br /> Stanford Chemical and Systems Biology

    1. On 2023-12-19 19:50:21, user Merrick Pierson Smela wrote:

      Cool paper, but I think there's a small problem:

      In the Methods section you say you're using 0.125 mM PD0325901 and 0.375 mM CHIR99021 for mESC culture. Are you sure those concentrations are correct? They are 125 times higher than the typical concentrations (1 and 3 µM, see for example https://www.sciencedirect.c... )

    1. On 2023-12-18 17:15:46, user Phil Stouffer wrote:

      I need to point out a mistake you made in reference to infrequent singing by Amazonian birds. You say 'Some Amazonian species have been shown to sing as infrequently as twice in 50 days (Jirinec et al. 2018).'

      This is not correct- from the second sentence of the paper you cite: ' Tracking was spread over 23 discrete days during July and August 2017...' The total number of days was 23, not 50. It is true that the bird only vocalized twice while it was being located, but the paper also notes that it was tracked during daylight hours to determine its space use throughout the day. In contradiction to the point you are trying to make, Grallaria varia seems to sing quite reliably before dawn (e.g. https://pstoufferbirds.file.... Thus it is not accurate at all to say that the species sings 'as infrequently as twice in 50 days.'

      I hope this clumsy interpretation of our anecdotal paper does not somehow get repeated as gospel.<br /> Phil Stouffer

    1. On 2023-12-17 02:34:02, user Vivianne Zoe wrote:

      This is a nice paper, old effector but explore the function from a new angle.<br /> BUT, it is known that Nudix effectors is wildly spread in plant pathogen species, including oomycete, bacteria, and fungus. It appears authors do not mention and cite the early research.

    1. On 2023-12-16 09:35:20, user disqus_mtg7x7eXMb wrote:

      In the previous comment (mtg7x7eXMb) posted on September 29th 2023 [1] to the earlier version of this manuscript (September 8th 2023, [2]), that can be found by clicking on the previous version of the Kim et al preprint https://www.biorxiv.org/con... , it was suggested that when increases in radiotracer uptake (SUV) in a given organ are accompanied by increases in radiotracer concentration in the plasma, it is necessary to adopt mathematical modeling techniques to extract the specific component from the non-specific component of the Time-Activity-Curves (TAC), in order to generate an estimate of the binding potential BP (the latter defined as the product of the ligand affinity (KA) and the molarity of the gp120 receptors in tissues (R0)). The modeling work by Mintun and colleagues in the early 80s shows that increases in probe uptake in the blood without changes in binding potential, leads to increases in SUV in the tissues but not to an increase in the ratio of the SUV in tissues over the SUV in the blood (relative-SUV). In other words, if an increase in SUV in a given organ takes place concomitant to an increase in SUV in the blood without changes in the ratio, the latter points to a fully non-specific component of the increase in SUV uptake in that particular tissue. The reason for my suggestion was because the images of the manuscript [2] clearly showed that increases in probe uptakes in several tissues were concomitant to an increase in blood pool activity uptake (which could be obtained through an ROI placed on the aortic outflow tract, which closely follows the uptake of the Heart, and that we will call, for simplicity in this document, as Heart SUV or blood-pool-activity BPA SUV), although the BPA SUV levels were not reported in the earlier version. <br /> On November 17th, the authors posted a new version of the Kim et al. pre-print [2], in which they have now disclosed in Supplemental Figure S3B and S3C the SUV of the Heart as well as the SUV of other organs normalized on the SUV of the Heart. Other criticisms in the previous September 29th comment [1] were also addressed by the authors who made several changes in their previous 2022 publication Samer et al. [3] (with respect to which, the current pre-print [2] is a follow up study) first on November 8th (by posting new supplementary files to modify videos, the fourth based on History Versions)) and later on November 22nd through an erratum corrige posted by the JCI-Insight. <br /> Through these corrections, the authors agreed that video 5\Figure 3F and video 8 of the previous versions (up to November 8th) of the Samer et al. [3]were incompatible, hence that there is no evidence of increase in probe uptake (post Galunisertib administration) in the Heart of any of the seven imaged animals; they also realized that the correct scale used to display the images of the 2022 publication was 0-1.0 SUVwb not 0-1.5 SUVwb like stated in the earlier versions (consistent with the content of the earlier September 29th comment [1]). See more under note z-1. <br /> The reason why the scale-correction does not show up in the erratum corrige published on November 22nd on the JCI-Insight website, is because the scale had been originally disclosed only in the legend of the supplemental material files, but not on the Figures or legends or text of the main paper, but changes in supplemental material are in general allowed without an erratum corrige, the latter being the only editorial tool that can keep track of the changes made in each version. This is a policy followed by some scientific journals, but its rationale is not clear to me as it seems to go against enhancing transparency in scientific publishing. <br /> Indeed, this change from 1.5 down to 1.0 seems relevant, because the new scale highlights a new concern about the images published in the 2022 [3] (which we now know that it displays images with scale 1.0) and the 2023 articles [2] (which displays images with scale 1.5, not 1.0). The baseline images (images acquired before the first Galunisertib administration) in the 2023 pre-print [2] reveal evidence of higher uptake in the heart compared to the images published in the 2022 paper [3]…the hearts appear more greenish in the 2023 pre-print Figure 2A\S1 Nov 17th version [2] and more blueish in the 2022 paper Figure 3 [3]. This difference is even more evident after we have now been informed that the SUV scale used to display images of the 2022 publication [3] is 1.0 and not 1.5. In other words, the two studies [2, 3], based on the newly corrected SUV scales, are showing clear evidence of differences in half-life of the radiotracer circulating in the plasma of the animals at 24 hr post radiotracer injection (higher Heart uptake (BPA) means longer half-life of the radiotracer). Could biophysical differences of the radiotracers used in- the 2022 and 2023 papers explain these seemingly different half-lives?

      Moreover, the new Figures S3B and S3C of [2] show that, as opposite to the absolute SUV levels, the relative-SUV levels are not statistically significantly increased at the later cycles of the Galunisertib administration, which confirms the weakness of the SUV mathematical operator, alone, in highlighting areas of specific uptake (a notion that resonates with a popular paper in nuclear medicine literature: SUV, standard uptake or Silly Useless Value…? ). In absence of dynamic modeling of the TAC(t), the latter evidence points to a fully non-specific uptake of the probe in those organs for the reasons explained above, hence, additional validation techniques (discussed in the previous comment [1]) would need to be provided before claiming again, as we read in the conclusion of the new version of the pre-print, that this dataset ”demonstrates that the Galunisertib-driven increases in SUV were mostly specific, ..(and with the new data presented as) consistent with previous studies validating the specificity of the PET signal for areas of enhanced SIV replication in gut and lymph nodes(Santangelo et al Nature Methods 2015 [4], Santangelo et al Mucosa Immunol. 2018 [5] and Samer et al JCI-Insight 2022 [3])”, i.e. before concluding that the new data supports the three previous non-human primate studies that fueled the rationale of the current pre-print study, and whose reproducibilities were questioned in the September 29th comment [1].

      In my opinion, the three earlier nonhuman primate studies [3-5] and the new corrections made by the authors after the September 29th comment [1] reveal a possible common denominator that I will try to articulate in the next lines.

      The first two nhp studies (Santangelo et al. Nature Methods 2015[4] and Santangelo et al. Mucosa Immunol 2018[5]) showed increase in the anti-env probe uptake of the blood pool (heart, BPA) of SIV infected animals, but the SUV levels of organs normalized on SUV levels of the Heart were not presented in those studies either. Based on the mean levels described for instance in Figure 1G of [4], the average increase in SUV in LNs of SIV infected animals compared to uninfected control is lower than the average increase in SUV of the Heart, hence the ratios (SUV of LNs over SUV Heart= BPA, relative-SUV) will most likely be lower or unchanged in the infected animals compared to the uninfected animals in both publications, which again points to a fully non-specific nature of the increase in uptake seen in those organs of the infected hosts. <br /> With respect to the third 7d3 study [3], as discussed in the September 29th comment [1], the images of the seven animals published in that 2022 article before and after Galunisertib administration show that the increase in uptake post-Galunisertib can be, in principle, fully explained by non-specific uptake of the probe. Specifically, the increase in uptake in LNs could well be explained by unintentional subcutaneous administrations of the probe (as visible in the post-Galunisertib images, but not in the pre-Galunisertib images). The main reason for my conviction that those uptakes are not related to binding of the probe to the gp120 is that increases in probe uptakes post-Galunisertib are seen only in the clusters of lymph nodes close to those injection sites that show evidence of subcutaneous injection of the probe but they are not seen in other clusters of lymph nodes (and with the latter unintentional mistake made, coincidentally, only for images acquired post-Galunisertib administration). This evidence, alone, should be sufficient to present the extravasation due to an unintentional subcutaneous injection of the probe as a reasonable explanation for the observed increases in LNs probe uptake post-Galunisertib, as further articulated in that comment [1]. The latter, in conjunction with other evidence in antibody imaging literature cited and articulated in the previous comment [1], points also to a non-specific nature of the probe uptake in gut seen in some of the imaged animals post-Galunisertib administration. Another reason for my conviction, as also articulated in the previous comment, is that higher doses for longer period of administration in the 2023 study [2] did not produce in any of the 8 animals (after cycles 1) those high probe uptakes seen in the 2022 paper [3] (in which only one cycle was administered in most animals). <br /> To recap, none of the 7 animals of the Samer et al. 2022 paper [3] showed evidence of increase in the (absolute) SUV of the Heart, and there are reasons to believe that the increases in probe uptakes seen in LNs and the gut of the same paper are fully non-specific. This is similar to what the authors reported following the first cycle of the Galunisertib in the 2023 Kim et al. pre-print [2], in which , in fact, no statistically significant increases in absolute SUV levels were found in any tissue compartment, nor in the heart. So, the Samer et al.[3] images and the images of the first cycle in the Kim et al. pre-print [2] show whole-body biodistribution properties that are different from those seen in the images of the 2015 and 2018 monkey studies [4, 5] as well as in the later cycles of the 2023 pre-print study [2] in which animals showed increase in absolute SUV levels in tissues concomitant to increase in SUV levels of the Heart.<br /> In other words, if we agree that the increases in uptake in the LNs of the anti-env probe in Samer et al 2022 paper [3] would not be visible in those published images, had the needle of the i.v. injection been properly inserted into the vein, the common denominator of all these studies is that the SUV levels increase in the tissues only in animals in which the SUV levels of the heart also increase. <br /> A well posed question seems: what could cause changes in SUV levels of the heart?<br /> In general, as anticipated above, higher SUV levels in the heart, or blood pool activity, means longer half-life of the radiotracer. Factors that could lead to increase in half-life of radiolabeled antibody\F(ab’)2 fragments include changes in isoelectric point , changes in the molecular weight of the radiotracer, level of dissociation of the radioisotope in the preparation or in vivo. Radio-HPLC analysis of the radiolabeled compounds could help to exclude that changes in preparations of the radiotracers are behind changes in probe uptake of the Heart at 24 hr (for the [3] and [2]) or at 36 hr (for [4, 5]) post radiotracer injection.<br /> In the Samer et al [3] or in the new pre-print ,[2], based on what we read in the Methods sections, no pegylation was performed. But the latter was performed in the earlier two anti-env imaging studies [4, 5] . Pegylation can, in principle, change molecular size, isoelectric point, clearance, half-life of a modified probe….<br /> The letter “p” is not defined in the article [3] but based on this PhD thesis online https://www.proquest.com/do... or the website www.resourcenhpir.com of the program that distributes these probes, it indicates the primatized version of the murine 7d3. <br /> Not only the new probe is a F(ab’)2 fragment, but it is based on a primatized version of the 7d3 (not on the murine intact IgG1 version used in the earlier two studies [4, 5]) and, for the reasons discussed above, it appears that it was not pegylated. Given all these differences, it would help not only to provide evidence that its binding affinity is retained but also that the product is stable and most of the radioactivity in the product is explained by the radiolabeled protein, including in vivo. These data were not provided in any of the five monkey imaging studies so far in literature (by including in this count the 2023 pre-print), produced from the same imaging team, claiming the feasibility of imaging the virus in vivo using anti-env radiolabeled probes and nuclear medicine cameras [2-6] .

      Moreover, the whole-body radiotracer retention could, in principle, highlight other differences in the preparations of the radiotracers. Indeed, the evidence that with SUV scale =1, most images of the 2022 papers reveal a liver that is green and some animals a kidney that is also green (hence with an SUV less than 1, because to be 1 or above 1 the organ must be red when displayed on a rainbow scale 0-1), suggests that there is quite low radiotracer retention in the body at only 24 hours post radiotracer injection. Why the radiolabeled fragment clears out of the body so fast?<br /> So, If the images have been correctly reconstructed with proper decay correction, it appears that the SUV levels in the whole-body are much lower than one would expect at baseline with a F(ab’)2 radiolabeled protein. This could happen for instance if the radionuclide dissociated early post radiotracer injection, and would be immediately excreted from the body without leaving traces of it. Only dynamic and serial imaging during the first 24 hours could highlight this phenomenon. If that is the case, i.e. if a high significant portion of the injected dose is excreted soon, QC data for each radiosynthesis would need to be carefully reviewed.

      Note-z1.<br /> Note, while changes were made on November 8th in Figures and Supplemental material files of the Samer et al 2022 [3] paper, the main text is, for some reasons, still left unchanged, which would make the reading of the article very difficult (given the changes made in some of the Figures and video links), for instance there is still reference to video 9 or reference to changes in the heart, NALT and spinal chord of A14X004…. <br /> A new link has been however added to the JCI-Insight Samer et al [3] called Supplemental data-2 https://insight.jci.org/art... , highlighting the corrections that need to be made to the main text to make it compatible with the new changes made on the Figures as well as on the Supplemental Figures\Videos, although the reason for this unusual editorial format (changes to the main text reported in a supplemental material link rather than directly on the main text of the paper online) is unclear.<br /> The erratum corrige https://insight.jci.org/art... added by the Editor states : “ Figure 3F and Supplemental Video 5 were incorrectly scaled by the analytical software due to an input error”, and “In the online version, Supplemental Video 5 has been updated. The original Supplemental Video 8 showed the correct scaling of animal A14X004 and has been removed to avoid redundancy.”;<br /> In my opinion, however, it is not correct to state that the original Supplemental Video 8 (published on November 2022, up to November 8th 2023 version) showed the correct scaling of animal A14X004, because it was stated, up to the version preceding the November 8th 2023 version, that the scale was 1.5 not 1.0, consistent with the content of the September 29th comment [1]). So to enhance transparency a more appropriate explanation seems the following “The original Supplemental Video 8 of animal A14X004 was displayed with a lower scale than the one stated in the earlier version, like it was the case for the images of all other animals in the previous version of the paper, but Video 8 has been removed in the new version to avoid redundancy with Video 5\Figure 3F”.<br /> Even the latter explanation for the erratum corrige would not be sufficiently clear to the readers that have not read the previous versions. As explained above and in the earlier comment, [1], the video 5 and video 8 were dual representations of the same animal (A14X004) displayed with different SUV scales, because the objective was to highlight small differences in low levels uptakes between baseline and post-Galunisertib administration in organs, like the gut, that could not be well visualized by keeping all the organs in the same field of view with a low SUV scale. After the erratum corrige, one would still expect to be able to appreciate those differences (e.g. increase in gut uptake) with the earlier dual representation, which now is missing. Indeed, it is not possible to appreciate such differences in the gut of the new Video 5. In other words, the two representations did not seem “redundant”, because the original dual representation was an attempt made to convey a new information about the whole-body biodistribution of A14X004. <br /> Note, the corrigendum also states : “Data from baseline (BL) and post-galunisertib weeks 1 and 2 (W1/2) were compared using Wilcoxon matched pairs nonparametric test, and the differences were nonsignificant with α > 0.05. The authors regret the errors.”<br /> However, there seems to be no explanation in the Methods section on how the SUV data are treated for animals that were imaged at both week 1 and week 2, and what statistical analyses were run (Wilcoxon or a mixed-effect model with the Holm-Šídák multiple comparisons test?). Finally, the removal of the NALT SUV analysis in the new version is a bit surprising, because, although there is evidence of PET and CAT misalignment for this animal, the latter is visible only at week 2, so it is not clear why baseline and week 1 SUV analysis of the NALT were also removed from Figure 3H. <br /> Note, as stated elsewhere in this document, that while the text of the 2022 Samer et al. paper is still unmodified, the Figures and videos have been all updated. However, the PMC link version of the paper https://www.ncbi.nlm.nih.go... still shows the older video 5 (scaled 0-0.3) and the older video 8 (by mistake placed in the video 1 link, now the new video 5 of the new version post-erratum-corrigendum of the Samer et al. 2022 JCI Insight publication [3]) or the older Figure 3H.

      Note, that Supplementary Figure S8 and video 8 of the new version ( JCI-Insight on November 22 2023 to the Samer et al 2022 [3]) still show rainbow scale 1.5, not 1.0 as stated in the new legends. Changing the scale from 1.5 down to 1.0 would further increase those red levels on the images for this “control” animal and highlight (although with n=1 only) potentially important differences in whole-body biodistribution of the isotype matched pIgG1 control (whose catalogue number or chemical nomenclature is missing in both papers [2, 3]) compared to the anti-env probe, not explained by its affinity to an irrelevant antigen.

      Finally, we read in Samer et al 2022 [3]“Two additional rhesus macaques, A8L057 and 08M171, were infected with SIVmac239M2 …and used as controls for, respectively, Supplemental Figures 8 and 9 at, respectively, weeks 56 and 32 postinfection” ; 08M171 is also an ID in the 2023 pre-print [1]. This looks an editing mistake, the descriptions of the infection timeline for 08M171 in the two publications are indeed incompatible.

      1. mtg7x7eXMb, comment to Kim, ...Villinger, Martinelli BioRxiv September 8th 2023. 2023.
      2. Kim, J., TGF-β blockade drives a transitional effector phenotype in T cells reversing SIV latency and decreasing SIV reservoirs in vivo. 2023.
      3. Samer, S., et al., Blockade of TGF-beta signaling reactivates HIV-1/SIV reservoirs and immune responses in vivo. JCI Insight, 2022. 7(21).
      4. Santangelo, P.J., et al., Whole-body immunoPET reveals active SIV dynamics in viremic and antiretroviral therapy-treated macaques. Nat Methods, 2015. 12(5): p. 427-32.
      5. Santangelo, P.J., et al., Early treatment of SIV+ macaques with an alpha(4)beta(7) mAb alters virus distribution and preserves CD4(+) T cells in later stages of infection. Mucosal Immunol, 2018. 11(3): p. 932-946.
      6. Obregon-Perko, V., et al., Dynamics and origin of rebound viremia in SHIV-infected infant macaques following interruption of long-term ART. JCI Insight, 2021. 6(23).
    1. On 2023-12-16 01:15:13, user Hovsep Sultanian wrote:

      This paper was very enjoyable to read as it had a lot of experiments that were well-thought-through and designed in an effective way. I have some general comments regarding the introduction and some of the figures outlined in this paper:

      Introduction: Tuberculosis is a dangerous and deadly airborne disease that can spread very easily. It would be nice to see statistics regarding the number of people infected in the world through the centuries or decades and more statistics of how many people die from this highly contagious strain, Mycobacterium tuberculosis (Mtb). Also, it would also be interesting to mention more information about the Mtb strain alongside the reason behind choosing this specific strain, instead of others.

      Figure 2: In Figure 2B, it was not indicated if the set of data shows gene expression of BL/6 compared to C3H, or the opposite way around. Alongside that, the data only shows that there is a significant difference, but does not show the magnitude of the significant difference. The y-axis is also supposed to have a negative in front of the “log10(padj)”. Also, figure 2C data did not specify which mouse strain compared to which, and it would be helpful if the sizes of the dots were specified, too. In figure 2D, there wasn’t a color bar and scaling, so it wasn’t known what the red and blue represented and the magnitude was also not known. It would also be nice for a statistical test to be performed to see which genes were statistically significant for figure 2D as well.

      Figure 3: Figure 3B-C should have a Two-way ANOVA done for its statistical test because the groups compared are not all independent of each other, where the different variables could affect each other. In addition, there could be a statistical analysis done for figures 3D-G between the two different doses in addition to a power analysis to measure the effect size. Figure 4: Figure 4B could use a time series to prove that the mice are losing weight statistically. Also, the numbers on the y-axis of figures 4L-Q are not the same in the same range, which can possibly throw off the reader.

      Figure 5: Figure 5B does not have a statistical test performed between the different ETO concentrations. It would also be helpful to show the “+” and “-” signs. Figures 5C-D could use a control mice without infection to show that it is not just ETO that’s affecting the cell number.

      Otherwise, the paper was engaging and enjoyable to read. Well done to all contributors!

    1. On 2023-12-16 01:06:09, user Ruth Nissly wrote:

      Thanks for sharing this analysis. I see the protocol uses a phosphorylated primer. Does it mean you skip the end-repair step in the Ligation kit and just go directly to adapter ligation? Or do you still recommend using the full Ligation protocol?

    1. On 2023-12-15 16:05:05, user Muhammad Ahmad wrote:

      Dear Authors, <br /> Very interesting article, I especially liked how the NPQ is induced and relaxed and differs between populations. I was looking at the method section to learn how you fit the model for NPQ and Phi PSII data. However, the link to r-scripts/code is not working. Would it be possible to update the working link? Thank you!

    1. On 2023-12-15 00:42:07, user Cristy Mendoza wrote:

      Hi. I found your work to be fascinating and very relevant due to frequent antibiotic resistance occurring with many different bacteria. The importance of the work was very well emphasized. However, there were parts of the paper that were confusing and restricted my ability to understand the conclusion of the work performed. Below are a few suggestions that I hope you take into consideration:

      • Besides a few experiments, the statistical analysis tests were not listed in the paper. It is highly significant the test that is being performed is well known to the audience so that they can interpret how the data's significance was gathered.
      • In Figure 3, there are significant stars on the graphs, yet no test is listed for the audience to know. Additionally, I am still unaware of what is being compared in these figures and what the significance ultimately represents.
      • Presenting Figure 7 earlier in the paper and fully describing the xenophagy pathway that is proposed before LC3 and Bafilomycin, for example, are introduced in experiments will be very helpful. Because there was little to no explanation of LC3 and Bafilomycin in the paper, I initially did not understand why they were tested in the experiments.
      • Carry out more experiments that prove the xenophagy pathway to be true. For example, in Figure 7, mTOR is shown to be in the pathway, yet no tests were performed to make sure this aligns with the theory.
      • Avoid using similar controls to statistically compare it to many experiments carried out, such as Figure 1. This is because the more statistical tests that are run using the same control for the different treatments, there is a higher chance that one may result significant out of chance. What would be suggested would be carrying out control experiments for each treatment group.
      • The sample sizes are never given for experiments. To rely on the data and that there is not too little power, it would be very helpful to know the sample sizes of experiments.
      • Labeling is preferred to be consistent. For example, if "Baf" is used to indicate Bafilomycin, that abbreviation should be used to refer to Bafilomycin in all figures.<br /> This paper is very interesting and has the potential to go further as it is very important to understand how and why bacteria can resist antibiotics. I wish you all the best with the paper!
    2. On 2023-12-12 23:50:40, user Meghan Buddy wrote:

      I enjoyed reading your paper and liked how you tailed off of previously published work from your lab! I do have some questions and a few suggestions as you move forward with publication:

      * Please make sure you define all abbreviated terms
<br /> * Provide sample sizes<br /> 
* Explain the purpose of 2-ΔCT
<br /> * Provide another cell line as a control so we can compare against SaOS2-OY
<br /> * Explain what statistical comparisons/tests were used for each experiment
<br /> * If looking at time dependence, maybe run a repeated measure ANOVA?<br /> * Avoid potential p-hacking by including all figures (5F)<br /> * Explain the potential xenophagy pathway and critical proteins before discussing how rifampicin and vancomycin modulate autophagy<br /> * Carry out more experiments with other intermediate factors involved in the proposed xenophagy pathway<br /> * It seemed as though acute and chronic infections were tested on randomly. For each investigation, maybe carry it out for both?<br /> * Perform co-IP to see if ubiquitin interacts with LC3A/B-II

    1. On 2023-12-14 13:13:43, user 宜青薛 wrote:

      First and foremost, I must express my admiration for the remarkable work presented in your study. The findings are incredibly intriguing, and it is noteworthy how closely they align with our own research published in EBioMedicine in 2021 (https://pubmed.ncbi.nlm.nih.... Your exploration of the impact of individual germline genomes on the determination of breast cancer subtypes, particularly emphasizing the significance of immune system functionality, adds valuable insights to the field.

      I find it particularly compelling that your results further validate and reinforce the observations we made in our prior work. In our study, which predates yours, we underscored the substantial role of germline genomes in determining the HER2 subtype of breast cancer. Recognizing the significance of your contributions, I recommend citing our publication to provide a comprehensive context for the understanding of the role of germline genomes in breast cancer development.

    1. On 2023-12-13 15:16:28, user Jan-Ulrich Kreft wrote:

      This preprint has been published in Bioinformatics:

      Moradigaravand D, Li L, Dechesne A, Nesme J, de la Cruz R, Ahmad H, Banzhaf M, Sørensen SJ, Smets BF, Kreft J-U (2023). Plasmid Permissiveness of Wastewater Microbiomes can be Predicted from 16S rRNA Sequences by Machine Learning. Bioinformatics 39: btad400

    1. On 2023-12-13 12:39:02, user Siegel Lab wrote:

      This preprint contains a great quantitative analysis that clearly shows a role for nuclear speckles in the regulation of gene expression. The observation that the distance of a gene from nuclear speckles correlates with the splicing efficiency of its transcripts is very interesting and fits very well with an observation we made in trypanosomes, a highly divergent eukaryote.

      Trypanosomes are unicellular parasites responsible for sleeping sickness in humans. In order to survive in the mammalian host, it is essential that they express only one of more than 2000 antigens at any one time. By frequently switching the antigen they express, they can alter their surface coat and evade the immune system – a mechanism known as antigenic variation.

      Interestingly, we found that the single actively expressed antigen gene is located in close proximity to a nuclear locus enriched in pre-mRNA splicing factors (Faria and Luzak et al., 2021, Nature Microbiology; supported by Budzak et al., 2022, Nature Communications). Inactive antigen-coding genes do not show such spatial proximity and are located further away from the processing hotspot. Similar to the findings in the preprint, which show that nuclear speckle association of genes is dynamic and changes between cell types, we find that the interaction of the antigen gene with the splicing hotspot in trypanosomes is dynamic: upon activation of another antigen gene, the interaction with the previously active antigen gene is released and then re-established with the newly activated antigen gene.

      Our hypothesis in trypanosomes is the following: The active antigen gene is strongly transcribed by RNA polymerase I, and in order to ensure efficient processing of such large amounts of antigen pre-mRNA and to avoid its premature degradation, the transcribed antigen gene is brought into close proximity to a processing hotspot that provides the machinery for RNA maturation.

      Very cool to find the common principle that RNA processing can be regulated by 3D genome folding in such divergent organisms!

    1. On 2023-12-13 01:25:27, user MBIO 600 class wrote:

      We reviewed this paper as part of the SDSU graduate course MBIO 600: Seminar in Molecular Biology. Our expertise ranges from ecology, cancer biology, and molecular biology.

      In this preprint, Sanhueza et al investigated the effect of different thermal conditions on stress response and welfare of captive fish (Atlantic Salmon). We all agreed that the paper was very interesting and was onto something, but feel that it may not have provided enough evidence to support all of their conclusions. Additional genes or stress pathway components would strengthen their work. To reach a broader general audience (that combines ecology and molecular fields for example), a more general description of some of the methods used would be helpful. More insight on how the natural and farm-raised populations behave would enable readers to put the authors’ results into context. It remained unclear what the final outcome was for the fish. We feel additional metrics to fish health and “wellness” would improve the manuscript. How do farmers “grade” their fish (for example weight; length over width; presence or absence of lesions? Overall, the manuscript offers potentially interesting findings that would be improved by additional clarifications and controls, as detailed below in further comments to follow.

    1. On 2023-12-11 17:03:02, user Cristian Villena Alemany wrote:

      Dear Kuzyk and to whom might concern,

      Thank you very much for your feedback. I am very happy that you enjoyed the preprint.<br /> As you suggested, I can provide some contextual information that will help on the understanding of the pointed issues.

      You indicate that our mention of AAP “phenology in freshwater lakes remains unknown” is inaccurate and there are already “few studies”. However, there is, up to our knowledge, no research that has been tracking the interannual variations and the recurrence (Phenology) of the AAP community in fresh waters. Here, we do not write that it is the first seasonal study of AAP in lakes, but the first study that focuses on the phenology of AAP community (three years in freshwater lake). In case you are aware of some studies that focus on AAP bacteria in freshwater lakes during several years, we would be very pleased.

      Regarding to the fluctuation of AAP abundance, you indicated that “it is unclear where this assertion comes from”, however, as described in materials and methods, AAP abundance was assessed using IR-microscopy. Since the paper is not based on manipulative experiments but observational samplings, no biological replicates were available and the AAP counts per sample (as described in M&M) can be found in Supplementary File S5. We agree that not statistical trends or deviations can be obtained from the graphical representation in Supplementary Figure S5, nevertheless, we tried to uncover relevant trends and show them also in the Supplementary Figure S7 and Supplementary File S6.

      AAP and bacterial numbers were calculated, as it is stated in M&M, using epifluorescence microscopy method as described in Piwosz et al., 2022 [17].

      As you point out it is correct that lakes that are stratified might become anoxic in hypolimnion. However, despite the fact that there is a reduction of oxygen in the hypolimnion during summer, this is not the case for the illuminated to the bottom CEP lake (Supplementary File S5) which always presented an oxic environment. Additionally, this trend (oxic hypolimnion even during stratification) has been observed before in this same lake (Villena-Alemany et al., 2023. Diversity dynamics of Aerobic Anoxygenic Phototrophic Bacteria). Therefore, since CEP lake represents a fully oxic environment, the occurrence of anaerobic anoxygenic phototrophs is negligible. Additionally, it is very well known that in anaerobic anoxygenic phototrophs the expression of photosynthetic apparatus is repressed by oxygen. This indicates that the BChl-a signals that we observe from the fully oxic environment originates from AAP bacteria. Not only this but, in Kolesár Fecskeová et al., 2019 the expression of AAP phototrophic genes in this same lake was already proven.

      Regarding the “phototrophic Myxococcota”, in the reference 100, in the Supplementary Data of that paper are stated the environments from where the MAGs were reconstructed. There, it is indicated that several Myxococcota MAGs were repeatedly reported from oxic environments. We can agree that further researches need to be done to unveil the functionality of this recently discovered phototrophic group, and by writing about “potential significance in microbial communities” of Myxococcota we meant that there is the possibility they have an important role since they were recurrently detected during 3 years. As they are newly recognized phototrophic group, member of AAP bacteria, there is more research needed, but we can observe that they can be found recurrently in freshwater lakes.

      Furthermore, the AAP percent contribution is not related with the pufM gene database. It is calculated as the percentage of total bacteria that are AAPs. As described in Piwosz et al., 2022, all bacteria are counted using DAPI and AAP bacteria according to BChl-a fluorescence. <br /> We are really grateful that you liked the database and we believe and hope that it will be an excellent tool for people studying anoxygenic photosynthesis. Yes, the MAGs had a great contribution in our database, and we included as much pufM gene sequences as we could to improve the taxonomic assignment pufM gene amplicons. Naturally, it also encompasses pufM gene of anaerobic anoxygenic bacteria and could also be used in anaerobic environments. Nevertheless, the category of AAP bacteria is a functional metabolic attribute, given to anoxygenic phototrophs in oxic environments, and the inclusion of anaerobic anoxygenic phototrophic MAGs into the database does not hamper the taxonomic assignation of AAP bacteria.

      I hope you find all the answers to the questions you stated here. I am really happy that you like the paper and to have this discussion. Looking forward if you have further contributions or thoughts.

      Best regards,

      Cristian

    2. On 2023-11-29 15:13:12, user Steven Kuzyk wrote:

      I very much enjoyed reading your preprint and have a few small comments:

      In your abstract it is mentioned that AAP “phenology in freshwater lakes remains unknown”, but a few studies already exist that have tracked AAP fluctuations compared to environmental factors in different lakes. It could be argued that there is still more to learn, each lake may have AAP populations fluctuating differently based on particular factors, and they can be monitored differently, but there have been previous studies nonetheless. Perhaps a small rewording will suffice, maybe suggesting your first integration of AAP into a PEG model, rather than first seasonal study of AAP in lakes.

      The paper suggests that AAP fluctuate by season, with a spring maximum and then a resurgence in the fall, but it is unclear where this assertion comes from. First, there are no numbers or variance provided, except for a singular maximum of 3.42 - 5.50 × 105 cells/ mL. How much did they vary per replicate vs per season or per year? Did they reach the same maximum each year or were less found for one particular season? Fig S5 is only a heat-map of data, and while the colours are nice, it is unclear if different years had similar trends/ deviation. It would appear that 2017 had an outright absence of enumerated AAP vs 2018 or 2019, and this discrepancy may hinder correlative analysis.

      In addition, it is not clear how the AAP numbers were calculated, was it from the BChl a autofluorescence microscopy counts? In the manuscript there is mention of the lake becoming stratified, which leads me to conclude that there is a strong possibility for anaerobic anoxygenic phototrophs to be present. As lakes have a fall turn-over (mixing), couldn’t the fall resurgence of detected BChl a autofluorescence not be of “AAP”, but instead be a mix (or predominantly) be made up of anaerobic anoxygenic phototrophs?

      What evidence is provided that your measurements aren’t of both aerobic and anaerobic anoxygenic phototrophs containing BChl a?

      Similar reasoning additionally questions if “phototrophic Myxococcota” are aerobic or anaerobic. The reference #100 shows that Myxococcota puf genes could be transferred into the anaerobic Rba. sphaeroides, which suggests it may be a (facultative?) anaerobe, not necessarily an aerobic phototroph. In addition, those authors suggested these Myxococcota have CO2 fixation – another indication of being anaerobic. Also, your nice story ends with a suggestion that while the Myxococotta “contribution was low”, that they are “potentially significant”. In what way are they significant? Perhaps showing some numbers into their contribution, or expanding upon this may support the implied significance.

      The puf database appears to be a really good asset. However, it is unclear how the AAP percent contribution of the bacterial community Fig S5E was calculated. While the puf database was used for looking into the variability of AAP populations within the community of anoxygenic phototrophs, there is no mention of how you chose AAP of the total community of bacteria that do not have pufM. Presumably you used the V3-V4 region of the bacterial 16S rRNA genes, but no list of AAP genera chosen for this analysis is provided or cited.

      Furthermore, while your puf database is indeed impressive, it is stated in-text to be composed of not just type material, but also MAGs (of high quality). While useful to show off diversity of sequences, MAGs do not provide concrete evidence of the respiration statues of any bacterium, particularly if the MAG is divergent from known AAP.

      Taken together, because your study followed the genes/ autofluorescence of not just AAP, but potentially of all anoxygenic phototrophs, I suggest providing content explaining how you sorted each group separately.

      If the levels of BChl a containing bacteria are detected by autofluorescence alone, then it is tracing all anoxygenic phototrophic populations, not solely aerobic anoxygenic phototrophs. If your pufM genes are closely related to MAGs that have not been proven to respire and produce BChl a aerobically, then those reads cannot be linked to AAP specifically. Instead, they are putative anoxygenic phototrophs with undetermined oxic tolerances.

      Looking forward to the final report.

    1. On 2023-12-11 09:47:13, user Simon wrote:

      In order to claim that their method is Physics-driven, the authors should show that the distance features learnt by the model actually emulate physical terms such as coulombic interactions. Just by analogy of distances this is not enough. A physics-driven method would also provide some form of binding energy. Since the output here is simply a distance matrix I don't think it's fair to call this a physics driven method.

      The model also lacks a way to indicate confidence or "binding energy" if you will. What happens if I run the prediction on a pocket that does not contain a metal site? The model would still place the ion somewhere, no?

      Authors should explain how DisDock has the potential to accommodate the flexibility of both ligands and proteins. In l.47 or l76 authors state that rigid protein structures are used.

      Table 1 is confusing. Are the percentiles referring to mean distance between predicted and experimental position? This is only mentioned in the text but not in the caption Is 25% the best predictions or the worst ones? This is not clear. <br /> The authors also justify that they do not compare against Metal3D because it only was trained on zinc, yet they compare the predictor by Wang et.al trained only on copper with their method. For Metal3D it was also shown that it performs well for 10 of the 16 metals in the training set for DisDock even if it was trained only on zinc.

      The authors should also provide a segmented analysis of the performance of their method for the different metal ions in the dataset in the main text of the paper. I don't think it makes sense to train the method on 5 CD sites and have actually 0 examples in the test set. In this case this metal should be excluded from training at all.

      For inference there seems to be a bit of divergence of where the actual metal is placed depending on the input search region. The authors should quantify this and provide a recommendation how many runs should be run starting from different location based on this analysis. Otherwise they cannot claim as in l.201 that the performance is consistent irrespective of the chosen initial location. In Figure S1 they just analyse the dependence on the starting distance. But there might also be an influence which equidistant starting point is used.

      For BioMetAll the authors should clearly detail in the methods section with what parameters the results have been computed and what is used as reference (any probe or just the cluster centers).

      It is also not correct that Metal3D takes the entire protein as input. Metal3D operates on residue centered voxel grids, that can be aggregated to compute a prediction for the whole protein but it is also possible to compute the binding probability around a specific residue.

      The authors should also clarify about code/data availability.

      Disclaimer: I am one of the authors of Metal3D (Simon Duerr).

      This review is licensed under CC BY 4.0.

    1. On 2023-12-11 08:35:50, user carlos alonso alvarez wrote:

      This preprint was finally accepted in Proceedings of the Royal Society B: DOI: 10.1098/rspb.2020.1067

      It is now entitled as: "Testing the carotenoid-based sexual signalling mechanism by altering CYP2J19 gene expression and colour in a bird species".<br /> The authors.

    1. On 2023-12-07 15:11:52, user Baudino wrote:

      Dear authors,<br /> We published in Science in 2015 a non canonical cytoplasmic pathway for geraniol biosynthesis in rose. In 2023 we showed that it was supported by a special G/FPPS and that IPP/DMAPP was provided by the MEP pathway. So you cannot say that "MEP pathway usually takes central stage for monoterpene biosynthesis under most scenarios" and it would relevant to cite our work :<br /> Magnard et al Science 2015<br /> Conart et al PNAS 2023

      Sincerely yours

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

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

    1. On 2023-12-06 19:13:50, user Dorsa Zahedi wrote:

      Dear Authors,<br /> This was a very interesting paper overall and provided great information on syncytium formation and the mechanisms through which curcumin prevents it. I liked that both curcumin and turmeric extract were studied so it could be discovered whether isolated curcumin is necessary for significant results or if turmeric extract is also sufficient. However, what was really missing for me was a statistical comparison between the effects of those two treatments. I would have liked to see whether there is a significant difference between those two treatments that would help clinicians lean more towards one treatment than the other. This would require ANOVA tests to be used instead of the t tests implemented in the study. It might also be helpful to include significance testing for the data in figure 1 to determine if the drop in viability from one treatment dose to another is actually significant or not.

    1. On 2023-12-06 15:11:38, user Virginie Courtier wrote:

      Very nice study! <br /> It seems that there is an error in Fig. S1C. The initial amino acid at position 25 does not seem to be Arg but Ala(GCA). If so, it should be p.A25* and not p.P25*. So the text (line 9 of page 10) of the main manuscript should be changed as well.

    1. On 2023-12-06 10:10:12, user Madeleine Aase-Remedios wrote:

      This is very cool! I have one comment about your reference to WGDs in insects. Li et al. used a gene-tree approach to detecting WGD and when re-examined with a synteny-based approach, there is little evidence for the WGDs Li et al. describe (https://doi.org/10.1073/pna.... Other well-supported arthropod WGDs to mention instead are in arachnopulmonates or horseshoe crabs.

    1. On 2023-12-05 11:50:24, user guest wrote:

      Overall, the story could be nice, but it is too light. In<br /> addition, sentences like “Our results challenge the prevailing paradigm that<br /> NLR proteins exclusively signal via their N-terminal domains and reveal a<br /> signaling activity for the NB domain of NRC-dependent sensor NLRs.” make me<br /> feel like the novelty is “hyped”. The signaling activity of the NB domain<br /> presented here is passive, similar to a guardee. The guard model is not novel.<br /> This gives a bad impression on an overall good study. The study should be complemented.<br /> 1 Most importantly, interaction data between RxNB (and other NBs) and NRCs<br /> should be provided. 2 The quality of blots in fig 2 should be enhanced. 3 I<br /> think the lettuce experiments should be complemented with Q-PCRs to show that<br /> defense genes can be upregulated by Rx/NRCs. This should be required before<br /> suggesting the use of sensor/helper pair for crop improvement. Here are a few<br /> comments:

      I find it disturbing that the introduction mostly cites<br /> previous papers from the Kamoun lab.

      I think the use of Rx halves expressed in trans does not bring<br /> much to the study and to the understanding of Rx function. Rx full length triggers<br /> the same phenotype so why bother using halves? In addition, these halves do not<br /> behave like full length Rx in term of oligomerization thus they seem biologically<br /> irrelevant.

      Figure 1B. Red circle, why not Rx full length in red too?

      Caption: “Experiments were repeated 3 times with similar<br /> results” Did you perform 4 replicates of each experiment? Repeat once = done twice.

      Figure 2 V5 panel is not great. I only see a smear on the<br /> whole gel. What is signal, what is noise in this gel? I don’t think we can<br /> conclude anything from this. Plus, why should we care about what this chimeric truncation<br /> of a protein does?

      Figure 3D: Why use time point? There is no difference between<br /> 3/4/5dpi. Most importantly, where are eGFP and CP-GFP?

      Figure 4 is nice; I like this part. I would like to have the<br /> WB data in the main figure, it would be easier to read (to understand that accumulation<br /> is correlated to activity). However, this part could really benefit from<br /> protein interaction data.

      Figure 5: The chlorosis symptoms on the adaxial part of the<br /> leaves are not clear. You should remove this and keep only the abaxial pictures<br /> to avoid misleading the readers.

      Figure 6: This schematic is not great. Is the p-loop<br /> required for Rx activation or for NRC activation? Is it even a 2step process?<br /> Isn’t it just that CP activates NRCs by changing Rx conformation? Also, what do<br /> we know about Rx halves interacting together? The structure of the NRC<br /> resistosome is unknown, membrane insertion is unknown but here these are<br /> presented as facts.

      I hope this helps. Good luck with the publication!

    1. On 2023-12-05 07:33:10, user ghujka wrote:

      -If the increase in the amounts of specific amino acids in the lumen is because of a lack of absorption from the lumen as a result of declined transporter expression, the aged lumen could better be mimicked by substracting the AAs instead of adding more. This should reveal systemic effects of inability to absorb. <br /> -It could be more informative to conduct the DUMP assay after the genetic intervention to confirm the change in AA transport.<br /> -The grayed out transporters could be validated with new qPCR primers.<br /> -Will the data about cholesterol and sugars added to this manuscript?<br /> -Slimfast antisense RNA might have off target effects and it is known to be toxic. Confirmation of the data using commercially available RNAi might increase the reliability of the results. <br /> -How does the undigested AAs change after feeding with aged AA medium?<br /> -Maybe you can combine AA measurements using hemolymph and DUMP using normal and smurf flies?

    1. On 2023-12-04 08:24:49, user Heather Etchevers wrote:

      This is a very interesting study and evolutionary question, given the importance of the circle of Willis in the cephalization and diversification of the neural crest-derived jaws and skull of vertebrates. I wanted to point the authors to an old study we had carried out on the strength of earlier work, concerning the embryological origins of the vSMC in these arteries. In the chicken, these anastomoses surround the ventral forebrain at a boundary between evolutionarily older and newer sources of myofibroblast-competent mesenchyme: https://pubmed.ncbi.nlm.nih...<br /> Congratulations to the authors on their judicious use of dynamics and modeling to reinforce what is known about this critical vascular junction in human health as well as in vertebrate diversification.

    1. On 2023-12-01 19:08:37, user nbrake wrote:

      The content in this manuscript related to sodium channel gating has now been published.<br /> Niklas Brake, Adamo S. Mancino, Yuhao Yan, Takushi Shimomura, Yoshihiro Kubo, Anmar Khadra, Derek Bowie; Closed-state inactivation of cardiac, skeletal, and neuronal sodium channels is isoform specific. J Gen Physiol 4 July 2022; 154 (7): e202112921. doi: https://doi.org/10.1085/jgp...

    1. On 2023-12-01 11:50:10, user Ready Boy wrote:

      Zaprionus tuberculatus was already reported from mainland France in 2020 (see Mouttet and al., Phytoma issue 738, november 2020). Z. tuberculatus is known from Hérault (2018) and also in Corsica (2020).

    1. On 2023-12-01 09:17:20, user Ward Decaestecker wrote:

      Hi Schreiber et al.,

      Really interesting paper!

      I agree with the comment of Geoffrey that it would indeed improve the paper if you could at least include the (estimated) number of T1 seeds for the Arabidopsis experiment. The number of dipped T0 plants might also be informative. Reading from the comments, I see that you will include that. Thanks!

      I'm looking forward to seeing your suggested experiment with a non-selectable target. Not sure which target you will pick, but I can recommend the restoration of the GL1 gene (Hahn et al., 2018). Alternatively, you might also think about the introduction of a fluorescent protein in the CRU3 gene (Shaked et al., 2005)

      Best regards,<br /> Ward

    2. On 2023-11-27 16:40:43, user Geoffrey Thomson wrote:

      Hi Schreiber et al.

      Very interesting paper with quite a few important observations. I especially like the testing of different homology arm lengths. The use of non-plant exonucleases is very innovative.

      I have one comment which I think is needed and would improve the paper .With the Arabidopsis transformants it reads like you are screening in the T1 generation. Is this correct? If so I think it should be made explicit in the paper. <br /> Assuming they are T1 plants I think it is important to indicate what proportion of T1 transgenic plants have undergone successful HDR using the donor template.

      It looks like your experimental design makes it difficult to quantify this as there is not another selection cassette on the T-DNA beyond the GL1 sgRNA which produces a phenotype which cannot be selected for in these mutants if not grown on thiamine. The growth of the thiC mutants on media lacking thiamine necessarily selects against transgenics where T-DNA integration has occurred and HDR has not. As it stands I think the number of seeds sown in this experiment needs to be documented at a minimum.

      However I would suggest repeating that experiment with a T-DNA which allows selection for transformants on thiamine selected media and either screening T1 plants via PCR and maybe growing on media lacking thiamine in the T2 generation, perhaps the editing of gl1 in the T1 generation can indicate which T2 families should be screened. This would give a better picture of the efficiency especially if people are interested in targeting loci without a phenotype so amenable to selection

    1. On 2023-11-30 14:11:09, user Luca Monticelli wrote:

      Dear Prof. Voth,

      thanks for taking the time to read our work. While we appreciate your feedback, we would like address here the points you raised.

      First, regarding the role of asymmetric tension. We appreciate that your J Phys Chem B (2022) paper tackles the role of membrane asymmetry in defining the budding direction. However, the role of asymmetric tension was reported first, to the best of our knowledge, by Thiam (DOI: 10.1016/j.bpj.2017.12.014), and then, in simulations, in our paper titled "Membrane Asymmetry Imposes Directionality on Lipid Droplet Emergence from the ER", published in Dev. Cell in 2019 (DOI: 10.1016/j.devcel.2019.05.003).

      Second, on maturation and budding. We read carefully your very interesting eLife paper, and we appreciate your contribution to understanding the role of seipin in scaffolding the neck of the nascent lipid droplet - as stated in our manuscript. However, lipid droplet maturation and budding are dynamic, non-equilibrium processes, in which the droplet grows and changes shape in time. We simulated growth and budding as dynamic processes, performing non-equilibrium simulations with non-constant number of particles, emulating lipid synthesis. The simulations in your paper (and all other simulations of lipid droplets in the literature, to the best of our knowledge) are equilibrium simulations, with a fixed number of lipids; they address equilibrium properties of *already budded* shapes, similar to experimental papers showing *already budded* lipid droplets. Our work, instead, addresses the mechanism of budding as a dynamic process. Being dynamic processes, we maintain that growth and budding were never simulated before (nor observed experimentally, due to the insufficient time resolution of current experimental methods).

      With best regards,

      Luca Monticelli

    1. On 2023-11-29 14:51:45, user Susana Wadgymar wrote:

      This is a fantastic list! I would like to suggest that you integrate at least one LO about phenotypic plasticity, or the ability for a genotype to produce more than one phenotype. Plasticity is ubiquitous across the kingdom of life and impacts a wide variety of traits, from physiological to morphological. In fact, genes and the environment routinely interact to influence phenotypes, which contributes to the enormous phenotypic variation we observe in nature. It is important for students to learn that many of our traits are not fixed and genetically determined. Importantly, the plasticity of a trait can be targeted by selection and evolve, making plasticity an important phenomenon in the evolution of local adaptation.

    1. On 2023-11-28 16:09:01, user Fraser Lab wrote:

      Deep mutational scanning has revealed the impact of individual point mutants on protein function and improved computational predictors of mutational effects. However, many mutations observed in evolution and disease are the result of insertions or deletions (indels) and the impact of these mutations are poorly predicted computationally relative to missense mutations. While a few other papers have profiled InDels in a systematic way, the major contributions of this paper are: 1) profiling indels across many different proteins (9), 2) profiling InDels for both stability and function, 3) introducing the concept of high throughput profiling of DelSub mutations (Mutations that remove an aa and substitute an aa in a single event)

      The authors make a library of substitutions, insertions and deletions of 9 protein domains. They carry out deep sequencing coupled to fragment complementation assay (aPCA) that is well correlated with expression/stability. They then go deep on two peptide binding domains, comparing functional mutational scans as well. This provides a rich set of data to compare to computational predictions, where notable limitations in current prediction methods are identified. The major limitation of the paper is in the presentation - there is a lot of data and the figures are quite complex - but the text is brief and difficult to follow in parts. An expanded text and breaking up the figures into more figures would likely improve the ability to extract insights from these impressive datasets. Additionally further discussion of the results within the context of past literature would be helpful in guiding interpretation of the study.

      **Major points:**

      • The authors included 9 domains that span classical motifs to include in their indel scan. From our reading, it is unclear what rationale the authors used to include these domains. What makes these a diverse set of domains (a/b content? size? eukaryotic/prokaryotic origin? other topological features? etc)? This will help the reader understand how to generalise the results.

      • In section “Evaluating indel variant effect prediction”, authors can comment on why PROVEAN is better suited to predict insertions and deletions relative to substitutions. In contrast, why CADD predicts substitutions better than PROVEAN? What design choices can distinguish PROVEAN from CADD? Is there a way a model could be trained that could perform well on both?

      • In the section “Structural determinants of indel tolerance,” the authors mention multiple features that seem potentially important for the effects of insertions and deletions. Currently specific patterns are discussed for the substitutions but the main draw of this manuscript it contains indel mutagenesis across many proteins and the discussion in this section regarding indels are vague beyond that indels are more tolerated at the N and C termini, that the secondary structure is important, and where the indel is seems to have an impact. Currently in our reading these are vague descriptions and perhaps it would be possible to describe general trends? What specific lengths are tolerated vs not? Which secondary structural elements are more sensitive? It would be helpful to clarify these trends with existing literature. For example, previous work in a potassium channel kir2.1 (Macdonald, CM, Genome Biology 2023) found that deletions were more disruptive than insertions in beta sheets especially. Is that also seen?

      • The authors train a model as described in the ‘Accurate indel variant effect prediction’ section to predict the effects of indels within all the proteins that are contained within the screen and another manuscript Tsuboyama et al. However, there are other previous indel scans that have been done including one within a viral AAV capsid protein (<https: <a href="http://www.science.org:ivXwDIUQkBXPV2UWulidgJP8J80" title="www.science.org">www.science.org="" doi="" 10.1126="" science.aaw2900="">), a potassium channel kir2.1 (<https: <a href="http://genomebiology.biomedcentral.com:943BjpX5R7uiOt0x_Pi3EL9GfPQ" title="genomebiology.biomedcentral.com">genomebiology.biomedcentral...="" articles="" 10.1186="" s13059-023-02880-6#citeas="">), and an amyloid protein that involved the senior author (<https: <a href="http://pubmed.ncbi.nlm.nih.gov:gwIcyO0GAbKMpgjpwXrER9h5FXA" title="pubmed.ncbi.nlm.nih.gov">pubmed.ncbi.nlm.nih.gov="" 36400770=""/>). It may be useful to test performance of the model on these datasets that were not generated within the same study and discuss where the model performs well vs those that do not perform as well.

      • The authors find that insertions can generate gain-of-function molecular phenotypes at higher rates relative to deletions and substitutions. Overall, the manuscript presents the results of deep indel mutagenesis on several protein domains, but lacks thorough discussion of the results. A discussion addressing the possibility of non-native ligand binding following indel mutations would provide an evolutionary perspective that contextualises this research.

      • The authors mention that some gain-of-function mutations occur due to short insertion mutations. While some domain insertions have been shown to have stabilising effects (<https: <a href="http://doi.org:Ard4WOdsVetuDE7Et54xGfFLGb0" title="doi.org">doi.org="" 10.1371="" journal.pcbi.1006008="">, <https: <a href="http://doi.org:Ard4WOdsVetuDE7Et54xGfFLGb0" title="doi.org">doi.org="" 10.1038="" s41467-018-08171-0="">, <https: <a href="http://www.nature.com:YVGN3sTg_ox1C7Zj0mUjeaM0zFI" title="www.nature.com">www.nature.com="" articles="" s41467-021-27342-0="">), the findings presented here are novel for being short insertions but prior work on domains and deletions of varying type being beneficial would be useful. Emphasising this in the “Insertions generate gain-of-function molecular phenotypes” section and considering how the insertions in the PSD95-PDZ3 domain might increase stability would enhance the understanding of the underlying mechanisms driving these gain-of-function phenotypes.

      • Related to gain-of-function: The last sentence of the discussion section references the potential usefulness of indel mutagenesis for protein engineering. As the paper notes, the results here will impact the protein engineering field significantly, so further discussion here will help extend the reach of this paper. Discussing what protein engineering strategies would be enhanced (e.g. directed evolution) would help the reader in evaluating the impact of the data presented. There are a few related papers in the literature (e.g. [https://doi.org/10.1073/pna...](https%3A%2F%2Fdoi.org%2F10.1073%2Fpnas.2002954117%3AQ9pKyGuK1_bwwFvIYOGl0Ed13dA&cuid=2634513 "https://doi.org/10.1073/pnas.2002954117") that could support this.

      **Minor points:**

      • Several of the figures are very information-dense. This manuscript would benefit from breaking up these figures and reorganizing them to make them easier to understand. It may be helpful also to focus the figures on the main points the authors would like to make within the manuscript as currently the sheer amount of data and analyses makes it difficult to follow the narrative. Additionally, figures could benefit from having secondary structure representations above or below heatmaps to aid in interpretation.

      • Figure 1 contains A-C labeled sections but contains 9 comprehensive experiments with 6 subpanels each. It is very difficult to evaluate the data when it is so densely represented. aligning an “unfolded” secondary structure below the heatmap for all domains might be clearer than colouring by secondary structure. We find secondary structure coloring to obscure the patterns.

      • Figure 2 would benefit from significant reorganization perhaps around SH3 and PDZ domains specifically. The spacing within this figure makes it difficult to follow the immense amount of comparisons contained here. Perhaps it would be worth separating this figure up or moving some of the comparisons to the supplement. As in Figure 1 secondary structure above or below the heatmaps would be helpful.

      • In figure 5E data is shown for GRB2-SH3 in which some mutants show high binding and low abundance. Additionally these mutants are most prevalent in the core and surface. What could be an explanation for such a phenotype for core mutants, since they are bound to have the most destabilizing effect. For the surface mutants, are those residues close to the binding site? Additionally the distributions of these two plots look fundamentally different (with a much higher correlation between binding and abundance for PDZ and a distinct change in the pattern of binding residues being gain or loss of function across the two domains). What does that say about the baseline stability of GRB2 vs PSD95? Or is this more representative of some methodological aspect of the dynamic range and sensitivity within respective assays when run on these specific proteins?

      • In the heatmap figures the coloring is confusing. Currently they are colored from red to white to blue - in the corresponding color bars next to the heatmaps white is not centered at 0. Presumably 0 is wildtype fitness and blue and red are greater and less than wildtype fitness, respectively. This should be explicitly stated within all figure legends. White should correspond to 0 otherwise it makes it difficult to determine the effect of a mutation.

      • Figure 3E contains violin plots however a boxplot is missing from these that indicates the median, interquartile range, and whiskers to represent non-outlier distributions. This would be useful in comparing across these variant types

      • In ‘Materials and Methods’ section, we were confused by the filtering steps taken in the ‘sequence data processing’ section. It would be helpful to include the minimum read cut-off.

      • Figure 4b should be explained more completely in the figure legend. It is very difficult to make out what the difference in colour for each panel means.

      • In the last paragraph of the introduction, it would provide additional support and context if you referenced other work with similar findings. These papers (<https: <a href="http://doi.org:Ard4WOdsVetuDE7Et54xGfFLGb0" title="doi.org">doi.org="" 10.1186="" s13059-023-02880-6="">, <https: <a href="http://doi.org:Ard4WOdsVetuDE7Et54xGfFLGb0" title="doi.org">doi.org="" 10.1101="" 2023.06.06.543963="">) would support the statement, “In general, indels are better tolerated in protein termini than in secondary elements.”

      Reviewed by Priyanka Bajaj, Karson Chrispens, James Fraser, Willow Coyote-Maestas

    1. On 2023-11-25 17:24:40, user Omid Karami wrote:

      Dear bioRxiv team,

      This manuscript has been published in Plant journal (DOI: 10.1111/tpj.16430). Please add a link to the published version.

      Kind regards,<br /> Omid

    1. On 2023-11-25 17:15:05, user Omid Karami wrote:

      Dear bioRxiv team,

      This manuscript has been published in current biology (DOI: 10.1016/j.cub.2022.02.060). Please add a link to the published version.

      Kind regards,<br /> Omid

    1. On 2023-11-24 01:48:09, user Prof. T. K. Wood wrote:

      Seems it is difficult to make decisions about the impact of phage-defense systems if the most-prevalent anti-phage system, toxin/antitoxin systems, are not included.

    1. On 2023-11-23 06:05:36, user Ying Cao wrote:

      Neural crest cells have intrinsic properties of fast migration and pluripotent differentiation potential. Why the intrinsic properties of neural crest cells must be attributed to/explained by the so-called EMT, which is defined by unknown cellular states?

    2. On 2023-11-23 05:54:49, user Ying Cao wrote:

      40000 EMT papers published so far and more to come, unfortunately nobody knows what the epithelial/mesenchymal states/properties are in the context of EMT. What is the scientific meaning of EMT study?

    1. On 2023-11-22 15:04:58, user Mel Symeonides wrote:

      Amazing work! This is an ingenious approach. I have a technical question regarding the insertion of the targeting sequences into the provirus. I see that you transformed the library into DH5alpha E. coli (or, well, NEB's version of it). In my experience, even when grown at 30C, proviral plasmids grown in this strain (as well as in NEB10beta) can exhibit LTR-LTR recombination, resulting in a heavily truncated form of the plasmid that excludes the entire provirus but retains the antibiotic resistance gene, which grows competitively against the full-length plasmid. The result of these recombination events is that you end up isolating what might look like "good plasmid" in terms of DNA yield, but actually makes very poor virus stocks as the majority of the plasmid in the prep does not even contain the proviral sequence.

      These recombination events can be monitored by simply running the uncut plasmids on a gel and looking for a prominent band around ~3 kb (corresponding to the supercoiled form of the truncated plasmid which should be ~5 kb in length). Long-read (e.g. Nanopore) whole-plasmid sequencing is another great way to detect and quantify the frequency of these recombination events. Switching to NEB Stable E. coli (also grown at 30C) is a fantastic way to reduce the frequency of LTR-LTR recombination.

      You did a great job dealing with the repetitive sequences in nef to prevent those smaller recombination events, but I am wondering if you have looked for any such LTR-LTR recombination events that would happen already in the E. coli transformants, and if so, do you know how frequent they might be?

      Mel Symeonides<br /> University of Vermont

    1. On 2023-11-20 09:22:39, user David S Gardner wrote:

      Hi, just reading with interest as investigating phenotypes associated with carnivory. You have categorised Dog as carnivore, same score as obligated carnivore - cat ?; almost certainly dog is an omnivore. Doubt it'll change your conclusions but just sayin.... https://pubmed.ncbi.nlm.nih...

    1. On 2023-11-18 09:41:41, user Marc Boudvillain wrote:

      This is a very interesting work that provides another example of multilayered regulation in bacteria. I would like to point out that in vitro evidence for the implication of Rho-dependent termination in btubB regulation has been provided previously in Nadiras et al, Nucleic Acids Res. (PMID: 29931073). The authors of the present work may wish to correct their manuscript accordingly.

    1. On 2023-11-18 04:20:07, user Vivian Zhong wrote:

      Is there data for transformation efficiency of R. rhizogenes transformed with wild-type pTiC58 plasmid, for comparison with the efficiency from introducing pDimple?

    1. On 2023-11-17 21:19:40, user Laura M. Walker wrote:

      This manuscript has been revised and is now published with G3 and is titled, "Parallel evolution of the G protein-coupled receptor GrlG and the loss of fruiting body formation in the social amoeba Dictyostelium discoideum evolved under low relatedness." https://doi.org/10.1093/g3j...