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... )

On 2023-12-19 09:12:21, user Thomas Tan wrote:

This article has been published by Nature Communications, DOI:

https://doi.org/10.1038/s41...

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

On 2023-12-18 01:11:51, user Tim Stasevich wrote:

This paper was published in Science Advances in Ocotober 2023: DOI: 10.1126/sciadv.adh4819

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.

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).

On 2023-12-16 08:24:51, user Kostas Kormas wrote:

The paper is now published in "Microbiology Spectrum" https://journals.asm.org/do...

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!

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?

On 2023-12-15 19:31:50, user Jordan C Rozum wrote:

This preprint has been accepted in PRX Life under a new title "Models of cell processes are far from the edge of chaos" and is openly accessible here: https://doi.org/10.1103/PRX...

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!

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!

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

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.

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

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!

On 2023-12-13 11:50:32, user Vasilis Promponas wrote:

An enhanced, peer-reviewed version of this work has now been published (open access) in the Journal of Medical Virology: https://doi.org/10.1002/jmv...

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

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

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

On 2023-12-12 21:37:59, user marius w wrote:

Very interesting work with Cas12f1. <br /> You may want to cite our previous work about drive-resistance in a viral gene drive: https://journals.asm.org/do...

On 2023-12-11 20:29:09, user JB wrote:

This has perhaps already been caught, but it looks like the URL should point to: https://openms.de/flashquant, not https://openms.org/flashquant

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

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.

On 2023-12-11 14:34:38, user tobiasbaril wrote:

A published version of this paper is now available in Genome Research: https://genome.cshlp.org/co...

DOI: 10.1101/gr.277820.123

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.

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.

On 2023-12-10 01:55:58, user Bernie Taylor wrote:

Rising Star Cave Engravings – Part II: The Terrestrial Plane https://beforeorion.com/cav...

On 2023-12-09 12:02:44, user Reinaldo dos Santos wrote:

On behalf of the authors, I would like to request. the inclusion of a link to the published version of our preprint, which has been published on Frontiers in Immunology; https://doi.org/10.3389/fim...

Sincerely,<br /> Reinaldo

On 2023-12-08 12:23:38, user Ilaria Callegari wrote:

This article has been accepted for publication in Brain Published by Oxford University Press

On 2023-12-08 02:37:31, user Nobutaka Numoto wrote:

The manuscript has been published as below:<br /> 10.1021/bk-2023-1450.ch006

On 2023-12-07 17:08:10, user Ryan Baxley wrote:

This paper has recently been published in Cell Reports. https://doi.org/10.1016/j.c...

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

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.

On 2023-12-07 09:39:51, user Max Sprang wrote:

Cool Paper,

there was recent work in a similar direction, without using Neural networks, but just the embedding, could be considered in the discussion:

https://www.mdpi.com/1422-0...

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.

On 2023-10-26 22:50:15, user ELSA COUVILLON wrote:

Dear authors,<br /> Overall, I felt your paper was interesting to read and highly relevant to the SARS-CoV-2 pandemic. These findings could be extremely valuable for identifying preventive measures for mitigating disease transmission, and I thought the experimental question addressed by your paper and the experimental design –incorporating a PSV entry assay and a syncytia formation assay– was cohesive. However, when reading and presenting at a journal club, some questions and comments came up that I would like to share with you.<br /> First off, I would like to point out a few simple fixes that I found. For one, Figure 4 is titled “Effect of turmeric extract and curcumin on PSV entry in 293/ACE2 cells,” however, the experimental results exhibited in Figure 4 only deal with curcumin, so I feel renaming that figure would be more accurate. <br /> Additionally, I’m curious as to why in Figure 4b, the PSV entry assay treatment conditions included the following:<br /> non-treated 30 mins/SARS-CoV-2 16-18h<br /> curcumin 30 mins/SARS-CoV-2 16-18h<br /> curcumin 30 mins/SARS-CoV-2+curcumin 16-18h<br /> but did not account for the effect of non-treated 30 mins/SARS-CoV-2+curcumin 16-18h? I was curious as to the logic for excluding that particular treatment combination, which I feel could’ve been a good control for comparing the infection rate of SARS-CoV-2 compared to SARS-CoV-2/curcumin without the additional variable/impact of the initial 30 min treatment. As a side note, it might be beneficial to readers to make more clear, what is meant by “curcumin pre-treatment”; does that refer to the 30 min “black arrow” segment, or the 16-18h “gray arrow” segment in which the virus has a “+ curcumin” label? <br /> One final thing I want to point out is the use of statistics in this paper. You state that a t-test is used throughout, however, I believe that an ANOVA might be more effective here, not only to reduce the number of tests you have to run on the data (and therefore reduce the risk of making a Type I error), but also so that you can show the readers comparisons between each and every group, and not just between each individual group and the control. Additionally, it would be helpful if there were asterisks or “n.s.” consistently shown for every statistic (for example, this is done on Figure 5b, but not Figure 4d), along with a key on every graph indicating the significance levels indicated by each asterisk, to help clear up some confusion about interpreting significance and statistics. Going back to the Figure 4 example, in the paper, it is stated that, “The results indicated that curcumin reduced PSV entry, especially for curcumin pretreatment before the addition of PSV (P = 0.035)” in reference to Figure 4d; however, in the actual graph, there isn’t sufficient statistical representation to confirm this conclusion (no statistics are shown for comparing Cur 1uM and the control) and additionally, I had a hard time determining what defined the pre-treatment when flipping between Figures 4b and 4d.<br /> Ultimately, I feel the paper is a great start and could mainly benefit from a few changes to encourage more clarity surrounding the ways in which different treatments were defined, the labeling and annotation of figures, and display/application of statistical tests. I look forward to following it through pre-print.

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.

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.

On 2023-12-06 07:47:05, user Jim Sutherland wrote:

BioE3 method is now published https://www.nature.com/arti... If you would like to try finding substrates for your favorite E3 ligases, the BioE3 plasmids are deposited in Addgene https://www.addgene.org/bro... Feel free to contact us for questions/feedback.

On 2023-12-06 05:27:36, user Timur wrote:

This preprint has been published in IJMS (https://doi.org/10.3390/ijm....

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!

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

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

On 2023-12-04 17:55:03, user pep wrote:

https://doi.org/10.3389/fmi...<br /> Reference to the first paper characterising PASEF (DDA and DIA) in metaproteomics is missing

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.

On 2023-12-04 07:25:52, user panos wrote:

now published in https://academic.oup.com/pl...

On 2023-12-03 15:18:27, user eldad-a wrote:

Growth of a Giant Single Cell is Intertwined with Daily Self-Rhythms

(News / Media outlet )

On 2023-11-24 14:29:41, user eldad-a wrote:

Afik, E., Liu, T.J.B. <br /> & Meyerowitz, E.M. Macroscopic waves, biological clocks and <br /> morphogenesis driven by light in a giant unicellular green alga.<br /> Nat Commun 14, 6204 (2023). https://doi.org/10.1038/s41467-023-41813-6

On 2023-12-03 13:14:16, user Joshua Flack wrote:

This article has now been peer-reviewed and is available online at https://www.mdpi.com/2310-2....

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.

On 2023-12-03 05:36:10, user Dovota wrote:

Nice preprint! are you aware of this recent work? https://www.science.org/doi...

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...

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).

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

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

On 2023-12-01 08:51:18, user Mari Sepp wrote:

Now published:<br /> https://www.nature.com/arti...

On 2023-12-01 00:33:58, user Rex Mitchell wrote:

Now published in Biological Reviews

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

On 2023-11-30 09:09:45, user Akira Kinjo wrote:

This paper has been published in Scientific Reports:<br /> https://doi.org/10.1038/s41598-023-47165-x

On 2023-11-30 09:04:00, user Heon-Jin Lee wrote:

Revised version of this reprint has been published in PLOS Pathogens (10.1371/journal.ppat.1011743).

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.

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

On 2023-11-28 15:50:39, user JustGuesting wrote:

https://www.pnas.org/doi/fu...

Would suggest that Ano6 does not belong in the subheading "Chloride Channels".

On 2023-11-27 17:15:59, user Omnes Res wrote:

This article has been published in Nature BME: https://www.nature.com/arti...

The journal changed the title so that may be causing linking issues.

On 2023-11-27 04:30:15, user 林佳葉 wrote:

The preprint has been modified and accepted for publication in CELL Reports. Please include a hyperlink to the full text of the paper: https://www.sciencedirect.c...

On 2023-11-26 10:32:35, user Justyna wrote:

Hi, this paper has apparently been published, you might want to update this: https://academic.oup.com/em....

On 2023-11-26 01:58:57, user Yarui Diao wrote:

This paper has been published in Genome Medicine:<br /> https://link.springer.com/a...

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

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

On 2023-11-25 17:04:50, user Omid Karami wrote:

Dear bioRxiv team,<br /> This manuscript has been published in The Plant Journal (https://doi.org/10.1111/tpj.... Please add a link to the published version

On 2023-11-24 14:55:11, user Guest wrote:

https://journals.plos.org/p...

On 2023-11-24 13:16:19, user Andrey Kruglov wrote:

The manuscript is published : https://www.sciencedirect.c...

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.

On 2023-11-23 12:12:01, user S. Mesnage wrote:

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

On 2023-11-23 09:42:30, user °christoph wrote:

Q: Why write "...of some ca. Omnitrophica (formerly..." and abbreviate "Candidatus" as "ca." instead of "Ca." ?<br /> See, e.g., Pallen et al. (2020) [https://www.cell.com/trends...]<br /> "ca." is the usual abbreviation for "circa" [https://en.wiktionary.org/w....]

On 2023-11-23 07:12:38, user Scientific Emily wrote:

Hi folks, this article was published on 20 Sept and can be found at this link: https://www.sciencedirect.c...

It is an open-access article. <br /> Can this pre-print link to that article, please? Thanks!

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?

On 2023-11-23 05:59:01, user Ying Cao wrote:

How can a mathematical model be generated to simulate a process/event about the change of unknown cellular states?

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?

On 2023-11-23 03:35:12, user Mark Gabriel Sagarbarria wrote:

We have published this paper in Transgenic Research.

https://link.springer.com/a...

On 2023-11-22 21:50:06, user Yingleong Chan wrote:

Published

https://www.cell.com/cell-r...

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

On 2023-11-22 15:02:33, user Shreeram Akilesh wrote:

This manuscript has now been published in Kidney International: https://doi.org/10.1016/j.k...

On 2023-11-22 06:55:03, user Florian Pfaff wrote:

The article is now published in Virus Evolution:<br /> https://doi.org/10.1093/ve/vead062

On 2023-11-22 03:50:11, user Rubayet Elahi wrote:

Now published in Nature Communications

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

On 2023-11-22 00:43:31, user Andrew Jiang wrote:

Apologies for the correction, the correct site should be https://launch.icarus-scrna...

An updated manuscript version will be accessible soon.

On 2023-11-20 13:21:03, user jaime pita wrote:

Please link this pre-print to the published manuscript in Frontiers in Endocrinology

On 2023-11-20 10:48:37, user Ksenia Mozhaitseva wrote:

The preprint has been already published in Genome Biology and Evolution

https://doi.org/10.1093/gbe...

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...

On 2023-11-18 20:24:25, user Ken Olive wrote:

The published version of this preprint is now avvailable at: https://aacrjournals.org/ca...

On 2023-11-18 10:30:11, user Brouce Lee wrote:

Now published in Cellular and Molecular Life Sciences: doi.org/10.1007/s00018-023-...

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.

On 2023-11-18 04:55:56, user Shweta Tyagi wrote:

This manuscript has been published in PLOS Biology<br /> https://doi.org/10.1371/jou...

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?

On 2023-11-18 00:51:21, user Advait Subramanian wrote:

The peer reviewed manuscript is now published and available @ https://www.nature.com/arti....

For a News and Views on our findings, please have a look @ https://www.nature.com/arti....

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...

On 2023-11-17 10:21:46, user EML wrote:

Really interesting paper, will dive into this deeper. Quick comment and question already:<br /> - would it be possible to do segregation testing in pedigree 1? As the affected individuals are still alive it might be possible to obtain DNA (no large amounts needed).<br /> - It would be helpful if the pedigrees in figure 6 would be redrawn in such a way that the first generation does not appear to have offspring of two females...<br /> best wishes,<br /> Elisabeth Lodder

On 2023-11-16 18:41:06, user Vernita Gordon wrote:

The Langmuir DOI for this article is 10.1021/acs.langmuir.3c01637.

On 2023-10-30 20:54:28, user Vernita Gordon wrote:

This manuscript has been accepted for publication in Langmuir.

On 2023-11-16 16:59:10, user MING YAN wrote:

The preprint is now published in Nature Communications. https://www.nature.com/arti...

On 2023-11-16 14:40:43, user disqus_46DQBV9D4A wrote:

Dear Wenderson,

thank you very much for the thorough feedback on our manuscript. We are very happy that you enjoyed the read! I think the preprint club in your lab is a great initiative!

As our manuscript was under review and ultimately accepted in the meantime (https://rdcu.be/drgAc), we did not manage to address all the issues you mentioned for the peer-reviewed version.

With respect to PC vs NMDS: we have adopted the method of doing both analysis since PC plots are limited to two variables in each iteration and this can cause statistical limitations with smaller sample sizes (as is the case here). NMDS collapses the variability to two dimensions and does not assume normal distribution. We use these two analyses in a complementary manner (one does not influence the outcome of the other, they are two independent methods). As our interest here was limited to inspect if the data is powerful enough to distinguish expression patterns at the respective time points, we did not focus on further discussion of these tests.

With respect to identifying secreted proteins, we used SignalP5 and TMHMM to identify candidate secreted soluble proteins. This was only mentioned int he methods section and we could have added this in the results section, as well.

We used the pipeline detailed in Figure 4 to identify the transcripts as noncoding. The tool CPC with a cutoff of an ORF length of 200 bp was used for that.

Once again, thank you for your feedback!<br /> Best regards,<br /> Stefan Kusch

On 2023-11-16 12:56:52, user Preprint Journal Club at MPIPZ wrote:

We recently reviewed this preprint in the PhD preprint journal club in the Department of Plant Microbe Interactions at the MPI for Plant Breeding Research. You can find the PREreview here: https://prereview.org/prepr...

On 2023-11-15 22:37:54, user Maxence Nachury wrote:

Just saw the paper in my Pubmed feed a few days ago and decided to present it in my journal club today. Superb imaging throughout. The evidence for 2 mechanistically distinct regimes of PC2-EV release is persuasive.<br /> Minor point: I was less convinced by the evidence of ciliary localization of the ∆4-desaturase. The signals are very weak in cilia. A few more images with increased contrast would help. The images in 4C and the counting in 4D suggest that the enzyme is required for both regimes. Could the differences I eyeballed com to significance when more data points are included? <br /> The major point has to do with the interpretation that the contact between the worm and bare glass generates a mechanical perturbation. If neither clear nor dense core vesicles are required for glass exposure to trigger PC2-EV release form the tip, then we could only see two surviving hypothesis: HYP1- the PC2-EV releasing cilia are intrinsically mechanosensitive. Can one tickle the worms with a non-glass micromanipulator? HYP2- the borosilicate surface triggers a physical response upon contact. Can one use plastic coverslips to squeeze the worms?

On 2023-11-15 21:29:07, user Arshia Zernab Hassan wrote:

Now published in Molecular Systems Biology https://doi.org/10.15252/ms...

On 2023-11-15 18:07:58, user Ryan Sheldon wrote:

This paper is now published in RNA Biology. https://www.tandfonline.com...

On 2023-11-14 21:36:11, user Sahar Melamed wrote:

Please note that the figures are incorrectly displayed in the preprint (some technical issue on BioRxiv). Please refer to the published version for the updated version and correct display of the figures.

On 2023-11-14 19:46:51, user M Fernanda Palominos wrote:

This paper has been accepted in Proceedings of the Royal Society B: Biological Sciences. Here's the link: https://royalsocietypublish...

Best fishes, <br /> feña

On 2023-11-14 16:49:20, user James Mallet wrote:

Congratulations on this provocative paper which I read with great interest.

However, I have some questions about the meaning of the results. Your paper suggests that previously, the prevailing belief has been that there is more hybridization, and therefore more gene flow between species, in plants than in animals. However, your preliminary discussion suggests that this is actually an artefact of “rely[ing] on morphological traits to arbitrarily define species (16),” where ref. 16 is Mallet 2005 in TREE. Although it is true that the data summarized in Mallet 2005 was indeed based largely on morphologically identified species (and their hybrids), it doesn’t rely on a morphological species concept. Anyone who knows taxonomy of any group of organisms knows also that morphology is a rather good, although not foolproof, guide to species status; two sister species, when they co-occur in sympatry, will typically display two modes in multivariate morphospace. Actually, Mallet in 1995 and 2005 argues for a genotypic cluster definition of species, which certainly applies to molecular markers as well as morphology. Two related species, if they co-occur in sympatry, will display a series of genetic differences that enables them to be identified, even if they hybridize. There are two modes in the multivariate genotypic distribution; the relationship with the classical taxonomist’s morphological identification of species is clear.

Then you argue “the emergence of molecular data ... enables substituting the human-made species concept with genetic clusters that quantitatively vary in their level of genetic distance (18),” where ref. 18 is Galtier 2019 in Evolutionary Applications. Now that is interesting, as I think Galtier proposes “Species are defined as entities sufficiently diverged such that gene flow (arrows) is very rare or inexistent” (his Fig. 1). In other words, he appears to have a species concept such that gene flow between species is zero. Any gene flow, he argues, would render the situation “ambiguous”.

Later, perhaps recognizing that this is too extreme, Galtier proposes using a reference species based system: “...to identify taxa in which large amounts of data are available, and species boundaries are consensual, or can be agreed on. Species delineation in any other taxon could thus be achieved so as to maximize consistency with the reference [taxa].”

Now perhaps this dickering about what is a species appears rather unreasonable, since I think we all know (and Nicolas Galtier certainly seems to agree) that there is a continuum between populations that are not species and those that are species. However, in order to disprove the prevailing narrative that plant species hybridize more than animal species, you really must take a stance on what you mean by a species, and what you mean by a population that is not a species. My natural history knowledge of flowering plants and animals such as insects and birds suggests that plant species that co-occur in sympatry really do have a higher rate of hybridization than animal species. Not only is a greater fraction of species involved, but when they do hybridize, there are usually a lot more hybrids.

But you will say perhaps: “that is not really the question we attempt to answer.” And indeed it is not, so perhaps you should not have complained that that finding about whether species hybridize was an artefact, which you appear to do.

The question you more attempt, I think, to answer is: “is introgression more common in plants than in animals for a given level of genetic divergence, DA?” Rather than a question about species, it seems to me you are asking a question here that is independent of what your (or the reader’s species) concept is (unless you argue that a species has a certain threshold level of genetic divergence).

After arguing that “the Tree of Life” is “interrupted by species barriers that are progressively established in their genome as the divergence between evolutionary lineages increases,” you then argue that “The consequences of reproductive isolation can therefore be captured through the long-term effect of barriers on reducing introgressing introgression locally in the genomes, which provides a useful quantitative metric applicable to any organism (4).”

Ref. 4 is Westram et al. (2022) J. Evol. Biol. “What is reproductive isolation?” Westram show that it’s actually very hard to measure overall reproductive isolation, RI, which they say is determined by the level of “effective migration” at neutral loci, or the fraction of the rate of neutral genes that actually establish (reduced due to species barriers) in the recipient population, me, divided by the rate of “potential gene flow,” m, into the population caused by the potential for hybridization and backcrossing, or RI = 1 - me/m. Effective gene flow depends on where in the genome you measure it; in which direction you measure gene flow; whether populations are parapatric or sympatric; whether you want to measure it using an “organismal” or “genetic” focus (in Westram et al.’s terminology). Furthermore, it depends on who is measuring it and how. Everyone who measures it seems to have somewhat different measures of reproductive isolation (Sobel, J. M., & Chen, G. F. (2014). Unification of methods for estimating the strength of reproductive isolation. Evolution, 68, 1511–1522). It doesn’t provide a very useful comparative measure applicable at the whole species level at all. My colleague from Boston University and I conclude from perusing the lengthy discussions in Sobel & Chen and Westram et al. that measuring overall reproductive isolation is unlikely to be useful, and we would be better off just accepting that it is a vague heuristic which expresses something about species (Mallet, J., & Mullen, S.P. 2022. J. Evol. Biol. 35:1175-1182). In contrast, one can readily measure some of its many components, such as “hybrid inviability”, “assortative mating” and so on, and these remain useful and interesting at the whole species level and as comparative indicators.

Again, it may seem a distraction that I am discussing what is reproductive isolation, but it seems important here, because you are using a measure of reproductive isolation, and then relating it to genetic distance. In Westram et al., the main concern was to develop an experimental measure of reproductive isolation. Westram et al cautioned against estimating reproductive isolation from sequence data, which is the method you employ here. Their reasoning is that sequence divergence is a consequence only of actual gene flow, me (after taking into account barriers to gene flow), and that there is no way of estimating “potential gene flow” from the same data. In the main part of the paper (e.g. the data points in Fig. 1A), there seems to be a non-continuous measure of reproductive isolation, such that “migration” has a value 1, whereas “isolation” has a value zero. It was not entirely clear to me why this should be so, since, whatever it is, it seems clear to me that reproductive isolation should surely be a continuous parameter. Delving into the supplement, I found that “genetic isolation” was indicated “when our ABC framework yields a posterior probability P(migration) < 0.1304. This threshold was empirically determined by the robustness test conducted in (Ref. 6).” Similarly, the same robustness test yielded “strong statistical support for ongoing migration ... when the posterior probability P(migration) > 0.6419.” Pairs of taxa with intermediate posterior probabilities were considered “ambiguous” and were discarded. Note that P(migration) is not the actual mixing rate of the populations, me, or the fraction of the genome exchanged, but, if I understand it correctly, the posterior probability that any gene flow at all occurs. This is a very different measure of reproductive isolation from that proposed by Sobel et al. or Westram et al., or anyone else.

I think the reason for your choice of a measure of reproductive isolation is indicated by the second question you ask in the introduction: “At what level of molecular divergence do species become fully isolated?” This is related to a common conception of species as irreversibly independent lineages, and the idea that speciation will be “complete” when gene flow becomes zero. But in fact, the “completion” of speciation in this sense seems rather unlikely. The progressive loss of compatibility between diverging lineages seems likely to follow some sort of continuous probabilistic failure law, similar to the way lightbulbs fail over time. The simplest failure law is log-linear with time, although more complex models such as the accelerating “snowball” model of hybrid incompatibility, or the likely “slowdown” model for selective reinforcement, are also possible (Gourbière, S., & Mallet, J. 2010. Are species real? The shape of the species boundary with exponential failure, reinforcement, and the "missing snowball". Evolution 64:1-24); but all have a long asymptotic tail. You seem to recognize this stretched out right-hand side timescale by plotting genetic divergence on a log scale in Fig. 1 (although why is “net divergence,” Nei’s DA, the correct scale on which to base such an analysis? You do not explain or justify this). Nonetheless, by making an argument for complete isolation as an endpoint, you ignore the asymptotic nature of compatibility decline to zero. Based on the data we analyzed, it is rather hard to estimate the shape of the failure curve, mainly because the accumulation of incompatibilities is so variable, even among closely related species, such as Drosophila fruit-flies, for example. This variability between pairs of species shows up only in the data, and not in the fitted curve in Fig. 1A, but is more evident from Fig. 1B.

Overall, I remain somewhat unconvinced that plants have a more rapid accumulation of species barriers than animals. I agree it is likely that many plants have “less efficient dispersal modalities” than most mobile animals, and that this might mean that actual gene flow becomes lower for plants at a distance from one another, but this is a little different from what I think one would mean by “species barriers.” Reproductive isolation and species barriers should generally be rather independent of geography; in other words reproductive isolation at close range is what we are primarily interested in. This is the problem of using a measure of reproductive isolation that depends purely on actual gene flow. I therefore remain unconvinced that my natural history observations of many plant hybrids in nature, and very few animal hybrids, are not reliable indicators of lower levels of reproductive isolation among plants than among animal species.

On 2023-11-14 09:58:04, user Thomas McCorvie wrote:

The authors should be aware that their FSC curves for two of CtRoco-NbRoco1-NbRoco2 maps indicate duplicate particles. This is shown by the FSC curves not dropping below a value of zero.These can easily be removed using the Remove Duplicate Particles in CryoSPARC. Testing different Minimum separation distance (A) values is recommended.

On 2023-11-14 06:19:25, user Malgorzata Biczysko wrote:

The article is now published https://link.springer.com/a...

On 2023-11-14 05:33:52, user HILA GELFER wrote:

I found the topic of your research incredibly fascinating and important as scientists try to better understand how to prevent cancer relapse among patients. Understanding the role of TAMs in preserving OvCSC presence serves an important purpose in identifying how to improve treatment responses to cancer patients. Here are some general comments I had on the statistics and figures in your paper.

1. Within Figure 3 (ex: 3B) you utilized medians as markers within your data, while earlier data utilized means. While both are proper statistical measures, the inconsistency in how the data is represented may be a little confusing for the readers. To improve coherence in their data, I would utilize either medians or means throughout the whole paper. Additionally, medians sometimes fail to note any skewness in the data. I think to further improve the representation of the data it may be a good idea to perform a Shapiro-Wilk test to determine the normality of the data.
2. Within several figures, including 3F, there were very few samples within each treatment group. Since no power analyses were performed, it may be difficult to determine the true statistical significance of the data. To improve confidence in your findings, I would recommend performing power analyses and adding more samples/replicates in future research to further increase confidence in your data as necessary.
3. Figure 1E indicates the percent survival of different cultured cells receiving different doses of treatments. Despite different cultures receiving different treatment doses, the points on the graph were connected making it difficult to decipher the differences between the samples. To increase clarity in the data, I would represent the data in the form of a bar graph or other similar form to better distinguish the differences between the samples.

Overall, I found your paper incredibly fascinating and hope to see further research on the topic to improve patient care!

On 2023-11-14 04:00:34, user JK Gamja wrote:

We represent our research about active microbial metabolite PLA produced by lactic acid bacterial. I hope this study help readers to provide the reason and mechanism of beneficial effects of probiotics.

On 2023-11-13 20:16:02, user Ebba Brakenhielm wrote:

Now published @CardiovascRes<br /> https://pubmed.ncbi.nlm.nih...

On 2023-11-13 20:14:26, user Ebba Brakenhielm wrote:

Now published @Naturecommunication<br /> https://pubmed.ncbi.nlm.nih...

On 2023-11-13 12:20:14, user paulanuneshasler wrote:

A peer-reviewed version of this article was published in Communications Biology on Oct 4th 2023 https://www.nature.com/arti...

On 2023-11-13 10:43:48, user Gary Mirams wrote:

This is a very nice use of a mathematical model of patch clamp compensations to account for artefacts in fast sodium recordings at 37C.

Just a little note that whilst Lei et al. (2020) introduced the artefact model without supercharging/prediction compensation, we expanded on that to include those effects in the artefact model version published in Chon Lok Lei's thesis: https://chonlei.github.io/t...

On 2023-11-13 02:04:39, user Partha Mondal wrote:

The paper is now published in Communications Biology (Narure Publishing). https://www.nature.com/arti...

On 2023-11-12 00:05:45, user Elizabeth Duncan wrote:

Recently, a group of trainees read and discussed this preprint as part of a journal club at the Markey Cancer Center at the University of Kentucky. We thought the findings suggesting that SETD1A may be driving the increase in H3K4me3 in MLL1 mutated cells (and possibly leukemic cells with MLL1 translocations) were very intriguing. However, we have several questions and suggestions:

In figure 2B (metagene analysis) and C (pie charts), you plot the mean read counts from H3K4me3 ChIP-seq. We interpret the unexpected lack of enrichment of H3K4me3 at gene TSSs in the WT sample as a reflection of the relatively significant increase of H3K4me3 at new gene loci in the MT1 and MT2 cell lines. Is this correct?<br /> If so, we believe this point could be made stronger by adding, for example, a Venn diagram of the genes with MAC2 peaks in the WT cells and those with peaks in the MT1 and MT2 cells. You could also create two separate metagene plots based on the data in Figure 2B: one looking at H3K4me3 in all three cells lines at genes with MACS2 peaks in WT, and one looking at H3K4me3 at genes with MACS2 peaks in MT1+MT2.<br /> Given that there is likely variability in the chromatin state in different iPSC lines, we also wonder if you performed these experiments and/or analyses using a separate iPSC line?<br /> It is unclear how you performed the differential expression analyses in figures 3, 4, and 5. The heatmaps show changes in both the WT and the mutated cell lines, even though we assume the differential expression is in relation to the WT cells? We appreciate there are many ways to perform these analyses, however we would like to understand the details of how they were done here to better understand their implications.<br /> What happens if you knock down SETD1 expression in the MLL1-R3765A cells?<br /> Do you see the same effects if you KO or KD MLL1? Versus this mutation that prevent association with WRAD?

We look forward to seeing your paper in publication.

On 2023-11-10 23:23:41, user Hui Wang wrote:

The study suffers from two serious flaws that undermine its credibility:

1. The authors overlook the fact that the aromatic ring of tyrosine 90 is essential for the SH3 domain hydrophobic pocket structure. They incorrectly present the Src90E mutation as mimicking phosphorylated tyrosine, ignoring the expected disruptive effect of almost any mutation at this site. This flaw raises doubts about the validity of their interpretation, as the observed data may be a result of the grossly disrupted SH3 domain binding site rather than of simulating Tyr90 phosphorylation.

2. The study neglects the tightly regulated nature of Src kinase activity through phosphorylation by Csk. Previous research by Erpel et al. 1995 has demonstrated that the mutation of Tyr90 to alanine impairs the interaction with Csk and the negative regulation of Src kinase. As the mutations Src90E and Src90A are supposed to disrupt the SH3 domain binding pocket in essentially the same way, the authors fail to acknowledge this crucial aspect. This oversight undermines the study's reliability and suggests that the similarities observed between Src90E and Src527F may be solely due to the impaired interaction with Csk.

These flaws significantly impact the study's findings and raise concerns about the thoroughness and accuracy of the authors' interpretation. Caution should be exercised when considering the conclusions presented in the study.

On 2023-11-10 16:44:10, user KJ Benjamin wrote:

Interesting approach, but I'm confused why the authors would model population instead of genetic ancestry? The authors use ADMIXTURE to show a great degree of mixed ancestry, but do not examine the effect of genetic ancestry, but "population grouping". This would be extremely influenced by environmental factors that are differences across and within continental groups.

On 2023-11-10 16:43:20, user Matt Jones wrote:

This manuscript has now been published in New Phytologist.<br /> https://nph.onlinelibrary.w...

On 2023-11-10 00:14:27, user Alan Rose wrote:

This is an impressive manuscript reporting a stunning amount of work that reveals an interesting and underappreciated feature of gene regulation in plants, namely that sequences downstream of the transcription start site (TSS) can have major roles in regulating expression. My only complaint is that it does not sufficiently cite previous work that reaches many of the same conclusions. Findings reported here that were previously published for Arabidopsis include the observation that sequences downstream of the TSS, in exons and introns, play a major role in controlling transcription (Rose and Gallegos, 2019, Scientific Reports 9:13777), that these sequences are unlike animal enhancers because they have no effect when moved upstream of the TSS (Rose, 2004, Plant Journal 40:744 and Gallegos and Rose, 2017, Plant Cell 29:843), that a motif containing the sequence GATC boosts expression in a dose-dependent manner and that mutating nucleotides within the GATC motif reduce its effect (Rose et al., 2016 Plant Molecular Biology 92:337). The Rose and Gallegos 2017 paper is cited but only as the reason for using the TRP1 promoter and for identifying the motif similar to GATC. I realize that the number of references is limited in some journals, especially those with a high profile (where I would really like to see this work published), but these seem too pertinent to omit.

On 2023-11-09 21:50:42, user lanka wrote:

very nice work! no supp table uploaded.

On 2023-11-09 17:10:12, user Reade wrote:

The biological concepts of the paper are easy to understand and follow as one experiment leads to another. The toy figures at the beginning of the figures outlining the experimental overview are very useful. There are some grammatical errors in the paper for example “casual relationship” which should be causal. Which statistical test is being used is unclear and at times I believe the wrong statistical analysis is used. For example, figure 1 states that Wilcoxon test or one-way ANOVA is used for comparison, but nothing indicates which analysis is used for which figure. Furthermore, when doing relative expression it is unclear what the expression is compared to, some graphs indicate it is relative to IRPL13a, but it looks like this is not true as in figure 2F the MFN2+/+ HEY1 and ID3 are both set to 1, suggesting that is what is being compared. The labeling of figures also makes it difficult to identify what is being compared, again in figure 2F it is unclear what the p values are indicating as they are over more than two groups. There are a few figures that I would like to see controls to compare against the date, example in figure 3A and 4I.

On 2023-11-09 15:17:53, user Bertram Klinger wrote:

Thank you for the nice explanation of expectation maximisation.

However, in my eyes your algorithm does not get rid of the spillover signal. <br /> In Fig3C the correlated distribution is the result of spillover from channel Yb172 into Yb173, as can be seen nicely in Fig3D where this correlation vanishes with the same antibody labeled to a channel which Yb172 does not spill into (Sm147D). Instead your algorithm seems to only set low signals to NA.

To undermine this point, Fig1a shows that the spill-in signal from Yb172 into the Yb173 channel is on average 2.7. Assuming the the mean of Yb172 bead to be of similar strength as Yb173 (~6.2) then for a cell population without Yb173 we would expect a difference of roughly 3.5 (in log scale) between the two channels if purely driven by spillover. Which is what can be seen in Fig3C for the CD3-low population ( i.e. they do not express CD3).

On 2023-11-09 15:12:20, user Matt Tudor wrote:

probably want to cite, discuss this prior work: https://doi.org/10.1371/jou...

On 2023-11-09 00:17:42, user Pooja Asthana wrote:

Summary:<br /> Identifying and modeling low occupancy structural changes and binding events is a major goal of protein crystallography. The most sensitive methods used to detect low occupancy changes require crystallographic datasets to be isomorphous, which often limits their applicability. To address this limitation, the authors have developed MatchMaps, an pipeline that performs map subtraction in real space rather than reciprocal space, thereby eliminating the need for isomorphous data. The MatchMaps approach takes measured structure factor amplitudes from two states: the ON state (the interesting/ligand-bound/perturbed state) and the OFF state (the ground/apo/unperturbed state). Next, the algorithm performs rigid body refinement of the OFF state model (e.g. a model built/refined using only the OFF state data) using both the ON and OFF structure factor amplitudes. The electron density maps are then aligned by a rotation-translation matrix derived from alignment of the ON and OFF refined models. The authors apply MatchMaps to four different cases studies and where applicable, compare the results with isomorphous difference maps

We tested the MatchMaps algorithm with some published datasets of the SARS-CoV-2 NSP3 macrodomain with ligand bound at 10-30% occupancy (​​https://zenodo.org/records/..., ligand-bound datasets UCSF-P0628, UCSF-P2193, UCSF-P2227 and apo dataset UCSF-P0110) along with some unpublished data. The program is well documented and easy to install. With some generous help from the authors, we successfully used MatchMaps to reproduce ligand density observed in isomorphous difference maps calculated using the same datasets. The initial issue we encountered had to do with the default solvent mask, but this was overcome based on their advice. We also successfully ran matchmaps.ncs to calculate a difference map between the two macrodomain protomers in the P43 crystal form (chain A and B of apo dataset UCSF-P0110).

Overall, the preprint is well written and the figures are clear and helpful. The major success of this work is the development of a method for the real-space subtraction of electron density maps to visualize structural changes between non-isomorphous datasets. This provides structural biologists with a powerful tool for visualizing structural differences between X-ray diffraction datasets and therefore will be of broad interest to the community. The major limitation is whether MatchMaps can be used to detect structural differences that are not detected using isomorphous difference maps, or to model structural differences that are not apparent by comparing refined coordinates. Although visualization is helpful, the real power in a tool such as this would be in its ability to detect and model low occupancy states.

Major points<br /> The manuscript could be strengthened by including an example where MatchMaps detects a structural change that was not detected by calculating isomorphous difference maps or by comparing refined coordinates. The authors show how MatchMaps removes artifacts due to misaligned models (Figure 3g), but it is unclear to us whether MatchMaps can detect new structural changes. Put another way, it’s unclear to us whether structural changes that result in non-isomorphous datasets would be better visualized using MatchMaps versus a simple comparison of coordinates.<br /> MatchMaps produces two maps by default, one with a solvent mask applied and one without. We are curious why a map with a solvent mask is calculated. This mask is based on the OFF model, so any features of the difference map corresponding to structural changes outside of the solvent mask will be removed. If the solvent mask is required to remove noise in the MatchMaps generated maps, then it would be helpful to discuss this and give examples where the solvent mask was necessary (because this goes somewhat against the claim made by the authors that MatchMaps maps are less susceptible to “uninteresting signal” - line 215). The solvent masking also was a challenge for us in detecting some fragments, but was resolved by working through different options with the authors. An expanded discussion of the merits and limitations of solvent masking (and when to depart from defaults) is therefore likely to be helpful to many users.<br /> Do the authors envisage that MatchMaps could be used to model structural changes or just to visualize them? Along these lines, a comparison with the PanDDA algorithm might be helpful (Pearce, N. M. et al. 2017, Nature Communications). PanDDA can be used to both detect and model low occupancy states, but is most effective when data sets are isomorphous (so is typically used to detect and model low occupancy ligands obtained by soaking). Can the authors imagine an extension to MatchMaps where multiple datasets are averaged to create the OFF map in a similar way to PanDDA? Improving the signal-to-noise of the OFF map might remove the need for the solvent mask.

Minor points<br /> Figure 3c/d. Can the authors comment on differences between the isomorphous difference map and the MatchMaps map? The density is similar but not identical. This is subjective, but to us the MatchMaps density looks a little noisier.<br /> Line 95. Are the data scaled with SCALEIT and then truncated (line 95)? Wouldn’t the reverse be more appropriate (e.g. truncation followed by scaling)?<br /> We were grateful for the -verbose flag in the command line, however, this only prints the output from SCALEIT/phenix.refine. Would it be possible to modify this flag to print the output from all the programs?<br /> Line 104. How are the maps placed on a common scale?<br /> Figure 2f. Is there positive difference density associated with the terminal ribose (or the unmodeled nicotinamide) in the MatchMaps? It would be helpful if the figure legend indicated what part of the model the maps are contoured around. <br /> Line 350. The text says ±2.5 σ but the figure says ±1.5 σ.<br /> Line 161-162 - figure references do not refer to the correct panels. <br /> Line 185-189 and fig. 4d label and text does not match- open:closed conformation/ H-bond

Review by Pooja Asthana, Galen J. Correy & James S. Fraser (UCSF)

On 2023-11-08 20:29:54, user P. Bryant Chase wrote:

Molecular basis for the "Abbott effect"? Bud Abbott was thrilled to know it was still being investigated in the 1980's, and would surely be thrilled to see this work if he was still living.<br /> Abbott BC & Aubert XM. (1952). The force exerted by active striated muscle during and after change of length. J Physiol 117, 77-86.

On 2023-11-08 11:55:26, user Philipp Rühl wrote:

An expression plasmid for rEstus is now available on Addgene:

https://www.addgene.org/209...

On 2023-11-08 09:39:25, user Patrik Simmler wrote:

An updated version of this manuscript has been peer-reviewed and published in eLife: https://doi.org/10.7554/eLi....

On 2023-11-08 06:55:56, user queeny dasgupta wrote:

A revised version of this manuscript is now published: https://www.nature.com/arti...