On 2024-11-18 19:32:52, user Jeffrey Bryan wrote:

This has now been published in peer-reviewed form in Communications Biology. Hoang MH, Skidmore ZL, Rindt H, Chu S, Fisk B, Foltz JA, Fronick C, Fulton R, Zhou M, Bivens NJ, Reinero CN, Fehniger TA, Griffith M, Bryan JN, Griffith OL. Single-cell T-cell receptor repertoire profiling in dogs. Commun Biol. 2024 Apr 22;7(1):484. doi: 10.1038/s42003-024-06174-w. PubMed PMID: 38649520; PubMed Central PMCID: PMC11035579.

https://pmc.ncbi.nlm.nih.gov/articles/PMC11035579/

On 2024-11-18 17:31:52, user Prof. T. K. Wood wrote:

Cyclic-di-GMP is not a quorum sensing compound.

On 2024-11-18 13:41:59, user Min-Ho Nam wrote:

This manuscript has been published in the Crispr Journal.<br /> DOI: 10.1089/crispr.2021.0025

On 2024-11-18 06:20:24, user Diana Duarte wrote:

Thanks for the interest in this manuscript. The peer reviewed article can be found on Plant Molecular Biology https://link.springer.com/article/10.1007/s11103-024-01523-z?utm_source=rct_congratemailt&utm_medium=email&utm_campaign=oa_20241101&utm_content=10.1007%2Fs11103-024-01523-z

On 2024-11-17 07:22:07, user Velyudhan Mohan Kumar wrote:

This study, identifying orthologs of human sleep-related genes in the Lokiarchaeota will certainly help in understanding human sleep physiology. The study supports the concept that sleep is a conserved biological function and evolutionarily distinct new features have developed later. The findings that the genes associated with energy metabolism and cellular repair, which play a primordial role of sleep in cellular maintenance in the Lokiarchaeota, suggest the origin and evolutionary purpose of sleep. The results are in line with the previous assumptions regarding the restorative and protective functions of sleep, including cellular maintenance and gene protection. Moreover, such researches can provide valuable information for drug development as conservation and evolutionary background of genes linked to disease could lead to the discovery of novel therapeutic targets.

This work has used both phylogenomics and bioinformatics to solve a biological problem. This research illustrates the scope of such studies in finding solutions to many biological problems. Why sleep is necessary and is maintained by evolution is not fully answered. From an evolutionary standpoint, phylogenetic approaches would help to unravel some of the unresolved mysteries of nature. The phylogenomics approach to sleep science is rather new, but combining phylogenomics and bioinformatics research on a range of species may help in understanding facts which are still unknown.

On 2024-11-05 08:41:43, user Seithikurippu R Pandi-Perumal wrote:

We greatly appreciate your constructive feedback and the recognition of Professor Imachi's significant contributions, which we have acknowledged in our manuscript. We fully agree that, ideally, certain wet lab experiments might have strengthened our findings to provide additional insights. However, there was no funding for our ongoing effort. If adequate funding were to be available, we would have pursued a certain set of experiments that we thought would add value to our work. This limitation is transparently discussed in our manuscript, and we consider our current data a valuable "stepping stone" for additional work in this exciting area of research.

Looking ahead, we are well-positioned to undertake follow-up experiments, contingent upon the availability of appropriate funding and other resources such as access to the strains. However, we do recognize the challenges involved in maintaining and rearing such specialized organisms in the laboratory setting. For example, replicating their native hydrothermal vent conditions and performing wet lab operations under those constraints condition pose additional significant technical and logistical hurdles. These unique challenges underscore the complexity and significance of our research. With support, we would eagerly and carefully advance this aspect of our research in the future.

Regarding your comment on sleep and circadian cycles in this organism, the identified sleep orthologs underscore that their functional characteristics diverge from those in human sleep processes. From the circadian clock perspectives, organisms whose circadian clocks have periodicities that correspond with those of their cyclic surroundings have survival advantage than those whose periodicities do not. If the development of circadian clocks was driven by circadian resonance, then animals with ~24h periodicity should be more fit in a 24-hour environment than in any other periodic or aperiodic time scale. Alternatively, the persistence of functional clocks and photoentrainment mechanisms implies that circadian clocks provide some "intrinsic adaptive advantage" to organisms in the absence of cyclical environments, where there is no apparent need to synchronize behavioral and metabolic processes with the geophysical cycles ( https://doi.org/10.1186/1740-3391-3-7) . The inherent benefit of clock mechanisms, there, is most likely derived from their ability to help coordinate the organisms' numerous internal metabolic functions.

In extreme habitats like hydrothermal vents, in the absence of photic cues, organisms likely rely on alternative timing mechanisms, such as temperature or chemical gradients, adapted to their unique environmental niche. In organisms such as Lokiarchaeota, the absence of circadian regulation due to the extreme hydrothermal environment may have driven the evolution of alternative timing mechanisms. Without light cycles, these organisms likely rely on other environmental cues, such as temperature fluctuations or chemical gradients, to regulate physiological processes. This adaptation might provide them with flexible metabolic and behavioural responses suited to their habitat. This suggests that evolutionary forces and selective environmental pressures unique to hydrothermal vents shaped a different evolutionary pathway for metabolic, and chemosynthetic mechanisms for survival than light-regulated, temporal mechanisms seen in surface-dwelling and terrestrial species. It's possible that daily and seasonal variations during evolution further honed the organisms for survival.

On 2024-11-04 12:05:23, user S. Krishnaswamy wrote:

Sleep in bacteria and archae is usually associated with a state of dormancy or persistency. But that is different from the circadian cycle of sleep. It is therefore surprising to see orthologs of human sleep genes in the deep sea vent dweller like this archae. Unless they had a different function there. It would be interesting to mutate some of the identified genes and see the effect in the cultures grown in the lab of Hiroyuki Imachi

On 2024-11-17 04:42:42, user De Novo Enzyme Enjoyer wrote:

Fantastic paper, breathtaking results. Just one minor suggestion—when I searched for RFam on GitHub, I found a repo with an almost identical name (Rfam) already exists, for an RNA bioinformatics tool. So to avoid confusion/difficulty locating the Rosetta Flow Atomic Matching codebase (and paper, whenever Ahern et al comes out), it might be a good idea to change the shorthand nickname for the model before publishing the repo (and the papers). RFlam, RoFAM and RoFlam all appear to have more unique namespace than RFam, while still being just two syllables to pronounce. Just a thought!

On 2024-11-17 03:09:55, user Diane Codding wrote:

This article has been published in The ADVANCE Journal: https://www.advancejournal.org/article/122098-the-clash-of-academic-hierarchy-and-inclusive-leadership-evolution-of-leadership-in-a-nationwide-diversity-equity-and-inclusion-initiative

On 2024-11-16 17:11:50, user Jishizhan Chen wrote:

Dear all, please read and cite the formal publication of this preprint here: https://doi.org/10.1016/j.ijbiomac.2024.137253

On 2024-11-16 16:34:07, user Yi Liang wrote:

This preprint has just been published in Science Advances as follows: Li-Qiang Wang#, Yeyang Ma#, Mu-Ya Zhang#, Han-Ye Yuan, Xiang-Ning Li, Wencheng Xia, Kun Zhao, Xi Huang, Jie Chen, Dan Li, Liangyu Zou, Zhengzhi Wang, Weidong Le, Cong Liu*, Yi Liang*. Amyloid fibril structures and ferroptosis activation induced by ALS-causing SOD1 mutations. Science Advances 2024 Nov 1, 10(44), eado8499.

On 2024-11-15 21:56:15, user Mikaela Follmer wrote:

this paper is now published in nature communications --> https://doi.org/10.1038/s41467-024-53642-2

On 2024-11-15 07:55:21, user Giovanni Bussi wrote:

Authors of the review

Olivier Languin-Cattoën, Giovanni Bussi

Summary

The authors use Molecular Dynamics with enhanced sampling techniques to gain insight in the directional catch-bond mechanism of Vinculin tail (Vt) interaction with F-actin. They construct two models of the Vt-actin complex that are hypothesized to represent a weak state and a strong state in a force-activated allostery model of the catch bond. They use enhanced sampling techniques to estimate the free-energy landscape of the unbinding process in each state, as well as the unbinding kinetics, in absence and presence of pulling forces of variable intensities and directions along the actin filament axis. Their results demonstrate higher kinetic stability for the strong state with respect to the hypothesized weak one, with unbinding kinetics in range of experimental expectations, confirming the viability of a “3-state” (2 bound states, 4 unbinding pathways) kinetic model of the bidirectional catch bond observed in single-molecule experiments. They additionally observe an increase in bond lifetimes for moderate constant pulling force (10-20 pN) in both directions, indicating an intrinsic catch-bond behavior within each “allosteric” state that may superimpose to the overall allosteric one. They show how an external pulling force affects the positioning of the H1 α-helix believed to act as a regulatory motif of the weak-to-strong transition, providing a compelling structural hypothesis for a force-induced allosteric mechanism. Finally, they provide molecular insight on the difference in stability between both states, highlighting the role of a C-terminal extension (CTE) and the redistribution of Vt-actin contacts under force.

Comments

* We wonder if the nomenclature “Holo” can be confusing at first glance for the reader, given the historical usage of the holo- and apo- prefixes to designate protein constructs with and without their constitutive prosthetic groups (usually non-proteic cofactors)

* We suggest that the authors make clearer the composition of the Holo and Aligned protein sequences given the different numbering in 6UPW and 1QKR (that we guess is due to the presence of Metavinculin instead of Vinculin), that in our understanding are identical except for the absence of the leading H1 helix.

* In FES calculations, since the constant force (that is, a linear bias) is applied on the same coordinate Q_‖, we expect that the resulting FES could be entirely predicted from the FES at zero force by simple addition of the linear slope, given sufficient exploration of the Q_‖ direction during the OPES-MetaD sampling. This fact could be used by the authors to assess the consistency between the FES computed at different forces. Alternatively, one may want to first aggregate the OPES-MetaD simulations at all forces using appropriate reweighting, and then estimate minimum free-energy paths and free-energy barriers at arbitrary force using the aggregated FES. This approach might lead to a better statistical use of the vast amount of simulations and a smoother estimate of the FES at all forces within the studied range. Finally, the multiple trajectories (20 replicates x 9 force values) could be used in a single bootstrap to assess the statistical uncertainty of the results (see below).

* Given the coarse nature of the set of chosen CVs (Q_‖ and Q_⊥) it is unclear whether the Vt is able to regain its canonical binding site during OPES-MetaD, notably because of free rotation with respect to the actin filament. <br /> - The authors acknowledge the difficulty in the methods (sec. 5.5) without explicitly stating whether such rebinding events happen at all in their simulations. We believe this is an important piece of information for proper understanding of the FES presented. We would suggest showing time series to clarify how many binding/unbinding events are observed.<br /> - One might expect that the absence of recrossing lead to a poor estimate of the free energy difference between the bound and unbound states as well as the height of the binding free energy barrier. On the bright side, the estimate of the unbinding barrier – which is the one they are the most interested in – can still be expected to be reliable.<br /> - The authors suggest to run multiple (20) separate OPES-MetaD simulations to compensate for this limitation. It should be acknowledged that independent runs starting from the bound state will not correct for the systematic bias caused by the absence of rebinding events. A bootstrap on these replicas would anyway estimate their statistical error.<br /> - We wonder if the use of the more specific CV Q_contact might allow for such recrossing to happen within OPES-MetaD, without the need of aggregating a high number of independent trajectories. In our understanding the authors only used Q_contact to assess the robustness of the free-energy barrier height to the precise choice of the projection space, but did not try to perform OPES-MetaD directly on this CV space, which could be instructive.

* The authors analyze the FES by determining a minimum free-energy path using the “String method” as a post-processing method. <br /> - The Methods section might benefit from some information about the use of the method, in particular that it is directly applied on the 2D CV-space projected FES (as opposed to a search of a minimum energy path on the full potential energy surface as originally proposed in [50]), and provide details about initialization (choice of end points for the string, number of nodes, initial interpolation) and robustness to these parameters in the converged paths and corresponding barrier estimates.<br /> - Since the FES are aggregated from 20 independent OPES-MetaD runs, it might be relatively straightforward to estimate errors (for example using bootstrapping) and provide error bars on Fig 2c. We believe this would strengthen the significance of the observed barrier difference.

* One may be concerned about the significance and reliability of the constructed “Aligned” state, since this state was constructed by aligning Vt to another conformation (in what we could refer to as “docking-by-homology”) with little experimental confirmation that such a state is stable in vitro. We understand that the model constitutes the core hypothesis of the whole computational approach, and that the consistency of the computational outcomes with single-molecule experiments themselves validates its plausibility. Nevertheless, it could be argued that lower binding affinity and lifetime are to be expected from a suboptimal binding partner in a suboptimal binding pose. This raises the question of whether the proposed model corresponds to a specific binding mode in reality, or if the results could be reproduced with a different alignment. Is this ruled out by the stability observed in the 500 ns simulations shown in Fig S1?

* In Sec 3.2 §4, the authors say “these results [...] do not quantitatively explain the observed experimental results, since the experimental changes in lifetime shown in Fig. 1D reflect a net 1.4 kcal/mol change in barrier in the negative direction, and 0.6 kcal/mol in the positive direction (if one assumes a constant prefactor the kinetic rate constant)”. It was somewhat unclear to us where these values come from (Are they computed from the fitted 3-state model in SI S1? At a specific value of the pulling force?) and how exactly they are compared to the computed barriers for Holo and Aligned to conclude to a discrepancy (Overestimated?)

* In Sec 3.4 §3, the authors convincingly remark that in two out of five simulations of Holo+H1 state pulled towards the barbed end, the conformation of H2–H5 becomes more similar to the Aligned (unbound Vt) structure, suggesting a first step in the strong → weak allosteric transition. We wonder if (i) the specific contacts made with actin and (ii) the specific intra-domain contacts of H1 with the H2–H5 bundle are also indicative of a displacement toward the Aligned state, since this would be an even stronger argument validating the proposed model.

* Given the relative simplicity of the supposed allosteric motif and the suggestive results of the Holo+H1 simulations, we cannot help but wonder whether the authors also tried to "unfold" the H1 helix in the Aligned model with a N-terminal pulling force since this seems a very natural test to look for equally suggestive indications of a weak → strong allosteric transition under force.

* Typos or writing remarks:<br /> - Fig 1B: The caption is inconsistent with the figure. In the caption, p₁ denotes the COM of Vt helices H2–H5, p₂ the COM of actin A1/A2 and p₃ the COM of actin A4/A5. On the figure instead (p₁, p₂, p₃) → (p₃, p₁, p₂).<br /> - Some repetitions that might be elegantly avoided<br /> x “capture the difficult-to-capture” (abstract)<br /> x “protein of interest [...] on our molecule of interest” (introduction §1)<br /> x “takes into account a Boltzmann weighted average over all possible configurations [...] a Boltzmann weighted average over all possible configurations” (introduction §4)<br /> - Typos<br /> x “FimH-manose” → “FimH-mannose” (introduction §8)<br /> x “which is the the direction” (3.1 §1)<br /> x “These approximate one-dimensional free energy pathways also give us a way to define when the system has crossed into the unfolded state” → Maybe the authors meant “unbound state” (3.1 §1)<br /> x “not all of the catch bond need come from” → “need to come from” (3.2 §3)<br /> x “we note that these results are suggestive, they do not quantitatively” → We wonder if the authors intended a formulation along the lines of: “we note that despite being suggestive, these results do not quantitatively” (3.2 §4)<br /> x “a constant prefactor the kinetic rate constant” → “a constant prefactor for the kinetic rate constant” (3.2 §4)<br /> x “grafted to our Holo structure random orientation” → “grafted to our Holo structure with a random orientation” (3.4 §2)<br /> x “TIP3 water” → “TIP3P water” (5.1 §2)

Acknowledgements

This report was written after a journal club given by OLC in the bussilab group meeting. All the members of the group, including external guests, are acknowledged for participating in the discussion and providing feedback that was useful to prepare this report. The corresponding authors of the original manuscript were consulted before posting this report.

On 2024-11-15 05:26:52, user Shigeaki Saitoh wrote:

The final version of this article is published in https://doi.org/10.1083/jcb.202404085 .

On 2024-11-14 20:30:07, user Patrick Neale wrote:

This prepint is now published, citation:<br /> PLoS ONE 19(10): e0305505. https://doi.org/10.1371/journal.pone.0305505

On 2024-11-14 15:48:57, user Mikel Garcia-Marcos wrote:

Based on information brought up to the attention of the authors, the passage below will be modified in the final published version. More specifically, it has been clarified that the isoformof Rap1GAP used in the EMTA does not affect nucleotide exchange and that additional evidence supports that the this system detects active Galpha(i/o). These conclusions are partially supported by evidence presented in paper that has been published (doi: 10.1126/scisignal.adi4747).

"For example, EMTA biosensors for G proteins of the Gi/o family are based on using Rap1GAP as a detector module, for which it is unclear whether it affects nucleotide exchange on different Gα subunits of this family or its preference for binding to Gα-GTP or Gα-GDP dissociated from Gβγ (35, 36), thereby raising questions about what is exactly represented by the BRET changes detected by this sensor."

On 2024-11-14 11:34:17, user John wrote:

Watch our PowerPoint presentation at the MMEE2024 conference:<br /> https://youtu.be/dwZRbpUDZq0

On 2024-11-14 08:45:38, user BioPioneer wrote:

Another paper had solved this problem and was published about two years ago:

Najafi, A., Jolly, M. K., & George, J. T. (2023). Population dynamics of EMT elucidates the timing and distribution of phenotypic intra-tumoral heterogeneity. Iscience, 26(7).

On 2024-11-13 14:57:57, user Jozef Nissimov wrote:

Very cool study Debbie and team. We recently did some work on the nblA protein as well that perhaps may be of interest: https://academic.oup.com/ve/article/10/1/veae082/7775594

On 2024-11-13 01:32:45, user mo wrote:

How can I get the data for the supplemental document, please?

On 2024-11-12 22:52:53, user Francesco Iorio wrote:

now published in Nature Communications<br /> https://www.nature.com/articles/s41467-024-53163-y

On 2024-11-12 14:14:42, user Velyudhan Mohan Kumar wrote:

In this report, human sleep-related gene orthologs have been studied in the unicellular green alga Chlamydomonas reinhardtii, which had been earlier considered as a model organism for the study of photosynthesis and flagella/cilia. Comparative genomics, which is an essential tool in biological research, is a very potent method that yields biological insights that are not possible with any other approach. It could also help us understand the human genome study, which has the potential to help us comprehend human sleep and sleep disorders. Phylogenomics gives a more profound comprehension of the discoveries made possible by comparative genomics.<br /> The results of this study indicate that the calcium signaling and synaptic transmission involved in sleep regulation are conserved across phyla, and a simple model organisms like Chlamydomonas may be useful in elucidating higher-order, complex behaviors such as sleep.

Chlamydomonas do not possess the complexity of the sleep seen in vertebrates. However, knowing their genetic makeup can help researchers to understand general molecular processes that may be used to understand sleep in more complex organisms. Comparative phylogenomic studies are also important in understanding human sleep disorders, and they may open new avenues for developing new therapeutic approaches.

On 2024-11-10 23:56:25, user Yi Shen wrote:

Dear bioRxiv team,

this preprint has been published in PNAS now.<br /> https://www.pnas.org/doi/abs/10.1073/pnas.2301366120

It will be great to link this preprint to the final publication. Thanks.

Yi Shen (First and corresponding author)

On 2024-11-10 18:38:49, user Michele Bonus wrote:

Hi! DataSAIL seems to be working well for me -- at least on protein structures using the built-in FoldSeek-based clustering.<br /> Regarding the paper: I believe there is something amiss with the coloring in Figure 2. In the lower sub-panel for the identity-based 1D cold split, shouldn't the rows be colored instead of the columns? Right now you are showing the same protein-identity-based split as in the upper sub-panel, and I believe you meant to show sub-optimal partitioning w.r.t. the "ligands" / chemical scaffolds.

On 2024-11-06 13:37:11, user Dieter Steinhilber wrote:

I recommend to read the paper by Alnouri et al. https://pubmed.ncbi.nlm.nih.gov/39365815/ and this one: https://pubmed.ncbi.nlm.nih.gov/35125345/ . From the experimental data, it is rather unlikely that there is a functional high affinity binding site for LXA4 and RvD1 at FPR2 and that the SPM ligands are functional at the mentioned GPCRs

On 2024-11-06 08:57:18, user himanshukhandelia wrote:

Very interesting work. I was curious about the surface tension being applied. For semi-isotropic pressure coupling, the tension is automatically set to zero, and does not need to be set explicitly. Why is this being done?

We previously demonstrated that even ATP could bind to lipid membranes. 10.1021/acs.langmuir.9b01240. So does UMP (shown by both scattering, fluorescence and simulations (https://doi.org/10.1039/C9S....

One caveat is that the charge on these nucleotide phosphates are most likely overestimated (see for example https://doi.org/10.1021/acs.jpcb.2c06077 and https://doi.org/10.1016/j.xcrp.2021.100343 . One way to address this is the rescale charges on the P and O atoms, as well as ions. However, a more systematic force field approach may be needed in the future. Pavel Jungwirth is working on this. https://www.developmentaid.org/organizations/awards/view/470320/doing-charges-right-modelling-ioncontrolled-biological-processes-with-the-correct-toolbox-q-scaling

On 2024-11-06 01:23:49, user sun zhen wrote:

The results of this paper are not credible.

Based on the gel result, we can see that only introns are removed by oligo dT purification, lots of precursor and nicked RNA still in the products. Obviously, there will be a decrease in dsRNA compared with RNA kit purification which removed nothing from the crude circRNA products.

When it comes to in vitro expression test, I am surprised to see even the expression of RNA kit circRNA is better than HPLC purified circRNA.We have also tested and compared RP-HPLC purified circRNA (circEGFP and circFLuc from GenScript) before, they got much higher expression than RNA kit purified circRNA or RNase R treatment crude circRNA.

As the purity of circRNAs is very important for their expression (which can reduce their immunogenicity) based on previous reports, the in vitro or in vivo data in this paper are not convincing.

On 2024-11-05 13:53:35, user Damien Garcia wrote:

The article has been published please link the archive to the published manuscript.<br /> https://doi.org/10.1093/plphys/kiab529

On 2024-11-05 13:49:01, user Damien Garcia wrote:

The article has been published could you link it to the publication:<br /> https://doi.org/10.1093/plcell/koae175

On 2024-11-05 11:17:02, user Guei-Sheung Liu wrote:

The paper has been published as below<br /> Proc Natl Acad Sci U S A. 2024 Nov 5;121(45):e2408345121. doi: 10.1073/pnas.2408345121. Epub 2024 Oct 30.<br /> Characterization of RNA editing and gene therapy with a compact CRISPR-Cas13 in the retina

On 2024-11-04 22:41:00, user Michael wrote:

typo in Sequence section? "pester and mortal" Did you mean pestle and mortar?

On 2024-10-29 04:24:41, user Dan MacNulty wrote:

The age estimates are impressive. To help readers appreciate the significance of these estimates, consider adding more background about what’s currently known about the age of aspen clones in general. Do estimates exist for other clones? If so, how do they compare to your estimates? How does your aging method compare to previous methods?

On 2024-11-04 22:27:12, user Brian Meckes wrote:

Link to published manuscript

https://www.cell.com/iscience/fulltext/S2589-0042(24)02240-5

On 2024-11-04 15:18:52, user Kasper van Gelderen wrote:

With my group we have been using this to 3D segment confocal timeseries, it works great! Recently, it has been updated and also works again via a Google colab form, so very low user threshold.

On 2024-11-04 15:12:37, user Jorge Hernández wrote:

Dear authors, is there a way to access the supplementary data? I am looking for all the Marchantia TFs tried to express as HALO fusions.<br /> Thanks in advance!

On 2024-11-04 12:14:36, user Chris Hunter wrote:

This paper was published in GigaScience in July 2023. https://doi.org/10.1093/gigascience/giad044

On 2024-11-04 10:28:20, user MRR wrote:

There are open reviews of this preprint Qeios:

https://www.qeios.com/read/WPW9WN

https://www.qeios.com/read/EHRP24

https://www.qeios.com/read/MGCKD5

On 2024-11-03 15:04:26, user B. Hendrich wrote:

I think you mean "potency". "Pluripotency" refers to the ability to make all cell types in the embryo. Trophectoderm stem cells cannot do that.

On 2024-11-01 16:14:55, user Haider, Saqlain wrote:

Dear Authors,<br /> Thank you for sharing this exciting work on the role of ABA and GI in recruiting CO at FT promoter to activate its transcription (and flowering). I appreciate the detailed analysis and valuable insights you've provided. I have a few questions and observations that may help clarify some aspects of the study:<br /> 1. Higher CO in aba1-6 mutants at ZT12<br /> In figure 2A, a slight increase in CO expression could be observed in aba1-6 mutants compared to WT plants though this expression declines at ZT16. Further, a higher CO expression is detected at ZT12 and ZT16 in SUC2::CO:CIT-aba1-6 compared with SUC2::CO:CIT (in WT background). It remains somewhat unclear as to why the lack of ABA is leading to a slight (yet) increase in CO transcription. It is noted that in 9 days old seedlings at ZT16 (figure 2B), there is a decline in CO expression yet aba1-6 mutants transformed with SUC2:CO:CIT in figure 2A have a higher CO at ZT16 than WT transformed with SUC2:CO:CIT. What could be the reason behind that. Finally, it will be valuable if authors could measure the CO-FT expression at ZT12 in 9 days old seedlings (like they did for 13 days old). <br /> 2. Higher Pol-II occupancy at CORE in WT<br /> In figure 4B, a higher enrichment/occupancy of RNA Pol II could be detected at TSS of FT in WT compared with SUC2::HA:CO aba1-6 and SUC2::HA:CO gi100. Yet, these lines flower much earlier than WT (figure 5B). How do authors explain this discrepancy as low Pol II occupancy would lead to a lower FT (and delayed flowering). Moreover, it will be interesting if authors could quantify and compare the FT expression in WT (Col-0), WT (with either SUC2::CO:CIT or SUC2::HA:CO), aba1-6 (with either SUC2::CO:CIT or SUC2::HA:CO), and gi-100 (with either SUC2::CO:CIT or SUC2::HA:CO). For example, do authors expect FT levels to be same in aba1-6, gi-100 (transformed with SUC2::HA:CO) as they flower nearly at the same time (Figure 5B). Moreover, as SUC2::HA:CO aba1-6 gi-100 show a slight delay in flowering compared with either SUC2::HA:CO aba1-6 or SUC2::HA:CO gi-100, it will be interesting to analyse FT transcript accumulation between genotypes. Taken together, addressing these points will increase the readers understanding and make the paper more concise.<br /> 3. ChIP and RNA Pol II occupancy assayed at ZT12 (not ZT16)<br /> In figure 3,4, and 5 the ChIP assays and RNA Pol II occupancy was quantified at ZT12. CO transcript peaks around that time point in long day (LD) conditions but the FT peak is observed at ZT16. Previously, it has been shown that reduced FT peak at ZT16 leads to delay in flowering ( https://doi.org/10.1111/pce.13557) . Authors mention CO binding is observed at ZT12 at CORE (line 329-331) but the delayed flowering phenotype is justified by FT levels so it will be interesting to check CO binding together with RNA Pol II enrichment at ZT16 and then compare both these assays (ZT 12 and ZT16). <br /> 4. RNA Pol II Occupancy at TSS in WT vs. SUC2::HA:CO abi1-6 and SUC2::HA:CO gi-100 with Delayed Flowering in WT <br /> SUC2::HA:CO: gi-100 and SUC2::HA:CO: abi1-6 lines show a drastic decline in Pol II occupancy at TSS of FT, yet these lines flower significantly earlier than WT. Would you consider discussing possible mechanisms by which this delay occurs, despite higher Pol II occupancy, to help clarify this interesting finding? Finally, the SUC2::CO:CIT gi-100 lines flower nearly at same time as SUC2::CO:CIT (figure 5B). Although in supplementary figure 9, an increase in bolting time (flowering in terms of number of days) in SUC2::CO:CIT gi-100 lines could be seen. A few additional details or a discussion of possible mechanisms might help contextualize this observation.

Thank you again for sharing this preprint. I hope these comments are helpful and supportive of your ongoing work. Please feel free to reach out if you'd like further clarification on any of my points.<br /> Thanks<br /> Saqlain

On 2024-11-01 14:02:04, user Cameron Thrash wrote:

Hello. May I respectfully recommend a few relevant references for the discussion of SAR11 recombination and speciation?

Natural variation in SAR11 marine bacterioplankton genomes inferred from metagenomic data<br /> https://biologydirect.biomedcentral.com/articles/10.1186/1745-6150-2-27

High intraspecific recombination rate in a native population of Candidatus Pelagibacter ubique (SAR11)<br /> https://enviromicro-journals.onlinelibrary.wiley.com/doi/abs/10.1111/j.1462-2920.2007.01361.x

The Evolutionary Success of the Marine Bacterium SAR11 Analyzed through a Metagenomic Perspective<br /> https://journals.asm.org/do...

A comparison of homologous recombination rates in bacteria and archaea<br /> https://academic.oup.com/ismej/article/3/2/199/7588207?login=false

On 2024-11-01 13:06:13, user Maxim Greenberg wrote:

Hello, this paper has now been published in Nat Struct Mol Bio. https://doi.org/10.1038/s41594-024-01405-4 Thank you!

On 2024-11-01 13:04:11, user Maxim Greenberg wrote:

Hello, This paper has now been published in Nucleic Acids Research: https://doi.org/10.1093/nar/gkae724 Thank you!

On 2024-11-01 09:15:08, user David Žihala wrote:

Dear Authors,

Thank you for sharing your impressive work in the form of a preprint. I have a question regarding one aspect of the study that I found a bit confusing. Could you clarify why it was necessary to decontaminate the samples from mouse cells? If two samples were excluded from downstream analysis due to high mouse cell content (over 65%), could you please provide information on the mouse cell content in the remaining samples? Additionally, I am curious about how this type of contamination could have occurred.

Best regards,<br /> David Zihala

On 2024-11-01 04:07:38, user Christoph Hagen wrote:

peer-reviewed and published: https://dx.doi.org/10.1038/s41564-024-01716-8

On 2024-10-31 23:46:29, user Derek Narendra wrote:

This has now been published here:

Lin HP, Petersen JD, Gilsrud AJ, Madruga A, D'Silva TM, Huang X, Shammas MK, Randolph NP, Johnson KR, Li Y, Jones DR, Pacold ME, Narendra DP. DELE1 maintains muscle proteostasis to promote growth and survival in mitochondrial myopathy. EMBO J. 2024 Oct 8. doi: 10.1038/s44318-024-00242-x. Epub ahead of print. PMID: 39379554.

On 2024-10-31 18:13:20, user Avi Flamholz wrote:

See detailed comments here https://prereview.org/reviews/14019384

On 2024-10-31 15:54:51, user Josh Mitteldorf wrote:

The comparison you have made supports the conclusion "that aging is not governed by a conserved universal program" but not the specific alternative you propose, "adaptations to damage and environmental conditions." I suggest that it may be that the ecological need for managing length of life is universal, but that it is implemented differently in different species. Salmon destroy their bodies with glucocorticoids; elephants fail to grow a 7th set of teeth and can't chew their food; mice get cancer; rats get heart disease -- but the underlying adaptive motivations might be universal. The best evidence for this is the role of insulin signaling, which accelerates aging across the animal kingdom.

On 2024-10-31 15:40:16, user James Amos-Landgraf wrote:

In your methods you state that you purchased C57BL/J mice from the four vendors. These mice are only available from Jackson Lab. The other mice you purchased are C57BL/6N substrains that specific to the vendor. NTac, NCrl, and NHsd. The biggest factor you need to address is the B6J have a mutation in the Nnt (nicotinamide nucleotide transhydrogenase) gene that dramatically influences metabolism. You should address this in your interpretation of bacterial community shifts. There is a good review of these differences that Jax has published on their web site and in various publications. https://www.genengnews.com/sponsored/heres-what-you-need-to-know-about-the-c57bl-6-substrains/

On 2024-10-31 14:51:48, user YC Han wrote:

This study holds considerable promise, but its findings are significantly undermined by the notable delay in developmental stages observed in embryos injected with CRISPRi/a + sgRNAs, as evident in Figures 1-3. For instance, the embryo injected with CRISPRi + tyr sgRNAs marked with 48 hpf in Figure 1B, appears to be developmentally equivalent to a 30 hpf wild-type embryo, which exhibits substantially fewer pigment cells than embryos at 48 hpf. This discrepancy raises concerns about the direct impact of the CRISPRi/a + sgRNAs injection, whether or not the delay development was caused by an unintended consequence.<br /> I strongly recommend that the authors reexamine their results and conduct a comparative analysis of CRISPRi/a + sgRNAs-injected and control embryos at equivalent conventional developmental stages, as described by Kimmel et al (1995, PMID: 8589427). This crucial consideration will not only enhance the study's rigor but also provide valuable insights to the zebrafish research community.

On 2024-10-31 14:41:16, user CT wrote:

It’s been known for decades that Blue Jay kents and IBWO kents differ spectrographically… duhhh. And a LOT of other sounds may mimic IBWO kents to the human ear as well (though the paper explores but one single avian species)… all being spectrographically different. This paper adds little to the known literature, except to try to reach further conclusions (actually redundant, re-stated conclusions in previous work) from unsubstantiated assumptions. <br /> I’d be amazed if one can find 2 or 3 (or even one) PhD. level scientists in the entire country who will defend the methods and exaggerated conclusions put forth here.

On 2024-10-30 21:32:37, user Vanesa Amarelle wrote:

The paper is now available at<br /> https://link.springer.com/article/10.1007/s42770-024-01512-w

On 2024-10-30 18:03:54, user Verónica Kovacec wrote:

This article has now been published in Microbial Genomics, under doi: https://doi.org/10.1099/mgen.0.001297

On 2024-10-30 14:15:51, user Anonymous wrote:

Dear authors,

as part of a group activity in our lab we discussed your very interesting paper with the goal to review it. The below review is the result of this exercise and therefor reflects the thoughts and concerns of several people. We hope this helps you with your way forward to publish the paper in a good journal.

Review:

The manuscript by De Tito et al. reports a hitherto unknown role for ATG9 in recruitment of PI4K2A to damaged lysosomes to control the levels of PI(4)P, lysosomal membrane contact site formation with the ER, and lipid transfer from ER to damaged lysosomes. They report that this mechanism is controlled by ARFIP2, which shuttles between the Golgi where it anchors ATG9 and lysosomes, and by the AP-3 complex, which mediates retrieval of ATG9 from lysosomes. Finally, the authors show a role for their proposed mechanism in lysosome damage induced by Salmonella infection. The manuscript deals with the timely topic of lysosomal damage repair and PI4K2A was recently emerging as a key player in this process. Mechanisms for PI4K2A recruitment to the sites of damaged lysosomes were so far elusive. Although the manuscript is certainly of interest for the field, the presented evidence on which the claims and the proposed model are based on seem not always substantial enough and further work is required.

Major concerns<br /> • Novelty and generalization: Although the findings suggest new functions for ATG9A and ARFIP2, they overlap significantly with previous work on autophagic and lysosomal repair pathways. The novelty of these findings, in comparison to earlier research, could be emphasized more clearly.<br /> • The claim that ARFIP2 "modulates lipid transfer for lysosomal repair" could benefit from additional direct evidence linking lipid transfer to lysosomal recovery and ARFIP2's specific role in this process. There is only one in vitro experiment directly showing lipid transfer modulation by ARFIP2 (Fig. 5K). The authors should use the ARFIP2 W99A mutant in this experiment to test whether lipid transfer modulation is specific and according to their model.<br /> • A central claim of the manuscript is a role for ARFIP2 in the repair of damaged lysosomes. The gold standard assay in the field is recovery of lysotracker fluorescence after LLOME-induced damage as shown in Fig. 2G. The way the experiment is presented does not instill confidence. The effects are relatively modest, it is unclear how the statistics were done, the representative images in the supplement only remotely resemble lysotracker stainings, how a reliable lysosome number from these images could be extracted is unclear, it is not shown whether and how much the cells express GFP-ARFIP2, the time points of the images do not match the relevant time points in the quantification, and the lysosome number in the different samples before LLOME treatment is not factored in. <br /> • The major lysosome repair assay in the manuscript is Galectin staining. This is a rather indirect assay as compared to the lysotracker recovery assay as it shows the damage rather than the repair. Could there be repaired lysosomes that still are positive for Galectin? Is the total Galectin expression level the same in the cell lines and conditions used? Furthermore, spot quantification for lysosomal damage markers like LGALS3 (Figure 2f) does not account for cell size or density, potentially leading to misinterpretation of the data. The authors further need to show lysotracker recovery in ATG9 loss of function to substantiate their claims.<br /> • The authors show that ARFIP2 and ATG9 affect PI4K2A localization in cells. The “delivery” of PI4K2A to lysosomes, however, which is a central claim in this manuscript, is insufficiently demonstrated. Same is true for the “retrieval” of ATG9: The authors claim that AP-3 and ARFIP2 are important for the retrieval of ATG9A vesicles. Even though they show effects of these proteins on ATG9A presence at the lysosomes, they never manage to show that retrieval really is impaired. I am not fully sure if it is possible to make videos that convincingly show this. Either they should try that, or they should not hammer so hard on the word retrieval when they never show it specifically. <br /> • The dynamic interactions of the relevant components need to be better demonstrated and characterized. E.g. interactions ATG9-PI4K2A interaction needs to be proven and characterized better (e.g. by co-IP, immunogold or similar techniques). Otherwise the role of ATG9 is much secondary, and should be a ARFIP2-mainly focused paper. The experiments in figure 4H and 4G seem ideally suited for this purpose but the authors fail to show the relevant components in the same blot.<br /> • The increased formation of membrane contact sites is insufficiently demonstrated. The authors need to use electron microscopy, super resolution microscopy, SPLICS or other appropriate techniques to make this claim<br /> • The way of quantifying protein localization to lysosomes (intensity lysosomes/total) seems heavily dependent on the lysosome coverage of the cells. In conditions where to my impression the overall number of lysosomes also changes, I would like to see a negative control that demonstrates that the enhanced lysosome localization is not just by chance, since there are more lysosomes. This applies for instance to Figure 4E+F and 5A+B.<br /> • I am not satisfied with the data analysis they presented. The authors should indicate the statistical test for each piece of data they analysed including an explanation, why did they chose particular test. For instance, in fig. 2g they compare the amount of puncta or lysosomes (it is not clear as well) in the cell in a pairwise manner. It would be more appropriate to implement a statistical test that can compare the curves fitted with the data points or a test which can compare each time point individually, i.e. Kaplan-Meier plot.

Minor comments<br /> • The zooms generally miss scalebars<br /> • Which part of the picture the zoom is from is often not indicated<br /> • Many of the images are hard to interpret because of low contrast, I would recommend to put their individual channels in grey<br /> • Fig3f. requires a better representative image and although there is a line indicated there is no line plot<br /> • Fig. 1c: show quantification for pS6K<br /> • Fig. 2b: data points are the same as figure 1b<br /> • Fig. 3: Live-imaging of ATG9A with AP-3D1 is needed

On 2024-10-30 08:51:15, user Thomas Munro wrote:

Given LM189's structural similarity to salmeterol and higher efficacy, I suspect there would be wide interest in whether it shares salmeterol's peculiar time course: activation that persists for hours after washout, but can be reversed by antagonists, and “reasserts” after antagonist washout. The cited patents claim “a particularly long duration of action”, which suggests LM189 may be even more extreme than salmeterol, but don't appear to give any results.<br /> It might be possible to get this data without the dreaded reviewer experiments. Glaxo (now GSK) offers to share clinical trial data. Presumably they would also be willing to share in vitro data for an off-patent compound like this, which has no privacy or IP concerns.

On 2024-10-29 14:01:42, user Amandine Véber wrote:

In this bioRxiv preprint, the authors question part of the methodology that we have used in Jay et al. (2022) , as well as the conclusions we have drawn from our study. In order to clarify the different points of misunderstanding that have led to these criticisms, in the document available at this link we provide a response which, we hope, will demonstrate the validity of our approach.

In this document, we first address the main questions raised by our colleagues and then carefully go through our paper to emphasize its main contributions. The format of this response makes it not eligible for publication in standard open archives, hence our non-standard way of making it available online. A link to it can also be found there .

The current debate which was generated by Jay et al. (2022) is grounded on the very interesting question of which control scenario is appropriate to assess the efficiency of the "sheltering effect" that we investigate there, and we hope to contribute to this discussion with sound logical arguments and relevant biological concepts.

Amandine Véber, Paul Jay and Tatiana Giraud

On 2024-10-29 10:08:15, user Jiayu Wang wrote:

Our database is available at https://datasync.ed.ac.uk/index.php/s/qlIgUlTTzW8KjXM

On 2024-10-29 09:45:14, user Stew Late wrote:

For Figures 4B and 4C, I suggest placing the "D. melanogaster" label in the same style as in Figures 4D and 4E.

On 2024-10-28 20:29:55, user Pedro H. Oliveira wrote:

Another recent large-scale screening of the bacterial defensome has been performed: https://www.nature.com/articles/s41467-024-46489-0

On 2024-10-28 15:02:24, user nuala oleary wrote:

Hi,

I wanted to point out a small issue with the references to NCBI Datasets. The author names are misformatted. It should read as follows:

O’Leary NA, Cox E, Holmes JB, Anderson WR, Falk R, Hem V, Tsuchiya MTN, Schuler GD, Zhang X, Torcivia J, Ketter A, Breen L, Cothran J, Bajwa H, Tinne J, Meric PA, Hlavina W, Schneider VA. Exploring and retrieving sequence and metadata for species across the tree of life with NCBI Datasets. Sci Data. 2024 Jul 5;11(1):732. doi: 10.1038/s41597-024-03571-y.

Thanks!

On 2024-10-28 09:39:36, user Isabella Capellini wrote:

A revised version of this manuscript is now available in Proceedings B:<br /> Mortlock E, Silovský V, Güldenpfennig J, Faltusová M, Olejarz A, Börger L, Ježek M, Jennings DJ, Capellini I. 2024 Sleep in the wild: the importance of individual effects and environmental conditions<br /> on sleep behaviour in wild boar. Proc. R. Soc. B 291: 20232115.<br /> https://doi.org/10.1098/rspb.2023.2115

On 2024-10-25 17:03:55, user Thomas Nicol wrote:

This article is now published in cardiovascular research https://doi.org/10.1093/cvr/cvad101

On 2024-10-25 12:37:11, user Yuta Takada wrote:

There was a mistake in line 80. The correct reference is figure S1A, not figure S2A.

On 2024-10-24 17:27:06, user Kyle Barrie wrote:

Published: https://www.nature.com/articles/s41594-024-01412-5

On 2024-10-24 16:51:29, user Allisandra Rha wrote:

Interesting work. The multimodal approach definitely enhanced the integration outcomes and is well-designed. While it is of benefit that AAV efficiently reaches the nucleus, moving forward would you consider evaluating other delivery methods for nuclear translocation to account for patients that are seropositive for AAV antibodies?

Also, the paper is under-referenced. I would encourage you to look at the text and provide more citations to support your claims.

On 2024-10-23 00:20:34, user CDSL JHSPH wrote:

I really enjoyed reading your preprint "Universal rules govern plasmid copy number." Your analysis of plasmid copy number across diverse bacterial genera is impressive and the discovery of a scaling law linking plasmid size and PCN is a huge contribution to the understanding of plasmid biology. I was wondering, based on your discussion on the intrinsic variability of PCN in high-copy number plasmids versus low-copy number plasmids, how these variations impact the fitness of bacterial populations, especially under different selective pressures. Additionally, given plasmids' role in gene transfer, how might the scaling law and PCN-size trade-off influence the evolution of plasmid-encoded traits, such as antibiotic resistance genes or virulence factors? <br /> I look forward to seeing how this research progresses! Thank you!

On 2024-10-19 00:01:15, user CDSL JHSPH wrote:

Thank you for sharing your research! I found your paper on PCN very insightful, especially the discovery of "universal rules" governing PCN. The large-scale analysis of plasmids provides valuable insights into plasmid number variability and regulation, with potential applications in both research and biotechnology. I do have a few questions. Could replicon dominance bring evolutionary advantage, particularly under natural conditions where plasmids may compete for replication resource? Additionally, under external constraints, is there a threshold where dominance shifts between replicons? <br /> I very much enjoyed the article, and look forward to how these findings evolve in future studies.

On 2024-10-23 00:19:08, user CDSL JHSPH wrote:

I really enjoyed learning about this new Bento Lab because it opens the doors for so many new positive things. I think the paper is written in a way that people will understand the differences between BL and TL and agree that there are so many advantages to the BL. I think your strongest part is how you explain your data from the figures since you talk about the how and identify how to understand the figures and tables. I would say for figure one; I was wondering if maybe also using bar graphs since it shows a clearer way of differentiation like Figure 2. I also think instead of tables, more figures will help the public understand their pros and cons better visually. I also think that the sludge's low read count should be addressed more. While it is mentioned that the Flongle's limitations might have affected the detection of less abundant entities, why do you think happened to that sample, and how could you express that to the audience? I think you could strengthen their argument by referencing specific species, ARGs, or plasmid types they suspect might have been missed due to the lower read count. To end this, I think the authors did a great job explaining the differences, and my only concerns were adding figures and explaining the sludge sample.

On 2024-10-19 18:05:46, user CDSL wrote:

This article is a good demonstration of the potential of portable DNA sequencing technology for rapid detection of pathogens and antimicrobial resistance in the field, especially for public health emergencies in low-resource or remote areas, and the results section, in particular, is very detailed. However, I feel that there are some deficiencies in the discussion part. It is mentioned in both the introduction and the results that the differences are evaluated from five aspects, but I do not seem to see obvious discussion about DNA yield and purity in the discussion part. Also, have you considered separating the restrictions into a separate section? That may help the reader to read in a more organized way.

On 2024-10-22 20:43:09, user Thomas Sorger wrote:

Erratum: The tree in Panel A of Figure 9 is incorrect. <br /> It will be corrected in a forthcoming revision.

On 2024-09-10 18:32:52, user Thomas Sorger wrote:

Please note that the date of the second reference (2003) is incorrect. The correct date is 1983:<br /> 2. Armstrong E. Relative brain size and metabolism in mammals. Science 220: 1302–1304 (1983).<br /> Tom Sorger

On 2024-10-22 19:27:34, user Andy Flies wrote:

This chapter is now published.<br /> Slyp, B., Darby, J.M., Flies, A.S. (2024). Conversion of Mouse-Derived Hybridomas to Tasmanian Devil Recombinant IgG Antibodies. In: Good-Jacobson, K.L. (eds) Memory B-Cells. Methods in Molecular Biology, vol 2826. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3950-4_17

On 2024-10-22 19:01:27, user CDSL JHSPH wrote:

I believe this is an excellent study and paper, with valuable contributions to the field. It was very well-written, and the methodology very clearly described and reproducible. Given the global clinical significance of malaria, the biological implications of this study can be used as a basis for further studies into the biology of circadian rhythms in malaria transmission, as well as clinical translation for efficient prevention and control strategies and more effective treatments.

Similar to what previous reviewers have noted, I noticed some inconsistencies between some of the figures and the apparently corresponding results in the text, especially as it relates to the percentages of the transcriptome displaying cyclic expression profiles, for both the mosquito salivary glands and sporozoites. The figures and/or results should therefore be corrected accordingly.

A possible limitation to the study, which I don't believe was addressed in the paper, was that different mosquito (Anopheles stephensi vs Anopheles gambiae) and parasite (Plasmodium berghei versus Plasmodium falciparum, vivax, ovale, malariae, or knowlesi) species from the ones which are primarily known to cause malaria in humans, were used for analysis in this study. This may limit direct clinical translation of these results for malaria in humans. There should be a statement addressing this in the discussion. Furthermore, the amount of gene homology between the species used in the study and the species causing disease in humans should be stated for reference. If there were any reasons it was not possible or necessary to use the species known to be most associated with disease in humans, this should be stated.

On 2024-10-22 01:49:09, user CDSL JHSPH wrote:

I thoroughly enjoyed reading your paper, and I believe this study brings valuable new insights into how circadian rhythms influence malaria transmission. This information will undoubtedly impact future strategies for combating malaria, but also other vector-borne diseases. However, I noticed some inconsistencies between the results and the corresponding figures. I might be wrong and misread it but I wanted to bring it to your attention and clarify it on my side as well. In certain sections, figures were cited but did not seem to align with the content discussed. I can mention for example:<br /> - “We found that 27-49% of salivary gland genes displayed a cyclic expression profile, (Fig. 1B-E) representing 5-10x more than what has been previously reported for the mosquito’s head or body”. For C-E, I don’t see how this is actually depicted. Could you please explain; even the next sentence as well I don’t understand how C-E are supposed to back the arguments you are trying to make <br /> - Some figures like 1G and 1H are mentioned in the legend of your figures and in the text as well but are not shown in the figures, why?<br /> Those are not the only inconsistencies I noticed, but before proceeding on citing them, I want to make sure that I am not misreading and not understanding.

If my assumptions are true, clarifying them would greatly improve the paper’s clarity and presentation of your significant findings. I am looking forward to reading your answer.

Best regards,<br /> Critical Dissection Class 2024/2025

On 2024-10-19 02:11:16, user Yak Nak wrote:

The manuscript provides an exciting and valuable look into how circadian rhythms influence malaria transmission by aligning mosquito feeding behavior and parasite activity. The use of RNA-sequencing to uncover rhythmic gene expression in mosquito salivary glands is a significant strength and offers important new insights into the mechanisms behind malaria transmission. The figures are clear and effectively illustrate how these rhythms correlate with mosquito feeding efficiency and parasite infection capabilities, though the figure legends could benefit from more detailed explanations, especially for readers unfamiliar with gene expression data. The introduction is solid but could be improved by providing a more detailed discussion of previous research on circadian rhythms in malaria parasites to better frame the novelty of this study. The discussion section does a good job connecting the findings to broader vector-borne diseases like Zika and dengue, but it would be even stronger with specific examples of how these results could inform practical malaria control strategies, such as optimizing the timing of interventions based on mosquito feeding times. Overall, this is a well-conducted study with important findings, and a few revisions could further enhance its clarity and impact. Two follow-up questions:

1. Could the authors clarify why specific time intervals (every 4 hours) were chosen for RNA-sequencing, and would more frequent sampling provide additional insights?
2. Also, how might environmental factors such as temperature or humidity influence these circadian rhythms, and could this affect transmission in different regions?

On 2024-10-18 18:57:00, user CDSL JHSPH wrote:

I really like this article. Thank you for sharing your article. Malaria remains a major global health challenge, and understanding the biological rhythms of mosquito vectors and malarial parasites is crucial for improving control strategies. However, previous studies have only revealed that mosquitoes have daily rhythmic behaviors. You provided new insights. First, about half of the genes in the mosquito salivary gland transcriptome show 24-hour rhythmic expression, and second, the gene expression of sporozoites in the salivary glands also shows circadian rhythms (parasite movement and infection ability). In addition, you mentioned in the discussion that the parasites and mosquitoes have evolved in coordination with the circadian rhythms, and jointly affect host infection by regulating parasite movement and mosquito blood feeding time. This is very interesting, and your findings provide a new perspective for understanding the temporal regulation mechanism of malaria transmission. At the same time, you mentioned optimizing the insecticide spraying time strategy according to the peak period of mosquito activity. In addition, your findings may also have reference effects on other diseases.

But I have some questions. First, it is good that you use 12h dark and 12h light to simulate day and night. But in the wild, the temperature and humidity vary between day and night. Perhaps the natural day-night cycle could be better simulated by changing temperature and humidity. Secondly, you mentioned that some observations may apply to uninfected mosquitoes, but you did not specifically discuss the potential differences in rhythms and behaviors between uninfected and infected mosquitoes.

Finally, thank you again for your contribution and new ideas.

On 2024-10-21 14:16:12, user MJ Muzzatti wrote:

This article is now published in the Journal of Economic Entomology http://dx.doi.org/10.1093/jee/toae208

On 2024-10-21 14:15:22, user Julian C wrote:

Accepted for publication in Journal of Proteome Research (comment added by the corresponding author).

On 2024-10-20 08:57:36, user Alireza Mani wrote:

This paper is now published at https://doi.org/10.1016/j.bbrc.2024.150854

On 2024-10-20 07:36:15, user XKM wrote:

This study has now been published in PLoS Biology.<br /> https://doi.org/10.1371/journal.pbio.3002615

On 2024-10-19 19:13:36, user sam wrote:

It would be good to see how altered the phosphoproteome is after FACS. Thats a long time for the cells and a lot can change in phosphopatterns in a matter of seconds .

On 2024-10-19 18:51:50, user Alessio Ciulli wrote:

This comment is to advise readers that our preprint has been published in Sci. Adv. 10, eado6492 (2024). https://doi.org/10.1126/sciadv.ado6492

On 2024-10-19 18:49:33, user Alessio Ciulli wrote:

This comment is to advise readers that our preprint has been published in Nat Commun 15, 8885 (2024). https://doi.org/10.1038/s41467-024-52871-9

On 2024-10-19 00:19:59, user CDSL JHSPH wrote:

This article is rigorous. I like the comparison between phages in gut and that in the sea water. And the analysis of phages in hCom2 mice is well designed. Yet I think the equations and the explanations in the result and the method part are not very readable. I’m confused about the meaning of lysogen-host ratio and relative coverage, the role of dilution rate and so on. Plus, figure 2B is not very informative and by just looking at it, I cannot have a clue of what you were doing to get the range of induction rate. But overall your results support your conclusions firmly and clearly.

On 2024-10-18 13:39:11, user Oscar wrote:

Dear James and co-authors,

I trust you are already working on submitting this manuscript to a reputable journal. As one of the researchers with the most publications on CAV1 in Ewing Sarcoma (Oscar M. Tirado), I would like to offer some comments and suggestions for your consideration:

It might be beneficial to mention that CAV1 is a demonstrated direct target of EWS::FLI1, as this could add further context to your findings.

Regarding the presence of caveolae in Ewing Sarcoma cells, I assume you've reviewed the manuscript (DOI: 10.1016/j.canlet.2016.11.020). In most of the cells studied, including TC71, the levels of Cavin-1, as well as caveolae, are extremely low. As such, the majority of CAV1’s functions in EwS are likely independent of caveolae, which aligns with the results you’ve presented.

I noticed the focus on the AKT pathway in your work. Have you considered discussing the MAPK pathway as well? It is also affected and likely plays a significant role in the processes you're describing.

In Figure 4, CAV1 emerges as a potential driver, along with EphA2. I’m sure you are familiar with this study as well (DOI: 10.1002/ijc.31405), which may provide further insights.

I agree with your assessment that CAV1 plays a key role in the progression of EwS, and your findings align with many of the conclusions we've drawn in our own research. I would be happy to assist further, either through this platform or via personal contact, as we've known each other for many years from various EwS meetings.

Best regards,<br /> Oscar M. Tirado

On 2024-10-18 01:51:53, user Tushar R. wrote:

Summary

Here, the authors sought to apply cryo-EM guided metainterference-based MD simulations to find modeling inaccuracies of flexible helical regions linked to their artificial representation by a single-structure model. They first applied their approach to the group II intron ribozyme from Thermosynechococcus elongatus, one of a few cryo-EM structures available in the PDB that featured mostly RNA and was obtained using a single-structure refinement procedure. They confirmed that remodeling with their approach mostly affected flexible regions at the solvent-exposed stem loops that were not phylogenetically conserved. They found that functional domains that were well-ordered, phylogenetically conserved, and were clearly represented by the cryo-EM density required no remodeling. Extending this analysis through other PDB structures revealed that poor modeling at flexible helical regions was broadly applicable to all RNA-containing cryo-EM-derived structures in the 2.5 - 4 Å resolution range.

Major Points:

It is difficult to keep track of the different parameters applied for each simulation. These are scattered in the text. It would be very helpful for the reader to understand these if the authors could make a table of all the MD simulations with specifications on helices restrained, approach, simulation time, force fields present, equilibration time etc.

Clarification regarding the helical restraints and the simulation time for the initial production run would be helpful—i.e. Why was a simulation time of 2.5 ns chosen, and would this be long enough for your purposes considering the relatively high complexity of the system?

Were restraints applied to the 3 helices (b,d,i) that unfolded in subsequent simulations or were they only applied to the other 6 helices?

In the section “Base Pairing Analysis of the Protein Data Bank,” it is mentioned that the analysis likely over-estimates the problematic modeling of helices in cryo-EM derived RNA structures. Given that 15 Å is larger than the expected 8-11 Å (Pietal, M. J., 2012) distance for N1/N9 distance, would this not underestimate the number of problematic helices?

A detailed analysis of H-bonding within the 6 helices would be useful to get a mechanistic understanding of why the 3 helices (b,d,i) unfold and the other 6 don't—for example, it might be helpful to provide a comparison with the model generated from a single structure approach to know if the same H-bonds exist in it or not as the model forwarded by this paper.

ERMSD approach: we know relatively less about non-Watson Crick base pairing apart from Hoogsteen and G-U wobble base pairs since so much depends on the context (specific region of the structure, ion concentration, pH etc.) (we’re still thinking about how to phrase this point).<br /> Balance between experimental measurements and MD sampled conformational states: lack of experimental validation when remodeling mismodeled regions for RNA - how do we know when specific interactions are too ideal?

How is variability between forcefield parameters for divalent metal ions or other ions addressed, especially considering that these are required for proper folding of RNA?

Dispersive interactions play a huge role in RNA integrity: how confident can we be about these parameters when comparing various nucleic acid specific force fields? Can these differences lead to unfolding of the 3 properly base paired helices?

With reference to the line on page 4: “The trajectories obtained with metainference simulations were then analyzed by back-calculating the corresponding averaged density map and comparing it to the experimental one”, was the solvent density included in the back-calculated density?

What proportion of the CC_mask arises just due to fitting of rigid part of the structure and how much of it arises due to the improved fitting of the flexible regions from the 9 loops? Would it be possible to separate these contributions?

Minor Points:

Would it be possible to show Fig 2C as a violin or box plot rather than a bar plot? This would allow visualization of the distribution of CC_mask for different conditions with clusters representing conformational states that may agree with the experimental data.

Is local resolution considered while plotting the RMSF? For this purpose, it would be useful to have a local resolution estimation for the map to help the reader understand whether the unfolding of helices occurs simply because the specific regions were not well defined or if these regions are inherently flexible.

How effective is DeepFoldRNA in filling gaps in structure as it is normally known for sequence based structure prediction—specifically regarding the modeling of the 38-nucleotide gap?

For benchmarking purposes, applying this approach to other RNA structures or providing additional validation against independent experimental data (e.g., SAXS or chemical shifts from short RNA motifs) could further strengthen the conclusions.

Aside from the pre-1r (6ME0) and pre-2r (6MEC) states, Haack et al. (2019) found additional 3D classes that indicated disordered density in conserved regions, which they did not investigate further. They also suggested that the post-2r complex may be captured in one of the 3D classes that yielded a low-resolution 3D reconstruction. Is it possible that one of the structures found by the metainference method could have sampled one of the structures that were not investigated further or the post-2r complex?

Why is there a sudden fall in the number of helices formed in 32 replicas (Fig 2E)?

Sentence on page 4: “As a result, 32 replicas appeared to be the best compromise between agreement with experiment and computational cost.” Has it been ensured that this is not a result of overfitting? One of the ways to do this can be to use half-map cross validation i.e. to check if the refined ensemble fits both the half maps equally.

Have the authors tried to change the weight for 1 μs reference simulation to improve the CC_mask?

Grammar/spelling mistakes <br /> Results, Test System and Preparation, 1st Paragraph: Change “run” to “ran” to retain past test throughout the paragraph in the sentence beginning “We then run 2.5 ns-long molecular dynamics (MD) simulations in explicit solvent…”<br /> Results, Ensemble Refinement, 1st Paragraph: “Within” is misspelled as “whitin” in the sentence beginning “Conversely, when restraining their helicity, whitin this single-replica refinement approach…”<br /> Results, Ensemble Refinement, 1st Paragraph: “Specifically, all the simulations attempted with a lower number of replicas were crashing reporting missing convergence in enforcing bond constraints, which indicates that the experimental and helical restraints were mutually incompatible.”

• Tushar Raskar, Sonya Lee, James Fraser

On 2024-10-17 18:28:16, user Christine Pye wrote:

This article has now been published in the Journal of Small Animal Practice https://doi.org/10.1111/jsap.13688

On 2024-10-17 14:50:43, user Elena Grassi wrote:

The revised version is now out:<br /> https://www.nature.com/articles/s41467-024-51909-2 <br /> https://doi.org/10.1038/s41467-024-51909-2

On 2024-10-17 14:12:50, user Sahil Sahni wrote:

Article published: https://www.nature.com/articles/s41467-024-52555-4

On 2024-10-17 14:05:57, user Christina Warinner wrote:

It is very nice to see the authors statistically confirm on a large number of samples that oral bacteria contribute to the thanatomicrobiome of archaeological teeth. They may want to note that this pattern has been previously observed and reported twice before: <br /> Mann AE, Sabin S, Ziesemer KA, Vågene Å, Schroeder H, Ozga A, Sankaranarayanan K, Hofman CA, Fellows-Yates J, Salazar Garcia D, Frohlich B, Aldenderfer M, Hoogland M, Read C, Krause J, Hofman C, Bos K, Warinner C. (2018) Differential preservation of endogenous human and microbial DNA in dental calculus and dentin. Scientific Reports 8:9822. DOI: 10.1038/s41598-018-28091-9<br /> Vågene AJ, Campana MG, Robles García N, Warinner C, Spyrou MA, Andrades Valtueña A, Huson D, Tuross N, Herbig A, Bos KI, Krause J. (2018) Salmonella enterica genomes recovered from victims of a major 16th century epidemic in Mexico. Nature Ecology and Evolution 1-9. DOI:10.1038/s41559-017-0446-6.

On 2024-10-16 00:18:50, user Hamidreza Karbalaeiheidari wrote:

Hello,<br /> My preprint has now been published in ACS Synthetic Biology Journal.<br /> https://doi.org/10.1021/acssynbio.4c00437

Thanks,<br /> Hamid Reza Karbalaei-Heidari

On 2024-10-15 20:51:50, user Alexa Jennings wrote:

Overall, this paper is significant to developmental biology. Thank you for your contribution and dedication to the field. As I read through your paper, I have a few comments I would like to offer.

Fig1: While I was intrigued by the several stainings and experiments carried out by your team, it would be helpful to understand your rationale for choosing the gestational ages of interest. Additionally, since this is a comparative review, it would be useful to visualize the corresponding gestational ages in mice. I also noticed you cited these phases in mice, but it would be much more memorable if there was a clear schematic comparing the two species' gestational ages to go along with the rest of the figure as well.

Fig2: I noticed a one-way ANOVA was carried out for several of the corresponding data. However, ANOVA's are designed to compare means of groups with samples >3. In the supplemental figures, several groups were either missing data, or had samples <3. Asterisks marking significance would also be useful to visualize significance on the graphs.

Fig3&4: Quantification of fluorescence would be useful to make statistical comparisons. As is, the evidence is too correlative to make any conclusions.

Fig5: For clarity, place comparative groups in alphabetical order (ex: 5a and 5c should be 5a and 5b). Additionally, 5k-q show dim fluorescence, and should be quantified to make accurate statistical comparisons. 5f should be compared via a multivariate-ANOVA.

Final remarks: Further manipulative, molecular experiments are required to make conclusions on similarity. A stronger argument could be made if additional evidence were included. Additionally, sex and cultural differences likely will cause variation among your data. Stating the sex of the samples can help make more accurate comparisons between data. Comparative experiments between sexes and across ethnicities in humans may also be useful.

On 2024-10-15 19:33:41, user Abdul Muntakim Rafi wrote:

the paper is now published at Nature Biotechnology<br /> https://www.nature.com/articles/s41587-024-02414-w

On 2024-10-15 18:37:28, user Jacek Majewski wrote:

This article has been published in Cell Reports:

Chromatin dysregulation associated with NSD1 mutation in head and neck squamous cell carcinoma

Cell Rep. 2021 Feb 23;34(8):108769. doi: 10.1016/j.celrep.2021.108769

On 2024-10-15 07:39:58, user turnersd wrote:

The paper states:

The source code is publicly available at https://github.com/ginnm/PROEDIT .

But the provided repository https://github.com/ginnm/PROEDIT is empty (README and license files only).

On 2024-10-14 20:52:21, user Patrick Varga-Weisz wrote:

We made basically the same observation in 2018 with crotonate and published in DOI: 10.1038/s41467-017-02651-5 | http://www.nature.com/naturecommunications <br /> (Fellows et al, Nature Coms 2018) see Figure 7d.<br /> Kind regards,<br /> Patrick Varga-Weisz

On 2024-10-14 06:17:39, user Ian Ross wrote:

As of Aug 23 (2024) the published version of this article is available at Plant Physiology Advance Articles https://academic.oup.com/plphys/advance-article/doi/10.1093/plphys/kiae427/7740471

On 2024-10-14 05:52:34, user PanosMoschou wrote:

Information missing from the Fig. legend of Fig. S1 panel I, in this version (data not present in the published version of the article in Plos Biology) represents a reconstructed image that fully reproduces the original dataset (in particular, the mating controls DDO and QDO experiments). If you wish to have access to the original dataset, don't hesitate to get in touch with the corresponding author. Furthermore, the group will soon post the original dataset in Zenodo.

On 2024-10-11 22:57:08, user aquape wrote:

Recent information, google<br /> "GondwanaTalks Verhaegen English"<br /> + see <br /> - M.Vaneechoutte cs 2024 Nat.Anthrop.2,10007 open access https://www.sciepublish.com/article/pii/94 <br /> "Have we been barking up the wrong ancestral tree? Australopithecines are probably not our ancestors” <br /> - id.2024 Nat.Anthrop.2,10008 https://www.sciepublish.com/article/pii/187 <br /> "Reply to Sarmiento E. “Australopithecine Taxonomy and Phylogeny and the Savanna Hypothesis…”"<br /> https://www.gondwanatalks.com/l/the-waterside-hypothesis-wading-led-to-upright-walking-in-early-humans/

On 2024-10-11 20:15:33, user Velizar Shivarov wrote:

May be you can refer to a recent publication: https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2024.1427810/full

On 2024-10-11 09:50:17, user bao zhang wrote:

I tried to run the script you uploaded on github, and the model.py script reported an error, "NameError: name 'p_SdC' is not defined", how is this p_SdC defined? I am looking forward to your reply, thank you.

On 2024-10-11 08:52:29, user Esther Broset wrote:

This article is highly informative, and the ionizable lipids show great promise. I was particularly impressed with the targeting strategy and the high expression levels observed in the lungs.

On 2024-10-11 08:50:00, user George Brown wrote:

Link to published article<br /> https://link.springer.com/protocol/10.1007/978-1-0716-3906-1_1

On 2024-10-10 22:17:31, user Delphine Destoumieux-Garzon wrote:

Now published in Science Advances:<br /> Gawra J, Valdivieso A, Roux F, Laporte M, de Lorgeril J, Gueguen Y, Saccas M, Escoubas JM, Montagnani C, Destoumieux-Garzόn D, Lagarde F, Leroy MA, Haffner P, Petton B, Cosseau C, Morga B, Dégremont L, Mitta G, Grunau C, Vidal-Dupiol J. Epigenetic variations are more substantial than genetic variations in rapid adaptation of oyster to Pacific oyster mortality syndrome. Sci Adv. 2023 Sep 8;9(36):eadh8990. doi: 10.1126/sciadv.adh8990.

On 2024-10-10 22:14:48, user Delphine Destoumieux-Garzon wrote:

Now published in PNAS: <br /> Oyanedel D, Lagorce A, Bruto M, Haffner P, Morot A, Labreuche Y, Dorant Y, de La Forest Divonne S, Delavat F, Inguimbert N, Montagnani C, Morga B, Toulza E, Chaparro C, Escoubas JM, Gueguen Y, Vidal-Dupiol J, de Lorgeril J, Petton B, Degremont L, Tourbiez D, Pimparé LL, Leroy M, Romatif O, Pouzadoux J, Mitta G, Le Roux F, Charrière GM, Travers MA, Destoumieux-Garzón D. Cooperation and cheating orchestrate Vibrio assemblages and polymicrobial synergy in oysters infected with OsHV-1 virus. Proc Natl Acad Sci U S A. 2023 Oct 3;120(40):e2305195120. doi: 10.1073/pnas.2305195120.

On 2024-10-10 07:25:37, user Flo Débarre wrote:

The points raised were dealt with in https://doi.org/10.1093/ve/vead077 . The paper was published after the preprint, but is cited and discussed in the peer-reviewed version of the work ( https://doi.org/10.1016/j.cell.2024.08.010 ).

As we do not believe that the comments made in this comment section have been in good faith, we will no longer reply.

On 2024-09-30 08:04:04, user Ema Nymton wrote:

Zach's reply does not really address the points in my previous comment. I note that it also does not address the points raised in Bloom 2024.

It only tangentially addresses 2 points in my post, both of which are apparently misconstrued.<br /> The first point that was addressed was about the p-values- however this response did not acurately portray the argument.<br /> My post never said it was misleading, but rather that the sampling bias towards the stalls would concentrate the highest p-values near the stalls, even more so than a simple relative-risk heatmap would, making the result of elevated p-values near the stalls expected whether or not the stalls were the origin of the outbreak.<br /> Compare: https://i.imgur.com/iDD6D93.jpeg to https://i.imgur.com/2YmYlZp.png

The second point "an argument that implicitly assumes all expectations for all coronaviruses are identical regardless of their hosts and modes of transmission,"<br /> No, it does not assume they are "identical", but rather that there are general similarities. I am asking for an explanation why the distribution is so different.<br /> Neither mode of transmission nor host explain this - nor does date of sampling.<br /> While not explicitly stated, there is a substantial focus on Raccoon dogs.<br /> You show the distribution of a virus with the same putative host (Raccoon dogs) and same transmission methods (respiratory). The distributions are strikingly different.<br /> Compare https://i.imgur.com/iDD6D93.jpeg to https://i.imgur.com/OFXpCIf.jpeg

This paper argued (in preprint), and it still argues )in final published form) that the 12 Jan RNA had more time to decay. Considering the market was closed at the same time, why did the SARS-CoV-2 reads decay to such lower values than other CoVs in samples from the same dates, and within the same samples, as shown by Bloom?<br /> See here: https://i.imgur.com/kIOTTYq.jpeg <br /> -even when stratifying by collection date, clear negative correlations still come up.

Overall, I find that a lot of speculation is offered, but it comes accross as excuses for the data not actually showing that the virus originated from the wildlife stalls.

I'll also raise additional critiques now:<br /> 1) The heat maps do not colocalize with the potential hosts:<br /> SARS-CoV-2 (RR p-values) and Raccoon dog reads shown here: https://i.imgur.com/AyAiag9.jpeg

In the paper, instead the focus is on the south-west portion of the market.<br /> As I see it, there are two possibilities:<br /> i) The analysis DOES have sufficient resolution: in which case the resulting heat map not co-localizing with the wildlife stalls would exonerate them<br /> ii) The analysis DOES NOT have sufficient resolution: in which case it must be noted that the eastern and northern parts of the market were very poorly sampled. In this case, finding more positive samples where more samples were taken does not allow any conclusions to be drawn.

2) The analysis of the SNPs of the Raccoon dogs clearly aligns with the the SNPs of locally caught wildlife (the C14859A + A15304G genome from Hubei, and the C14372T, C15102A, C15252A, T15306C genome from Hubei). Despite the evidence clearly pointing to locally caught raccoon dogs, speculation is offered (again) that maybe racoon dogs with these SNPs are found further south.<br /> Notably, Hubei (the local province) is not a plausible origin of the progenitor. Pekar seemingly agrees: https://www.biorxiv.org/content/10.1101/2023.07.12.548617v1

In essence, this paper shows:<br /> i) 3 lineage B sequences 1 low quality lineage A sequence have a (very very wide) tMRCA confidence interval that overlaps with a tMRCA calculated from many more A and B lineage genomes: hardly anything surprising or something that conclusions can be drawn from

ii) Within the heavily sampled area of the market, the areas of elevated relative risk don't actually overlap with the locations of the wildlife stalls<br /> -There is just one stall on the periphery of an area of elevated risk, the one closest to the entrance/mah jong rooms/bathrooms, which is where the higest elevated risk is.

iii) The raccoon dog genomes from that stall suggest that they are locally caught, and do not come from areas with potential reservoirs of SARS-CoV-2's precursors

On balance, the sum of this evidence seems to suggest that the HSM wildlife stalls in general, and raccoon dogs specifically, were not the origin of this virus.

Much speculation and many excuses are offered, but it must be reiterated that these excuses and speculation, not evidence.

The correlation analysis of Bloom can point right to the hosts of other viruses. The heat maps produced for this paper can point right to the hosts of other viruses. SARS-CoV-2 continuously fails to produce evidence and associations that other animal viruses at the market produced.

The way these findings are presented in the paper and to the general public through the media seems to be at odds with what the data actually shows.

On 2024-09-21 04:17:05, user Zach Hensel wrote:

I have a short response to the two comments on this preprint, which, of course, we took into consideration while revising the manuscript, which is now published following peer review here: https://doi.org/10.1016/j.cell.2024.08.010

One commenter, David Bahry, has taken to social media to call myself and co-authors "frauds" who are "trying to pretend" and made some vulgar comments that can't be repeated in this comments section because, he says, we "ignored" his comments. This is not true, so it's appropriate to respond here.

Both commenters note that low p-values for relative risk maps (Figure 2 and Figure 4) require sufficient sampling density to obtain a low p-value. Of course, this is correct. Both commenters argue that this is misleading. I disagree. No co-author, peer reviewer, or editor involved was misled. News articles on the paper thus far have almost all portrayed the data and analysis accurately. We do not argue in the paper that SARS-CoV-2 could not have been found in places that were not sampled; I'm unaware of anyone making this argument based on our paper; it's not misleading if no one is misled.

On top of this, the proportion of positive samples in each location is displayed along with the relative risk p-value. The n=1/1 sample that Bahry complains "shows little heat" is clearly indicated as a sampled location with high positivity in Fig 2A. And the underlying data is available in supplementary figures and tables. It's demonstratively not misleading.

The other arguments the two commenters make consist of (1) arguing against a different paper published years ago, (2) a demand for a citation of an irrelevant paper, (3) arguing for an alternative analysis method without demonstrating that it would have more statistical power, (4) an argument that implicitly assumes all expectations for all coronaviruses are identical regardless of their hosts and modes of transmission, and lastly (5) a typo in a citation. We addressed the last one -- showing that, in fact, we didn't ignore these comments.

On 2024-10-09 16:05:10, user Rosângela Maria Rodrigues Barb wrote:

Very interesting. I wonder if it works with urban pests (mosquitoes)

On 2024-10-05 16:00:52, user Walter S Leal wrote:

We are delighted to share this preprint on BioRxiv. It is the fruit of a couple of years of research collaboration with the citrus growers of the State of Sao Pauli. Fundecitrus has a unique model – they fund research (like most commodity groups do) and have PhD-level research scientists who engage in research activities. Please look at the number of replicates (as well as the raw data in SI) to further appreciate the work's depth. Walter Leal

On 2024-10-08 19:42:41, user Reena Sharma wrote:

Plant Microbe interaction is the epitome of resilience and symbiosis.

On 2024-10-08 19:41:38, user Reena Sharma wrote:

The key message of the study is that heavy metals like cadmium (Cd) and mercury (Hg) pose significant threats to plant health, but legumes, including Medicago truncatula, exhibit genetic variation in their ability to tolerate and accumulate these toxic metals. By conducting a transcriptomic analysis of plants with varying levels of tolerance to Cd and Hg, the study identified tissue-specific, genotype-specific, and metal-specific gene expression patterns.

Notably, plants inoculated with mercury-tolerant rhizobia strains carrying a mercury reductase (Mer) operon experienced less reduction in nodule number, plant biomass, and iron distribution under Hg stress. This suggests that Hg-tolerant rhizobia can mitigate Hg toxicity in plants, enhancing resilience in contaminated environments. These findings highlight the potential to optimize legume-rhizobia interactions for improving plant tolerance to heavy metals and reducing heavy metal transport to edible parts of the plant, which is critical for food safety.

On 2024-10-08 19:30:54, user Bogdan Olenyuk wrote:

This article is now published in Nature Nanotechnology. doi: 10.1038/s41565-024-01786-z

On 2024-10-08 18:44:34, user Paul wrote:

Please note that the preprint's result is subsumed by that in the published AMB paper. In particular, the AMB paper has modified Algorithm 1 so that it computes S in a right-to-left scan, rather than left-to-right. In this way, S does not need to be stored in memory, eliminating the quadratic space requirement. The space usage of the algorithm is now O(\ell \log \ell).

On 2024-10-08 09:43:09, user Bruno Cenni wrote:

Very nice and comprehensive dataset and overview across almost all BTKi. A note with regards to the data in Table 2 and Figure 4. For remibrutinib a BTK potency of 1.3 nM as “Kd or IC50” is listed. While the data is correctly referred to Angst et al 2020, this manuscript lists the IC50 for biochemical BTK enzyme inhibition. The same Angst et al 2020 publication also includes the Kd of remibrutinib for BTK (measured in the same assay all the others in the present manuscript) which was 0.63 nM. This is the value that should enter Table 2 and Figure 4.

On 2024-10-08 00:33:10, user Alexis Rohou wrote:

This comment is based on the version of the article published by Cell.

I have concerns about the validity of the cryoEM result presented in this paper.

The authors claim they obtained “a structure of the NINJ1 segment at 4.3 Å”. At this resolution, the following features should be clearly resolved in a cryoEM map of a protein containing alpha helices:<br /> • The pitch of alpha helices (~5.5 Å)<br /> • Large amino acid side residue side chains (e.g. Phe or Tyr, of which there are several in Ninj1)<br /> Neither of these features are demonstrated in the figures prepared by the authors.

Figure panels S2D and E are consistent with a resolution of ~ 8-10 Å, where alpha helices are resolved as tubular features. No regular indentation or ridging corresponding to the helical pitch is apparent in the figures, or upon visual inspection of the deposited map ( https://www.ebi.ac.uk/emdb/EMD-42301) . No features corresponding to Phe or Tyr side chains are visible. At the resolution claimed by the authors, features corresponding to the side chains of residues Phe100, Phe117, Phe127 and Phe135, which are all located in alpha-helical segments (not in loops), would be expected to be resolved in the map, but they are not.

In my view, based on this map inspection the authors should not have made this resolution claim.

To support their resolution claim, the authors present Fourier shell correlation (FSC) curves from the cryoSPARC software in Figure S2C.

While the FSC curve shown by the authors does cross the 0.143 threshold at ~4.3 Å, the FSC curve exhibits pathologies, which should have alerted the authors to the possibility that the 4.3 Å resolution estimate may be unreliable and that the map should be interpreted with caution. Most notably the curve starts dropping off at around 15 Å, indicating that the signal-to-noise ratio in the map is significantly deteriorated at resolution of ~15 Å and beyond.

After completion of 3D refinement, cryoSPARC also outputs a second FSC figure, which includes an additional curve (“Corrected”), which accounts for effects of masking on resolution estimation by the FSC. It is unfortunate that the authors didn’t include this in their manuscript.

The most likely explanation for this pathological FSC resolution estimate and its mismatch with the features resolved in the map is that the 3D refinement failed (due to high noise, or preferred orientation, or other pathologies in the dataset or in the refinement parameters), leading to overfitting to a local minimum in the scoring function.

Indeed, the validation report for the deposited map and PDB (EMDB: 42301; PDB: 8UIP; https://files.rcsb.org/pub/pdb/validation_reports/ui/8uip/8uip_full_validation.pdf ) contains evidence of overfitting during refinement. The orthogonal projections of the raw map (Section 6.1.2 of the validation report) show overfitting artefacts, as do the orthogonal standard deviation projections in false color of the raw map (Section 6.4.2).

Such overfitting causes artifically inflated FSC values, which may help explain why the FSC-based estimate of resolution was wrong in this case.

Given a map of this quality, it’s unclear how the authors built an atomic model of Ninj1. In that respect, the publication’s methods section is not detailed enough. Table S2 indicates that the authors started from a computational structure prediction from AlphaFold2. Given the lack of any features in the map to help place any residue side chains, I assume the location of key residues mentioned in the paper originated from this computational prediction rather than from the cryoEM result itself.

The lack of support for the modeled atomic coordinates from cryoEM is also made evident in the EMDB/PDB validation report by the unusually poor Q and atom inclusion scores for a map/model of the claimed resolution. A Q-score of 0.04 is unusually low and shows very little map-to-model fit.

These metrics, together with a visual inspection of the map and model as deposited, suggests that a significant fraction of the sequence was built outside the map and that the cryoEM result does not support the authors’ atomic model beyond the general shape and relative orientation of parts of the alpha-helical segments. The positioning of individual amino acid residues is not directly specified by the cryoEM result and may have come mostly from the AlphaFold model the authors used as a starting point.

Note: the above observations should be not be taken as having any bearing on other key cryoEM-based observations in the paper, such as the curvatures of Ninj1 assemblies, which are supported by two-dimensional class averages of cryoEM images and not affected by overfitting in the later 3D refinement process.

On 2024-10-07 14:40:02, user BindCraft Enjoyer wrote:

I like the ‘Design-Until’ architecture of the BindCraft pipeline, but one thing I couldn’t find in the paper is any quantification of BindCraft’s in silico design success rate. In the Introduction, you note that one drawback of RFDiffusion/MPNN-based pipelines is the need to screen thousands to tens of thousands of designs in silico before finding the 10-100 that pass the quality metrics and can be tested experimentally with good success rates. Does BindCraft also require screening of thousands to tens of thousands of designs, or is it more efficient in silico than an RFDiffusion pipeline? You mention in the paper that BindCraft outputs statistics from each design run, and that biasing away from alpha helical binders reduces the in silico design success rate; so it sounds like you have the statistics ready to hand, at least for the targets reported in the paper. I’d love to see these design success rates added to a table, either in the main paper or the SI.

Another thing I’d like to see is some quantification of the compute time and cost required to run the 4-step pipeline until 100 designs pass the in silico filters. I understand this cost scales with target/binder size and target difficulty, but I would imagine you have the data required to calculate these metrics at least for the design campaigns reported in the paper. I saw on Twitter that you’re working on a direct BindCraft / RFDiffusion pipeline comparison; I hope you’ll include the computational hardware and total CPU/GPU time for each side of that design campaign.

Great work!

On 2024-10-07 08:20:31, user Mike Pitcher wrote:

This article is now published with new title: "Double-negative B cells and DNASE1L3 colocalise with microbiota in gut-associated lymphoid tissue" in Nature Communications, 2024: https://www.nature.com/articles/s41467-024-48267-4

Michael Pitcher, co-author

On 2024-10-06 09:17:57, user Phil Jones wrote:

This article has now been published:<br /> https://www.nature.com/articles/s41588-024-01891-8

On 2024-10-06 09:16:37, user Phil Jones wrote:

This article has now been published as<br /> Self-sustaining long-term 3D epithelioid cultures reveal drivers of clonal expansion in esophageal epithelium,<br /> https://www.nature.com/articles/s41588-024-01875-8

On 2024-10-06 03:14:31, user David Grahame wrote:

I cannot find the references for the Supplementary material

On 2024-10-06 00:56:48, user Rishav Mitra wrote:

What is the endogenous TDP43 concentration in neuronal cells and how does that change with aging?

Are there neuron-specific gene regulatory mechanisms like alternative splicing, microRNAs, transcription factors, etc. that control TDP43 expression and consequently the intra-condensate localization inside SGs and liquid-to-solid transition?

Can microscopy as performed here distinguish between rapid coalescence of distinct TDP43 and G3BP1+ condensates and intracondensate localization?

Is the phenotype titrable with concentrationof arsenite, i.e., more number of intracondensate TDP43 foci at higher [arsenite]?

Does demixed TDP43 recruit/co-condense with other SG-resident proteins?

How can we correlate age-associated oxidative damage with the length of arsenite exposure?

Does solid-like demixed puncta of TDP-43 having higher local concentrations and slower internal dynamics compared to more liquid-like droplets potentially impact fixing and antibody staining? Is Correlative light and electron microscopy a better approach here than conventional immunofluorescence?

On 2024-10-05 08:38:14, user Martin GIURFA wrote:

Great work, congratulations! <br /> You may be interested in having a look at the following works, which relate to your findings:

° Pheromones modulate reward responsiveness and non-associative learning in honey bees. Baracchi D, Devaud JM, d'Ettorre P, Giurfa M. Sci Rep. 2017 Aug 29;7(1):9875. doi: 10.1038/s41598-017-10113-7

° Pheromone components affect motivation and induce persistent modulation of associative learning and memory in honey bees. Baracchi D, Cabirol A, Devaud JM, Haase A, d'Ettorre P, Giurfa M. Commun Biol. 2020 Aug 17;3(1):447. doi: 10.1038/s42003-020-01183-x.

Good luck with the next steps!

On 2024-10-04 16:24:29, user Gregory Way wrote:

We read this paper as part of a journal club, and have decided to compile a collective review and publicly share it with the authors. This was inspired by the Arcadia Science Preprint Review Pizza Party Initiative, and this represents our fourth preprint review.

Ji et al. present a transformer-based foundation model, called Prophet, which stands for Predictor of Phenotypes. The authors train Prophet on a variety of data modalities including gene expression, cell viability, chemical structures, and cell morphology using publicly-available sources. The authors should be commended for using such vast and disparate resources for such an innovative approach. The task of Prophet is to predict assay endpoints, such as cell viability, and to learn a useful embedding space which can be mined to identify novel, and potentially impactful, relationships. Most often, the phenotype prediction is in the context of some form of perturbation. The authors present a variety of benchmarks comparing Prophet to other methods, and they present both in vitro and in vivo applications to demonstrate potential use-cases. The applications range from looking up untested compounds that are similar to clinically-relevant compounds and predicting zebrafish cell type proportions after gene knockout. Overall, Prophet is methodologically interesting and the applications demonstrate that the method may help generate hypotheses at a low cost. However, we have several major and minor concerns mostly to do with clarity, performance, and software.

Major concerns:

1. Unclear and inconsistent terminology and definitions.<br /> a. It is unclear exactly what the authors mean by “phenotype”. It seems that sometimes the term is being used interchangeably with prediction/output but other times it is being used to describe observable physical properties. Additionally, the authors refer to gene expression as a phenotype, and, while technically true, it could be confusing given the authors are also using cell morphology as a phenotype. Furthermore, it is unclear if the authors are describing the collection of genes in the gene expression vector, for example, as the phenotype, or, if it is just a single gene. This confusion is also related to our confusion about model outputs (whether the output is a single element or a vector representation; see below). Given the word is in the article’s title, it seems particularly important.<br /> b. The terminology of “experiments” is also unclear. The authors claim to use 4.7 million experiments, but does this refer to plates, conditions, samples, something else? How did the authors calculate this count?<br /> c. At times, it is unclear what format the input data are and at what level of processing. What are the different possibilities of input data and how might a user decide which to use? Can a user input multiple kinds of data? Did the authors apply any sort of post-processing or quality control?<br /> d. The output of Prophet is ambiguous. Does Prophet predict a single value per input (or different inputs), or, does it predict a cell state vector? The authors describe outputting a one-hot encoding. Does this refer to the output phenotype? What is this structure? The authors write: “we train Prophet to predict cell viability, compound IC50, Cell Painting morphology features, mRNA transcript abundance, and cell type proportion.” Does this mean Prophet will output all of these predictions if the data you have is only morphology features? Does a user have control over these decisions? Furthermore, the authors write "Prophet’s transfer learning capability is not limited to phenotypes seen during the pre-training stage. We did not pre-train Prophet on any morphological measurement, but Prophet fine-tuned on JUMP outperformed both the Prophet-individual model trained only on JUMP and the baseline (Fig. 2b).” What is being predicted from the JUMP data? Morphology feature profiles? Images? Drug class or MOA? This needs to be expanded upon to make these claims. Please clarify the output structure and how a user will interact with the output. Figure 1C does not make this clear.<br /> e. Critical methodological details are discussed without sufficient detail. For example, the data split and validation strategies were ambiguous. How were training, test, and validation splits handled? What partitioning methods, if any, were used? The three-fold cross-validation procedure also lacked clarity. Were all datasets used in cross-validation? How did individual data-set models training differ and influence the full model fine-tuning? What is the specific pseudobulking procedure for RNA?
2. Unclear justification for Prophet architecture decisions<br /> a. The authors present table S3, which provides hyperparameters. How are these justified? For example, the choice of GeLU over alternatives like ReLU or SiLU. Why use an embedding dimension of 512? Were alternative configurations explored? How would modifying individual architecture decisions impact performance?<br /> b. 20.1 M parameters is a fairly small transformer, will this model need to grow as more perturbations or data types are added? In other words, how long will this model be foundational until the next best model is released? <br /> c. Encoders sequentially relate inputs with a positional embedding. Does this architecture use a positional embedding in the encoder? What does the position represent?
3. Concerns about model comparisons, baselines, and performance<br /> a. The authors compare Prophet to much simpler machine learning models (random forest, MLP, linear regression), individual Prophet models (trained using only one modality), and a mean baseline representing the average value of that intervention. The authors write: “This approach follows the same strategy as current foundation models (19, 36, 37), which are pre-trained on large amounts of data and then fine-tuned for specific datasets using the pre-trained model as a backbone.” Why not use these current foundation models as benchmarks for Prophet? The authors should also consider comparing different transformer architectures and non-transformer models (e.g., state-space models) as well.<br /> b. Prophet’s performance is low. The highest R2 value is 0.27 with a low of -0.03 and many predictions that perform the same as the mean baseline. Given the low, variable performance, it is difficult to trust Prophet’s output, or, at best, understand which outputs may have incorrect predictions. The authors claim an R2 improvement as low as 0.04 represents a 13x increase in number of hits, but it is unclear how the authors calculate this value. The authors also claim Prophet reduces “the number of experiments needed for viability screens by at least 60x” What statistics are calculating this estimate? <br /> c. The ML model comparisons compared to baseline are incredibly low. Results in Figure 2B suggest that the mean is a better predictor than nearly 100% of ML-based predictions (mean baseline is better in 41/45 comparisons). Our guess is that something might be going wrong in the model training or evaluation procedures.<br /> d. It is confusing if there is only one “final” Prophet model or if there are multiple “final” Prophet models since each must be fine-tuned to single datasets. Why not fine-tune on all datasets? Figure 2A suggests that for each time you perform inference in a new data category (e.g., cell morphology vs. gene expression), fine-tuning is required? If so, this strategy will lead to variable predictions and perhaps unexpected results and this is not a foundational model, but, at best, a foundational architecture.<br /> e. The authors performed a critical analysis, in which they restricted the amount of training data by 50%, 30%, 20%, 10%, and 5%. The authors state: “We found a clear trend: the more treatments and cell states seen by Prophet, the higher the confidence in the predictions (Fig. 2g).” This is an expected result, however, it is unclear how Figure 2G supports this statement. What is an “axis holdout”? Standard deviation of R2 for which predictions? What do the different points represent? How does this show confidence?
4. Concerns about limited discussion of technical artifacts<br /> a. The authors mention technical variation in the limitations section, but this should be elaborated upon. How might Prophet’s performance be impacted by technical artifacts?<br /> b. Earlier in the manuscript, the authors write: “We universally decompose each experiment into a unique combination of three fundamental elements—the cellular state, the treatments being performed, and the intended phenotypic readout.” Signals from technical artifacts are likely a fourth fundamental element, or, at the least, there should be an experiment to test the impact of technical artifacts.
5. Concerns about publicly-available source code on Github<br /> a. We provide a full GitHub review following our manuscript review below.

Minor concerns<br /> - Pg. 3 ln 5, the authors write: “To train it, we collected 9 perturbational datasets to create the largest compendium of publicly available screening datasets to date:...” The language suggests that these datasets were all collected by this group, but the datasets are all publicly available. Also, the Figure S1 reference probably should point to Figure S2. JUMP is listed three times in Figure S2 (one for compound, one for genetic treatment, and then one for both) Why is it listed for both when it is already split into the two perturbation types? Also, the right subplot in S2B (the complexity trade-off) is a bit misleading. JUMP has far higher complexity than PRISM, for instance, but this graph would suggest otherwise. Perhaps the missing piece not described here is readout complexity?<br /> - Figure 1B is a bit difficult to understand. For example, the bullet points for the “intervention” box don’t seem like interventions? It seems like these should be listed elsewhere or the heading should be changed. How is a “gene sequence embedding” an intervention data transformation?<br /> - The authors should tamp down claims. For example, the authors write “We did not pre-train Prophet on any morphological measurement, but Prophet fine-tuned on JUMP outperformed both the Prophet-individual model trained only on JUMP and the baseline”. The performance is only marginally improved (0.01). The authors retrained the zebrafish Prophet model, but it would be helpful to see performance for the original prophet model applied.<br /> - What is a pre-built plate? “To do so, we used a setup with existing pre-built 384-well plates, each with 352 unique drug perturbations applied to 9 cancer cell lines (Table S9). In total, there were 16 pre-built plates.”

GitHub comments and concerns<br /> 1. Documentation and Usability<br /> a. The README provided by the author is well-structured, offering clear instructions on installation, usage, and licensing, which provides a strong starting point for using Prophet. This level of clarity is especially valuable for researchers and users who are new to the tool. <br /> b. While the README provides a good overview, the documentation around model training is sparse. It would be beneficial to include an explanation in the README on how the model was trained and provide a small explanation of the embeddings captured. More detailed usage/inference examples would enhance comprehension. It’s also unclear how users can apply the method to their own datasets. This will offer quick and easy access for users to understand the functionality and purpose of Prophet.<br /> c. Commented-out code and TODOs are scattered throughout the scripts. Best practices suggest removing unused code to reduce clutter and confusion. Example: prophet/ http://model.py #L12.<br /> 2. Software Environment and Dependencies<br /> a. Users may face reproducibility challenges when trying to set up the software, particularly due to missing environment isolation. Creating an isolated Conda environment or improving instructions around environment setup would help ensure users avoid dependency conflicts.<br /> b. The project uses an older version of Pandas (1.5.x), despite newer versions being available with important fixes. Updating the Pandas version would improve compatibility and performance.<br /> c. A http://setup.py and requirements.txt are both provided but are not used together, creating confusion over proper environment management.<br /> d. No Python version range is specified in the http://setup.py , which led to issues with earlier Python versions (3.8, 3.9). Python 3.10 worked, but this should be clarified for future users.<br /> e. The provided notebook requires a specific version of NumPy that differs from what is stated in http://setup.py . Errors occur with newer versions. NumPy 1.24.4 was found to work, but this should be addressed in the dependencies.<br /> f. We were able to install it into a Linux machine. However, an error occurs when attempting to install the software on macOS. The error reports: “ERROR: No matching distribution found for scipy==1.14.0”<br /> g. A Jupyter notebook is included, but Jupyter is not listed as a dependency in the http://setup.py or requirements.txt, which prevents seamless execution within the provided environment.<br /> 3. Other comments<br /> a. The repository does not include any software tests or automated testing via GitHub Actions or similar tools. Incorporating automated testing would help validate the code’s functionality and improve its robustness.<br /> b. The code does not pass several linting checks (e.g., through dslinter), highlighting the need for improved code quality and adherence to data science best practices.<br /> c. The repository lacks key community health files like http://CONTRIBUTING.md and http://CODE_OF_CONDUCT.md , which are important for guiding open-source contributions and user interactions.<br /> d. Data provided in .xlsx format (e.g., via Figshare) can cause formatting errors and is less open-access friendly than formats like .csv. Switching to a more stable format would improve accessibility and avoid errors.<br /> e. In the tutorial notebook, the code blocks are not executed in a sequential order, which can lead to potential bugs. This lack of sequential execution means that changes made in earlier cells may not be reflected in subsequent cells, resulting in inconsistencies or errors in functionality. <br /> 4. Recommendations for Improvement<br /> a. Address installation issues: Fixing the bugs related to installation and setup should be a priority, as they could deter users from exploring Prophet further. Ensuring an isolated environment setup (e.g., using Conda) would help resolve these issues.<br /> b. Enforce version control for dependencies: Better organization of http://setup.py and requirements.txt, as well as enforcing version control for dependencies, would enhance reliability.<br /> c. Expand the README: Adding a table of contents, additional usage examples, and sections on contributing guidelines and testing procedures would make the README more comprehensive.<br /> d. Adopt best software practices: Implementing clear setup instructions, enforcing dependency version control, and organizing the code more effectively would increase usability and accessibility for a wider scientific audience.

This is a signed review:<br /> Gregory P. Way, PhD<br /> Erik Serrano<br /> Jenna Tomkinson<br /> Dave Bunten. MEd<br /> Michael J. Lippincott<br /> Cameron Mattson, MSc<br /> University of Colorado Anschutz Medical Campus, Department of Biomedical Informatics

On 2024-10-03 21:49:13, user Francesco Del Carratore wrote:

At the end of the methods section it is written 'To facilitate reproduction of these findings, all shareable data and code are available in a single structured file, with instructions and links for the non-shareable data, in S1 Data.'. This is great, but where can I find the S1 data as well as the code used for the analysis and figures (S1 code and S2 code)?

On 2024-10-03 21:10:43, user Gino wrote:

if I may ask, will this paper ever get out of the preprint stage?

On 2024-10-03 19:22:07, user Kaitlin Tagg wrote:

Hi bioRxiv!

This paper has now been published under a different title. Please see published version here: https://journals.asm.org/doi/full/10.1128/msystems.00365-24

Thank you,<br /> Kaitlin Tagg

On 2024-10-03 07:45:33, user Michiel wrote:

Interesting article on relevant topic. The recent article Identification, Elucidation and Deployment of a Cytoplasmic Male Sterility System for Hybrid Potato is a additional useful resource on this topic: https://www.mdpi.com/2079-7737/13/6/447

On 2024-10-02 20:15:19, user Prof. T. K. Wood wrote:

1. The authors fail to cite the relevant literature:

a. Line 59: the first report of ROS inducing persistence for any species is doi:10.1111/j.1751-7915.2011.00327.x (published in 2012, 12,000-fold increase) and should be cited. An erratum was issued to ref 20 by the Conlon group for failure previously to cite this reference.

b. The first high-throughput screen (10,000 compounds) for waking persister cells was doi:10.1111/1462-2920.14828 (published 2020), which reports persister cells wake by modifying ribosomes with pseudouridine.

c. The first report showing lower ATP increases persistence is doi:10.1128/AAC.02135-12 (published in 2013) and should be cited.

1. It is odd that the S. a. strains have 10% persistence (Fig. 1AB) when most strains have persistence far less than 1%.

On 2024-10-02 18:44:54, user Yasas Wijesekara wrote:

Congratulations on your discovery! This indicates that there is still so much biology waiting to be discovered. I'm interested in examining the genes on these Inocles myself. However, using the provided accession number, I couldn't locate the sequences. Would they be available soon?

On 2024-10-02 16:40:25, user Iva Tolic wrote:

This manuscript is related to our manuscript "Kinetochore-centrosome feedback linking CENP-E and Aurora kinases controls chromosome congression," doi 10.1101/2024.09.29.614573. This manuscript here expanded significantly during the revision from version 1 to version 2. As a result, we decided to divide it into two separate manuscripts: version 3 (doi: 10.1101/2023.10.19.563150v3) and a new manuscript (doi: 10.1101/2024.09.29.614573).

On 2024-10-02 16:36:02, user Iva Tolic wrote:

This manuscript is related to our manuscript "CENP-E initiates chromosome congression by opposing Aurora kinases to promote end-on attachments," doi 10.1101/2023.10.19.563150v3, which expanded significantly during the revision from version 1 to version 2. As a result, we decided to divide it into two separate manuscripts: version 3 (doi: 10.1101/2023.10.19.563150v3) and this one (doi: 10.1101/2024.09.29.614573).

On 2024-10-02 09:06:19, user Frédérique Reverchon wrote:

We discussed this paper in our Journal Club and the comments arising from our Discussion are the following:<br /> 1) Why focus on “rhizosphere soil” rather than “ bulk soil” to study the dynamics of fungal functional groups in fields of different age? The rationale behind that choice is not clearly explained, most of the references used in the Introduction and Discussion sections to explain the effect of time since planting on functional groups come from soil studies rather than rhizosphere studies. The term “field age” is also misleading as it suggests that bulk soil was sampled, and other terms such as “plant age” could maybe be considered.<br /> 2) There were no replicates per field age, as only one field per age was considered, although we understand that different plots were sampled per field. Other parameters than “field age” could cause the observed effects on fungal functional guilds, such as differences in soil nutrients between fields, for example. As seen in the PCoA, fields C and D (both classified as “old fields”) are different in terms of fungal community structure. Including replicate fields would have allowed to avoid possible confounding effects.<br /> 3) Important details are missing from the field description. Considering the Discussion on the possible effects of fertilization and fungicide applications, information should be added on the agrochemical management of these plots. Was it similar across fields? Across sampling years? Information regarding the soil nutrient status, and possible climatic variations between the two years of sampling, should also be included.<br /> 4) Methodological bias was also discussed. For example, most fungal taxa identified with the UNITE database would not be able to be assigned to a functional guild. How did you account for these taxa that could not be included into a functional category? Another possible bias that was discussed is the fact that relative abundance data may not represent what is actually occurring in the rhizosphere: absolute abundance data would also be needed and should be used for the implementation of microbial networks.<br /> 5) The discussion could also benefit from adding some consideration on the “functional group” categorization. How categorical is a “functional group”? Mortierella for example is listed as a saprotroph but it can also act as a plant pathogen. Mycorrhizal fungi included both EMF and AMF, yet strawberry only form symbiosis with AMF. As the focus of this study is on strawberry rhizosphere, why not discuss possible strawberry pathogens and symbionts?<br /> 6) An effect of plant genotype was not detected on relative abundance nor richness of fungal functional guilds. However, significant effects may be found in terms of taxa composition. Why not include such analysis in the study?<br /> Other minor comments are enlisted below.

Abstract<br /> Results seem to report differences in soil fungi rather than rhizosphere fungi. Why the focus on rhizosphere and not bulk soil? Plant genotype is not mentioned in the Results either, yet it was mentioned in the Background section as an important determinant of microbiomes.

Introduction<br /> L50: “The essential functions of soil microbiomes, such as pathogenesis, mutualism, and decomposition…”. Is pathogenesis a function? Functions of the soil microbiomes could rather include maintenance of soil structure, fertility, plant productivity… <br /> L66: Research gap: “Despite their significance, our knowledge of the dynamics of different fungal functional groups that co-exist in rhizosphere soil microbiomes is limited (Zanne et al. 2020; Martinović et al. 2021)”. The importance of filling this research gap is not clearly explained. <br /> L69: “Soil fungal functional groups can be dynamic in the plant rhizosphere. For example, fungal pathogens can accumulate over time, especially in monocultures or ecosystems with low plant species diversity (Cook 2006; Li et al. 2014; Peralta et al. 2018; Wang et al. 2023a)”. These references are for soil, not rhizosphere.<br /> Second paragraph of the Introduction: mostly on soil build-up of pathogens, and possible decrease of AMF over time. But this section seems to address the effect of long-term cropping, not plant age (rhizosphere). The rationale behind the selection of rhizosphere as the soil compartment to be studied is needed. You may want to check paper by Sun et al. (2022), published in Applied Microbiology and Biotechnology, on the dynamics of fungal functional guilds in the rhizosphere of wheat.

Methods<br /> Why establish and sample plots (20 x 20 cm) and not plants if rhizosphere soil was considered? Is there only 1 plant per plot (i.e. 3 plants sampled per genotype and field age)? It is unclear.<br /> Were the same plants sampled in 2021 and 2022? If not, a plant effect could be observed between both years.

Results<br /> L205: The increase in pathogens in the older fields is observed for one sampling year only (the pattern does not hold in 2022), yet it is described (and discussed) as a general finding.<br /> What happened in 2022 with AMF that decreased dramatically in all fields (both in relative abundance and in richness)? It is briefly discussed as possibly caused by the fertilization regime, which is not described.<br /> Differences in community composition could be assessed statistically to test for significant differences in taxon relative abundance between fields or sampling years. <br /> Regarding your Figures, stating the age of the field in the “x” axis rather than letters “A”, “B”, etc., could be more informative.

Discussion<br /> The discussion on the accumulation of pathogens over time is also based on references on soil microbiomes, not rhizosphere microbiomes. Were the same plants sampled in 2021 and 2022?<br /> L301: fungicide applications are mentioned to explain the “levelling off” of pathogens in older fields (C and D). Any data on application rates and application dates? This is important if fungal communities are studied. How specific were these agrochemicals? How were they applied?<br /> L311-312: Fusarium is a large genus with many species, not all are pathogens. Making inferences such as “the causal agent of Fusarium wilt in strawberry” is a bit far-stretched. Which Fusarium species are actual strawberry pathogens?<br /> The whole discussion regarding the potential accumulation of pathogens in the rhizosphere calls for symptom incidence data. It is something that was measured?<br /> L387: “the similarity in fungal functional groups across genotypes observed in this study may indicate functional redundancies and stable essential functions of microbiomes.” The microbiome was not studied and differences may be reflected in bacterial communities rather than fungal ones.

On 2024-10-01 20:16:33, user Beiyan Nan wrote:

This paper has been published in eLife https://elifesciences.org/articles/99273

On 2024-10-01 18:51:48, user Maria Valle wrote: