On 2025-06-20 15:37:18, user Lotus N wrote:

Great paper! I was wondering if the code will be published for public use soon?

On 2025-06-20 14:55:31, user Avery Davis Bell wrote:

Now published at Geneics:<br /> https://academic.oup.com/genetics/advance-article-abstract/doi/10.1093/genetics/iyaf110/8159564?redirectedFrom=fulltext

On 2025-06-20 03:38:00, user Manoz Pokharel wrote:

Published version of this article (published in a peer reviewed journal) is available now at this link: https://www.sciencedirect.com/science/article/pii/S2667010025001192

On 2025-06-18 15:11:05, user Ahmed wrote:

This preprint has now been published in a journal: https://doi.org/10.1016/j.molmet.2025.102182

On 2025-06-18 10:57:08, user Delia Belelli (Staff) wrote:

Where can I access the supplementary material the authors refer to and list in the preprint? Many thanks

On 2025-06-18 10:48:15, user Oliver Stuart wrote:

This has been published in MBE: https://academic.oup.com/mbe/article/42/4/msaf079/8105428

On 2025-06-17 16:51:14, user paul knoepfler wrote:

I've discussed this paper journal club-style in the broader stem cell and cellular reprogramming fields here: https://ipscell.com/2025/06/some-skepticism-as-shift-bioscience-reports-secret-purported-rejuvenation-gene-sb000-in-preprint/

On 2025-06-17 16:33:06, user Olivia Fromigue wrote:

Hello,<br /> This manuscript is now published:<br /> C-terminal binding protein-2 triggers CYR61-induced metastatic dissemination of osteosarcoma in a non-hypoxic microenvironment.<br /> Di Patria L, Habel N, Olaso R, Fernandes R, Brenner C, Stefanovska B, Fromigue O.<br /> J Exp Clin Cancer Res. 2025 Mar 5;44(1):83. doi: 10.1186/s13046-025-03350-6.<br /> PMID: 40038783

On 2025-06-16 20:48:24, user Dr. Anne F Simon wrote:

Hi, this work has been published:<br /> Yost, R. T., Liang, E., Stewart, M. P., Chui, S., Greco, A. F., Long, S. Q., McDonald, I. S., McDowell, T., McNeil, J. N., & Simon, A. F. (2021). Drosophila melanogaster Stress Odorant (dSO) displays the characteristics of an interspecific alarm cue. J Chem Ecol, 47(8-9), 719-731. https://doi.org/10.1007/s10886-021-01300-y

On 2025-06-16 19:58:20, user Alex Poliakov wrote:

The open source implementation of S3Mirror is available online here:<br /> https://github.com/dbos-inc/dbos-demo-apps/tree/main/python/s3mirror

On 2025-06-16 14:29:28, user Anthony Rish wrote:

This manuscript was published in Nature Communications (June 2023) https://doi.org/10.1038/s41467-023-39250-6

On 2025-06-16 14:18:32, user Anthony Rish wrote:

The final peer reviewed published version of this manuscript can be found in the journal Molecular Cell DOI: 10.1016/j.molcel.2025.02.001

On 2025-06-16 13:43:56, user Joey wrote:

Dear authors, thanks for sharing this interesting work! <br /> Could I ask how did you manage to fix both cells on your Au NP surface? Was there any surface chemistry used to achieve that? As I would imagine both cells in your study would be floating and moving around during the secretion analysis process, if there is no other modification on the surface to 'capture' them.<br /> Looking forward to your reply. Thanks and good luck with the publication!

On 2025-06-16 06:59:15, user John Cavitt wrote:

This preprint represents a much-needed step in synthesizing long-term patterns of annual female survival across Wild Turkey subspecies. Given the widespread concerns over population declines it is an effort that is overdue and highly relevant. The authors provide compelling evidence that reductions in female survival since 2012 may be a primary driver of these trends, and their use of matrix models to link vital rates to population growth trajectories is illuminating. While the causative mechanisms remain unresolved, this work effectively highlights critical knowledge gaps and frames urgent research priorities for the wild turkey research and management community. It has strong potential to now guide targeted and effective conservation actions.

On 2025-06-16 05:52:19, user Jack Durant wrote:

Potential bias for gram-negative bacteria introduced in the DNA isolation or amplification methods? There are several reports of the isolation of Gram-positive bacteria from the symbiotic cavities.

On 2025-06-14 18:39:51, user Shubhada Nagarkar wrote:

Where is the data set located for readers?

On 2025-06-13 14:17:16, user Prof. T. K. Wood wrote:

YgiW is NOT part of y-ome (been re-named VisP) and not a “putative stress response gene” (p 17 btm). ygiW was first studied in 2009 (doi:10.1111/j.1365-2672.2009.04611.x) and found to respond to H2O2, Cd, and acid stress and found to be regulated by AriR (doi:10.1016/j.jmb.2007.07.037); both should be cited.

On 2025-06-13 13:20:50, user Anonymous wrote:

Dear authors,<br /> as part of a group activity in our lab we discussed your very interesting manuscript with the goal of reviewing it as well as improving our reviewing skills. The below review is the result of this exercise and reflects thoughts and comments of several people. We hope this helps you with your way forward to publish the paper in a good journal.

Summary, strengths and limitations of the paper<br /> The manuscript by Lee et al. investigates the mechanisms behind lysosomal damage in CLN4, a form of neuronal ceroid lipofuscinosis caused by dominant mutations in the DNAJC5 gene. The authors demonstrate that CLN4-associated DNAJC5 mutants aggregate on lysosomal membranes, leading to membrane disruption and severe lysosomal damage in neurons derived from human iPSCs and in a Drosophila disease model.<br /> In non-neuronal cells, a protective ubiquitin-dependent microautophagy pathway is activated, helping degrade these toxic aggregates and preventing lysotoxicity. Through CRISPR screens, the ubiquitin ligase CHIP (STUB1) was identified as a critical regulator of this pathway. CHIP facilitates the ubiquitination and lysosomal degradation of CLN4 aggregates, thus preserving lysosomal membrane integrity and preventing the lysosomal-driven cell toxicity.<br /> Importantly, overexpression of CHIP in CLN4 mutant neurons and flies restores lysosomal function, reduces lipofuscin accumulation, and rescues neurodegeneration, highlighting CHIP as a potential therapeutic target for CLN4 and, potentially, other lysosome-related neurodegenerative diseases.<br /> A strength of this study is its comprehensive mechanistic investigation into CLN4 disease, identifying lysosomal membrane damage as a key pathological feature and clarifying the still open question about how DNAJC5 aggregates cause neurodegeneration. In addition, the identification of the ubiquitin ligase CHIP as a key regulator of a protective microautophagy pathway represents a key discovery. CHIP-mediated ubiquitination of DNAJC5 aggregates enables their lysosomal degradation, effectively preserving lysosomal integrity. These findings are of important translational relevance, since CHIP overexpression restored lysosomal function and reduced neurodegeneration in both human neurons and flies, highlighting a promising therapeutic application.<br /> However, the study also displays some limitations. While the neuron-specific vulnerability to CLN4 aggregates is a central focus, the mechanistic basis for this selective sensitivity remains only partially explained. Additionally, the therapeutic relevance of CHIP modulation is based on genetic overexpression, which, while illustrative, does not yet translate to practical interventions such as small molecules or gene therapy strategies. The broad role of CHIP in cellular protein quality control also raises questions about potential off-target effects of systemic modulation. Despite these challenges, the paper makes a strong contribution to the field by establishing a mechanistic link between lysosomal membrane damage and CLN4 pathology and identifying CHIP-mediated microautophagy as a potential neuroprotective pathway.

Major Comments<br /> 1. In Figure 1A, why do monomeric DNAJC5 levels change in the mutants? L116ΔHT seems to have comparable levels to WT, which is not the case for the other heterozygous mutant nor for the homozygous of the same mutation. Are the overall DNAJC5 levels changed in the different lines? Maybe testing by qPCR or checking the fully solubilized protein by WB can be options.<br /> 2. In Figure 1D, can the authors prove that the loss of lysotracker signal in day16 neurons is not simply because these cells are dying? Can they stain cells with calcein-AM as well?<br /> 3. In Figure 1L, why do they not find CHIP in the proteome? Based on figure 5A, they should find a difference in its levels between WT and L116Δ HM. Do they check the proteome at a timepoint that is too early, or are CHIP levels overall too low to pick up changes? Minor comment: in the text, they define a log fold change cut-off of 1.5. Please illustrate this in figure 1L.<br /> 4. In Extended Data Figure 2E, where does the HMW form of DNAJC5 fractionate? And does the organelles fraction change in mutants? It would be of help to see the fractionation of the mutant too, showing also the HMW DNAJC5. This will also help to understand why the authors see the downregulation of DNAJC5 in mutants in mass spec (Figure 1L). <br /> 5. The authors show that the lysotracker phenotype in mutants is likely not linked to V-ATPase dysfunction. However, it is not fully clear what is happening to V-ATPase. In Extended Data Figure 3A, the WB shows a decrease in the interaction between mutants FLAG-DNAJC5 and membrane-bound ATP6V1G2. What is the authors’ hypothesis of this phenotype?<br /> 6. In Figure 3, the authors conclude that ubiquitin, HGS and DNAJC5 colocalize on lysosomes. Can they add a lysosomal staining (e.g. LAMP1) to really prove this point and show the lysosomal localisation of mutants? And, more in general, the authors should include a colocalization analysis (e.g. Pearson’s everytime they claim it - Fig. 1H, 3A, 3C, 4F, 4H and 6C).<br /> 7. In Figure 3E,F, it seems that also the monomeric version of DNAJC5 accumulates inside lysosomes and this is impaired when microautophagy is blocked. Can the authors comment and expand in the result section about the WT phenotype? <br /> 8. In Figure 3H,I, the treatment with TAK-243 induces the reduction of lysotracker signal also in cells overexpressing the WT isoform. Why? The authors can include the quantification of untreated cells too. <br /> 9. In Figure 4 (A and B) and Extended data Fig.5, the authors employed CRISPR screens and identified CHIP as a candidate regulator and then they further supported this finding through KO and rescue experiments. The claims they made here can be sufficiently supported by the data they showed. However, one concern is the noticeable difference between the screening results of the WT and ΔJ conditions, as there are relatively few overlapping hits. What could explain this divergence? How feasible is it to use Keima-DNAJC5 with L116Δ for CRISPR screen instead of using DNAJC5 WT and ΔJ? Does the Keima-DNAJC5ΔJ mutant have the same aggregation and/or lyso-toxicity phenotype as observed with the L116Δ mutation? Considering that both DNAJC5 WT and the ΔJ mutant are involved in misfolding-associated protein secretion (MAPS) and microautophagy (PMID: 35506243), but the ΔJ mutant lacks MAPS activity, is it appropriate to use the ΔJ mutant as a substitute for the L116Δ mutation? How is this choice justified in the context of the study?<br /> 10. In Figure 4D, is the observed leftward shift, particularly in case of sh-CHIP, substantial enough to confidently conclude that there is a decrease in the association of Keima-DNAJC5 WT with lysosomes? It would strengthen the claim if this was quantitatively assessed and supported by statistical analysis.<br /> 11. In Figure 4E, we would expect similar immunoprecipitation efficiency for all FLAG-tagged proteins using FLAG beads. The recruitment of the various FLAG-DNAJC5 constructs to the beads should be comparable—consistent with what is shown for FLAG-DNAJC5 WT, L115R, and L116Δ—in order to confidently conclude that the co-immunoprecipitation demonstrates CHIP can bind both WT DNAJC5 and the CLN4 mutants independently of the J domain. Alternatively, if transfection efficiency or expression levels of FLAG-DNAJC5 ΔJ present an issue, the protein level of FLAG-DNAJC5 ΔJ in the input should be provided to clarify this point.<br /> 12. In Figure 4F, from the representative confocal images, Ci-L116∆ mutant in CHIP-KO appears to be localized also on cell periphery or boundary along with punctate localization. Also, it would be better to show the status of ubiquitin and HGS staining in CHIP’KO cells without any over-expression of CLN4 mutants to appreciate the role of CHIP in microautophagy of CLN4 mutants.<br /> 13. In Figure 4G, I and K, the figure legends for the graphs do not clarify how the normalization of the Ub and HGS areas was done with respect to their untransfected (UT) cells. Did the authors use neighbouring untransfected cells from the same coverslip or did they use a common untransfected control for all the samples? Also, it would be more informative to add the untransfected column in the graphs shown in Fig. 4G and Fig.4I, similar to Fig.4K, to have a better comparison in the data.<br /> 14. In Figure 5, the overall rescue effect of CHIP in this system is weak. Maybe the fact that their promoter only activates from d8 onwards is part of the problem? Would it be possible to start expressing CHIP earlier?<br /> 15. In Figure 5A, the authors overinterpret their results and claim from only the fact that CHIP is in the NP40-insoluble fraction that it must be inactive. Could they check whether it really ends up in the HMW L116Δ aggregates, and maybe even perform an in vitro assay to determine its activity in either version?<br /> 16. In Figure 5G, the authors show increased cell death in immature neurons which lack the lysosomal damage phenotype. What is the authors’ explanation for this phenotype? Is it linked to CHIP aggregates accumulation?<br /> 17. In Figure 6, how do the different levels of lysosomal translocation make sense with their model? Shouldn’t the brain have the lowest level of translocation, since this is the only tissue where a phenotype occurs?<br /> 18. We would recommend moving Figure 7 to the extended data, and spend more time in the text to explain the relevance of Tsg101. Currently the figure comes a bit unexpected and does not allow the authors a strong finish to the paper, since the phenotypes are less convincing than the ones in Figure 6. In addition, is there any quantitative analysis of the rough eye phenotype that can give a more objective assessment for the phenotype?<br /> Minor comments<br /> ● In Figure 2F, the authors should also show the WT DNAJC5-treated cells. It will make the data more complete and solid, confirming that the WT isoform is not interfering with lysosomal homeostasis.<br /> ● In Figure 2H, in the L116Δmono panel a control cell (intact) is missing. <br /> ● There are no supporting images for Figure 5H (also not in extended figure).<br /> ● In Extended Figure 6B and C, why does the WT go down too? Showing the graph like this is a little confusing, and also the normalisation.. and they should add also earlier time point to see if at d12 something is happening<br /> ● Try to avoid using red-green as a combination in figures, to make the paper accessible to colorblind people.<br /> ● Authors can homogenize how they show statistics in their graphs, either deciding to not show the p-value when it is not significant or to include it every time. Also, why do they often use n=2 and do statistics on individual data points? Why not add n=3 and do statistics on experiments?<br /> ● The authors claim a microautophagy-based system that cleans up the DNAJC5 aggregates, which end up inside lysosomes. However, if the aggregates can damage the lysosome membrane from outside, why would they not do the same from the inside?

On 2025-06-12 20:54:21, user vicgarcia4 wrote:

Hi! Really interesting paper, its great to understand more and more about antiviral mechanisms! I wanted to point to some data we have on the lysosome that may be interesting in light of your experiments: https://www.science.org/doi/10.1126/sciadv.adn5945 . In Fig 6 we looked at lys-2 mutants and saw a dramatic increase in viral load (comparable to the levels obtained with drh-1 mutant, which should give roughly the same as rde-1 mutants). We were able to confirm these results later on with an independent CRISPR lys-2 mutant, although that's not included in the paper. Thought you may like to know, good luck publishing!

On 2025-06-12 16:36:16, user Ian Lamb wrote:

This manuscript is now published in the Journal of Proteome Research: https://doi.org/10.1021/acs.jproteome.5c00079

On 2025-06-12 13:57:46, user Ignazio Verde wrote:

Hi Dorrie, Sook and Chris;<br /> I just have a quick look at your manuscript. I would like to highlight something desplayed in fig 2. In the centromeric region of Pp03 I see a couple of translocations and inversion. Rather than structural variations between your assembly and v2 I guess these are some misorientation and mis order of scaffolds in v2. I don't have the coordinates of your assembly but you can better check in our paper Verde et al 2017 where we describes this mis orders and mis orientations of these scaffolds. This region (12 Mb to 17 Mb) was really complicated since we hadn't many markers to anchor and the few we had cosegregated. Other maps published after we established the assembly highlighted these discrepancies that we reported in the paper.

On 2025-06-12 11:56:02, user Evolutionary Health Group wrote:

We at the Evolutionary Health Group ( evoheal.github.io ) really enjoyed this paper.

Here are our highlights:

Demonstrated the sequence space of autocatalytic self-replicating molecules, specifically ribozymes, is much larger than previously thought

Computational filtering through direct coupling analysis (DCA) enabled evaluating this large sequence space while achieving a high rate of experimental validation

Thoughtful experimental design for testing autocatalysis

Approach will scale well with increased sequencing depth

Method serves as a template for the functional prediction of other catalytic processes

Opens the door to quantitatively assessing different origin of life scenarios

On 2025-06-12 04:20:37, user Saswat K. Mohanty wrote:

This publication has now been published at: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-025-03635-1

On 2025-06-11 22:20:37, user alexei rankin wrote:

AUCell scores are inherently rank-based [1.1, https://bioconductor.org/packages/devel/bioc/vignettes/AUCell/inst/doc/AUCell.html ], reflecting the relative ordering of gene expression within a cell.<br /> Given this, I have questions about the modeling assumptions described in Section 2.1.2.

How does this multiplicative assumption account for the rank-based nature of AUCell scores?<br /> A clarification or justification would be appreciated.

On 2025-06-10 13:43:31, user Roberto Battistutta wrote:

Nice work. Connected to this topic, it could be useful the citation of Fornasier E, Fabbian S, Shehi H, Enderle J, Gatto B, Volpin D, Biondi B, Bellanda M, Giachin G, Sosic A, Battistutta R. Allostery in homodimeric SARS-CoV-2 main protease. Commun Biol. 2024 Nov 4;7(1):1435. doi: 10.1038/s42003-024-07138-w. PMID: 39496839; PMCID: PMC11535432.<br /> Best.

On 2025-06-09 00:27:59, user Binks Wattenberg wrote:

Hi! This is a very interesting study. As the authors no doubt know, there is an alternate SPT subunit, SPTLC3, that can substitute for SPTLC2. When included, SPTLC3 drives the formation of alternate sphingoid base composition by changing acyl-CoA specificity. In most tissues SPTLC3 is expressed at low levels. Did you measure levels of SPTLC3 and in your lipidomic analysis did you measure non-d18 sphingolipids?

On 2025-06-08 01:17:05, user Misha Koksharov wrote:

"The following supporting information can be downloaded at: http://www.mdpi.com/xxx/s1 "

The supporting information is not present at this link.

On 2025-06-06 20:42:19, user Jayson Zhao wrote:

Cite the Annambed paper? Same idea: https://academic.oup.com/nargab/article/6/4/lqae172/7928174

On 2025-06-06 09:00:46, user Michal Mokry wrote:

Peer reviewed article is now published: https://doi.org/10.1093/cvr/cvaf077

On 2025-06-05 19:48:36, user Igor Kryvoruchko wrote:

Dear Authors! Thank you for this valuable, convincing study! Your findings and conclusions align very closely with our observations on iORFs in the model plant Medicago truncatula. It would be interesting to consider one more scenario. What if an amino acid sequence encoded by an iORF becomes part of a long alternative splicing form of a canonical protein instead of or in parallel with acting as a microprotein? Suppose such a form provides a biological advantage regardless of its origin. In that case, the iORF region may be conserved strongly at the amino acid level in the alternative frame but much less in the reference frame. In other words, alternative splicing may probably explore and utilize the coding potential of alternative frames even without the production of a microprotein. In both scenarios, mutations neutral for one splicing form may be affecting the amino acid sequence of a different splicing form. This would be one more reason to consider such mutations in many contexts, primarily in studies aimed at the identification of the molecular basis of genetic diseases and acquired cancer types. Unfortunately, such mutations are still being ignored in most studies. Your work will undoubtedly contribute to changing this state of things in the future. Best of luck with your research at the very frontier of modern biology!

On 2025-06-05 12:44:28, user Carli Peters wrote:

This research is very interesting. One thing I was left wondering was the sample size used for the analysis, how much soil (in g/mg) did you use for each sample?

On 2025-06-05 11:10:50, user Nicolas João Silva wrote:

This study is now accepted and available at Animal Behaviour: https://doi.org/10.1016/j.anbehav.2025.123223

On 2025-06-05 09:54:34, user °christoph wrote:

I noticed a small flaw in line 400/401, the missing verb in square brackets:<br /> "We suspect that the fragmented symbiont genomes of some planthoppers, which we were not [able to] assemble and chose not to characterize here, may represent a similar situation."

On 2025-06-04 20:55:49, user Katia Lopes wrote:

Here is a video of Juliana Godoy talking about the manuscript in 7 min https://www.youtube.com/watch?v=p4LMS0fEl54 .

On 2025-06-04 14:51:37, user Michelle Anderson wrote:

This is now published: https://doi.org/10.1038/s41598-023-47777-3

On 2025-06-02 17:22:57, user Karl Milcik wrote:

We reviewed this paper as part of our regular journal club. Below is a collection of the comments made by the various group members:<br /> --- 1 ---<br /> It's unclear why asymmetry in the latent embeddings is required.

No mention of the model predicting trivial results during training due to the symmetric KL? Ablation might reveal that the loss weights require very careful tuning to avoid predictions or that the reference distribution is extremely important.

There are a number of implicit assumptions being made with the model architecture, primarily that there is sufficient information to align two datasets. It becomes an issue when combining datasets from very different modalities (e.g. scRNA-seq and sc proteomics). Adding multiple modalities is definitely possible, but the overlapping information becomes smaller and lose additional information. It would be good to see where the model stops working. Small datasets will similarly carry little information: is there a minimum number of samples for the model to function as expected (exact number not required, but getting a sense with a few datasets of different modalities would be informative). As-is, we wouldn't expect the model to apply to most single-cell datasets.<br /> Aligning modalities that are of extremely-different dimensionality implies either redundant information in one modality or information loss. This should be discussed.

Specifics of training, hyperparam optimization, etc. would be better in a supplemental (assuming the targeted venue allows it). The main contribution appears to be the combination of the various losses. The article could be shortened by focusing on that when describing the method.

Re: training procedure. No mention of balancing the different modalities. "Difficult" modalities would be more difficult to learn. early stopping could be preventing complex modalities from being sufficiently mapped because the simpler modalities are overfit faster than the complex ones are learned.

Evaluation metrics: NMI is very similar to the symmetric KL that is used to train the model. I'm not sure if it's a reliable metric for this.

Fig. 2a: the figure amounts to "the model removed information," which is the point of batch correction but doesn't quantify what other information was lost. Fig. 6 suggests that there is quite a bit of biological information is lost.

Fig. 3: scRNA reconstruction is producing high values for some genes when it shouldn't (purple cluster, top). If one were to use this, we would conclude that those genes are highly differentially expressed when they are not in the original data. This is a fatal problem.

--- 2 ---<br /> 1. Lack of Evaluation in Downstream Biological Applications<br /> While UniVI shows strong performance in latent space alignment and cross-modality prediction, its utility in downstream biological tasks (e.g., identifying novel cell subtypes, inferring regulatory programs, or reconstructing differentiation trajectories) remains under explored. Demonstrating improvements in real biological discovery would substantially enhance the manuscript's impact.<br /> 2. Insufficient Validation of Generalizability Across Conditions<br /> The datasets used in evaluation are mostly standard and clean (e.g., PBMCs from 10x Genomics). It is unclear whether UniVI generalizes well to more diverse or challenging settings (e.g., different sequencing technologies, species, or tissues).<br /> 3. No Ablation Studies to Justify Model Design<br /> The architecture includes several important design choices (e.g., β-VAE, shared and private latent spaces, MoE layers), but the manuscript lacks ablation experiments to validate the contribution of each component.<br /> 4. Lack of Interpretability for Latent Space Representations<br /> The latent space is central to UniVI’s function, but its biological interpretability is not addressed. It is unclear which features (genes, peaks, proteins) drive the alignment, or how latent dimensions relate to known biology.<br /> 5. Failure Cases and Limitations Are Not Discussed<br /> The manuscript does not address situations where UniVI might fail or yield poor alignments. Understanding when and why the method breaks down would be critical for end users.

--- 3 ---<br /> 1) They mention that scATAC-seq is not reliable for determining cell type specificity, then why did they necessarily include ATAC-seq?

2) The dataset they use are reliable but I think it would be good for them to mention why exactly they preferred these dataset and databases, there is not much information about this

--- 4 ---<br /> Figure 4: recommend labeling panels rather than referring to top left, etc. In the boxplots at the top left, uniVI and totalVI seem really similar in NMI, ARI, ACC but no formal statistical comparison done<br /> usability may be limited if you have to manually fit the model with your own data<br /> is overfitting a problem with very small datasets? is computational time a problem with very large datasets (eg early stopping used)?

--- 5 ---<br /> -Use of the model to generate new data is stated and referenced throughout, but I felt the true utility of this is underexplored. Why would someone want to do this? The authors mentioned data augmentation, but the authors could be more explicit on any other uses.

-Did the authors consider using alternative methods to grid search for their training procedure (e.g., neural architecture search)? Also what were the ranges of values searched and with what step sizes?

-For adding >2 modalities, are there any considerations with computational complexity and training time at a certain point? How would this scale to K>2?

-In general, the paper is well organized and detailed, but almost to a fault. I suggest moving details less relevant to the average reader into a supplemental section. For example, knowing the function calls and variables probably isn't relevant to most readers. Those that want to know that could look in the code or point the reader to a supplement. These somewhat irrelevant details to the figures were also mixed with critical details such that I felt a little lost on trying to pick out the most important parts of the methods.

-On the same note, simple details are often over-explained or restated multiple times in the text (e.g., the explanation for subsetting the data to obtain non-overlapping labels is repeated several times), while more complex concepts such as the Beta term, mixture of experts model, etc. are often underexplained in my opinion.

-For Figure 1, I am still confused on what exactly UniVI provides a benefit over in some panels versus just looking at individual UMAPs and annotating by the labels, since these are already known? More specific explanation on why a shared latent space is usual to find new biology would help.

-Exploring more on the fringe cases in which data does not align is interesting. For example, the authors mention cell 59 aligning closer to a Dendritic cell than B cell. They mention this could be biological variation or technical error, but exploring more about this 'misalignment' in this and other datasets could be be a key way of identifying unique insights from this model, though would require biological validation. Perhaps the authors could suggest some such experiments as future work to tie in dry and wet lab approaches/experimental designs that would complement this model in the lab.

--- 6 ---<br /> In the paper authors mention that approximately 1% of the dataset shows inconsistent alignment. Could you elaborate on how this might be interpreted as reflecting dynamic cellular states in continuous development? A deeper discussion of this would be very helpful.

--- 7 ---<br /> Figure 7: how to prove that the reconstruction retains the biology signal or better illustrate:<br /> It’s weird that the error did not increase significantly with the higher dropout rate.<br /> As well as for the Correlation<br /> When no dropout is applied, the correlation between the raw and reconstructed data is only 0.52. Does this suggest that the pathways have changed significantly? It may be necessary to check which pathways have changed and which have not.

--- 8 --- <br /> Lack of QC metrics and if there were any filtering involved for the data. Transparency is missing in the QCs.

--- 9 ---<br /> A limitation is that this must be only used for measurements made from the exact same cells - we cannot apply this framework to cells measured in parallel with different methods

Figure 2 not sure that they compared to CCA or OT as those were introduced alternatives in the beginning.

Figure 2 : I like that they show the measurement pairs for each cell - can they quantify this globally somehow?

The distinction between “imputation” and alternative mode reconstruction is unclear from their description; they mention fitting a gaussian mixture model with their data and then using that for input - does that mean they use the true values from one measurement modality and then use all zeros for the other? Why not simply run a forward pass from the one modality encoder and then use the opposite decoder?

They comment on higher expression levels having higher reconstruction MSE - this is a common feature of autoencoders that compress the range of predictions so as to minimize error from any large magnitude predictions. The methods claim to have used pp.scale() which should have removed this effect of the measurements original magnitude?

It would be interesting to know what are the limits in terms of minimum (or maximum) features per modality and minimum measurements for training.

Based on figure 4, the claim that uniVI “outperforms existing state of the art integration methods does not appear to be statistically supported. It appears to be indistinguishable from TotalVI and perhaps even Seurat. The authors should compute p values using random samples of the data with replacement (I think these experiments used identical samples, which would violate the assumption of independence for t-testing). TotalVI appears to have been published over 4 years ago in Nature Methods. However they claim that TotalVI requires “modality specific priors”. This “prior” appears to be a specific model term that is learned from the data to account for background, so I agree that uniVI is more generalized but not by as much as I thought before seeing this prior work.

The authors should be careful about statements of distance based on UMAP “The model preserved meaningful cellular distinctions, with closely related populations remaining spatially proximate in the latent space, underscoring UniVI’s ability to harmonize intra-modality variation while retaining biologically relevant structure.”

Figure 6C is a neat application of this data. Does this scale beyond this data and how can it be less slushy in the representations?

Can this be fit on very deep single cell omic data and then applied to predict missing depth from more shallow studies?

It would be interesting to repeat the dropout experiment with multiple random dropouts to get a sense of variance in the genes that are dropped out.

I’m confused why the pre and post reconstruction heatmaps in figure 7 bear no resemblance even with 0% dropout. Are these hierarchically clustered differently or should we be able to compare the shapes between them.

Is there overlapping information between true SCP and SCT (beyond cite-seq where the proteomic measurement part is substantially limited based on the number of antibodies)?

Does this work well beyond measurements from blood cells (what seems like an easy case)?

--- 10 ---<br /> I was hoping to see more of the unified cell state concept play out in its experiments. I feel like they got sidetracked (or rather, realized they didn’t have enough to really fulfill that ambition), but it would be nice to have that addressed more clearly.

I was wondering if weights trained for a single modality as paired to a second modality could be transferred to a third modality comparison. Doubtful, but it would be interesting to explore.<br /> Not sure if this is something that you actually want to include in the review. It was more what I was focusing on and was somewhat dissatisfied by.

The text in the figures is too small to read, generally speaking. I found issues with all figures with the possible exception of the first.<br /> Figure 1b, Cell-Cell Alignment is not intuitive. It goes from a UMAP to decode as a graph figure, and is not consistent with the batch correction element of the same subfigure. It’s an odd inconsistency.

On 2025-06-02 10:50:42, user cy duan wrote:

Is there releated code available ?

On 2025-06-01 02:37:56, user jorge rocha wrote:

this work was published in a peer reviewed journal... https://www.sciencedirect.com/science/article/pii/S2590207525000322?via%3Dihub

On 2025-05-31 12:31:35, user Elio Rossi wrote:

Published: https://journals.asm.org/doi/10.1128/aac.00280-25

On 2025-05-31 10:09:46, user MaMo wrote:

In the Methods section, you stated that you used an extinction coefficient of 118,000 M⁻¹ cm⁻¹ for OCP holoproteins at 476 nm. However, your absorption spectrum in Figure 1c does not reflect this value. An extinction coefficient of 118,000 M⁻¹ cm⁻¹ at 476 nm would yield an absorption of approximately OD = 1.8. Therefore, the measured absorption should be much lower unless your sample contained a significant amount of apoprotein. This would suggest that your preparation might consist of roughly 50% apo/holoprotein.

On 2025-05-31 04:43:34, user Kostas Kormas wrote:

This paper is now published: https://doi.org/10.1016/j.microb.2025.100396

On 2025-05-30 15:41:15, user Ricardo wrote:

Hi! Very cool and interesting work, I really appreciate the amount of work you put into it.

I have a question about your ITC data. From the methods section, it seems you only obtained thermodynamic binding data at 25C. Have you considered determining the relationship of dH with temperature -- the heat capacity of binding? If the dCp is negative then the reaction becomes more enthalpically-favorable as temperature increases (and, by compensation, the entropic contribution becomes more unfavorable). Since environmental temperatures at ancient times were higher (which matches with the observation of increased Tm in Anc1 and Anc2), it's possible that the binding reaction to BMDG/fucose is also driven by enthalpy at higher temperatures (which would be the normal temperatures experienced by the TFs at the time). Of course, this would be highly dependent on the magnitude of dCp. Making this characterization would be a very useful because it could strengthen your main argument about enthalpy-entropy trade-offs or it could even suggest that an enthalpy-driven mechanism was conserved over time, which it's also very cool and interesting.

I recommend reading Cooper, A. (2005). Heat capacity effects in protein folding and ligand binding. Biophys Chem 115(2-3): 89-97 and related literature to evaluate if my comment makes sense. I hope this helps!

On 2025-05-15 19:40:40, user Ugo wrote:

Summary:

It is a known ideological fact that life as we have come to know it has passed through various stages of evolution. These evolutionary transitions are not spared of the biomolecular constructs that make up this life, which include proteins and DNA. These big and subtle changes which have been identified through various evolutionary studies, have led to recent understanding as to why various biomolecules behave the way they do while still maintaining their functionality across varying ancestors. In this work, the authors set out to examine how the Lacl/GaIR family (LGF) of transcription factors (TFs) exhibits entropy-enthalpy trade-offs. The study focuses on 4 ancestors (Anc1-4) which likely emerged after a fusion of solute-binding proteins with DNA-binding domains and the extant E.coli Lacl proteins. The authors utilized methods like ancestral sequence reconstruction to characterize the structural variations of the LGF TFs. The sets the stage for mapping the thermodynamic variability across the Lacl/GaIR family, and how the entropy-entropy trade-offs are achieved. To address this, the study introduces parameters like binding affinity and melting temperatures to characterize the evolutionary transition from Ancestor 1 through 4 and the extant E. coli Lacl. The major success of this paper is characterizing the free energy of binding into the enthalpic (delta H) and entropic (-TdeltaS) contributions. This laid a foundation that helped in understanding their primary aim, which is the entropy-enthalpy trade-offs that biomolecules have had to undergo in order to maintain their relevance. The authors demonstrated structural insight into binding specificity of d-Fructose to ancestor 1, which has a more open binding site for its ligand, and the specificity of ancestor 4 and extant Lacl, which has a more closed binding site with beta methyl D-galactosides (BMDG) as its preferred ligand. This work establishes a framework for understanding why biomolecules with the same ancestral line possess mutational changes both in residue sequence and conformational characteristics. The authors attributed these changes to the environmental temperature changes over geological time and general environmental changes that biomolecules adapt to in order to retain their evolutionary functions.

Major Points:

The limited effect of thermodynamics: While I might not have a clear understanding of evolutionary changes, especially with respect to this study, I think that thermodynamics might not be the only major factor.

Although one of the main aims is situated around the entropy-enthalpy trade-offs as classified using the properties that constitute the free energy of binding calculations. I suggest that thermodynamics might not be contributing solely to this characteristic observed

The effects of kinetics, which are not addressed, might have some contribution, probably of equal magnitude with thermodynamics contributions

Limited evolutionary trajectory:

Since the family is said to have existed for approximately 3 billion years, analyzing thermodynamic properties of just 4 ancestors and an extant Lacl might be speculative rather than factual about the whole family

Minor point:

Overall, the paper was well written, but one misspelling that caught my attention is the paragraph on page 14 below Figure 5 caption, in the last sentence. “It should be noted that he apo and d-fucose....” where ‘he’ should be ‘the’

The SEM bar on Figure 2 (a) appears to be slightly invisible, I suggest changing it to a darker color, or an overall bar graph color that is well-contrast with the error bars.

Since the major points might not contribute directly to this paper based on the author's aim, the paper can be published as is after incorporating the minor edits.

On 2025-05-30 14:57:30, user Yasushi Tamura wrote:

This paper was accepted and already published in iScience. https://www.cell.com/iscience/fulltext/S2589-0042(22)01634-0

On 2025-05-30 13:49:38, user Haiyue Hou wrote:

Great work! But how broadly applicable is this CCS library in glycan structure sequencing? As we can see, the examples cited in the paper are cases with significant CCS differences in glyco-epitope fragments (Fig. 4). However, many fragments exhibit minimal CCS distinction—within 2% (e.g., F1 and F2)—which falls within the typical error range for CCS measurements. In such cases, determining glyco-epitopes might be challenging?

On 2025-05-30 10:57:29, user Nikolai Slavov wrote:

A labeling protocol is available from protocols.io :

PSMtag - Anhydrous Labeling Protocol, doi: https://dx.doi.org/10.17504/protocols.io.kqdg3w5yzv25/v1

On 2025-05-29 16:20:33, user Manish Jain wrote:

Fascinating study Vedant! You've elegantly dissected the role of MAB-5/Hox in regulating posterior migration of Q lineage neuroblasts in C. elegans, revealing a staged process controlled by distinct target genes (vab-8, lin-17, and efn-4). The link between migration and differentiation is particularly intriguing, with premature dendritic protrusion formation upon migration disruption. Great insights into the Wnt signaling pathway and transcriptional programs governing neurodevelopment!

On 2025-05-29 08:29:11, user Jeffrey Ross-Ibarra wrote:

Podcast on this work: https://share.transistor.fm/s/bed2ffa6

On 2025-05-28 16:48:42, user Jonathan Eisen wrote:

Nice work. One quick comment - I think the use of the term "green algae" in reference to Ostreococcus and Bathycoccus is not ideal. I suggest either replacing this or at least adding the scientific name for the group in which these taxa are found (Chlorophyta).

On 2025-05-28 07:34:39, user Anne N wrote:

How to access the supplementary files, please?<br /> also https://trace.ncbi.nlm.nih.gov/Traces/index.html?view=study&acc=ERP132253 reports 'mature' samples in addition to the embryonic ones, and this is not explain in this publication

On 2025-05-28 06:52:46, user Dr. Christos Chinopoulos wrote:

I have listened to the lecture by Ben-Sahra at the Tumor Metabolism conference in Vancouver on March 17 regarding this work, and I have the following question: they showed that an increase in succinate (even by DE-succinate) led to inhibition of mTHF by succinylation of SHMT2, which negatively affects purine synthesis. But succinate does not have the capacity to succinylate, succinyl-CoA does. Succinate and succinyl-CoA are in equilibrium through succinyl-CoA ligase, and as I saw in their slides that they are able to knock-down (or knock out) both isoforms (ATP- and GTP- isoforms of succinyl-CoA ligase) by knocking down or out SUCLG1, the common subunit. A lot of succinate, means a lot of succinyl-CoA through reverse operation of succinyl-CoA ligase. So, is the effect of succinate (DE-succinate, or SDHi) together with 6-MP on diminishing cancer cell proliferation abolished if SUCLG1 is also inhibited or knocked out/down?

On 2025-05-28 03:43:13, user Robert George wrote:

The timing, 'origins' and reason for the 'eastern shift' in post-Neolithic Anatolia is very interesting.

Some other factors to consider<br /> - note the individual I0679 from Krepost (Mathieson 2018), also showing the shift, as far as ~ 5600 BC Bulgaria. Also a singleton case<br /> - aside from a 200 year time gap, if Buyukkaya has this shift whilst West Mound neonates lack it, could Buyuk's northern geography be a factor ? <br /> - what was happening in the broader west Asian sphere c. 5500 BC which might explain the appearance of new groups in Anatolia ? There are important clues here<br /> - even though the east shift occurs as early as 5500 BC, it really accelerates after 4000 BC, and especially 3000 BC. I.e probably multiple streams & causes

On 2025-05-27 19:17:07, user Shipra Agrawal wrote:

Published: Journal of the American Society of Nephrology<br /> https://journals.lww.com/jasn/abstract/9900/rna_alternative_splicing_and_polyadenylation_and.659.aspx <br /> May21, 2025

On 2025-05-27 19:12:26, user Mehtab Singh wrote:

This paper is now published in Plant Biotechnology Journal. <br /> Link: https://onlinelibrary.wiley.com/doi/10.1111/pbi.70146

On 2025-05-27 15:30:22, user Inaam A. Nakchbandi wrote:

An international patent application has been filed (WO2011097401) for the peptide molecule GLQGE and its use in the treatment of fibrotic conditions characterized by an excess accumulation of extracellular matrix in a tissue and/or an organ. It was granted in CN and JP, and is under examination in EP, US and IN. For more information, please contact Max-Planck-Innovation ( http://www.max-planck-innovation.de ).

On 2025-05-27 14:25:49, user Jonathan Eisen wrote:

It would be useful to show the tree in Figure 6 as a phylogram (i.e., with branch lengths)

On 2025-05-27 12:30:56, user Noah Mishkin wrote:

Article has been published in Comp Med - https://aalas.kglmeridian.com/view/journals/72010023/74/6/article-p373.xml?%3Crelated_content%3E=mostCited-184386

Mishkin N, Carrasco SE, Palillo M, et al. Chlamydia muridarum Causes Persistent Subclinical Infection and Elicits Innate and Adaptive Immune Responses in C57BL/6J, BALB/cJ, and J:ARC(S) Mice Following Exposure to Shedding Mice. Comparative Medicine. 2024;74(6):373-391. doi: 10.30802/AALAS-CM-24-057

On 2025-05-27 07:16:30, user Christian Panse wrote:

see also SIB in-silico talk https://youtu.be/acDiXq2xbOw?si=IKYyG2b_kRTTf_vk

On 2025-05-26 19:38:41, user Jalees Rehman wrote:

Now published in Nature Cancer: https://www.nature.com/articles/s43018-025-00975-6

On 2025-05-26 13:30:56, user Dan Bolo wrote:

I wonder how to contact the authors. I can't find an email :(

On 2025-05-26 12:27:08, user David Ron wrote:

https://uploads.disquscdn.c...

On 2025-05-26 11:33:58, user None wrote:

From your paper: "For the sampling of the skin of breast (n=231) eNAT® swabs (Copan, Brescia, Italy) were used for microbiome profiling. Participants moistened the swabs with a sterile pre-moisture buffer (50 mM Tris buffer [pH 7,6], 1 mM EDTA [pH 8,0], and 0,5% Tween-20) and gently rubbed the area for 30 seconds to collect sufficient biomass."

Did you tell the participants WHERE on the breast to sample? (over the nipple? in the underfold? (if there was one)). Did you classify the samples came from moist (e.g. with larger breasts) vs dry (e.g. with smaller breasts).

On 2025-05-25 13:33:25, user Bjarke Jensen wrote:

Dear authors,

You are to be commended on addressing a really interesting question. Considering Burmese pythons are born with a mass of ~100g and can reach ~100.000g, it almost has to be the case that hyperplasia underlies some/most/all of the ~1.000-fold growth.

Tracking mitosis of cardiomyocytes is notoriously difficult in mammals, in part due to the challenge of verifying which cell type the cell-cycle-positive nuclei belongs to, e.g. endothelial vs. fibroblast vs. cardiomyocyte (e.g. PMID: 20457832). Detection of the cardiomyocyte-expressed PCM1 has alleviated the situation substantially (PMID: 26073943). In reptiles however, it has been suggested the situation is more challenging because of a relatively small size of cardiomyocytes and at present there is not an equivalent marker to PCM1 (PMID: 31712265).

In the present manuscript, the authors use a good marker of cell cycle activity (pHH3) and the immunohistochemical detection of cytoplasmic sarcomeres (= cardiomyocyte cytoplasm) is also good; however, it is far from clear how the authors correctly assign a pHH3-positive nucleus to its cardiomyocyte cytoplasm (especially when markers of endothelial cells and fibroblasts are not employed). The shown microscopy in the present manuscript is almost interchangeable with didactic examples of the false-positives shown in Figure 2 of PMID: 20457832.

When the authors report on pHH3 detection, the values are double-corrected, first for tissue area (good) and then converted to fold change. The second step renders the reported values too abstract and it is difficult to understand which measurements varied between samples and treatments.

The authors, surprisingly, interpret the transcriptomic signatures of cell cycle activity as stemming from the cardiomyocytes, whereas the cardiomyocytes (likely) only comprise one-quarter to one-third of the total number of cells of the heart (e,g, PMID: 26846633). That is, a priori one could expect most of the cell cycle activity to originate from endothelial cells and fibroblasts rather than cardiomyocytes.

The authors conduct the interesting experiment of applying plasma from fed pythons to neonatal rat cardiomyocytes. They have previously done this with plasma from pythons 3 days into digestion (which allegedly induces hypertrophy). It then seems like a missed opportunity that the authors did not run in parallel batches of neonatal rat cardiomyocytes treated with 3DPF plasma (hypertrophy?) and 6DPF (hyperplasia?).

The authors are attempting an interesting study while there is scope for increasing the weight of the evidence.

On 2025-05-24 22:37:20, user Prof. T. K. Wood wrote:

Not clear to me why the most-prevalent defense system, toxin-antitoxin systems are completely ignored. This is a problem of relying on DefenseFinder, which mostly ignores TA systems. Note the first TA system studied in P. a. is HigB/HigA (doi: 10.1002/mbo3.346).

On 2025-05-24 17:00:48, user Misha Koksharov wrote:

I just like to commend this manuscript for writing bioRχiv with a Greek letter "χ", as in the logotype, rather than "bioRxiv". I think it's nicer but it seems like it's the only paper that does it so far. ????

On 2025-05-23 20:01:15, user Solene Moulin wrote:

Please visit https://academic.oup.com/ismecommun/article/4/1/ycae055/7645736 <br /> For the final published version of this paper.

@Biorxiv, can you point to the final peer reviewed publication<br /> https://academic.oup.com/ismecommun/article/4/1/ycae055/7645736

On 2025-05-23 18:54:57, user Lars Bode wrote:

Interesting data! But why did you use 5 mg/mL as your final concentrations? Concentrations in amniotic fluid are less than 5 ug/mL (a thousand fold less than what you are using here). Are those results physiologically relevant?

On 2025-05-23 18:27:25, user Minwoo Kang wrote:

This preprint has been published in a journal.<br /> https://biosignaling.biomedcentral.com/articles/10.1186/s12964-025-02227-7

On 2025-05-23 17:44:25, user L. Collado Torres wrote:

Thank you for this interesting proof of concept work! My team did a journal club about it (see https://bsky.app/profile/mbarse.bsky.social/post/3lps3ygzicc2a for details), as our colleagues are considering using your approach for a new dataset.

The pre-print is well written and easy to follow, kudos to you! If you have time, I'd greatly appreciate if you could answer some questions we have.

Question 1: applicability to other brain regions

* Figure 3d and Table 2 show two set of confidence bands (narrow and wide), which would lead to excluding 4.9% or 12.75% of the cells respectively. That's when using data from the same brain region for both training the model and applying the model. Figure 5 shows the results from training the model in data from the VTA brain region and then applying it to the NAc brain region. How do the confidence bands look like in this case? Is the percent of cells excluded in those bands similar to the results from Figure 3d and Table 2? It's hard to guess this from comparing Figure 3b and Figure 5c.<br /> * For context to the above question, we are wondering whether we would have to train the model on new data or not before we can apply it to another brain region (not the VTA). If the answer is that the % of cells excluded doesn't change much, then there's not that much to gain from re-training the model on data from the same new brain region (which would involve generating pilot data if none is publicly available). Of course, assuming the cell types are somewhat similar in both brain regions: particularly the sex-dependent effects on neuron transcriptional activity.

Question 2: code

We appreciate that you shared your code on GitHub (we checked version https://github.com/Jeremy-Day-Lab/Twa_etal_2024/tree/2cf2eff2241a8caf9e1405c2f29b6e68f1d6850e ). In particular, the "sex-prediction-model" HTML and Qmd files are very comprehensive. However, we haven't been able to find your model predictions. It seems to me that we would need to re-run some of your analyses before being able to apply your method to new data. Maybe this is outside the outscope of your project, but, do you plan on providing the objects and some easy to follow steps for applying your models to other data? Maybe you are planning on converting some of your code into functions and bundling them together as an R/Bioconductor package. If you have questions about that process, we can help a bit.

Thanks again for sharing a pre-print of your great proof of concept!

Best,<br /> Leo

PS. Questions are written relative to figure numbers from version v2 of this pre-print.

Leonardo Collado Torres, Ph. D.<br /> Investigator, LIEBER INSTITUTE for BRAIN DEVELOPMENT<br /> Assistant Professor, Department of Biostatistics<br /> Johns Hopkins Bloomberg School of Public Health<br /> 855 N. Wolfe St., Room 382<br /> Baltimore, MD 21205<br /> http://lcolladotor.github.io

On 2025-05-23 14:45:55, user Yi Xue wrote:

Should APEX2 be 'engineered ascorbate peroxidase_2', rather than 'Apurinic/Apyrimidinic Endodeoxyribonuclease 2' as written in article?

On 2025-05-23 12:04:52, user Malte Bødkergaard Nielsen wrote:

The preprint has been peer-reviewed and published (open access) in 'FSI: Genetics' with DOI:<br /> 10.1016/j.fsigen.2025.103291

On 2025-05-22 22:05:51, user Lori Burrows wrote:

PilY1 is not a minor pilin, it is a large pilus-associated adhesin that forms a complex with minor pilins PilVWX ( https://doi.org/10.1074/jbc.M114.616904) . This complex was previously shown to regulate expression of the AlgR-dependent operon that encodes it ( https://doi.org/10.1371/journal.ppat.1007074) .

On 2025-05-22 15:29:14, user SM wrote:

The manuscript has been revised and published:

Mohammed S, Kalogeropoulos AP, Alvarado V, Weisfelner-Bloom M, Clarke CJ. Serum and plasma sphingolipids as biomarkers of chemotherapy-induced cardiotoxicity in female patients with breast cancer. J Lipid Res. 2025 Apr 5;66(5):100798. doi: 10.1016/j.jlr.2025.100798. Epub ahead of print. PMID: 40189207.

On 2025-05-22 15:24:16, user Manuel Kleiner wrote:

Great article! Very cool discovery!<br /> The phenomenon sounds very similar to what is described here<br /> https://www.biorxiv.org/content/10.1101/2024.12.31.630880v1.abstract <br /> might be good to cite this article

On 2025-05-22 11:49:26, user Amirhossein Nayeri Rad wrote:

Nice article. Just a point to mention: Authors have used the Seurat tool function to label the cell cycle phase of single cells, however, this function has been established based on the gene expression in proliferative cells (Also stated by the authors). Senescent cells already exhibit downregulation of many genes involved in S/G2/M phase (PCNA, MCM2, MCM4, RRM1, MCM6, GMNN, HMGB2, NUSAP1, BIRC5, TOP2A, AURKB, BUB1, KIF11 etc.) causing these cells to be mislabeled as G0/G1 while they might be genuinely in S/G2/M Phase (as some are 4N). Interpretations related to cell cycle phase solely based on gene expression might not be appropriate for stressed cells, which should be addressed.

On 2025-05-22 07:27:48, user Anthony Smith wrote:

Can the authors please respond to my comment below?<br /> Thank you. Anthony Smith

On 2025-04-29 07:57:38, user Anthony Smith wrote:

I read your preprint manuscript - a nice read and congratulations on development of this cool tool.

I went online to test the tool and searched for “Salmonella genomes from South Africa”. I was concerned to only find 125 genomes, as there actually should be thousands of genomes, as we now routinely sequence all our clinical isolates of Salmonella.

All our data is submitted to the EnteroBase platform, made immediately publicly available, and the data is automatically submitted to GenBank. https://enterobase.warwick.ac.uk/species/index/senterica ; <br /> https://enterobase.warwick.ac.uk/species/senterica/search_strains?query=strains:country:South%20Africa

Could you please revisit finding all Salmonella data from South Africa and make these data visible on the http://amr.watch platform.

Thank you.

Dr. Anthony Smith<br /> Principal Medical Scientist<br /> Centre for Enteric Diseases<br /> National Institute for Communicable Diseases<br /> South Africa

On 2025-05-21 16:46:14, user Alice Deal wrote:

Some results/conclusions in this text are strange or not rigor: 1. Why were Scx+ and Scx-lineage cells so few-to-no in the hematoma and callus in the long bone midshaft fracture model? Several published abstracts/papers using the same models showed substantial Scx+ or Scx-lineage cells within the callus 7 days or 14 days post fracture. Are you sure this data is correct or integral? Why don't you use multiplex-IF or multi-reporter model to verify the relationships of Scx-lineage cells and Prg4-lineage cells? We have to know clearly about their true identities to get biological explanations. 2. I don't understand why you do the so called second fracture experiments. What sense does it make? How do you distinguish the periosteum-sourced Td+ cells from the Td+ cells originating from any other structures after second fracture? There are many published examples showing different types of mesenchymal cells giving rise to other structures after long time lineage tracing. 3. Can you do some single cell RNA sequencing on the FAPs, Scx+ cells and fractured callus cells followed by trajectory analysis to show how they differentiate in vivo? That will be much stronger evidence for your final conclusions. 4. Can you do the muscle transfer experiments between Prg4ER/Td/DTA mice and their WT siblings followed by your cell ablation and fracture experiments? Because we know very well Prg4 labels superficial cartilage cells, tendon cells and any other cells expressing this proteoglycan for lubrication, there is no doubt that Prg4+ cell ablated model will have musculoskeletal movement disorder, healing defects and other phenotypes. How do you simply attribute this overall effect to your so-called Prg4+ FAPs by only using Prg4/DTA model? It didn't even exclude Prg4+ Scx+ co-expressing cells when Scx-mutant mice were already demonstrated with bone healing defect based on publications.

On 2025-05-21 15:59:16, user Hindra H wrote:

Please link this preprint to the published article: https://doi.org/10.1128/msystems.01368-23 <br /> The title is different after peer-review process.

Requested by Hindra (hindraf@mcmaster.ca)

On 2025-05-21 11:40:21, user Stefano Pagliara wrote:

This manuscript has now been published in PLoS Biology https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3003155

On 2025-05-20 21:39:39, user Chase Clark wrote:

https://github.com/medema-group/BiG-SCAPE/tree/master/Annotated_MIBiG_reference

Doesn't exist in the current master branch, needs to be updated to reference the permalink

e.g. https://github.com/medema-group/BiG-SCAPE/tree/bb2264c8101f8f7204f2799e41aeef2a2505da72/Annotated_MIBiG_reference

On 2025-05-19 23:28:03, user Thomas Gilmore wrote:

Undergraduate students in BI576, “Carcinogenesis”, at Boston University had the assignment of reviewing a preprint paper. This was one of the preprints that they reviewed. They provided a number of comments on the paper. The students fully acknowledge that they are not scientific experts. Nonetheless, they hope that some of their comments will be useful to the authors as they prepare their work for submission to a peer-reviewed journal. <br /> The interplay between diet, the gut microbiome, and cancer risk, particularly colorectal cancer (CRC) was a fascinating topic for us to dive into. Finding links to gut chemistry with cancer risk in C57Bl/6 mice and human colorectal cancer patients, through investigating how a Western style diet affects gut microbial composition, sulfide production, and intestinal epithelial function through a combination of murine models and a meta-analysis of human patient data, we believe laid a strong foundation. These results suggest that sulfide metabolism serves as a functional biomarker for dietary impact on CRC risk, with gene expression analyses showing diet-driven suppression of sulfide detoxification enzymes in intestinal stem cells, particularly an enrichment of Erysipelotrichaceae leads to the strong possibility of CRC association.<br /> This dual species approach strengthens the relevance of the findings. However, there were some limitations in current methodologies, such as small sample sizes and insufficient controls for geographic dietary variation. Along with providing evidence of between Erysipelotrichaceae and high sulfide concentration in the gut. Despite these caveats, the study contributes meaningfully to the growing body of literature linking nutrition, microbiome dynamics, and oncogenesis. The findings warrant further exploration into metabolic pathways influenced by diet and underscore the need for broader, better controlled human studies to confirm causative links between microbial sulfur metabolism and colorectal cancer. Below are our group's critiques of the paper.<br /> Figure 1 presents an informative and visually accessible graph, though several refinements could improve its readability and clarity. One immediate issue is the placement of axis titles and labels, which currently overlap with axis ticks on both the x- and y-axes. Ensuring a clearer separation between these elements would enhance legibility. Additionally, the bacterial names on the graph may benefit from abbreviation or reformatting to make individual taxa easier to identify. Organizing the bacteria according to taxonomic hierarchy could also aid interpretation, helping readers quickly distinguish between different microbial groups and understand their relative significance. It would be useful to explicitly note in the figure legend that bacteria from the same taxonomy are color coded similarly, making visual groupings more intuitive. Furthermore, revising the color palette to accommodate colorblind friendly options would broaden accessibility and ensure all viewers can accurately interpret the data.<br /> The accompanying discussion of Figure 1 effectively contextualizes the findings and links them back to the overarching theme of diet and microbial sulfide production. One suggested edit for clarity and consistency is to revise the phrase “Importantly, we show that” to “Importantly, we showed that,” aligning with the past tense scientific narrative. Similarly, the assertion that “diet is the strongest determinant” of gut sulfide production might be more accurately phrased as “diet is a strong determinant,” since other potential influences were not investigated in the study. Supporting this revised claim with references to other research examining factors that affect gut sulfide production would help reinforce the conclusion.<br /> Notably, the concluding sentence “Thus, we posit that sulfide production can be considered a community level functional readout of dietary sulfur nutrient input that is potentially linked to multiple effects on the mucosa, including the intestinal remodeling characterizing IBD and tumor risk” is particularly effective. It succinctly captures the relevance of the findings and articulates their broader implications for understanding how diet may influence mucosal health and disease risk. <br /> Figure 2 aims to demonstrate that Erysipelotrichaceae abundance is elevated in mice fed the NWD1 diet compared to those fed the AIN control diet. While the figure successfully communicates a statistically significant difference—supported by the inclusion of both P- and Q-values (P = 0.019917, Q = 0.025892)—its overall presentation could be improved to enhance readability and interpretability. Most notably, the use of different shapes to represent mouse sex adds unnecessary complexity, as these distinctions are not analyzed or discussed within the figure or the main text. Similarly, the use of red and blue color coding for diet groups is redundant, given the clear labeling on the x-axis. Simplifying these elements and ensuring the figure employs a colorblind friendly palette would make the data more accessible. Importantly, statistical significance is only indicated in the caption, not visually within the figure itself. Including an asterisk or bold text to highlight the difference in NWD1 would make this clearer to readers. Additionally, the figure would benefit from stating the sample size directly, including clarification that both sexes were equally represented. Overlaying a median bar or converting the scatter plot to a box plot would also improve visual clarity, particularly where individual data points overlap.<br /> Although the inclusion of the P- and Q-values is appreciated, the caption does not describe how these values were calculated, nor does it provide adequate information on sample size, data collection methods, or statistical models used. For example, while the MaAsLin2 model incorporated "Sex" as a fixed effect, the figure and accompanying text do not explore whether an interaction exists between sex and diet—a potentially informative variable. This omission is particularly notable given the use of sex-based symbols in the graph. In terms of content relevance, the data in Figure 2 might be more appropriately placed in the supplementary material, particularly since Figure 3 appears to carry the narrative forward more directly. Supplemental figures like Figure 5, which show trends in microbial abundance over time for individual mice, are valuable for highlighting inter individual and potential sex-based variation. However, more detail should be provided on how bacterial groups were selected for inclusion, as well as on the decision-making process for which data were elevated to the main figures. Finally, while the use of standardized sequencing and DNA extraction methods (e.g., PowerFecal kit, V4-V5 region) strengthens methodological consistency, the absence of a control group to account for batch effects (such as collection date or sequencing run) is a limitation that should be addressed in future studies.<br /> In Figure 3 the study presents compelling associations between dietary intake, gut microbiota, and colorectal cancer (CRC) risk, yet several aspects warrant deeper scrutiny and refinement. Notably, the experimental design utilized small cohorts, with only eight mice per diet group (n = 8), which limits statistical power and the generalizability of the findings. A central biochemical focus of the study is hydrogen sulfide (H₂S), which the authors implicate in mucosal damage. The original phrasing, “Sulfide has been shown to damage the mucosa by cleaving disulfide bonds found in mucus and has been linked with diseases with marked mucosal damage,” can be more clearly articulated as: “Sulfide cleaves disulfide bonds in mucosa, damaging the tissue and linking it to diseases with marked mucosal damage.” However, while this biochemical mechanism is plausible, the study lacks direct experimental evidence of mucosal degradation or impaired mucosal function, such as histological analysis or barrier function assays, to substantiate this claim within the context of their model.<br /> Furthermore, the relationship between Erysipelotrichaceae abundance and sulfide production remains correlative rather than causal. Although the meta-analysis shows an increased prevalence of Erysipelotrichaceae in CRC patients and mice on a Western style diet, no mechanistic data address whether this microbial family is directly responsible for enhanced sulfide synthesis. The current framing risks overstating the significance of Erysipelotrichaceae without isolating its metabolic role. Additionally, the claim that sulfide is a sufficient marker for mucosal damage and CRC risk oversimplifies the complex pathophysiology involved. Line 412-413 should be removed to avoid this overgeneralization, while retaining the more nuanced correlation stated in Line 411. Lastly, the diets used in the study differ across multiple nutritional variables, including fat, fiber, calcium, vitamin D, and sulfur containing amino acids. The conclusion attributing increased sulfide production to a single component overlooks this complexity. A more accurate interpretation would acknowledge the composite nature of the Western style diet and the need for controlled studies isolating specific dietary variables.<br /> Figure 4 provides valuable insights into how dietary interventions affect the expression of mitochondrial enzymes involved in sulfide metabolism, yet several improvements would enhance the clarity and interpretability of the data. Figure 4a offers a helpful schematic that illustrates the localization of sulfide detoxification enzymes—such as SQR (Sqrdl), ETHE1, SUOX, and TST—within mitochondrial structures and identifies the directionality of reactant flow. However, the figure would benefit from a more thorough introduction describing each enzyme’s specific role in sulfide detoxification and the broader significance of this pathway. Without this context, it is unclear why all four enzymes are studied, especially since ETHE1, SUOX, and TST appear to serve overlapping functions. Furthermore, the inconsistent labeling of sulfide quinone reductase-like protein referred to as SQR in Figure 4a and Sqrdl in Figure 4b and the text should be standardized across the manuscript to avoid confusion. The visual consistency between Figures 4a and 4b also requires attention, particularly the shading conventions, which imply relationships between data that are not explicitly connected.<br /> Figure 4b effectively illustrates differential gene expression between diet groups, demonstrating that expression of Ethe1, Suox, and Tst is significantly altered in response to the NWD1 diet. However, the figure would be clearer if significance were marked with asterisks above the relevant bars rather than listing precise p-values, which could instead be moved to the figure caption or results section. Importantly, Sqrdl expression did not show a significant difference, which raises interpretive questions. Including a hypothesis in the discussion about why Sqrdl expression was unchanged—despite the other enzymes showing significant variation—would help contextualize the data. Additionally, the discussion of these results would be strengthened by addressing why the observed changes in Ethe1, Suox, and Tst expression are biologically meaningful and warrant further study, as briefly noted in the concluding sentence of that section. Greater emphasis on the relevance of sulfide metabolism in intestinal stem cells could also help bridge the mechanistic findings to broader health implications, such as inflammation or tumor development.<br /> The associated methods section also lacks critical detail. The repeated mention of “bulk RNAseq” without a quantitative description (e.g., read depth, sample pooling strategy) is insufficient and should be clarified rather than deferring entirely to the Gene Expression Omnibus. Additionally, it is unclear how mRNA expression was normalized in the analysis—a fundamental detail that should be explicitly stated either in the methods or the figure caption. Lastly, phrasing such as “at sacrifice” could be revised to more standard terminology like “at the time of euthanasia” to maintain scientific professionalism. Addressing these issues would greatly enhance the transparency, rigor, and impact of the study’s findings.<br /> Figure 5 provides a valuable cross-species summary of the association between Erysipelotrichaceae taxa and colorectal cancer (CRC), but several refinements would improve its clarity, consistency, and interpretability. In Figure 5A, the addition of an x-axis label—specifically identifying it as the “AUC Score”—would aid reader comprehension, particularly for those unfamiliar with this metric. To further clarify the analysis, the figure caption or discussion should define the AUC (Area Under the Curve) score, explaining that scores below 0.5 may indicate taxa enriched in CRC cases, but that statistical significance is determined by whether the 95% confidence interval crosses the 0.5 midline. Highlighting this explicitly in both the figure and caption would prevent misinterpretation. Visually, increasing the size and contrast of the asterisks denoting significance—and using colorblind friendly alternatives—would enhance accessibility. The midline at 0.5 should be made more prominent to emphasize its role as a visual reference for enrichment or depletion in CRC.<br /> In Figure 5B, the rationale behind using cysteine desulfhydrase genes as markers for sulfide production should be more thoroughly explained in the caption or Discussion. A brief explanation of their role in hydrogen sulfide synthesis would provide essential context, especially since the paper hinges on microbial sulfide metabolism as a mechanism linking diet and disease. Terminological clarity is also needed: the phrase “presence/absence” should be replaced with “presence or absence” for proper usage, and minor grammatical errors (e.g., “cysteine desulfhydrase genes”) should be corrected. Consistency between panels A and B would also enhance the reader’s experience. This includes presenting the bacterial species in the same order along the y-axis, capitalizing figure labels (“Figure 5A” and “Figure 5B”), and starting the caption for each panel on a separate line. Shortening bacterial species names (e.g., using D. fastidiosa instead of Dilemma fastidiosa) could make the y-axis more readable without sacrificing clarity.<br /> Overall, these changes would not only improve the figure’s visual clarity but also support a more nuanced understanding of the results. Aligning terminology, improving visual accessibility, and strengthening the interpretive context particularly regarding the AUC metric and sulfide related gene markers would make Figure 5 more informative and persuasive within the broader narrative of dietary influence on gut microbiota and CRC risk.<br /> Lastly our group proposed several edits aimed at improving the clarity, consistency, and grammatical precision of the manuscript. Key terms such as “AUC” (pg. 16), “Lgr5hi ISCs” (pg. 5), and “IACUC” (pg. 6) should be defined upon first use to aid reader comprehension. We also recommended standardizing terminology particularly by using “Western style diet” consistently throughout the text. Edits included grammatical corrections (e.g., appropriate comma placement, hyphenation, and noun verb agreement), as well as restructured sentences to enhance readability and provide necessary methodological details. Several phrases were revised to reduce ambiguity and better support the study's conclusions.<br /> ● Pg 2, line 47: “Western-style diets, characterized by high fat and protein content and low micronutrient levels, promote...” (added commas for clarity).<br /> ● Pg 2, lines 52–53: “NWD1 is a purified Western-style diet that induces sporadic intestinal and colonic tumors in the absence of genetic predisposition or carcinogen exposure.”<br /> ● Pg 2, line 69: “...human CRC patients, suggesting microbial signatures of CRC and gut ecosystem...” (clarified phrase structure).<br /> ● Pg 3, line 80: “Colorectal cancer (CRC) is the second leading cause of cancer deaths and the third most common cancer worldwide.” (deleted unnecessary comma).<br /> ● Pg 3, lines 84–85: Added a comma: “...mortality in both human and mouse studies, and can shape...”<br /> ● Pg 3, line 89–90: Clarified structure: “...with early and late-stage CRC, but how diet, microbes, and gut chemistry influence cancer development is not well understood.”<br /> ● Pg 3, line 91: Changed to “microbially derived” (removed incorrect hyphen).<br /> ● Pg 3, line 93: Clarified chemical notation: “sulfide (including H₂S, HS⁻, and S²⁻).”<br /> ● Pg 3, line 96: “sulfur-containing amino acids” (standardized compound adjective).<br /> ● Pg 4, line 112: “...is NWD1, which was formulated...” (added comma for flow).<br /> ● Pg 4, line 117: “...tumors in wild-type mice, which rarely, if ever, develop...” (added second comma).<br /> ● Pg 4, line 123: “...donor nutrients; and increased fat content that reflects levels linked to increased CRC risk.” (refined punctuation).<br /> ● Pg 4, lines 115–118: Reworked sentence for clarity: “Feeding mice NWD1 accelerates and amplifies intestinal and colon tumors in many genetic models and is unique in reproducibly causing sporadic tumors in wild-type mice, which rarely develop such tumors on other diets.”<br /> ● Pg 5, line 130: Suggested defining “Lgr5hi ISCs” on first use.<br /> ● Pg 6, line 153: Recommended defining “IACUC” (Institutional Animal Care and Use Committee).<br /> ● Pg 8, line 191: “Freshly voided fecal pellets were collected from individual mice...” – suggested specifying the collection timing for clarity.<br /> ● Pg 8, line 201: “...using a –p-trim length of 301, and a feature table was created.” (added optional comma for smoother reading).<br /> ● Pg 9, lines 214–215: “...single-cell suspensions were prepared and sorted by FACS...” – clear and concise.<br /> ● Pg 11, line 275: “(Supplementary Fig. 4, 5)” – recommend using full form for clarity, depending on journal style.<br /> ● Pg 12, line 284: “Samples from male mice are represented by open triangles; female mice by filled dots.” (revised for parallel structure).<br /> ● Pg 13, line 310: “...control AIN76A diet or NWD1, a Western-style, colorectal cancer-promoting diet, from weaning.” (added comma for clarity).<br /> ● Pg 13, line 311: “A total of three female mice were fed AIN, and three female mice were fed NWD1.” (deleted redundant “3”).<br /> ● Pg 14, line 323: “...knockout of a key gene downregulated by NWD1...” (moved line break and cleaned structure).<br /> ● Pg 14, line 329: Deleted extraneous comma after “(Sqrdl)”.<br /> ● Pg 16, lines 367–368: Suggested moving AUC definition earlier in the text (e.g., pg. 9) and clarified phrasing.<br /> ● Pg 16, line 372: Changed “presence/absence” to “presence or absence” for grammatical accuracy.<br /> ● Pg 16, line 373: Corrected to “cysteine desulfhydrase genes” (fixed redundancy).<br /> ● Pg 17, lines 388–390: Streamlined sentence for clarity: “...associated with CRC in a nine-study, 1,650-sample meta-analysis of human colorectal cancer and control metagenomes.”<br /> ● Pg 17, lines 391–393: Removed line break and clarified phrasing.<br /> ● Pg 18, line 417: “...within four days of the shift to NWD1.” – removed article “the” before “NWD1”.<br /> ● Pg 19, lines 446–448: Revised for grammar and punctuation: “...dynamics of dietary alterations in bacteria; the microbial metabolite, sulfide; and gut epithelial gene expression. As such, we acknowledge several limitations...”<br /> ● Pg 19, lines 448–449: Sentence fragment—needs completion or merger with another sentence.<br /> ● Pg 19, lines 453–455: “...cannot distinguish whether the association of Erysipelotrichaceae with CRC patients...” – clarify structure.<br /> ● Pg 20, lines 455–456: Removed redundant “of”: “...is a consequence of consuming a Western diet.”

On 2025-05-19 19:31:22, user Melise wrote:

I couldn't run this program because BlastN uses the input directory as the search path. I can't put the sequences.fasta from the database in this directory because the program uses it as input. How to solve?

On 2025-05-19 17:48:24, user Mobin sikder wrote:

This manuscript explores the reasons behind how collagen mimetic peptides (CMPs) self-assemble into diverse hierarchical supramolecular structures like fibrils, nanotubes, nanosheets. The study shows that by alternating electrostatic charge pair and cation-π interactions enables control over the symmetry, periodicity and morphology of assemblies and reveals how pH-dependent behavior affects structural transitions. Researchers employed CD, Cryo-EM, SEM, SAXS and coarse-grained MD simulations to validate these findings.<br /> This work offers a comprehensive understanding in sequence-structure correlation and hierarchical assembly behavior. The use of several advanced characterization methods and simulation to cross-validate morphological hypotheses adds strength to the conclusions.

Major Points<br /> I think the Cryo-EM reconstruction that shows D-banding in FDY2 fibrils is one of the most exciting aspects of this work, especially the attempt to propose detailed stacking models like the 8+2 and 16+4 arrangements (Figure 4e–h). However, the resolution is around 8 Å, I believe it is important to be a bit cautious with how confidently these structural models are interpreted. At this resolution, the overall shape and periodicity are certainly visible but distinguishing the exact number or orientation of individual triple helices can be quite difficult.<br /> In my view, the proposed lateral packing models are valuable as hypotheses and help to guide future exploration, but they might be more appropriately described as possible interpretations rather than definitive conclusions. It could also help to clarify in the main text that higher-resolution methods or related techniques like SAXS-based modeling would be needed to confirm these specific arrangements.

Minor Points:<br /> 1. On page 18, the authors acknowledge that their coarse-grained model cannot capture nanotube or nanosheet formation, yet: They do not sufficiently discuss why their model fails to capture these.<br /> 2. In “Introduction” part, “The synthesis of collagen-like molecules that recapitulate the hierarchical assembly of collagen also presents an opportunity to” this line is incomplete, I think.<br /> 3. In figure 1, (panel b) where lateral and axial interactions are shown can be designed more carefully.<br /> 4. In figure 3, (panel d & f), scale bar can be labeled on the image like panel e & g.<br /> 5. On Small Angle X-ray Scattering section: “Raw data were processed were processed using Python 3.11.” duplicate phrase “were processed” appears twice.<br /> 6. CD melt derivative curves (Fig. S10) can be labeled with Tm values directly on graphs.

Overall, I believe the manuscript is innovative and methodologically sound. The experimental claims are generally well supported though some discussion sections would benefit from more quantitative clarity and acknowledgment of limitations. With minor textual and figure clarifications, the work should be suitable for publication and will likely be influential in the field of biotechnology.

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

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

eLife Assessment

This study presents a useful array of analyses of the effects of training and/or instruction to use the method of loci during episodic encoding and retrieval. A major strength of the experiment is the impressive recruitment of memory athletes and the training of novice athletes to use the method of loci, long known to improve the precision of memory recall. That said, the sheer number of results and their organization should be addressed; streamlining the results and placing them, whenever possible, in a theoretical framework. As it stands, the presented work is incomplete with respect to the major conclusions that training itself leads to neural differentiation of prefrontal cortical neural patterns, and the authors need to temper these claims.

Reviewer #1 (Public review):

Summary:

The question of how or whether "extensive memory training affects neocortical memory engrams" (to use the words of the authors) is an interesting question and an area where I think there is room for advancing current knowledge. That said, I do not think the current paper succeeds in meaningfully addressing this question. At a conceptual level, I really struggled with the predictions and interpretations of the findings. There are also several elements of the experimental paradigm and analysis decisions that feel incompatible with the claims that are made. While the manuscript does demonstrate that several measures of neural pattern similarity differ between the various groups of individuals, the issue is that it is difficult to draw clear conclusions from these findings.

Strengths:

(1) This is a very unique dataset. Being able to recruit and enroll high-level memory athletes is impressive.

(2) In principle, comparing memory athletes to control subjects, active control subjects (who received working memory training), and trained subjects (who received method of loci training) is very appealing.

(3) In several ways, the authors were rigorous in their analyses.

(4) In principle, the question of how memory training influences neural similarity vs. dissimilarity is of potential interest.

Weaknesses:

(1) As far as I can tell, the training manipulation is fully confounded with instructions. That is, subjects were only instructed to use the method of loci if they had completed method of loci training (or if they were the memory athletes). For the training group, in the pre-training session, there was no strategy instruction (subjects could do whatever they wanted), but post-training, they were told to use the method of loci. I understand the argument, of course, that naïve subjects might not be very good at using the method of loci if they had no experience with it. But, it does seem entirely possible that some (or even many) of the observed fMRI results that are attributed to "extensive training" are better explained by strategy use. That is, maybe the effects can be explained by TRYING to use the method of loci as opposed to actual proficiency with the method of loci. It seems impossible to address this, given the design of the experiments. As such, any claims about the effects of memory training, per se, feel inappropriate. It feels equally plausible that the effects are due to the strategy instruction. If the same results could be obtained through a simple strategy manipulation without ANY training at all, that would radically alter the interpretation of the effects. I think the strategy use account is, in fact, quite viable because it is very easy to improve subjects' memories with a method of loci instruction (relative to no strategy instruction) without ANY practice at all. Obviously, practice does improve memory performance with the method of loci, but my point is that even without any meaningful practice, there is likely to be SOME immediate benefit to adopting the method of loci as a strategy. There is also the question of why the effects for the memory athletes weren't obviously stronger than for the trained group, given that the memory athletes have much more experience with the method of loci. Ultimately, the problem with the current design is that I don't see how one can tease apart the role of training, per se, vs. strategy use.

(2) There is no clear theoretical framework for the predictions or interpretations. The Results section is mostly a list of lots of different permutations of analyses (similarity within a group, between groups, between trials, across trials between subjects, during encoding vs. retrieval, frontal vs. hippocampal vs. parietal ROIs, etc). For each analysis, I did not have an intuition for what the prediction should be (e.g., should athletes have higher or lower pattern similarity?), and even after seeing all the results, I still do not have an intuition for how to interpret them. For the main results related to dissimilarity in prefrontal cortex, I would have, if anything, predicted the opposite: that when individuals are trained to use a common strategy, there would be MORE similarity between them. The Discussion acknowledges a very wide range of possible factors that might contribute to measures of similarity/dissimilarity, but I am ultimately left feeling that I have no idea how to interpret the results because the design and analyses were not structured such that any of these interpretations could be teased apart.

(3) Same theme: the analyses shift from frontal regions (when looking at encoding) to hippocampus and precuneus (when looking at temporal recency). This shift in ROIs is confusing. The analyses (encoding vs. recognition) are essentially confounded with the ROIs (frontal vs. hippocampal/precuneus), so it's hard to know whether different analyses yielded different patterns or different ROIs yielded different patterns. Why were the frontal regions that were important for encoding ignored for the temporal recency judgments? And the fact that medial temporal lobe regions showed opposite effects to the frontal regions during encoding did not get much attention. Given that there were opposing patterns (dissimilarity vs. similarity) across different brain regions, the framing of the paper (that "the method of loci may bolster uniqueness") feels like a very selective representation of the data.

(4) One of the more surprising aspects of the analyses (or at least one of the analyses) is that representational similarity analyses (RSA) are used to compare the average activity pattern (averaged across all trials) between different individuals. At a conceptual level, this really just reduces to a univariate analysis. It is not standard (or intuitive) to think about RSA that is essentially blind to the actual representational content. In other words, averaging across trials obviously washes out the content, and what is left are process-level effects. For process-level analyses, univariate analyses are far more common and seem more straightforward. However, these 'RSA' analyses are described as reflecting the "uniqueness of each word-location association" (an account which strongly implies content-level effects). This feels like an inappropriate description of what the analyses actually reflect.

(5) I think the analysis looking at trial-by-trial similarity during word encoding (showing greater dissimilarity among the experienced individuals) is a somewhat interesting result, but again, I think the interpretation is very difficult. It is hard (or, impossible, I think) to get a clear sense of what is driving those differences. Is it the association of a unique spatial context? Is it somehow a product of better encoding, per se (as opposed to distinct spatial contexts)? These things could be tested by actually manipulating the spatial contexts in a more controlled way. For example, the paper by Liu et al. that is cited several times - and also a just-published paper by Christopher Baldassano (Nature Human Behaviour) - each used a very controlled paradigm where the (imagined) spatial location associated with each item was known/manipulated. However, the design of the current study does not allow for these things to be teased apart.

(6) Relatedly, the training group seemed to receive instruction on a common spatial route, but, surprisingly, "Participants were free to choose which route and how many they would use to anchor the 72 items." Thus, if I understand correctly, we don't know whether the trained individuals were using common or distinct locations. And the fact that they learned a 50-location route but then studied a 72-word list is also a bit strange. Not having control or knowledge of the location that was associated with each word (sequence position) is a major limitation and also a major difference between the current study and other recent studies. For that matter, the word order was also randomized, so there was no control over whether the words and/or locations matched. These issues really complicate interpretation.

(7) Again, same theme: for the result showing lower trial-by-trial similarity (within-subject similarity), the question is why, exactly, training/experience is associated with lower trial-by-trial similarity. Does training specifically or preferentially lead to greater differentiation between temporally-adjacent trials (as in Liu et al)? Does it lead to greater differentiation IF subjects associate each word with a unique location? Or maybe there is a more abstract effect of sequence/position that is independent of spatial location? Importantly, each of these three possibilities that I mention here has a precedent in prior studies that were more tightly controlled. But here, there is no way to tease these apart because of the experimental design, limiting the conclusions.

(8) The ISC analysis described on p. 9 (line 328) is confusing. If I understand correctly, correlations between different trials were not computed (e.g., subject 1 trial 1 was not correlated with subject 2 trial 2). Rather, trial 1 was always correlated with trial 1 (in other subjects). Thus, it is not clear whether trial-level alignment matters at all. Maybe the same results would be obtained if there were no correspondence across subjects in trial number. Or if the trial order was shuffled within the subject. Given this, I simply don't know how to think about the data. And why did memory athletes show higher pattern similarity in this analysis as opposed to lower pattern similarity (as in some other analyses)? And why was this analysis performed by comparing memory athletes to each other as opposed to memory athletes to non-athletes? And, conceptually, why was this selective to the memory athletes or to the precuneus? And why was it selective to the temporal order test and not encoding? I am not asking the authors to answer each of these questions; rather, the point I am trying to make is that this analysis, and many of the analyses, seem to raise more questions than they answer.

(9) The ISC analyses are interpreted in terms of scene construction and context reinstatement, but these conclusions go (very) far beyond what the data actually shows. Again, I don't see how this analysis lends itself to a meaningful conclusion. And this general critique applies to many of the analyses reported in this paper.

(10) The fact that words were in random order per subject also makes the ISC analysis even more confusing to think about. The memory athletes had unique spatial routes (that they used for the method of loci) and unique word lists. So, why would it make sense to look at trial-level ISC? At a conceptual level, I simply don't understand what this is intended to capture.

(11) Differences in the pattern of results between the encoding and temporal memory recognition task are hard to make sense of and are not addressed in much detail. Why would it make more sense to have across-trial similarity during recognition than during encoding? I think any account of this is very speculative.

Reviewer #2 (Public review):

The authors aim to understand how intensive training with the method of loci changes the brain systems that support memory in both elite "memory athletes" and previously untrained adults. They combine a cross-sectional comparison of athletes and matched controls with a longitudinal training study including mnemonic training, active working-memory training, and passive control groups, and use fMRI pattern-similarity analyses to characterise how brain activity patterns during learning and temporal-order judgments become more distinct or more shared within and across individuals.

The dual design is a major strength. It combines findings from both real-world expertise and experimentally induced training and adds well-matched control groups. The representational similarity analyses are appropriate and reveal a clear, internally consistent picture in which learning with the method of loci leads to more idiosyncratic prefrontal and posterior cortical patterns during encoding, and more shared hippocampal-precuneus patterns during temporal-order retrieval, observed in both athletes and trained novices.

However, the study is complex and the manuscript dense, and some secondary analyses feel less central or are difficult to interpret. More importantly, while the neural evidence for training-related changes in representational format is compelling, the behavioural relevance of these changes is less clearly supported. The key per-group brain-behaviour correlations are weak and inconsistent, and the direct association between neural and behavioural change across all subjects is not clearly presented.

Overall, the work convincingly shows that extensive mnemonic practice reorganises neural representations in specific networks, but the strength and specificity of the claimed link to long-term memory improvements should be viewed as more tentative.

Reviewer #3 (Public review):

Summary:

This study sought to explore how neural representations during encoding change with expertise or proficiency in the method of loci (MoL). To do this, the authors compared three groups: memory athletes (experts in MoL), naive controls, and naive participants before and after 6 weeks of MoL training and analyzed how similar their encoding-related activity patterns were across groups and training. They found that in lateral prefrontal, inferior temporal, and posterior parietal regions, pattern similarity decreased with expertise and training. They also found that changes in similarity between pre- and post-training were associated with improvements in memory performance measured 4 months later. Additionally, in a follow-up exploratory analysis on the temporal order recognition task, neural patterns were more similar for those proficient in MoL - a contrast to the decrease seen at encoding. Taken together, the authors interpret these findings as evidence that proficiency with the method of loci is associated with distinct encoding representations: Broadly, the findings suggest that greater representational differentiation at encoding may be associated with better memory.

Strengths:

(1) The manuscript is impressively rich with analyses. Their general claim that neural differentiation increases between individuals with MoL experience is thus addressed in this work. Specifically, the authors effectively explore different levels of granularity to tackle the question of whether a participant's neural representation (with MoL experience) looks more similar to that of another (with less experience) during encoding.

(2) The authors connect their hypotheses about neural representational differences caused by training to behavioral data (and 4 months later at that).

(3) Although exploratory, they not only look at encoding-related differences, but also retrieval-related differences.

(4) The authors provide many supplementary figures with complementary and interesting findings. As I read, I found myself curious about exploratory analyses, which were then addressed in supplementary figures.

Weaknesses:

(1) The manuscript is impressively rich, but the number of analyses and levels of comparison (and how they are presented) made it difficult to follow. The paper would benefit from an anticipatory introductory paragraph (or an introductory Results paragraph) that explicitly states the hypotheses and which sections of the results addressed them. Additionally, given how this is a Methods-last formatted paper, the manuscript would benefit from a few introductory sentences at each Results section describing the methodology.

(2) One of the motivations needs strengthening. Given the introduction, the manuscript seems to be motivated by two complementary questions: (i) whether neural differentiation effects reported with short-term MoL training (as done in Liu et al., 2022) extend with longer-term training and expertise and (ii) whether training might lead individuals towards a canonical "expert" representation that can only be acquired through training as has been previously shown in other work (e.g., Meshulam et al., 2021).

The first motivation is clear and compelling. The second one, however, does not feel as well grounded. In studies like Meshulam et al., alignment is expected because participants are exposed to the same stimulus or concept. In contrast, as the authors note, a user of the method of loci is encouraged to create unique, vivid representations of their loci and to-be-remembered items - here, neural alignment is at odds with the premise of the technique. As such, the described tension between increased pattern similarity across the studies cited in the second paragraph of the introduction and individuals proficient with MoL feels underdeveloped (despite the reference-rich second paragraph).

The authors would benefit from articulating why the counterfactual of "increased neural alignment" might be expected, specifically, in the method of loci. In other words, why should we expect trainees to become more similar to experts when the strategy itself promotes idiosyncratic representations? Perhaps, the authors could distinguish between alignment at the level of knowledge representations vs the process of encoding (e.g., the act of placing items into loci).

(3) Relatedly, terminology referencing the employed methodology is a bit unclear. In some of the papers cited that look at pattern similarity across people (like Meshulam et al., or Koch et al.), the spatial patterns of individuals are compared with 'template' patterns that reflect the canonical representation of a concept or episode. However, the manuscript does not include this type of template-based comparison. This is understandable because there may not be a representative canonical pattern when each participant has their own idiosyncratic palace. In this case, a pairwise comparison may be more fitting as it focuses on the distances between people's representations instead of the distances between them and a group template. Although both comparisons (pairwise and template-based similarities) are related, they have different interpretations. A clearer justification for why pairwise similarity, instead of template-based similarity (as in many of the cited papers), is the more appropriate metric in this paradigm early on would add to the clarity of the work. Additionally, this slight difference in methodology was confusing because some portions of the text (including the figures) say "group average", but in others, we see "pairwise".

Minor Comments:

A recent paper (Masis-Obando et al., 2026, Nat Hum Behav) shows that stable and distinctive spatial representations can support later reinstatement of items placed within those contexts. Their conclusions seem to support your hypotheses and results here. In parallel, prior work (like Robin et al., 2018, J Neurosci) emphasizes the importance of spatial contexts for the representation of events. Given how MoL encoding relies on vivid context-item binding, including these perspectives in the Introduction (and/or discussion) may help frame the current findings within the broader memory literature.

Overall, this work provides rich and valuable contributions to the field.

On 2025-05-19 15:18:21, user Cody Loy wrote:

The following article has been published in J.Med.Chem ( https://pubs.acs.org/doi/10.1021/acs.jmedchem.5c00485 )

On 2025-05-19 09:10:53, user Thomas REHER wrote:

This preprint has now been published in Agronomy for Sustainable Development (2025), Volume 45, Article number 25, under the title:<br /> "Agrivoltaic cultivation of pears under semi-transparent panels reduces yield consistently and maintains fruit quality in Belgium."<br /> The final version is available at: https://link.springer.com/article/10.1007/s13593-025-01019-0 <br /> DOI: https://doi.org/10.1007/s13593-025-01019-0

On 2025-05-17 13:11:07, user Antony Merlin Jose wrote:

This preprint was split into two papers published in microPublication Biology. One describing the development of the assay ( https://www.micropublication.org/journals/biology/micropub-biology-001514 ) and another describing the difficulty reproducing associative learning when butanone is paired with starvation ( https://www.micropublication.org/journals/biology/micropub-biology-001568 ).

On 2025-05-17 06:44:22, user 11 wrote:

Summary<br /> The manuscript “An online GPCR drug discovery resource” describes an online resource of GPCR drugs, clinical trial agents, targets and disease indications. This resource offers unique reference data, analysis and visualization, and is availed as a new section, ‘Drugs and Agents in trial’ integrated in the GPCR database, GPCRdb. Furthermore, it includes a target selection tool for prioritization of receptors for future drug discovery.<br /> The major weakness of the paper is that the paper fails to include olfactory receptors in the database, the reliance on Open Targets disease association scores might miss novel hypotheses with weaker genetic backing. The validation of the newly introduced selection tools and visualizations, how the prioritization recommendations compare to existing approaches or human expert selection are unclear.<br /> This paper helps identify strategies and trends in current GPCR drug discovery and give insights into which already drugged, or yet untapped targets have the largest potential in specific diseases.<br /> Major Points<br /> 1. Validation of the Target Selection Tool<br /> The target selection tool offers a powerful yet swift means to prioritize GPCRs for future drug discovery based on disease indications, characterization, tissue expression and more. While there is no validation to show the tool’s performance. How does the tool perform when testing a now-successful GPCR target before it was clinically pursued? Consider revising by running the tool on historical data and show how it would have prioritized GIPR before the approval of tirzepatide.<br /> 2. Missing Method Comparison<br /> You described several new visualization methods (e.g., GPCRome wheel, intersecting Venns, Sankey plots) but how these tools are different from existing tools are not explained. Why are these tools better than existing tools such as those in Open Targets, ChEMBL, or DrugBank? consider adding a contextual comparison section to clarify the advantages and disadvantages of those tools, explain the innovations of the new visualization methods.<br /> 3. Disease Classification and Filtering<br /> The disease annotation relies on ICD-11 mapping from Open Targets (using EFO/HPO/MONDO). While manual curation of disease terms is not clearly defined. Adding a supplemental table listing these modifications would be great.<br /> 4.Exclusion of Olfactory Receptors Have Limitations<br /> Your research excludes olfactory receptors--a large family of GPCRs which play essential roles in cancer, reproduction, and metabolic regulation. Databas such as CORD (Comprehensive Olfactory Receptor Database) provides better annotations of olfactory receptors than those in GPCRdb, ligand binding and diseases linkage are also included. Including olfactory receptors in your research may reveal potential drug discovery opportunities.

Minor Points<br /> Technical Questions<br /> 1.Definition of "Agent" vs. "Drug" should be explained in the Abstract or Introduction section.<br /> 2.Explain why do you choose 0.5 as the association score cutoff from Open Targets.<br /> 3. The classification rules of “pharmacological modality” of agonist, antagonist and allosteric modulatorare not clear.<br /> 4. Non-human GPCR targets (e.g., mouse models) are not included.<br /> Stylistic Issues<br /> 1."the platform contains 516 drugs, 337 agents…" is later repeated again in Figure 1 text.<br /> 2. “bioactivity data” and “bio-activity” are used inconsistently.<br /> 3. PDSP Ki database is used without prior expansion.<br /> Unable to Assess:<br /> Data integration are made from tools like Pharos, GTEx, and the Human Protein Atlas, especially for expression-based filtering. I cannot offer expert feedback on the transcriptomic expression atlas validation and the correctness of the tissue expression normalization procedures.<br /> Final Reflection<br /> This paper made great contributions to the GPCRs field by offering an online resource of GPCR drugs, clinical trial agents, targets and disease indications. The platform will help streamline drug development pipelines, helps identify strategies and trends in current GPCR drug discovery and give insights into which already drugged, or yet untapped targets have the largest potential in specific diseases.

On 2025-05-17 05:58:02, user PSarker wrote:

Peer Review: The Immune Response Against Cancer is Modulated by Stromal Cell Fibronectin<br /> Summary :<br /> The study conducted by Lubosch et al. explores how bone marrow stromal cells, particularly fibronectin, affect the immune response to cancer. The main goal was to analyze how these fibroblasts impact tumor development, with a specific emphasis on the mechanisms responsible for their noted tumor-suppressive effects.<br /> Strengths:<br /> This study offers an in-depth examination of the intricate interactions among stromal cells, the extracellular matrix, and the immune response within the tumor microenvironment. Its well-structured approach enhances our understanding of these complex relationships and their implications for cancer biology.<br /> 5. The discovery of a particular subpopulation of bone marrow stromal cells (osterix+, CD31-, CD105-) and their essential contribution to the production of fibronectin in facilitating anti-tumor immunity through Ly6G+ cells is a significant and groundbreaking finding.<br /> 6. The research utilizes a diverse set of advanced methods, such as various murine cancer models (including syngeneic and immune-deficient), transgenic mouse strains for conditional gene deletion (fibronectin and β1 integrin), cell sorting along with flow cytometry, proteomic assessment, and in vitro functional assays (migration, cytotoxicity, signaling). The incorporation of littermate controls and suitable statistical analyses enhances the reliability of the results<br /> 7. The paper provides an in-depth analysis of the molecular mechanisms underlying tumor suppression, focusing on several critical components. Key receptors such as α5β1 integrin and Toll-like receptor 4 (TLR4) are highlighted for their pivotal roles in mediating cellular responses to the tumor microenvironment. The study further elucidates the involvement of the NF-κB signaling pathway, which is crucial for regulating immune responses and inflammation as well as orchestrating the tumor suppression process. Additionally, the research identifies Ly6G+ neutrophils as important effector cells that contribute to the anti-tumor immune response, revealing their mechanisms of action and interaction with other cellular players in the tumor microenvironment. This comprehensive examination sheds light on the complex interplay between these molecular elements and their collective impact on inhibiting tumor growth.<br /> 8. The correlation that exists between low CD105/ENDOGLIN expression in melanoma tumors and improved patient survival offers a potential translational link for the findings, suggesting it could serve as a prognostic marker or therapeutic target.<br /> Weaknesses:<br /> While the study is robust, there are some areas that could be further clarified or expanded upon:<br /> 3. The fragmented presentation of figures, such as "Figure 1" being separated from "Figure 1 cont.," significantly disrupts the logical flow of information and complicates the interpretation of data for readers. <br /> 4. The paper discusses the unexpected tumor-suppressive role of stromal cell-derived fibronectin, which contrasts with its usually pro-tumorigenic role. It notes that the CS1 fragment and TLR4 activation provide a mechanistic understanding, suggesting further exploration into why stromal-derived fibronectin behaves differently, such as the influence of specific isoforms and context-dependent signaling.<br /> Clarity :<br /> 3. Supplement: Label tables more clearly (e.g., "Proteomics Data")<br /> 4. Writing: Define abbreviations (e.g., "pFN") at first use.<br /> This manuscript by Lubosch et al. presents a significant and original body of work that enhances our understanding of how stromal cells can positively influence the anti-tumor immune response through fibronectin. The study is methodologically rigorous, the results are impactful, and the mechanistic insights are insightful. Although there are minor limitations and some areas where additional exploration could strengthen the conclusions, the fundamental findings are solid and well-supported. <br /> The recommendations for improvement aim to further increase the clarity, depth, and significance of this already strong contribution. This work is of great interest to those studying tumor immunology, cancer biology, and the tumor microenvironment.

On 2025-05-17 03:57:12, user thegradstudent wrote:

Summary<br /> This study introduces a novel computational pipeline for the de novo design of peptides that localize preferentially at the interface of biomolecular condensates. These condensates are membrane-less compartments created by protein and RNA molecules that form ‘dense’ and ‘dilute’ phases. The interface between these phases has been shown to promote the aggregation of the proteins that are part of the condensates and the formation of disease-associated fibrils of hnRPNA1. Previous literature has demonstrated preferential interfacial partitioning of a few proteins, but not of small molecules or peptides.

This technique combines coarse-grained molecular simulations, mixed-integer linear programming (MILP), and machine learning. The authors use this workflow to design peptides that localize at the interface of biological condensates, hnRNPA1, LAX-1, and DDX4, which are formed by intrinsically disordered proteins. They test these designed peptides in vitro and show that they exhibit their intended surfactant-like activities using confocal microscopy. They also identify how the charge of these peptides is a crucial element of their physicochemical features.

Overall, this study successfully shows that these short peptides preferentially distribute between the interface of the biomolecule condensate and the surrounding environment, showing surfactant-like properties. They also show that the net charge and the amino acid composition of these peptides in relation to their biomolecular condensate are crucial to determining whether they will preferentially partition at the interface.

The authors have opened the potential to study more complex condensates using this rigorous strategy. This paper is exceptionally well written and thorough. I recommend this paper for publication with minor revisions.

Major Point<br /> To experimentally validate this computational pipeline, you fluorescently label the selected peptides. This may show my lack of knowledge on this subject, but my one concern is regarding the potential effects of the fluorescent tag on the condensate system. This JBC paper from 2023 shows that fluorescently tagging a protein can promote phase separation , in this paper specifically huntingtin exon-1 with red fluorescent protein ( https://pmc.ncbi.nlm.nih.gov/articles/PMC10825056/ ). So, what is to say that the Cy5- fluorophore isn’t playing a role in creating these surfactant-like properties of the designed peptide?

Minor Points<br /> - Figure 1: Placing the label descriptors of the figure in front of the written text makes it clearer when reading, instead of having them at the end.<br /> - Figure 1C: The grey color used for the box is a little dark, making it slightly hard to read the words and it is very close to the grey coloring within the figure. Maybe switch this box into an outline or go with a lighter shade of grey.<br /> - Figure 1A and the figure in the abstract: The question marks are a little confusing to me. There may be a better way to describe what you mean without them.<br /> - Figure 5C & D: There is green text next to red text, which can be confusing to the color impaired.

On 2025-05-17 03:17:17, user UrNewStepDaddy wrote:

In this study, the authors refine an established FDA method (FDA C010.02) originally developed for extracting PFAS from food to enable analysis of smaller volumes of Dolphin milk than previously possible, demonstrating that Dolphin milk may be a major source of PFAS for nursing calves. The major success of this paper was the ability to quantify the concentration for 13 targeted PFAS species and additional untargeted species in dolphin milk over a 2-year nursing period for the characterization of the PFAS most likely to be transferred from mother to calf. The major weakness of this study is the presence of several unsupported claims which undermine the rigor of the manuscript and weakens the credibility of the interpretations made. Nevertheless, this study provides important groundwork for future research on the transfer of accumulated PFAS from parent to offspring and the effect of PFAS on the development of aquatic newborns. Although the environmental accumulation of PFAS is well established, further research is needed to elucidate the horizontal transfer of these forever chemicals.

Major Points<br /> 1. The manuscript states that dolphin milk was stored at the Smithsonian’s Mammal Milk Repository at -20C for the past 30 years, but provides no detail regarding the type of containers used or whether potential contamination from storage materials was assessed. Since PFAS are hard to avoid and known to leach from plastics, contamination from the storage material could significantly impact results, leading to the question: Were these samples stored in a plastic container that could have leeched PFAS into the milk? According to pictures from the Smithsonian website ( https://nationalzoo.si.edu/conservation/news/making-sense-animal-milks) the dolphin milk seems to be stored in plastic containers, however, the milk was harvested 30 years ago before the widespread knowledge of PFAS contamination. Were any blanks, storage container controls, or background correction measures collected to account for the PFAS introduced during storage? This study reports these chemicals in nanograms so even minimal leaching from storage materials could have introduced measurable contamination.

1. The methods sections states that the fish fed to the dolphin, Slooper, at the Naval Command Control and Ocean Surveillance Center were not screened for persistent organic pollutants (POPs). This raises a critical concern on whether the PFAS detected in the milk were primarily pre-existing maternal PFAS levels or diet-induced? Is there a chance that a species of fish had a higher amount of POPs within them than the others? At the same time, was there a regular feeding schedule that regularly spaced out the type of food? If Slooper was fed a more common/affordable food that happened to be more abundant in POPs at the beginning of the milking period, it could explain why there were so any PFAS detected initially and then tapered down later when she was fed other fish lower in POPs. Hence, the potential for dietary exposure to skew the results should be addressed in the discussion.

Minor points<br /> 1. Citations are needed for the sentences listed below:

-Line 43: “Since scientists have recently suggested that humanity has surpassed the planetary boundary for PFAS, major uncertainties must be addressed.”

-Line 217: “Additionally, most studies have only performed targeted quantitative PFAS analyses and not looked for new and unknown PFAS.”

-Line 283: “Previous studies have demonstrated that the lactational burden of POPs decreases following birth.”

-Line 386: “Although research on neonatal PFAS exposure is expanding, many epidemiological studies examine only one compound, failing to capture the complexity of mixtures encountered in the environment.”

-Line 467: “Although previous studies have linked traditional legacy PFAS, PFOS and PFOA, to adverse outcomes in dolphins and other marine mammals, there remains virtually no data on the impact of these chemicals or their replacement compounds on growth and development of neonatal marine mammals, especially with dosages of this magnitude.”

None of these claims are backed up by any evidence, which only helps to erode the work done within this study.

1. Some scattered typos are listed below:<br /> -In line 110, the title of the section has the method name wrong. It is correctly stated in the section as FDA C10.02.<br /> -In line 201, Administration does not need “ ’s ”.<br /> -In line 313, there is a red underline in the space in “illustrated that”.

2. Weekly tolerable intake of PFAS from the European Food Safety Authority and Food Standards Australia New Zealand is specifically stated twice within this paper (lines 22 and 368), however, it is only revealed during the 2nd mention that this is the weekly tolerable intake of PFAS for humans. From the source it could be surmised that the data was for humans, but having those numbers used for dolphins implies an equivalence between humans and dolphins that is not properly justified or supported with data (line 462). If human values are used for reference purposes, this should be clearly labeled or explicitly stated.

3. The wording in the methods section about sample collection and handling is unclear. In the sentence starting on line 90, does “During this time” refer to during the process of being milked or during the 603-day period in which Slooper was milked or during her life at the Naval station?

4. This manuscript would benefit from references to recent studies on PFAS amounts in dolphin carcasses: Sciancalepore G et al. (2021) and Foord CS et al. (2024) to name a couple. From this paper alone, PFAS do not seem like a huge problem, but put into the context of the papers I listed, it paints a more concerning picture.

On 2025-05-16 23:27:04, user Andie Souder wrote:

Summary: <br /> The major goal of this paper was to understand FAD binding to Cry4b isoform in vitro. This was done by in vitro binding assays, simulations of FAD binding to Cry4b and solvent accessibility, and mRNA transcription levels of in vivo and immunoprecipitation. The major success of this paper is establishing protocols for optimization and finding new methods to work with the Cry4b isoform. The major weaknesses stem from a lack of reliable experimental data. But this paper brings to attention the need for thorough and rigorous protocols. It also highlights how little is known about these proteins and sheds light on areas that need to be explored.

Major Issues:<br /> Perhaps I am misunderstanding the conclusion of this paper, but it seems like the results of your experiments do not support your conclusion. In the introduction, it states that genome analysis of Cry4b exon has stop codons in the intrinsic region and asks if the mRNA is translated into function protein in vivo. How do we know that the samples collected didn’t have a nonfunctioning version of mRNA being translated?

https://pubmed.ncbi.nlm.nih.gov/32978454/ This paper states that the Cry4b is expressed only at night, were the specimens harvested during the day vs night to compare Cry4 isoform expression? Could that be the reason for the discrepancy in the MS results? As stated in the paper: “...the latest avian genome analyses showed that the CRY4b-specific exon carries loss-of-function mutations (e.g., stop codons), a pattern characteristic for intronic regions [29]This poses the question whether the ErCRY4b mRNA isoform is translated into a functional protein product in vivo.” Is this a factor that impacts the results of the experiments done?

Considering that the major goal of this paper is to understand FAD binding in vitro, why weren’t those experiments thought out more carefully? It seems as if the inclusion of the vivo studies as well as the simulations were done in an attempt to reinforce the weak results of the experimental data. But the experimental data is hard to draw concrete conclusions from. In the paper, it states that the Cry4b might be misfolded, is there an experiment that verifies the fold of the protein? That is an important thing to consider, especially because these experiments are the basis of the paper. Does the solubility tag block FAD binding? What about the chaperone?

Minor Issues:<br /> Resolution quality of figure 1 does not match the other figures<br /> Please add the confidence of the alpha fold generated structure<br /> Add to the figure 1 caption that the structures were generated using alpha fold<br /> Please clarify if there are competing interests as the bioRXIV webpage states that there is not but paper states that competing interest is present

On 2025-05-16 22:11:44, user Bio76 wrote:

Summary of work:<br /> This study presents the first cryo-electron microscopy (cryo-EM) structure of the human TWIK-2 potassium channel. TWIK-2 is a member of the two-pore domain potassium (K2P) channel family and has been shown to play an important role in NLRP3 inflammasome activation and inflammatory disease. In this work, the authors resolved the structure of TWIK-2 at 2.85 Å and of TWIK-2 bound to the FDA-approved antipsychotic drug, pimozide, at 3.17 Å and described the features of these structures.

While they observed that TWIK-2 shares core structural features with other K2P channels, such as the presence of lateral fenestrations which house acyl chains, this work also highlighted several distinctive characteristics:

A unique positioning of Tyr111: Tyr111 is found in the sixth position of the selectivity filter 1 (SF1) sequence—only one other K2P channel shares this feature. In the TWIK-2 structure, Tyr111 adopts an "up" conformation, which the authors speculate might be a binding site for channel modulators.

Identification of a novel SF1-P1 pocket: A previously uncharacterized pocket behind the selectivity filter was discovered, potentially contributing to channel gating or modulation.

After purifying the TWIK-2 protein, the authors used a liposome-based flux assay to demonstrate that purified TWIK-2 was functionally active, similar to TWIK-2 found in its native environment. This assay showed that TWIK-2 mediates K⁺-K-selective currents and can be inhibited by known blockers (Ba²⁺, ML365) and pimozide, which exhibited higher apparent potency. After testing the bioactivity, the cryo-EM structure of the pimozide-bound channel revealed that the drug binds within the central cavity below the selectivity filter and displaces acyl chains, which led them to conclude that it likely blocks ion conduction. The binding pocket overlaps with sites used by other K⁺ channel inhibitors and is accessible via the membrane, suggesting that other hydrophobic molecules could serve as viable TWIK-2 inhibitors.

In summary, the study provides the first high-resolution structural and functional characterization of TWIK-2, offering key insights into its gating, conductance, and targetability. These findings lay a foundation for developing selective TWIK-2 inhibitors as potential therapeutics for inflammatory diseases involving the NLRP3 inflammasome.

Major Successes:

The researchers were able to determine high–quality cryo-EM structures of previously undetermined apo (2.85 Å) and inhibitor-bound (3.17Å) TWIK-2 structures. They obtained well-resolved features that enabled highly confident modeling of transmembrane and pore regions.

Provided Reports of novel structural features:

Identification of unique 'up' conformation of Try111 in the selectivity filter in the TWIK-2 structure.

Discovery of the SF1-P1 pocket as potential targetable site for drugs.

Provided insight into the mechanism of inhibition:

Successfully demonstrated the inhibition of TWIK-2 by pimozide, an FDA-approved drug.

Provided evidence to show that pimozide occupies the central cavity below the selectivity filter and displaces the acyl chain.

The work demonstrates therapeutic relevance.

TWIK-2 has a strong linkage to NLRP3 inflammasome activation, which makes it a potential target for the druggable anti-inflammatory targets.

Major Shortcomings:

It might be beyond the scope of the study, but there is no mutational or dynamic data to confirm Try111's role in inactivation gating or binding with channel modulators.

Minor Technical questions.

Was Tyr111 mutagenesis attempted to assess its influence on gating or conductance?

How reproducible is the liposome assay? What were the number of technical vs biological replicates?

Was negative-stain EM used during sample quality control?

Stylistic issues:

Line 5: This is a significant sentence, as it sums up what was done in the study, but to improve the flow and logical progression of this sentence: "We report the cryo-EM structure of human TWIK-2," I would suggest adding a purpose statement first: For example: "To better understand its structure and inform future drug development, we report the cryo-EM structure of human TWIK-2".

Line 80: ““The NLRP3 inflammasome also been shown…” → should be “has also been shown…”

Line 301-304: The sentence "Inactivation in TWIK-2 is not complete...for certain C-type inactivating K+ channels. " though very strong, could be broken up into 2-3 sentences to improve readability. Perhaps you could consider rewriting the following:

Inactivation in TWIK-2 is incomplete — for instance, even at low external K⁺ concentrations (5 mM), currents decrease to only about half of their initial value (13). This suggests the selectivity filter may not experience the marked protein backbone conformational changes typically associated with C-type inactivation in other K⁺ channels.

Reviewer's Recommendation:

The author presented a well-executed structural and functional study of TWIK-2. The cryo-EM reconstructions are of high quality, and studying the pharmacologically relevant pimozide binding to TWIK-2 is novel and has therapeutic capabilities. While the study would benefit from functional validation of the role Tyr111 plays through mutagenetic studies, there are no fundamental issues that should disqualify this manuscript from publication. I recommend that this paper be published pending minor grammatical revisions.

On 2025-05-16 22:09:33, user Bio76 wrote:

Summary of work:<br /> This study presents the first cryo-electron microscopy (cryo-EM) structure of the human TWIK-2 potassium channel. TWIK-2 is a member of the two-pore domain potassium (K2P) channel family and has been shown to play an important role in NLRP3 inflammasome activation and inflammatory disease. In this work, the authors resolved the structure of TWIK-2 at 2.85 Å and of TWIK-2 bound to the FDA-approved antipsychotic drug, pimozide, at 3.17 Å and described the features of these structures.

While they observed that TWIK-2 shares core structural features with other K2P channels, such as the presence of lateral fenestrations that house acyl chains, this work also highlighted several distinctive characteristics:<br /> - A unique positioning of Tyr111: Tyr111 is found in the sixth position of the selectivity filter 1 (SF1) sequence—only one other K2P channel shares this feature. In the TWIK-2 structure, Tyr111 adopts an "up" conformation, which the authors speculate might be a binding site for channel modulators. <br /> - Identification of a novel SF1-P1 pocket: A previously uncharacterized pocket behind the selectivity filter was discovered, potentially contributing to channel gating or modulation.

After purifying the TWIK-2 protein, the authors used a liposome-based flux assay to demonstrate that purified TWIK-2 was functionally active, similar to TWIK-2 found in its native environment. This assay showed that TWIK-2 mediates K⁺-K-selective currents and can be inhibited by known blockers (Ba²⁺, ML365) and pimozide, which exhibited higher apparent potency.  After testing the bioactivity, the cryo-EM structure of the pimozide-bound channel revealed that the drug binds within the central cavity below the selectivity filter and displaces acyl chains, which led them to conclude that it likely blocks ion conduction. The binding pocket overlaps with sites used by other K⁺ channel inhibitors and is accessible via the membrane, suggesting that other hydrophobic molecules could serve as viable TWIK-2 inhibitors.

In summary, the study provides the first high-resolution structural and functional characterization of TWIK-2, offering key insights into its gating, conductance, and targetability. These findings lay a foundation for developing selective TWIK-2 inhibitors as potential therapeutics for inflammatory diseases involving the NLRP3 inflammasome. <br /> <br /> Major Successes:<br /> The researchers were able to determine high–quality cryo-EM structures of previously undetermined apo (2.85 Å) and inhibitor-bound (3.17Å) TWIK-2 structures. They obtained well-resolved features that enabled highly confident modeling of transmembrane and pore regions.  <br /> Provided reports of novel structural features: <br /> - Identification of the unique 'up' conformation of Try111 in the selectivity filter in the TWIK-2 structure.  <br /> - Discovery of the SF1-P1 pocket as a potential targetable site for drugs.  <br /> - Provided insight into the mechanism of inhibition: <br /> - Successfully demonstrated the inhibition of TWIK-2 by pimozide, an FDA-approved drug. <br /> - Provided evidence to show that pimozide occupies the central cavity below the selectivity filter and displaces the acyl chain.  <br /> The work demonstrates therapeutic relevance.  <br /> TWIK-2 has a strong linkage to NLRP3 inflammasome activation, which makes it a potential target for the druggable anti-inflammatory targets.  <br /> Major Shortcomings:<br /> It might be beyond the scope of the study, but there is no mutational or dynamic data to confirm Try111's role in inactivation gating or binding with channel modulators.

Minor Technical questions. <br /> - Was Tyr111 mutagenesis attempted to assess its influence on gating or conductance? <br /> - How reproducible is the liposome assay? What were the number of technical vs biological replicates? <br /> - Was negative-stain EM used during sample quality control? <br /> Stylistic issues:<br /> - Line 5: This is a significant sentence, as it sums up what was done in the study, but to improve the flow and logical progression of this sentence: "We report the cryo-EM structure of human TWIK-2," I would suggest adding a purpose statement first: For example: "To better understand its structure and inform future drug development, we report the cryo-EM structure of human TWIK-2". <br /> - Line 80: “The NLRP3 inflammasome also been shown…” → should be “has also been shown…” <br /> -Line 301-304: The sentence "Inactivation in TWIK-2 is not complete...for certain C-type inactivating K+ channels. " though very strong, could be broken up into 2-3 sentences to improve readability.  Perhaps you could consider rewriting the following: <br /> Inactivation in TWIK-2 is incomplete — for instance, even at low external K⁺ concentrations (5 mM), currents decrease to only about half of their initial value (13). This suggests the selectivity filter may not experience the marked protein backbone conformational changes typically associated with C-type inactivation in other K⁺ channels.<br /> <br /> Recommendation:<br /> The author presented a well-executed structural and functional study of TWIK-2. The cryo-EM reconstructions are of high quality, and studying the pharmacologically relevant pimozide binding to TWIK-2 is novel and has therapeutic capabilities.  While the study would benefit from functional validation of the role Tyr111 plays through mutagenetic studies, there are no fundamental issues that should disqualify this manuscript from publication. I recommend that this paper be published pending minor grammatical revisions.

On 2025-05-16 18:30:17, user Stella Rose wrote:

Major issues related to data analysis or interpretation of the work:

Since these findings are compared to vertebrate mammalian intestine stem cells quite often, would it be possible to elaborate more on the findings for those cells as well to have an idea of how the two systems can be related together or whether we cannot compare them directly.<br /> Bortezomib and Actinomycin D are the two chemotherapeutics that are used throughout the paper to look at the cytotoxic protection that these stem cells and progenitor cells employ. For figure 1e, they performed an experiment to certify that Bortezomib does not reach its target in the stem cells. These experiments were also done for other chemotherapeutics but not for Actinomycin D.

Could there be an additional similar experiment to demonstrate that Actinomycin D also does not reach its target in the stem cells? This would be to fully certify that this statement applies to all the drugs used in the study.<br /> More elaboration on the techniques and experiments corresponding to each figure would be helpful to allow the audience to better understand the figure itself. On the figure description itself, there is very little mentioned on the techniques used. Overall, by not having a materials and method section in the article, it is even harder for the reader to understand how these experiments were done.

Presenting more quantitative data alongside the qualitative data presented would be great to validate the importance of the findings. For example, figure 1 lacks a quantitative representation of the visual data, which would make it simpler for the audience to digest by being able to align a number to what is observed.

Is there an equal contribution from stem cells and progenitor cells or are there differences between the two? This topic is lightly discussed in the results/discussion, but it would be significant to distinguish between the two which one has more impact in these findings.

Minor technical issues:<br /> In page 7 for the first paragraph, it refers to a 2-fold increase in stem/progenitor dye retention found in Figure 2e, but that figure only has parts A-C. This might be a typo but could be a quick fix.

When it comes to the structure of the article, it was difficult to follow the different sections of the paper. I was not able to access the materials and methods part of the paper to further read on the way these experiments were done. Separating the results and discussion of the article would definitely help with the digestion of the information. It might just be a problem with BioRxiv itself but adding the materials and methods at the end of the article would be very helpful. Would it also be possible to show us how to access the supplementary information as well.

Keeping the same nomenclature throughout for the genes and proteins would significantly help with the clarity of the paper.

Overall, the manuscript is a major contribution to stem cell and cancer research by identifying possible factors that directly interfere with the efficacy of chemotherapeutics. I would recommend this paper for publication after addressing the major revisions mentioned before. Very few extra experiments would be needed for these revisions and would mostly rely on the elaboration of techniques. Giving the paper more structure would be an easy fix that would make the greatest difference in how this paper is digested and understood.

On 2025-05-16 17:48:12, user Adil Muneer wrote:

Published version of this article titled "Targeting G9a-m6A translational mechanism of SARS-CoV-2 pathogenesis for multifaceted therapeutics of COVID-19 and its sequalae" is now available: <br /> https://www.cell.com/iscience/fulltext/S2589-0042(25)00893-4 <br /> DOI: 10.1016/j.isci.2025.112632

On 2025-05-16 00:48:41, user Reviewer wrote:

This preprint examines the impact of C-terminal amidation on the structure and membrane interactions of the antimicrobial peptide Uperin 3.5 (U3.5). Using all-atom molecular dynamics simulations, the study compares the amidated (U3.5-NH2) and non-amidated (U3.5-OH) forms of the peptide in both monomeric and tetrameric (amyloid-like) states interacting with a model bacterial membrane made of POPE:DOPG lipids. <br /> One of the key strengths of the study is its clear demonstration of how a single post-translational modification can affect peptide behavior. The simulations show that amidation improves peptide-membrane binding, promotes α-helical structure formation, and supports a “carpet-like” mechanism of antimicrobial activity. The paper also suggests that the amyloid form may act as a reservoir, gradually releasing active monomers at the membrane surface. <br /> However, the study has some limitations. The conclusions are only based on computational work without experimental data to support the findings. Additionally, providing a clearer explanation for the selection of the tetrameric structure would improve the clarity and reproducibility of the work. <br /> Overall, the paper provides useful insights into the structural role of amidation and its impact on antimicrobial function. I think it contributes to the broader understanding of AMP design and peptide-membrane interactions. <br /> Major Comments:<br /> 1. The study is entirely based on molecular dynamics (MD) simulations without any experimental validation. While the results are interesting, it is difficult to fully trust the conclusions without supporting experimental data. <br /> 2. The paper does not explain why the specific tetrameric structure was used. It was taken from a cryo-EM study, but it is not clear why these four specific peptides were selected or how well they represent amyloid structures. Explanation in the methods or results would improve clarity and help readers understand the biological relevance of the setup.

Minor Comments: <br /> 1. The paper does not mention which method was used to calculate the secondary structure (like α-helix and 310-helix). Since this is important for interpreting related results, it would help to state which tool or algorithm was used. <br /> 2. In Figure S5, the RDF plots show how close the peptide is to the lipid, but the paper does not specify what distance counts as a real interaction. Adding a sentence to explain this cutoff would help readers understand the strength of the peptide-lipid binding. <br /> 3. There is a typo in the caption of Figure S7: “three of the four peptides where in an α-helical conformation” should be corrected to “were” instead of “where”.

I recommend this manuscript for publication with major revisions because some conclusions are too strong without experimental support, and the choice of the tetramer structure needs better explanation to show its biological relevance.

On 2025-05-15 23:14:17, user Melanie Cocco wrote:

Major revisions are in progress. In the time since we submitted this manuscript we have discovered that the stoichiometry of binding described in the literature is not correct. The data presented here are currently being re-evaluated to confirm the stoichiometry of binding for these measurements on metHb.

On 2025-05-15 12:43:38, user Alexandros Abdel Massih wrote:

Hello, in the supplemental figure you use gating on the Berkley mouse but in the methods you refer to the Townes mouse

On 2025-05-15 12:36:30, user Eglė Jakubavičiūtė wrote:

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

On 2025-05-15 09:25:24, user Nitika wrote:

The flanking region that has been chosen for adding the specificity is too far (27 bases) from the G4 forming site,is there any specific reason for that?

On 2025-05-15 08:29:21, user Jvas wrote:

This study proposes a thermodynamics-driven framework to improve DNA origami folding yield by selecting scaffold sequences with minimal off-target binding. The authors introduce four metrics (M1–M4) based on free energy calculations using a NUPACK-based model: M1 captures staple-scaffold misbinding, M2 scaffold self-binding, M3 staple-staple co-folds, and M4 staple internal structures. These were used to score and filter candidate scaffolds for a triangle and rectangle origami design, sampled from de Bruijn, biological, and synthetic sequence pools.<br /> Key findings include the identification of scaffold variants with reduced off-target binding that folded more reliably, as validated by AFM and optical tweezers. Variants T1 (de Bruijn) and T2 (Pareto-ranked biological) showed higher yield and structural uniformity, while T3 (reverse-front biological) frequently misfolded or aggregated. The authors suggest that sequence-dependent kinetic traps are a key factor in these outcomes.<br /> The method’s strength lies in isolating sequence as an independent variable, showing that even when the overall design and staple routing remain constant, modifying the scaffold sequence alone can have a significant impact on folding behavior and yield. The dual-lab blind testing protocol further strengthens the validity of their conclusions. <br /> Overall, this paper contributes a practical and scalable method for improving DNA origami reliability at the sequence level, and sets a precedent for sequence-aware design tools in the field.<br /> Major Issues<br /> - Interpretation of structural disorder in NUM clustering (Figure 6) requires clarification. The use of hierarchical clustering to assess force-extension profiles is appropriate, but the thresholds for cluster assignment are not discussed. To support the interpretation that T3 exhibits high disorder, it would be helpful to clarify how clustering thresholds were selected and whether alternative settings affect the assignment. Otherwise, the interpretation may overstate differences that are method-dependent.<br /> - Unclear relationship between individual metrics and folding success. Since the paper uses a multi-metric ranking scheme, a brief textual reflection on whether any single metric (M1–M4) appeared more predictive than others would help strengthen interpretation and validate the ranking approach.<br /> - Quantitative folding yields not consistently mapped to metrics. While AFM results suggest strong differences in folding success among variants, the M1–M4 scores for each scaffold are not explicitly tied to observed outcomes. A summary paragraph or table linking these values in the main text would help reinforce the link between off-target minimization and folding performance.<br /> - In Figure 4, the authors present AFM images and gel results comparing the triangle variants T1, T2, and T- While the visual differences are compelling, the description in the main text could more clearly connect specific off-target metrics such as a high M2 score in T3 with the observed aggregation. Similarly, the quantification of "well-folded," "semi-folded," and "misfolded" structures is valuable, but the criteria for classification are not clearly defined in the figure legend or main discussion. A short paragraph clarifying how structures were binned into categories, and whether this was done blindly, would strengthen the interpretation of this key figure.<br /> - Equal weighting of thermodynamic metrics lacks justification. The authors use equal weights for the four thermodynamic metrics (M1–M4) in ranking scaffold sequences, but don’t explain why these should contribute equally. Since scaffold-scaffold binding (M2) might have a larger influence due to its intramolecular nature, this assumption could bias ranking. I recommend either providing a rationale for equal weighting, performing a sensitivity check, or softening claims that treat the metrics as equally impactful.<br /> - Claim about aggregation lacks direct support. The interpretation that aggregation in high off-target sequences (T3 and R3) is caused by scaffold-scaffold interactions seems plausible, but no direct evidence is shown. To improve clarity, the authors could mention that while the aggregation is consistent with such interactions, alternative causes such as staple misfolding or variations due to concentration aren’t ruled out. Rephrasing this point or adding supporting gel data could improve the argument.<br /> - Interpretation of aggregation in poorly folding structures is speculative. The authors report aggregation in samples with high off-target scores, suggesting scaffold-scaffold interactions as the likely cause. However, no direct evidence is provided to support this interpretation. Alternatively, the authors could strengthen the interpretation by referencing any existing gel patterns or noting whether aggregation varied with concentration during folding trials or revising the interpretation to make clear that the aggregation is consistent with, but not direct proof of, scaffold-scaffold binding.<br /> - Limited diversity of tested origami shapes. The validation is limited to a triangle and rectangle—both 2D shapes with relatively simple geometries. Since many applications depend on 3D or more densely routed structures, it would be helpful to include a brief discussion on how this method is expected to generalize, or acknowledge its current limitations in scope.<br /> - Folding outcome analysis lacks dynamic insight. The authors mainly use end-point assessment (AFM, gel) and unfolding profiles (OT) to evaluate folding. However, different scaffold sequences may lead to different folding pathways, which aren’t explored here. While additional kinetic experiments are not needed, it would strengthen the paper to mention that folding dynamics could be sequence-sensitive and suggest this as a potential future direction.

Minor Issues<br /> Minor technical questions and clarification points:<br /> - GC content relevance should be clarified. While GC content is widely understood in the field, it would be helpful to explain why it is relevant at the point of first mention. For example, does higher GC content promote more stable on-target binding, or does it also increase risk for strong off-target duplexes? Clarifying this would help readers unfamiliar with its tradeoffs in origami design.<br /> - Supplementary findings deserve mention in the main text. Several results, such as the comparison between biological and synthetic sequences, and the minimal effect of scaffold rotation, are relegated to the supplementary notes. Summarizing these briefly in the main results would help reinforce the conclusions.<br /> - Ambiguity in outcome terminology. Phrases like “largely failed” to fold (used for T3 and R3) should be supported by specific quantitative results such as the proportion of well-formed structures visible in AFM or the frequency of trace groupings in OT clustering to clarify what degree of misfolding is being referenced.<br /> - Timing of de Bruijn explanation. The paper introduces de Bruijn sequences early but delays explanation of why they were selected. Briefly noting their core property—uniform distribution of k-mers and minimal short repeats—in the introduction would help orient readers unfamiliar with their significance.<br /> Stylistic and clarity-related issues:<br /> Figure legends could include more interpretation. Some figure legends (notably Figures 4 and 5) mostly restate what was done rather than what is seen. Including brief interpretations (e.g., which variant performed best) would improve their usefulness.

Recommendation<br /> Accept with minor revisions<br /> This study presents a solid combination of computational design and experimental validation. It offers a practical way to improve DNA origami folding by taking sequence effects into account. A few areas could be explained or expanded better, but overall the work is strong and would be a good contribution to the field.

On 2025-05-15 07:43:09, user David Skerrett-Byrne wrote:

Now published in Reproduction: https://doi.org/10.1530/REP-25-0105

On 2025-05-15 02:18:12, user Guoyong Xu wrote:

This has been published on Nature communications: https://www.nature.com/articles/s41467-025-56570-x

On 2025-05-14 22:16:23, user Anonymous wrote:

This paper investigates the human gut microbiota’s ability to break strong carbon-fluorine (C-F) bonds that have been introduced into the human body via pharmaceuticals and environmental pollutants. The researchers developed a 96-well colorimetric fluoride assay to screen culturable bacteria in the human body and identified dehalogenases (an enzyme that degrades environmental pollutants) in gut bacteria, including Clostridia, Bacilli, and Coriobacteriia, that<br /> hydrolyze fluorinated amino acids. This enabled the researchers to identify key amino acids important for defluorination. Then, the researchers successfully converted dechlorinating dehalogenases into defluorinating dehalogenases by substituting the carboxyl (C)-terminal 41 amino acids with those from naturally occurring defluorinating dehalogenases. Whole protein alanine scanning, molecular dynamics simulations, and chimeric protein design facilitated the<br /> identification of the role of the C-terminal region of dehalogenases in defluorination. The researchers also trained machine learning models to understand the structural and sequence differences between defluorinating and non-defluorinating dehalogenases. These novel predictive models were trained on the 41 amino acid segments of the C-termini and predicted defluorination<br /> activity with 83% accuracy and 95% accuracy when based on the full-length protein features. This study ultimately discovered that the human gut microbial enzymes are capable of cleaving C-F bonds.

The figures in this paper are clear and well-organized, which lent themselves to effectively conveying complex data. The methods section of this paper goes into great detail, describing the different types of equipment used, explaining multiple validation steps, and noting where protocols were modified from the original workflow. The researchers provide GitHub links for their data, which shows that their methods are easily reproducible. They also have several<br /> replicates for their experiments and test why certain aspects of their experiments did not work. They also succeeded in developing a novel assay for alanine screening.

This study is significant and relevant because of the prevalence of fluorinated drugs and environmental pollutants such as Per-and polyfluoroalkyl substances (PFAS), commonly referred to as “forever chemicals,” in our environment. This study uncovers the ability of the human gut microbiome to metabolize fluorinated compounds and gives insight into developing engineered<br /> enzymes to mitigate the effects of fluorinated pollutants and drugs in the human body. Their novel predictive model could help in the development of interventions to address environmental and human health concerns associated with fluorinated substances.

Major points:<br /> There are a few major aspects of this paper that could be improved:<br /> 1. I think this paper could benefit from an explanation or discussion of where they speculate the fluoride ions migrate to in the human body after cleavage of the C-F bonds. This may provide more transparency between the author and the reader about what is happening in the human body, especially since this paper addresses human health concerns and environmental pollutants.<br /> 2. Also, in order to strengthen the results and increase the reproducibility of the work, I recommend a more detailed description of the statistical analysis employed by the researchers.<br /> 3. I do appreciate the acknowledgement of the limitations of the study by the authors:<br /> o The range of fluorinated compounds is limited to overexpressed recombinant<br /> dehalogenases sourced from the gut microbiota<br /> o The comparison of in vivo and pure culture experiments is lacking<br /> 4. I acknowledge that it is beyond the scope of this study, but for possible follow-up experiments, the researchers could expand the range of fluorinated compounds by testing more substrates and could conduct more experiments in vivo.

Minor points:<br /> There are a few minor aspects of this paper that could be improved:<br /> 1. In the editing stage of this publication, it would help the reader for the authors to standardize the terminology they used. Also, there were instances where the authors used scientific jargon when introducing the study. For example, it would be helpful if the authors explained or wrote out what “LB” referred to in their methods. This would strengthen the author’s ability to communicate science to a general audience.<br /> 2. I recognize English may not be the authors’ first language, however, to increase the clarity of the presentation of this work, I suggest editing the minor typos in the second to last paragraph of the Introduction section: “Here,e” and “which us motivated…”<br /> 3. Throughout the paper, the authors switch tenses, going from past tense to present tense and then back to past tense again. There are also some grammatical errors that need to be addressed in order to improve the sentence structure. This would help with the overall flow of the paper.<br /> 4. Lastly, the figures could have color palettes that are more accessible for people who are colorblind.

In conclusion, I would recommend this paper for publication with minor revisions. I did not find any fundamental issues with this paper that would disqualify it from publication. I acknowledge that I do not have the expertise in biochemistry to comment on the exact experimental methods utilized, however, I do not think there was experimental misconduct or unfounded claims made<br /> about the data. This work involves a novel experimental design, and this study was well thought out and executed. I think this work would be very impactful to the scientific community and human society.

On 2025-05-14 14:33:18, user Mark Shein-Idelson wrote:

This paper has been published in PLoS Biology:<br /> https://doi.org/10.1371/journal.pbio.3002411

On 2025-05-14 07:39:06, user Christian Rödelsperger wrote:

The results of this study were now published as a part of a larger collaborative work in Molecular Biology and Evolution doi: 10.1093/molbev/msaf097

On 2025-05-13 23:19:04, user Jillian Evans wrote:

Great paper. Clearly shows mTORC1 4E-BP1/eIF4E translation initiation axis is important in human fibrosis. RMC-5552 should be in clinical trials for IPF and other fibrotic diseases.

On 2025-05-13 22:37:57, user Chris C. wrote:

Summary<br /> The main objective of this paper is to test the konjac glucomannan (KGM), exercise, and both together on body weight and lipid metabolism in obese rats versus their control. Their idea for testing KGM is based on the findings from Chen et al. 2019 where they tested glucomannan on type 2 diabetes in rats. The added benefit with this paper is the addition of exercise with KGM. The groups that they used were high-fat diet (HFD), normal control group (CON), aerobic exercise group (HAE), KMG group (HKM), and combined treatment (HKE).

I believe that the authors were successful in establishing that KGM and exercise likely reduce body weight, improve lipid metabolism and oxidative status in obese rats. They validated their findings through their rigorous testing of liver lipids, liver enzymes, lipid metabolism, insulin sensitivity, and antioxidants. Their statistical analyses are good and are held to a low p-value with high significance. The authors included a figure for the underlying mechanisms of konjac and exercise on lipid metabolism, which makes it easy to understand.

Overall, the study adds in the benefits of both konjac and exercise. This study also repeats how konjac and exercise can separately improve obesity as well as together. Previous literature has established that konjac and exercise improve obesity through multiple mechanisms, as mentioned in your introduction. While the authors' findings are not particularly surprising, they have provided rigorous and sufficient data to support their hypothesis. The overall impact of the paper is modest.

Major Points

The figure captions could be more descriptive.<br /> For example, figure 5 would benefit from a more detailed explanation of the role and relevance of each antioxidant and a clear indication of which groups showed the most favorable results in the figure caption.

I am unsure if this statement, “Exercise is reported to enhance the ability of skeletal muscles to utilise lipids as opposed to glycogen, thus reducing lipid levels.” is completely true. <br /> It appears to be an oversimplification of the findings from Mann et al. (2014). Current literature suggests that utilization depends on exercise intensity and other factors. Clarifying that the statement applies to low-intensity steady-state cardio would improve its accuracy. Please let me know if you think otherwise.

Minor Points

If you could please write out the groups in the abstract or introduction that would be helpful for me to understand what the groups are from the beginning.

Figure 2 may be more interpretable as a bar graph, which could better illustrate group differences. It would help me see the differences easily.

I am confused about Figure 4. Please clarify the labeling—specifically, whether Figure 4A represents a control or a healthy adipose tissue sample. This will help me understand it easily.

Ensure all figure panels are clearly labeled and legible. Enlarging Figure 4 slightly might improve readability as well as getting a better resolution for figure 3.

Recommendation<br /> I recommend publishing this paper with intermediate/minor revisions. Here are the key revisions required:

More comprehensive figure captions

Clarification in the introduction regarding lipid vs. glycogen utilization during exercise.

Improved labeling for Figure 4.

Optional suggestions:<br /> Reformatting Figure 2 as a bar graph for clarity.

Slight enlargement of figures to improve visual accessibility.

Write out full group names at the beginning of the preprint for easier readability

On 2025-05-13 08:53:45, user Karel Břinda wrote:

Published version: https://doi.org/10.1038/s41592-025-02625-2

On 2025-05-13 06:17:44, user Vojtech wrote:

Published here<br /> https://doi.org/10.1186/s12866-025-03915-7

On 2025-05-13 06:13:38, user Vojtech wrote:

Now published here<br /> https://doi.org/10.1099/ijsem.0.006552

On 2025-05-12 20:47:52, user Elzi Volk wrote:

A large improvement in detail, scope, and sophistication compared to the Petri et al (2021) paper. <br /> A bit disappointing that C. latrans was absent from Fig. 3. <br /> Very glad to see Canini from South America included in analyses. And avoiding couched claims that C. latrans originated in Eurasia. There is no solid evidence…yet. (Perhaps an offshoot study could be revealing.)

On 2025-05-12 12:24:19, user Lukas Roth wrote:

Dear bioRxiv team, please link this preprint to it's published version:

https://doi.org/10.34133/plantphenomics.0185

Thank you!

On 2025-05-12 12:22:38, user Murrall, Katy wrote:

The main figures can be found in the Supplementary Material section

On 2025-05-12 07:24:17, user Mirko Lampe wrote:

Great collecting work, but, I dont see the novelty. This system was communicated to the market long time ago by company VIUM, boungt by Recursion. Many described hardware components are also already since years in use for Digital Home Cage Monitoring Systems such as iMouse ( https://imouse.info/ ) or Olden Labs ( oldenlabs.com ) even Actual Analytics ( https://www.actualanalytics.com/ ).

On 2025-05-11 08:35:09, user Gernot Langer wrote:

My sincerest apologies for simply refering to the very context of our paper only in a very first reaction.<br /> More specifically - in our paper we specifically pointed out, that a combination of high exogenous PAC1-R levels (in a commercial cell overexpressing the receptor) as well as the use of IBMX in cAMP accumulation assays didn‘t result in any tractable matter in a first screen for SMOL antagonists. We finally succeeded when going for HEK‘s endogenously expressing the receptor and ommitting IBMX - the best explanation for this strategy to us being a clear hint towards a high (cAMP) coupling efficiency of the agonist stimulated receptor that may mask any inhibitory pharmacological read-out by SMOL inhibitors in comparison to peptides antagonists. Thus I wonder wether running the very same assays you performed in the absence of IBMX would be more likely to prove BAY268.. activity. I can also imagine that transiently transfecting your reporter construct (only) into native HEK‘s may also confirm BAY268.. activity - once the presence of the endogenous receptor has been demonstrated, of course.<br /> Achieving appropriate (assay) conditions to demonstrate receptor inhibition by SMOL modulators has been a major issue not only for us but also for an other pharma research group studying VIP antagonism (see reference in our paper). Thus, I do hope these suggestions may be of help.

Yours sincerely

Gernot Langer

On 2025-05-11 05:24:31, user Gernot Langer wrote:

The authors are only partly right in pointing out the lack of well-characterised small molecule inhibitors and I would therefore like to draw their attention to the following paper published in 2023 in Molecular Endocrinology<br /> Discovery and in vitro Characterization of BAY 2686013, an Allosteric Small Molecule Antagonist of the Human PAC 1 Receptor

On 2025-05-11 04:17:56, user Rob Williams wrote:

Please contact Rob Williams or Danny Arends is you have questions or suggestions for improvement.

On 2025-05-10 17:20:18, user sebastiangregoricchiophd wrote:

Now published on Genome Biology:<br /> https://doi.org/10.1186/s13059-025-03596-5

On 2025-05-09 21:18:48, user Michael White wrote:

This is now published in Cell Systems with a minor change to the title. PMID: 39778579

On 2025-05-09 09:09:24, user Lilian Marie Boll wrote:

Please link this preprint to its final publication on Nature Communication ( https://doi.org/10.1038/s41467-025-56462-0 ).

On 2025-05-09 02:32:29, user Alizée Malnoë wrote:

The manuscript by Stanhope et al. investigates the role of the one-carbon metabolism enzyme S-Adenosylhomocysteinase (Ahcy) in the alteration of neurodegeneration gene expression in Drosophila. The authors found that oxidation of C195 of Ahcy inhibits its activity, leading to neuroprotective changes in gene expression through an unknown mechanism. The findings highlight the importance of C195 oxidation in Ahcy, potentially limiting enzyme activity in a metabolism-dependent manner. We agree with the authors’ conclusions and we provide major and minor comments below to assist in the clarity and ease of interpretation of the manuscript.

Major comments <br /> - Please provide more information on Ahcy and AhcyL1, e.g. how many cysteines does Ahcy contain? Does AhcyL1 respond to blue light stress? <br /> - Line 55, please include a brief explanation of the redox proteomics approach and whether it was concluded that C195 is involved in a disulfide bond. We suggest discussing early on from structural prediction of Ahcy whether the two cysteines (C195 and presumably C228) are in close proximity for a disulfide bond to form as otherwise it is unclear what is meant by oxidation of C195 (it could be e.g. a sulfenic acid) and what the ‘second’ cysteine is. <br /> - Fig1E, include samples with reductant and oxidant +/- labeling to show where the fully reduced or oxidized protein migrates. There appears to be a slight band present in the “second labeled cys” in the C195S, which would contradict the statement in Line 99. Could you comment on it. Although not required for the conclusions, including protein samples from flies that are blue light-treated would inform on whether Ahcy-C195 becomes oxidized. <br /> - We found it quite striking that the RNAi lines for AhcyL1 were also displaying a low level of Ahcy. Could it be an issue of sequence identity between Ahcy and AhcyL1 leading to off-target effects? Or could AhcyL1 positively regulate Ahcy gene expression? Also, could you explain the modest changes in the expression of AhcyL1 in the AhcyL1 line?<br /> - Fig2D, E, how do you explain the variation in AhcyL1 and Gnmt between the three Ahcy RNAi lines? Consider revising line 126-127: “without any corresponding changes in AhcyL1” as there are changes. <br /> - Line 255, it would be of interest to provide more discussion of the discrepancy between this work and the previous work which showed Ahcy-dependent enhancement of H3K4me3. Line 273, consider revising this sentence as it was not shown here that Ahcy promotes methylation, which data indicate this point?

Minor comments <br /> - Figures, figure panels are mostly small, with large white space between them. Increasing panel size and image fidelity would help to fully visualize data (e.g. Fig. 2A, 4E). Figure 1E, increasing the size and brightness/contrast would be good too.<br /> - Line 43, consider revising 'higher' to another term (e.g. multicellular organisms) because 'higher’ assumes that complexity increased with the evolution of eukaryotes, which is not always the case. <br /> - Line 70, briefly describe S2 cells and why they are being used here. <br /> - Line 77, define TMT. <br /> - Lines 81-84, repeated information from introduction that could be removed or condensed. <br /> - Fig1A, we found it unusual to include previously published data, is that because it wasn’t displayed as a graph in the previous study? <br /> - Fig1B, add arrowheads and acronyms by the molecules (SAM, SAH) and an arrow indicating inhibition of methyltransferases. <br /> - Fig 1E, define “IGMR”. <br /> - Line 108, cite also Fig1C. <br /> - Line 113, refer to Fig2A instead of Fig1A. <br /> - Line 115, add Fig2A for the Ahcy interaction with itself. <br /> - Line 140, refer to Supp Fig2B instead of 2A. Could you describe the data presented in Supp Fig2B in the legend. <br /> - Lines 146-147, refer to Fig 3A only instead of 3A & 3B, as you are describing catalytic efficiency (kcat/km). <br /> - Line 158, could you discuss further how else C. elegans Ahcy differs from Drosophila? Maybe provide an alignment of the full protein sequences in supplemental. Are there other key differences between Drosophila and C. elegans Ahcy in structure or function? The rate shown in Fig. 3B appears different at lower SAH rates specifically, is this relevant? <br /> - Line 170, refer to Supp Fig2A instead of 2B. <br /> Figure 3E, add adenosine label. Could adenosine come from cleavage of NAD+ in these conditions? Or is the product of SAH hydrolysis (adenosine) still complexed with the enzyme Ahcy upon purification?<br /> - Line 180, refer to Fig3A instead of 3B. <br /> - Line 181, consider including the structure of the C195S mutant (does it accumulate to wild type level or does the mutation destabilize the protein?) <br /> - Lines 191-192, do you have other evidence than structural ones that suggests that C195 is completely protected from solvent (i.e., H2O2) when NAD+ is bound?<br /> - Line 207, add the word cells after "photoreceptor" <br /> - Figure 2, could you include in the legend what is meant by 3% input? In “(A) Ahcy, AhcyL1, and AhcyL1 in S2 cells” should read Ahcy, AhcyL1, and AhcyL2 in S2 cells. <br /> - Fig2A, there’s a lower band for AhcyL1-V5 in the input, is that a N-term truncation product (if V5 is in C-term)? <br /> - Fig 2C-E, the style of displaying the statistics on these graphs are initially a bit misleading as the straight lines across multiple bars seem to indicate significance across all the bars under the line, but on further inspection it might be representing a statistical comparison between the bars at the end of each line. It might be helpful to use bracketed lines instead of straight lines that point directly to the bars been compared.<br /> - Fig2C-F, the RNAi lines used in these panels are referenced in the Supplementary Table 1, but this table is missing.<br /> - Line 250, when discussing redox sensing, and the lack of Set1 oxidation in your previous analysis, clarify whether only cysteines were analyzed. It could be that methionine for example are oxidized in their sulfoxide form and possess a regulatory role. <br /> - Lines 263-265, maybe rephrase “when compared both with untreated Ahcy RNAi flies and with mCherry RNAi flies exposed to blue light” to “when compared with mCherry RNAi flies exposed to blue light” because the untreated Ahcy RNAi flies had lower levels than the treated ones, and the original wording suggests that the treated is lower than the untreated.<br /> - Lines 296-297, missing space between using and anti. <br /> - Line 332, include the specific type of qPCR kit used. <br /> - Figure 4B, could you explain in the legend what the dashed arrows represent or are showing? <br /> - Figure 4E, could you orient the non-initiated reader as to how these data are compared, how can one tell the differences observed e.g. between Ahcy RNAi blue light vs. control blue light is not significant?

Participants of a course on Peer Review (Indiana University Bloomington) - not prompted by a journal; this review was written within a Peer Review in Life Sciences graduate course led by Alizée Malnoë with input from Sally Abulaila, Kim Kissoon, Michael Kwakye, Madaline McPherson, Madison McReynolds, Mandkhai Molomjamts, Habib Ogunyemi, Octavio Origel, and Warren Wilson.