On 2025-10-16 16:56:00, user Raj Charamata wrote:

May I know what communities you exactly picked and if I can have results as per community. Also I would be glad to get contact details or email address of researchers

On 2025-10-16 07:59:15, user Christoph von Beeren wrote:

Congratulations on the nice discovery! <br /> Could you mark the exit whole in Figure 3D. I guess it is the rather large roundish structure on the right site, but it would be good to mark it with an arrow to be sure. Have you checked for exuvia of earlier instars in the infested pupae? And are there any feeding marks on the cuticle of the infested ants? Have you checked that? Maybe it is also worth barcoding the clerids and checking their genitalia as they were collected from different host ant species. Just to have an idea whether it is likely a single or possible several species. Anyhow, nice find :)

On 2025-10-15 23:44:42, user Date Hidetoshi wrote:

I was deeply impressed by your excellent research.<br /> Do you have any plans to publish the list of OMIM-associated genes (biallelic, biased, monoallelic) shown in the Venn diagram in Figure 3A?

On 2025-10-15 19:30:36, user Chenxi Sun wrote:

This paper presents the first single-cell RNA-seq atlas of Trypanosoma cruzi, mapping gene-expression profiles across all major life-cycle stages. The study identifies ten transcriptional clusters and two distinct trypomastigote subtypes, revealing a continuous differentiation trajectory and highlighting RNA-binding proteins as key post-transcriptional regulators.<br /> The main contribution of this work is providing a comprehensive reference atlas that transforms our understanding of T. cruzi development from discrete stages to a continuous, dynamically regulated process. The methodology is convincing, combining single-cell and bulk RNA-seq validation, though it remains limited by static sampling and the lack of experimental confirmation for specific regulatory factors.<br /> Overall, the paper is clearly written, logically structured, and conceptually strong. It offers a lasting impact by establishing a foundational dataset for exploring gene regulation and parasite differentiation at single-cell resolution.

On 2025-10-15 17:54:53, user Pui-Yan Kwok wrote:

This paper is now published in Nature with an updated title: "The Taiwan Precision Medicine Initiative provides a cohort for largescale studies”<br /> DOI: 10.1038/s41586-025-09680-x<br /> URL: https://www.nature.com/articles/s41586-025-09680-x

On 2025-10-15 00:31:44, user Gabriel Ferreira wrote:

A recent study by Emond-Rheault et al. provided an extensive analysis of the coding and non-coding transcriptome of Leishmania infantum developmental stages using Nanopore direct RNA sequencing (PMID: 40597600). The considerable depth of their sequencing—achieving 5.2 to 6.9 million reads and approximately 150-190% genome coverage for both promastigote and amastigote stages—enabled statistically powerful comparative studies. Their work precisely determined the primary spliced leader (SL) and poly(A) sites for the majority of L. infantum protein-coding transcripts, accurately defining their 5’ and 3’ ends and the lengths of their UTRs. It also provided further evidence for SL cleavage site motifs and better defined the genomic context for cleavage and polyadenylation. The accuracy of the transcriptome annotation was supported by cross-validation with Illumina RNA-Seq. Furthermore, the study quantified the frequency of primary SL and poly(A) sites and compared alternative polyadenylation events between the developmental stages. Additionally, this analysis uncovered a rich repertoire of newly annotated lncRNAs, which generally exhibit distinct expression patterns from their cognate 3’UTRs. The protein-coding potential of a subset of these lncRNAs was rigorously investigated by LC-MS/MS and the expression of proteins of selected lncRNAs was confirmed using CRISPR–Cas9-mediated tagging and western blot analysis. Several of these lncRNAs are developmentally regulated, showing higher expression in amastigotes, underscoring their potential importance during intracellular development.

The current work by Dumetz et al., which combines PacBio HiFi de novo assembly with Oxford Nanopore direct RNA sequencing of Leishmania donovani promastigotes and axenic amastigotes, would be greatly strengthened by considering the recent study on L. infantum. A comparative analysis between these two closely related species could address compelling biological questions regarding gene expression, UTR conservation and length, RNA processing, and the presence of known and novel 3’UTR regulatory motifs. However, the low number of Nanopore reads obtained for L. donovani axenic amastigotes (only 648,426) drastically reduces the statistical power and reliability of comparative analyses between the two life stages.

The discovery of putative RNA G-quadruplexes in over 56% of L. donovani 3’UTRs is a novel finding. It would be interesting to investigate whether these sequences are conserved in the 3’UTRs of the closely related L. infantum. Nonetheless, in the absence of experimental validation, it is premature to suggest these motifs are main drivers of post-transcriptional regulation, particularly given the many other validated 3’UTR regulatory elements known in Leishmania spp. It is a limitation that the authors did not search for these established elements in their analysis. Finally, the crucial role of 3’UTRs in controlling mRNA fate in Leishmania has been widely documented, and it is regrettable that the authors did not discuss their findings within this broader context.

I hope these suggestions are helpful in strengthening the study.

Respectfully,

On 2025-10-14 20:45:37, user Yu Lee wrote:

This paper, as well as the previous papers of M. P. Nikitin, is based on the concept of DNA commutation, where affinities for interactions are calculated using custom NUPACK scripts developed by the authors. Could the authors deposit the developed code along with the new paper, since it is central to this manuscript as well as to their previous publications?

The previous papers, which also relied on the same NUPACK scripts, did not include the code despite Nature’s code availability guidelines.<br /> https://doi.org/10.1038/s41557-022-01111-y

On 2025-10-14 15:48:16, user Asya Makhro wrote:

Thank you very much for your comment, we greatly appreciate your interest in our preprint.<br /> Our publication focuses on the nonimmune incompatibility between two species, which is why we concentrated on compromised oxygen delivery between mother and fetus. I have read the paper you mentioned and completely agree that multiple mechanisms were most likely involved in the decline of archaic populations. While Piezo1 also defines a blood group, the amino acids responsible for blood group type are located in the extracellular domain and do not influence Piezo1 channel properties (PMID: 36122374, Figure 4) or, consequently, RBC metabolism.<br /> The choice of the Ser307Gly variant was inspired by Svante Pääbo’s lecture, in which he compared archaic protein variants with modern ones based on 1000 Genomes data. A list of the proteins can be found in the supplementary materials of PMID: 24679537. At that time, protein alleles with low frequencies (<1%) were not detectable in such a small sample of modern genomes.<br /> The Ser307Gly variant appears to have undergone negative selection. For example, the archaic allele of a Piezo1-linked gene, MC1R, reached up to 70% in some East Asian populations (PMID: 24916031), but the archaic Piezo1 variant is absent in these individuals, as genomic data were obtained from the 1000 Genomes Project. Another archaic allele of Piezo1, E1839K, is preserved at higher frequencies. I did not verify this variant in archaic genomes, but its designation as archaic is based on comparative analysis. Approximately 5% of Asians (gnomAD) and all other modern hominid species carry K instead of E, reflecting a prevalence rate more consistent with neutral expectations for an archaic allele.<br /> Thank you for mentioning Denisovans. I did not examine their sequence, but I would expect that most archaic humans carried this variant, particularly if our hypothesis regarding the reproductive barrier is correct.

On 2025-10-07 06:57:42, user Stéphane Mazières wrote:

The fetomaternal incompatibility between Neanderthals and H. sapiens is a very original hypothesis brought from "novel" genetic markers in the era of genomics: red cell polymorphisms. We have previously raised it in 2021 in our study of red cell blood groups (PMID: 34320013, not cited in the preprint)<br /> I have few more methodological concerns about this preprint: <br /> - the study does not precise which Neanderthal genome(s) has been studied and how the authors have identified the p.Gly307Ser.<br /> - all 3 high-quality Neandertal genomes have several other. missense polymorphisms in the PIEOZ1 exons. I don't understand why the authors have focused only on the p.Gly307Ser.<br /> - By the way, Denisova also carries the p.Gly307Ser.

On 2025-10-14 15:45:20, user Diana Duarte wrote:

Thanks for the interest in this manuscript, a peer reviewed version is available at https://molhort.biomedcentral.com/articles/10.1186/s43897-025-00176-w

On 2025-10-14 14:26:40, user Steffen Ostendorp wrote:

This work has been published in Journal of Experimental Botany ( https://doi.org/10.1093/jxb/erae495 )

On 2025-10-14 12:24:57, user Fabio Alfieri wrote:

This preprint has now been published in Functional Ecology ( https://doi.org/10.1111/1365-2435.70178 )

On 2025-10-13 23:43:20, user CDSL JHSPH wrote:

Thank you so much for diving in the attenuation of LAMV vaccine. Particularly, you find out that acute induction of IFNa and NF-Kb in immune cells is the crtical determinant of LAMV attenuation. You have contributed the field by discovering the molecular level findings of LAMV attenuation, which provides possible strategies on designing future vaccines. <br /> This is a breakthrough contribution, since such detailed observation of vaccine attenuation have never been done before. The methodology is convincing, since you have applied In-vitro and In-vivo to support your findings. These experiments are well-designed and their results corresponds to each other, which strengthens your conclusion. Conflicts of the results have also been explained. The most important limitation of these two approach is that they could not fully explain the mechanism of attenuation in human bodies, considering differences between human and experiment animals. <br /> Overall, the narrative is great, which makes the article extremely easy to follow. I don’t have to re-read the section, since each section is structured in similar format : purpose of method, result of method, interpretation of method. <br /> Although this paper proofs and explains everything in detail, there are some questions need to be solved. For example, in the discussion, you have demonstrated possible correlation between virus particle formations and reduced symptoms. More researches need to be done to proof the hypothesis.

On 2025-10-13 19:10:56, user Ya'el Courtney wrote:

Really interesting work. Impressive scale and thoughtful masking. One analysis that could sharpen the biological interpretation of the ~10–11% EBV DNAemia group is per-individual, gene-level EBV coverage profiles.

A simple addition could be:

(1) Per-sample coverage heatmaps across the EBV genome (normalized depth, with masked loci excluded).

(2) Summary indices contrasting latency-associated regions (e.g., EBNA1/2/3, LMP1/2, RPMS1/BARTs) vs lytic genes (BZLF1/BRLF1/BMRF1/BALF5/late capsid).

(3) A “flatness/entropy” metric to distinguish uniform episomal coverage from focal peaks consistent with fragmentation or reactivation.

It would really help interpretation because gene-level coverage tells you what biology you’re actually capturing in that ~10–11% tail. If coverage is flat across the EBV genome, that’s more consistent with cell-associated episomes and high-load latency; if you see focal enrichment over immediate-early/early/late loci, that points toward reactivation or fragmented cfDNA. That distinction changes how we read the HLA/ERAP2 signals (antigen presentation under lytic pressure vs control of latent burden), tightens the PheWAS (different disease links are expected for latent-heavy vs reactivation-enriched samples), and avoids over-generalizing a single “DNAemia” phenotype. It also matters for downstream hypotheses and clinical framing: a reactivation-prone subgroup suggests monitoring/treatment angles very different from stable latent carriage.

Thanks for sharing this!

On 2025-10-13 16:17:56, user Gunjan Sharma wrote:

Hello, this preprint has been published in Cell Reports with the doi: 10.1016/j.celrep.2025.116330 . IGF2BP3 redirects glycolytic flux to promote one-carbon metabolism and RNA methylation. It will be great if you could link this with the preprint. Thanks!

On 2025-10-13 00:21:15, user CDSL JHSPH wrote:

The preprint presents a clear and compelling framework for understanding plant and fungus interactions as a trio among pathogens, host and microbiome. Especially AMAPEC, this key method, is a supervised predictor that integrates the sequence and structure feature and short motif patterns to identify secreted fungal proteins with antimicrobials potential. I am appreciated for this key method and so impressive of how well of the work it done. Scaling AMAPEC across 150 genomes reveals that the antimicrobials like secreted proteins are enriched among widely shared and conserved clades. Moreover, from the prediction of biology for “wet-lab” tests indicate that five canonical effectors display direct antimicrobial activity in vitro. The Vd424Y knockout experiment is also impressive. Especially for the figure 4 Panel B, the direct field plant control groups are super impressive. It also emphasized the Vd424Y knock out only presented the impact on plant with the microbes enrichment soil. In contrast, in the absence of the microbe soil has no big gap between either WT and Knock out for the vitro observation. Overall, I wan tot say the APAMEC is a credible tool cross validation and cohesive model that reframes the pathogenesis as both host manipulation and ecological aspect of the plant microbiome.

On 2025-10-12 22:30:47, user JW wrote:

You did a very inspiring piece of work! Your idea: fungal effectors originated from ancient antimicrobial proteins adds a new dimension to my understanding of plant and microbe interactions. The AMAPEC tool is a powerful and creative contribution that will definitely help future research in effector biology and fungal evolution.

On 2025-10-12 06:26:52, user Chenxi Sun wrote:

Overall, the paper makes a strong and interesting contribution by showing that macrophage mitochondria transferred to cancer cells don’t serve as energy sources but instead act as ROS-producing signals that activate ERK and drive proliferation. I’d rate its significance a 4 out of 5, since this finding changes how we think about mitochondrial transfer, giving it a new signaling role in tumor biology. The experiments are carefully designed — live-cell imaging, RNA-seq, and biosensors all support the conclusions — but most data are from in vitro models, so it’s still unclear how much this process matters in real tumors. The main limitation is the lack of in vivo validation, which would make the story more convincing. Overall, the methods are solid, the logic is clear, and the conclusions mostly follow from the data, even though more physiological testing would strengthen it.

On 2025-10-10 13:27:59, user Kasper Hansen wrote:

This is now published in Genome Biology, DOI: 10.1186/s13059-025-03601-x

On 2025-10-09 20:36:10, user CDSL JHSPH wrote:

Overall, I think this paper had interesting research about circadian rhythm mediating malaria transmission potential. In the abstract, I believe that for the beginning of the paper it would beneficial to explain right away the importance of the research and it’s significant to the public health field. <br /> I would also suggest that the gaps that existed from previous research should be included in the introduction part of the research article. But, I do believe that this research was essential to challenge the dogma that sporozoites are “quiescent” and instead are transcriptionally active. <br /> Furthermore, it’s interesting to know that disrupting these circadian clocks that exist within these mosquitoes could reduce transmission. Which, can help mitigate the transmission of these disease to humans. Not only in malaria transmission but in other vector-borne diseases as well. <br /> And I wonder if you furthered the research and tried to implement specific novel strategies to prevent these circadian rhythms from occurring and transmitting malaria? I do also wonder if the prevention would even be beneficial at all? There could be a possibility that the system could a way to still transmit malaria even though it’s disrupted. I do also wonder since the research is based off of a small sample pool size of around 20 mosquitoes if this is masking individual variation? I believe this part should have been stated somewhere in the research paper.

On 2025-10-09 18:57:38, user Donovan Parks wrote:

Hello. I read your preprint with much interest. I have recently been looking at Zinderia insecticola and Stammera capleta genomes at NCBI. Genomes from these species also appear to lack tRNA-Trp(tca) and only contain tRNA-Trp(cca). I am wondering if you have looked at these genomes? Do they also have a tRNA-Trp(cca) with a 4-bp anticodon stem? This would strengthen your finding that this modified tRNA-Trp(cca) can recognize UGA.

On 2025-10-09 15:13:19, user Kurt Runge wrote:

A version of this article is now published. See PMID: 41016932

On 2025-10-09 12:24:19, user Vitor Neves wrote:

A new version of this manuscript has been published online on the Journal of Clinical<br /> Periodontology ( https://onlinelibrary.wiley.com/doi/10.1111/jcpe.70048)

On 2025-10-09 12:20:41, user Paul Macklin wrote:

Published in Journal for Theoretical Biology

https://doi.org/10.1016/j.jtbi.2025.112266

On 2025-10-09 10:37:53, user Emma Chambers wrote:

This article is now published https://onlinelibrary.wiley.com/doi/10.1111/acel.70249

On 2025-10-09 07:53:55, user Prof. T. K. Wood wrote:

Yes, since serine recombinases are themselves an anti-phage system as disclosed earlier this year. Please cite https://www.biorxiv.org/content/10.1101/2025.03.28.646004v1 and the final version to be on-line in the next few days.

On 2025-10-09 07:16:25, user Ralf H. Adams wrote:

In a recent preprint at bioRxiv (Yang et al., https://doi.org/10.1101/2025.10.02.679940) , a group of authors led by Dr Anjali Kusumbe challenges an article from my lab published in 2024 (Koh et al. 2024, PMID: 39537918). The new preprint refers to Extended Data Figures and Supplementary videos that are unfortunately not provided at bioRxiv. Nevertheless, the manuscript text and the 5 main figures plus a proposed model contain a couple of major issues that I will cover here. A thread containing my comments plus useful illustrations is available on X ( https://x.com/ralfhadams/status/1976179960558539006 ).

But before I do so, let me give you a little background. Several papers over the recent years have proposed that the bone marrow (BM) in skull is specialized and acts as an immune cell reservoir for the brain parenchyma and the meninges, a multilayered tissue structure that encloses and protects the brain and spinal cord. Key evidence comes from landmark publications by the groups of Matthias Nahrendorf (Herisson et al. 2018, PMID: 30150661), Jonathan Kipnis (Cugurra et al. 2021, PMID: 34083447), Ali Ertürk (Kolabas et al. 2023, PMID: 37562402) and others. Kolabas et al. 2023, for example, provide compelling results showing that skull “has the most distinct transcriptomic profile compared with other bones in states of health and injury” with potential relevance for neurological pathologies. The same publication also reveals that skull shows a strong response to stroke, arguing for interactions between the brain and a nearby bone marrow compartment.

Our paper from 2024 shows that the BM of skull is undergoing substantial expansion in adult life but also during aging, which applies both to stromal cells (including vasculature) and a large range of hematopoietic cell subsets including stem and progenitor populations. Our paper makes use of well-established immunolabeling and imaging enabled by the injection of antibodies into living animals before they were sacrificed for tissue isolation. The removal of external tissues, including the meninges/dura mater on the brain side and dermal tissue on the skin side enabled undisturbed insights into the organization and expansion of bone marrow vessels, which exhibit the distinct dilated and irregular morphology typical for the sinusoidal vasculature of the BM. It is also noteworthy (and will be relevant later) that the expansion of the BM vasculature starts in frontal and parietal bones at the edges (near the sutures) and gradually progresses toward to center in young adult mice (10-14 weeks), whereas the interparietal bone is already filled with vessels (and hematopoietic cells) at this stage.

Apart from imaging, we provide an extensive set of flow cytometry data in a large (22 page) supplemental file accompanying the paper, which confirms the expansion of stromal, endothelial, total hematopoietic cells and various hematopoietic cell subsets during adulthood and aging but also in response to various challenges (such as pregnancy and stroke).

But even the skull of older mice is not spared from signs of aging. We observe that the expression of pro-inflammatory cytokines is elevated in old skull in comparison to samples from younger mice. However, this affects fewer cytokines and involves lower levels of upregulation relative to age-matched (young and old) femur samples.

Functionally, we show that shielding of either the head or the hindlimb allows the survival of young mice exposed to a lethal dose of irradiation (without BM transplantation). In irradiated old mice, however, only shielding of the head ensures survival, whereas animals with shielded legs gradually succumb over a period of 200 days, presumably reflecting the exhaustion of hematopoietic stem cells and early progenitor populations. These results and other evidence presented in the article led us to the conclusion that skull BM is more resilient to aging than its counterpart in femur.

Now, let’s take a look at some of the claims raised by Yang et al. in their recent bioRxiv preprint. In Figure 1, they have reanalyzed selected subsets of scRNA-seq data from our publication to show that samples from old skull show hallmarks of aging compared to young skull. It is not clear (and is not explained) why certain samples from our data (available at GEO under the accession number GSE275179) were included in their analysis whereas other, equally suitable samples were excluded. In any case, it is not surprising and also not controversial that cells from old skull show an increase of aging markers compared to samples from young mice. We have never claimed that the skull BM stays young forever and, instead, we have reported the upregulation of pro-inflammatory cytokines mentioned earlier together with other evidence such as myeloid-biased hematopoietic differentiation.

Figure 2 of the Yang et al. preprint shows a comparison of mass spectrometry data from young (10 weeks) and old skulls/calvaria (79 weeks), which also indicates that markers of DNA damage and inflammation are upregulated, whereas markers of angiogenesis, osteogenesis and mitochondrial activity are downregulated. One would have liked to see a more detailed description of the sample preparation (e.g., were the meninges and other adherent/external tissues removed from the skull samples?), but the findings themselves are not surprising or controversial. Of course, old tissue samples show evidence of aging and are different from young skull samples.

Things heat up a little in Figure 3 of the Yang et al. preprint. The authors claim that a dense, continuous network of vessels is found throughout skull samples from all ages and thereby challenge our observation that the calvarial vasculature and BM are dynamically expanding in adult mice.

The evidence provided by Yang et al. includes transversal sections through the skull roof. The low magnification overview images in Figure 3b are of poor quality/resolution so that it is not possible to see much detail. The DAPI nuclear staining in the higher magnification images, however, suggests that the enlarged areas primarily correspond to tissue near the sutures, which already contain vasculature and some BM even in young frontal and parietal bones. Clearly, one would like to see better quality overview images and an unbiased comparison of central vs. peripheral areas from frontal and parietal bones.

Things get more interesting and controversial in Figure 3c and d. Yang et al. use tissue clearing and light sheet microscopy to show the presence of a “continuous, dense vascular network throughout the skull frontal, parietal and interparietal regions in young mice”. The CD31 immunostaining in Fig. 3a, however, shows predominantly the meningeal vasculature, easily identifiable by hallmarks such as the sagittal and transverse sinuses consisting of large diameter blood and lymphatic vessels. Periosteal vessels might be also labeled, but it is not obvious that any sinusoidal vessels of the bone marrow are stained in this sample.

Higher magnification images in Fig. 3d show more details of the meningeal (and perhaps also periosteal) vasculature in mice from different age groups. Again, it is very obvious that sinusoidal vessels with their distinctive morphology and large caliber are not captured in these samples. This is not surprising because expression of CD31 is low in sinusoidal (also termed type L) endothelial cells, as previous work from my group has established (Kusumbe et al. 2014, PMID: 24646994).

Taken together, it is clear that Yang et al. have not carefully separated the vasculature of the skull and the adjacent meninges, leading to confusing results and wrong conclusions. Future research in this important field needs to clearly distinguish what is bone marrow, periosteal tissue, meninges or the surface of the adjacent brain.

There is another small but interesting nugget hidden in Fig. 3c of the Yang et al. preprint. The freshly dissected skull sample prior to clearing shows distinctive areas of red blood cells in the interparietal bone but also in parts of the frontal bone. Our work has revealed a similar pattern of red blood cell distribution, representing regions of bone marrow, in freshly dissected young skull. In samples from older mice, these areas increase substantially, confirming the expansion of calvarial BM even in the absence of any immunostaining. Kolabas et al. 2023 (PMID: 37562402) also show a strikingly similar distribution of Nr4a1+ and propidium iodide (PI) labeled immune cells in calvarial bone marrow of young adult mice. Note the absence of immune cells in central regions of the frontal and parietal bone, whereas labeled cells (blue and red) are abundant in the interparietal bone. This is totally consistent with our own findings.

Unfortunately, the sample showing aged skull is not included in the Yang et al. bioRxiv preprint so that a further comparison of young and old bone marrow will have to wait until the Extended Data Figures become publicly available.

Before I move on, I would briefly like to address a technical detail. In the Yang et al. bioRxiv preprint, the authors claim that our Endomucin staining shows “aberrant nuclear localization”, which, according to them, suggests “that the current staining is artefactual and calls into question the reliability of their vascular imaging data”.

This is just one of many strongly worded claims in the preprint. In reality, however, the Endomucin immunostaining shown by Yang et al. in the preprint is strongly overexposed, preventing any insight into the actual intracellular distribution of the antigen. The red (Endomucin) signal covers pretty much every part of the stained cells. The impact of different imaging and data presentation modalities, whole-mount vs. tissue sections and maximum intensity projection vs. isolated optical planes, also need to be considered.

Furthermore, I have already mentioned that our study has used an in vivo labeling approach involving the injection of antibodies before the animals were sacrificed. This might lead to a slightly different pattern in comparison to post-fixation staining. Nevertheless, it is unambiguously clear that the bone marrow vasculature is stained reliably in Koh et al. 2024 (PMID: 39537918), revealing striking details of vessel architecture and regional differences.

Before I continue with the next figure of the preprint, let’s have a quick look what other recent publications say about skull bone marrow.

Chang et al. 2025 (PMID: 40970910), using advanced tissue clearing and light sheet imaging, report bone marrow expansion in a comparison of 2-month-old and 2-year-old skull samples. The data shown in Figure 5 of their publication independently confirms that large parts of the young calvarium are devoid of VEGFR3+ sinusoidal vessels, whereas older samples show a profound expansion of vessels, consistent with our own findings.

A recent publication from the group of Warren Graham (Horenberg et al. 2025, PMID: 39984434) reports that “Emcn+ vessels demonstrated drastic morphological changes with aging, as bone marrow-resident sinusoidal blood vessel signal increases with age”.

A recent publication from the group of Shukri Habib (Reeves et al. 2024, PMID: 39692737) has evaluated calvaria from mouse of different age groups by micro-computed tomography and other methods. In Figure 1A+B and the corresponding text, they state that “overall thickness from outer surface to inner surface of the parietal bone significantly increased with age” and they also show a striking increase of cavities (which, as we know, represent bone marrow areas) compared to young and adult calvarial bone.

Overall, there is more and more emerging evidence supporting that the bone marrow in adult skull is expanding dynamically in adult mice, which involves changes in sinusoidal vessels but also in stromal and hematopoietic cell populations.

Let’s return to the preprint by Yang et al. with a focus on Figure 4, which is another reanalysis of scRNA-seq data from Koh et al. 2024 (PMID: 39537918). This time, the focus is initially on hematopoietic stem and progenitor cells (HSPCs) and CD45+ cells from young and old femur samples. According to the figure legend, this analysis is based on three samples – one from young and two from old femurs – that were subjected to the removal of hematopoietic cells prior to single cell sequencing. This depletion method is necessary to capture and enrich stromal cell populations, but these are obviously not samples that one would use for the analysis of hematopoietic cells. The remaining hematopoietic cells are residual contaminating cells that have escaped the depletion step, which is never totally complete.

I would dispute that the analysis of contaminating cells allows meaningful conclusions about the abundance/enrichment of hematopoietic cell populations in aging femurs, but this seems to be exactly what Yang et al. have done. Could they have selected the wrong samples from our deposited data or mixed up the sample numbers? Who knows? In this context, I would like to point out that we had provided a substantial amount of information about our scRNA-seq data, the pretreatment of the different samples and other aspects of our methodology per email to Dr Kusumbe in early June 2025.

Back to the preprint and Figure 5, which provides bulk mass spectrometry data comparing proteins from old vertebra and skull, which led the authors of the preprint to the conclusion that vertebrae are relatively protected from aging hallmarks in comparison to skull. The description of the methodology is unfortunately very brief. It is not clear which vertebrae were analyzed exactly (cervical, thoracic, lumbar, sacral, or all?). It is also not clear whether adherent muscle, fat, spinal cord and intervertebral discs were removed, all of which should influence substantially what is seen in the proteomic profile. In any case, it is obvious that this analysis is not confined to bone marrow and functional data for BM cells from vertebrae in comparison to other BM compartments is lacking. Obviously, future work will have to address how aging processes affect different BM compartments and thereby the function of the hematopoietic system.

We are almost done, but I want to highlight another controversial aspect in the Yang et al. preprint, namely the presence (or absence) of lymphatic vessels inside bone. Based on the reanalysis of our scRNA-seq data from Koh et al., it is claimed in Fig. 1 and the accompanying text that lymphangiogenic markers are downregulated in aged skull samples. I would dispute that the presence of Cdh5+ Pecam1+ Prox1+ Ptprc- Flt4+ endothelial cells in the data supports the presence of lymphatic endothelial cells inside the skull bone or marrow. Chang et al. 2025 (PMID: 40970910), a publication that was already mentioned earlier, have examined this question in detail and they state that lymphatic vessels are present outside the skull in the periosteum but not inside bone marrow.

The preparation of samples of skull and other tissues for scRNA-seq analysis is a race against time because it is essential to preserve RNA integrity as much as possible. This means that the removal of adherent, periosteal tissue residues including lymphatic vessels from the meningeal or the dermal side has to be done quickly, is most likely incomplete and might lead to residual contaminations. Essentially, the proposed presence of putative lymphatic endothelial cells in our data, which we have not validated ourselves, cannot resolve where these cells originate from.

I want to conclude here and thank all of you who have read this thread till the end. As I have not been able to cover all issues in the preprint, I am considering another detailed thread once the Extended Data becomes public.

I also hope that I have remained factual and sufficiently polite throughout my posts even though I perceive the language in the Yang et al. preprint as sometimes overly harsh and many claims unsubstantiated and wildly exaggerated.

Final full disclosure: I am not only the last author of Koh et al. 2024 (PMID: 39537918) but also the former postdoctoral supervisor of the two last authors of the Yang et al. preprint.

On 2025-10-09 00:18:55, user Adam Smith wrote:

Congratulations to all authors on a strategic and collaborative article that has identified challenges and important opportunities for citizen science

On 2025-10-08 15:32:30, user M.A. wrote:

Great work. It would seem that the "baseline setting" (Figure 2) is unfairly favoring the semi-supervised methods. The same labels are used as input to guide integration AND for performance evaluation; this allows ss-methods to overfit the data, especially scGen and scDREAMER, which have many parameters. Wouldn't it make sense to report the rankings based on a more realistic scenario, such as one with partial annotation or partially incorrect labels?<br /> On another note, silhouette coefficients have been reported to be suboptimal for this kind of benchmarks, and more appropriate metrics have been proposed, see e.g. https://www.nature.com/articles/s41587-025-02743-4

On 2025-10-08 14:25:37, user Michal Tal wrote:

Since I was asked to review this paper several months ago and waived my anonymity on review, I'm sharing my review publicly here as a comment. The TL/DR is that I think this paper is both very informative, and very important. However, it does need to be contextualized as a deep study of a recovery cohort, which is then being compared to public data from cohorts with a significant percentage of people who are not recovering, and that needs to be accounted for. Comparing immune cells from the PBMC fraction of blood of people who all went on to recover to cells from tissue of cohorts including those made up of 40% people who did not go onto recover does not allow for making conclusions about differences between the blood and the tissue without accounting for the differences in immune responses of those on a trajectory to recover and those who are not. Those immune responses could look very different, both in the blood and in the tissue.

Here is my full review:

This is an important and comprehensive study by Rostomily et al., "Multiomics Reveals Compartmentalized Immune Responses and Tissue-Vascular Signatures in Lyme Disease," which significantly advances our understanding of the immunopathology of acute Lyme disease (LD). I found it easy to read, and the figures were clear and compelling. By employing a longitudinal, multiomics approach integrating plasma proteomics, metabolomics, and PBMC immunophenotyping, supplemented with a meta-analysis of skin lesion transcriptomics, the authors present a compelling narrative of compartmentalized immunity. They propose that the robust alterations in circulating plasma proteins and metabolites, linked to endothelial barrier stability, metabolic reprogramming, and symptom severity, are predominantly driven by local immune processes within the skin and associated vasculature, while systemic PBMCs remain largely quiescent. It is quite surprising to see the PBMCs and metabolites show such fast resolution, and it feels like this is likely related to the complete recovery seen in this cohort. This work offers novel insights into effective immune responses against Borrelia burgdorferi and the kinetics of recovery from infection, particularly highlighting vascular involvement, and provides a valuable resource for future biomarker discovery and therapeutic development in LD. <br /> A critical aspect for the authors to address, perhaps in the limitations or discussion, is the high recovery rate observed in their patient cohort. The manuscript states, "Following antibiotic treatment, symptoms resolved in most patients, with only a few reporting mild symptoms attributable to LD at 6 months or at 1 year post-treatment". This contrasts with broader literature suggesting that 10-20% of LD patients develop Post-Treatment Lyme Disease Syndrome (PTLDS) with persistent symptoms. It would be beneficial for the authors to discuss why their cohort experienced such a high recovery rate. Were specific exclusion criteria applied that might have inadvertently selected for individuals less prone to PTLDS (e.g., absence of certain co-morbid conditions known to be risk factors, that’s very interesting to speculate)? The methods section details exclusions such as fibromyalgia, chronic fatigue syndrome, traumatic brain injury, prolonged undiagnosed somatic complaints, morbid obesity, sleep apnea, autoimmune disease, uncontrolled cardiopulmonary or endocrine disorders, recent malignancy, liver disease, major psychiatric illness, or substance abuse. While extensive, it's worth considering if these fully account for the low PTLDS rate. Additionally, the cohort demographics (Figure 1B) show a skew towards male patients (27 male vs. 22 female). Given that some infection-associated chronic illnesses, including potentially PTLDS, may skew female, could this gender distribution contribute to the observed recovery outcomes? Clarification on these points would help contextualize the study's findings regarding the typical immune trajectory of acute LD.<br /> Major Points:<br /> 1) Comparability of Meta-Analysis Cohorts: The conclusions regarding skin-derived systemic signals rely heavily on meta-analyses of public datasets (GSE63085, GSE154916, GSE169440). It is crucial to provide a more detailed comparison of the clinical characteristics (symptoms, treatment, PTLDS rates) of these external cohorts with the primary study cohort. For instance, the GSE63085 PBMC dataset is from a cohort with a reported PTLDS-like symptom rate of ~46%, substantially different from the near-complete recovery in the current study's cohort. These differences should be explicitly discussed as they could influence the nature and interpretation of immune responses. I wonder if the major differences seen in the skin vs PBMCs here are driven more by immune differences in people who are on a trajectory to recover vs those who are not. There are public datasets available on PBMCs as well, such as from the SLICE cohort including those on a trajectory to recover and those who are not. These should be compared in the analysis.<br /> 2) The finding of largely quiescent PBMCs in the face of infection and systemic mediator changes is surprising. The authors should expand their discussion to contextualize this observation against other types of infections, or Borrelia infections where people go on to develop borrelia infection-associated chronic illness. For example, how does this compare to PBMC responses in other chronic infections, tissue-localized (versus systemic/blood-borne) infections, or infections caused by slow-growing (like B. burgdorferi) versus fast-growing bacteria? Or, back to point #1, is this more just what PBMCs look like in someone who has been successfully treated with an antibiotic for a bacterial infection, and is this just what being on track to a full recovery looks like? That would explain why this looks so different from PBMC profiles in chronic illnesses like TB/HIV/HCV/ T. cruzi, but would make more sense in the context of a cleared infection and recovery. One additional thing to consider is that most of the immune granulocyte cells will be spun out of the PBMC fraction, but that does not mean those responses aren't circulating in the blood, they just won't be found in the PBMCs.<br /> 3) The T3 timepoint seems to stand out compared to T2 or T4, and it’s not clear why, and this isn’t adequately addressed in the discussion or limitations.<br /> Minor Points (Organized by Figure):<br /> Figure 1: Study overview and clinical manifestations <br /> Panel B: The gender distribution is skewed male. It would be useful to know if any sex-based differences in the measured parameters were analyzed, as this was not apparent throughout the manuscript.<br /> Panel C: The T3 (6 months) timepoint for C6 ELISA is missing; only T1, T2, and T4 are shown. Is that because T3 looks weird throughout, and you didn’t want to show it? <br /> Panel F: It would be helpful to indicate which correlations are statistically significant (e.g., using asterisks or by highlighting significant bubbles).<br /> Figure 2: Differential expression of circulating proteins and their correlation with symptoms <br /> Panel A: The separation into fast- and slow-resolving clusters is a very interesting and insightful presentation. However, the text states PRDX5 remained significantly elevated at T2, but this is not immediately clear from the heatmap's visual representation for PRDX5 in the T1-T2 comparison. Only IL17C is labeled as significant (T2-T3) in the slow responding genes.<br /> Panel B: It is unclear why not all rows are labeled as they appear in A across all pathway comparisons, which makes it harder to assess the full dynamics. Maybe this circles back to the fact that some of them were significant T1-T2, and not T1-3, but then again yes T1-4 so maybe it looked messy to show it that way? But this way you only show the first set it was significant for, and not the dynamics in between…<br /> Panel C and D: The rationale for selecting T1 versus T3 for this heatmap of cardiovascular, metabolism, and organ damage proteins could be clarified, especially as Panel A focuses on pairwise comparisons across all timepoints. And at other times T3 seems to be intentionally excluded. Displaying patient-based trends rather than just row-based averages might also be informative. The asterisks on the left indicating significance are somewhat hard to read on the opposite side from the label.<br /> Panel E: This is a visually appealing figure, though the bundling can make specific correlations slightly challenging to trace.<br /> Figure 3: Integrated community analysis and diagnostic modeling <br /> Panel A: Could the authors add descriptions of any shared features or overarching themes among the analytes within each of the three largest communities beyond endothelial disruption/protection? The rho scale for symptom correlations (-0.4 to 0.6) suggests many correlations are not very strong; indicating that adding statistical significance for these symptom correlations would be beneficial.<br /> Panel B, C, D: These ROC curves are interesting for diagnostic potential. Suggestion: If data are available, showing a baseline ROC curve using standard clinical diagnostic features (e.g., EM presence, basic serology if used for initial classification rather than just inclusion) could provide a useful comparison for the multiomic models.<br /> Figure 5: Minimal peripheral changes in acute LD <br /> Panel A: The highest variance explained by PC1 in the PCA of PBMC abundances is relatively low (18.4% for patients, 16.2% for controls), suggesting considerable heterogeneity not captured by the main principal components.<br /> Panel B: The decrease in plasmablasts over time would possibly be expected if it aligned with the development of memory B cells. But that doesn’t seem to be the case from this data. That might be a fit with what Nicole Baumgarth has described in B6 mice, and definitely warrants further discussion.<br /> Panel C: The UMAP visualization shows minimal separation. Without non-recovered patients, it's difficult to discern disease-specific trends versus inter-individual variability.<br /> Figure 6: Dramatic changes in a case with severe disseminated disease<br /> The boxplots effectively highlight how different the severe outlier patient is. This case underscores the point that systemic activation can occur. I really wonder if compared to publicly available data from people who did and did not recover after their acute infection, if you would see a lot more of this. Replicating this in a dataset with more patients with severe, non-recovering disease would be necessary to draw broader conclusions about this hyperinflammatory state.<br /> Figure 7: Skin immune responses reflect plasma protein and metabolic signatures <br /> Panel A: the source/location of "unaffected skin" biopsies can influence cellular profiles. This should be addressed.<br /> Panel C: The differential expression of CXCL8 (IL-8) across various skin-resident cell types is very interesting as is LILRB4 expression in skin-resident cells which would support the tissue-based regulation hypothesis as long as we had more comparators between the symptoms and inflammatory state of the individuals these cohorts.<br /> I think this paper is both very informative, and very important. However, it does need to be contextualized as a deep study of a recovering cohort, perhaps being compared to cohorts with more people who are not recovering, and that needs to be accounted for.

On 2025-10-08 09:45:42, user Ayansh R wrote:

Published in Computational Biology and Chemistry, 120, 108647. https://doi.org/10.1016/j.compbiolchem.2025.108647

On 2025-10-07 17:56:56, user Marcelo R. S. Briones wrote:

This preprint is now published as peer review paper in Nucleic Acids Research Molecular Medicine https://doi.org/10.1093/narmme/ugaf034

On 2025-10-07 13:37:08, user Evolutionary Health Group wrote:

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

Here are our highlights:

This study explores how simple molecular interactions (partial hybridization between RNA sequences) can change the course of evolution. The authors show that product inhibition, which might seem to slow things down, actually helps populations maintain diversity and obtain new adaptations FASTER.

Without hybridization, the fittest sequences rapidly outcompete all others, driving the population into a low-diversity state centered on a local fitness peak. This SLOWS adaptation, because new mutations can arise only from a narrow set of closely related sequences. With hybridization, all sequences partially inhibit each other, preventing any single clone from sweeping the population. This maintains a broad pool of genetic diversity for longer periods, effectively flattening the adaptive landscape and accelerating the discovery of fitter variants.

Hybridization transforms individual fitness from a fixed property into a population-dependent quantity: the reproductive success of a sequence depends on the presence and abundance of other sequences. This introduces frequency-dependent selection, reshaping the evolutionary dynamics without altering mutation rates or external conditions.

In classical models, large networks of nearly neutral genotypes allow populations to drift across genotype space. Product inhibition breaks this neutrality by making fitness context dependent. Sequences that were previously neutral can become advantageous or deleterious depending on the surrounding population, creating a constantly shifting fitness landscape.

On 2025-10-07 13:14:52, user Evolutionary Health Group wrote:

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

Here are our highlights:

Conventional assays have a lot of noise, making it hard to pick out subtle genetic influences. This method increases the signal-to-noise ratio, lowering the barrier to detecting genetic effects in vitro. Future studies could use the Townlet method to evaluate how rare mutations shape cellular phenotypes.

Typical GWAS studies require hundreds to thousands of participants to account for confounders and small effect sizes. The GWAS-in-a-dish method used here measures each donor's proliferation/survival repeatedly over time, which removes confounders, clarifies the genetic signal, and drastically cuts the required number of participants to draw the same conclusions as a much more difficult study design.

We appreciate Townlet's ability to identify differences in proliferation and viability through large-effect mutation, normal genetic variation, and treatment-dependent states. The village method shows promise for broad application, especially when used with an analytical tool that properly adapts to the unique constraints of compositional data.

On 2025-10-07 12:59:05, user Evolutionary Health Group wrote:

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

Here are our highlights:

This study investigates bacterial adaptation to cold shock tracking the fitness time dynamics of thousands of mutants.

The authors demonstrate that adaptation unfolds in distinct phases: early survival depends on membrane fluidity and cell wall remodeling, while later recovery relies on RNA-level regulation and ribosomal function. They highlight a synergistic role of ribosomal methyltransferases in restoring protein synthesis after translational arrest.

We admire the time-resolved design + comparative analysis of two model species, which helps distinguish conserved from species-specific elements of the cold shock response.

On 2025-10-06 17:40:27, user Bruno Do Rosario Petrucci wrote:

Has anyone been able to find the annotated genomes anywhere? I'd like to include them in one of my datasets, and can't seem to find the zenodo repository anywhere.

On 2025-10-06 16:58:22, user Richard Shuai wrote:

This preprint has since been peer-reviewed and published in ICML https://icml.cc/virtual/2025/poster/45963

On 2025-10-06 12:17:30, user Alex Cope wrote:

This was published earlier this year under a new title: https://doi.org/10.1126/science.ads2658

On 2025-10-06 07:48:15, user Frederik Heurlin Aidt wrote:

Very interesting paper and work, congratulations! I cant seem to find the supplementary information cited in the main paper, can you please upload it to biorxiv?

On 2025-10-05 11:39:34, user Yufeng Wu wrote:

This paper has been published advanced online at Genome Research on September 3, 2025. doi: 10.1101/gr.280542.125 . URL: https://genome.cshlp.org/content/early/2025/09/03/gr.280542.125.abstract

On 2025-10-04 21:10:39, user Kim Wagner wrote:

The preprint has now been published in Scientific Reports:<br /> https://www.nature.com/articles/s41598-025-16222-y

On 2025-10-04 19:58:19, user annonymous wrote:

The authors claim that fitting a DDPM to a single MD dataset and subsequently generating samples from the trained model results in ‘enhanced sampling’. This claim is highly dubious as the results seem to indicate that the ‘enhanced sampling’ they refer to is equivalent to adding small amounts of guassian noise to samples already present in the MD training data. In fact, this result is expected - when generating samples from a DDPM, a simple prior distribution is iteratively transformed to a sample from the data distribution through noisey purturbations in the direction of maximum likelihood. Consequently, it comes as no surprise that generated samples are highly similar but not exactly identical to samples from the training data. Upon additional training of a DDPM on a single dataset, one expects that these deviations should asymtotically decrease until the DDPM ‘memorizes’ the training data and generate nearly exact copies of the training data. In the context of MD, adding gaussian noise to pre-existing samples from long-time MD is generally not considered a useful enhanced sampling method, unless noise perturbed structures are subsequently evaluated with a potential energy function and are subject to some aceptance criteria, as is done in metropolis hasting / MCMC sampling methods. I do not belive that the ‘enhanced sampling’ the authors claim to obtain from over-fitting DDPMs on single MD datasets is of the same nature as that expected from standard enhaced sampling methods for MD like replica exchange or metadynamics, which aim to explore completely new regions of phase space that are otherwise difficult to access and not yet characterized. The authors present no evidence that their procedure is able to generate samples that are substantially different than those already present in the MD training data - therefore, I would not consider this an enhanced sampling method in any sense. Moreover, the DDPMs presented here are trained to maximize the likelihood of generating samples that are similar to the training data distribution - there is no incentive for the model to explore new regions of phase space and one could argue that if their model were producing samples highly dissimilar to the traing data - it would suggest that the model is either under-fit or systematically incapable of appropriately modeling the training data.

On 2025-10-04 14:48:07, user CDSL JHSPH wrote:

Dear Dr. Clare et. al,

This article proved to be an amazing opportunity to be able to review your research.

I found this research paper to be interesting because a lot of previous research before has not discussed how DNA from terrestrial animals can be found in the air and this information could help with biomonitoring similar to how eDNA is utilized in aquatic settings.

I understand that this type of research has never been done before and it demonstrated how this technology can detect species in zoos and demonstrates how it can serve to be useful in the biodiversity field.

I do wonder since there are high levels of contamination within these air samples how reliable could the samples be? I also understand that the method of sampling each information costs a lot and I wonder how this could be accessible to various laboratories dedicated to protecting biodiversity? I’m curious to know if you were able to develop further tools to mitigate the effects of contamination within the data samples collected.

I thought it was incredible that in a zoo setting the biodiversity monitoring was able to identify specific species and the location of those species. Another question I have with this information is if you have given thought if the information could have been different if the tool was placed in a wild life setting instead of a controlled environment such as the zoo?

Overall, I think this paper and the research done with various methods will help with further research as to how DNA from the air can be utilized to revolutionize biodiversity further in the future.

Thank you for presenting this research.

On 2025-10-04 11:33:43, user scarlett johansen wrote:

This preprint contains serious scientific flaws that undermine its validity. The most critical issue is the use of HEK293T embryonic kidney cells as a “normal control” for gallbladder cancer, which is biologically inappropriate and invalidates much of the comparative analysis. The reported patient validation is based on only 3–5 samples, which is statistically underpowered and cannot support strong conclusions.

The claims of discovering “novel miRNAs” are based solely on in silico prediction, with no experimental validation. The central mechanistic assertion — that miR-17~92 directly targets Cdt2 — is not demonstrated with luciferase reporter assays, which are a gold-standard requirement in miRNA research. Moreover, the paper overstates therapeutic implications without any in vivo data, making such claims premature and misleading. Overall, this work reads as a descriptive sequencing dataset inflated with overstated conclusions. Without appropriate controls, larger cohorts, independent dataset validation, and rigorous mechanistic experiments, the manuscript does not meet the standards expected for credible cancer biology research.

On 2025-10-03 15:37:19, user Clemence Fraslin wrote:

This preprint has now been published in Genetics Selection Evolution in August 2023. Would it be possible to add the link to the published version? Thanks

Fraslin, C., Robledo, D., Kause, A. et al. Potential of low-density genotype imputation for cost-efficient genomic selection for resistance to Flavobacterium columnare in rainbow trout (Oncorhynchus mykiss). Genet Sel Evol 55, 59 (2023). https://doi.org/10.1186/s12711-023-00832-z

On 2025-10-03 14:19:38, user Pielak, Gary Joseph wrote:

Two of the citations to my work in this paper (references 11 and 70) do not exist.

Gary J. Pielak, Ph.D. 裴盖瑞<br /> Kenan Distinguished Professor<br /> Department of Chemistry<br /> Integrative Program for Biological & Genome Sciences<br /> Department of Biochemistry & Biophysics<br /> Lineberger Comprehensive Cancer Center<br /> (919)962-4495<br /> 3250 Genome Sciences Building<br /> University of North Carolina<br /> Chapel Hill, NC 27599-3290<br /> gary_pielak@unc.edu<br /> http://pielakgroup.web.unc.edu

On 2025-10-03 13:55:11, user Rhonda Witwer wrote:

This paper was published at https://dx.doi.org/10.1016/j.csbj.2020.10.042

On 2025-10-03 08:55:58, user Tomáš Strečanský wrote:

Dear authors,

Great work on this study, thank you for sharing it as a pre-print.

I have a few quick questions about the methods that would be helpful for clarification:

What was the centrifugation speed and time used to obtain the blood plasma?

Could you specify the brand and material of the filter used for the plasma filtration?

What were the sample plasma volumes used for the cfRNA extractions?

For the sequencing, could you clarify the number of samples pooled per PromethION flowcell and the resulting sequencing depth and per sample number of reads (for both long and short reads) for each sample?

Thanks in advance for the details. I'm looking forward to the next version of the paper!

All the best,<br /> Tomas Strecansky<br /> PhD student<br /> Institute of Molecular Biomedicine<br /> Comenius University in Bratislava

On 2025-10-03 03:29:16, user Amoon Jamzad wrote:

???? Published version:<br /> This work is now published as: Jamzad, A., et al. MassVision: An Open-Source End-to-End Platform for AI-Driven Mass Spectrometry Imaging Analysis. Analytical Chemistry (2025). https://doi.org/10.1021/acs.analchem.5c04018

On 2025-10-02 18:40:09, user Luciano Panepucci wrote:

I just found that this preprint got a post on preLights at: https://prelights.biologists.com/highlights/paclitaxel-compromises-nuclear-integrity-in-interphase-through-sun2-mediated-cytoskeletal-coupling/ . It's a bit sad that they do not add their post as a comment here.

On 2025-10-02 09:42:37, user Gaby Schneider wrote:

This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this article is published in Environmental and Ecological Statistics, and is available online at https://doi.org/10.1007/s10651-025-00675-5 .

On 2025-10-02 09:02:43, user Riccardo Rizzo wrote:

???? Exciting news! Our group at CNR-Nanotec and TecnoMedPuglia (Biotech Lecce Hub), in collaboration with Domenico Russo (CNR-IEOS, Naples) and a fantastic network of national and international partners, is thrilled to share that our study has been accepted in Advanced Science!<br /> ???? In this work, we identify a previously unrecognized mechanotransduction pathway in which extracellular matrix stiffness controls the secretory route via the Src–FAK–AMPK–GBF1 axis, positioning the Golgi as a key mechanoresponsive organelle.<br /> This mechanism has important implications for diseases like fibrosis and cancer, where matrix stiffness fuels pathological secretion loops.<br /> ???? Huge thanks to all the amazing collaborators from TIGEM, INGM, UNIMI, EPFL, and University of Alabama who made this possible.<br /> Stay tuned for the published version!<br /> hashtag#Golgi hashtag#Mechanotransduction hashtag#AMPK hashtag#GBF1 hashtag#SecretoryPathway hashtag#MatrixStiffness hashtag#Fibrosis hashtag#CancerBiology hashtag#CNR hashtag#AdvancedScience hashtag#Bluesky <br /> https://lnkd.in/dhAC-AMv <br /> https://www.linkedin.com/posts/riccardo-rizzo-352662154_mechanical-cues-regulate-cargo-sorting-and-activity-7379439913412333568-Mt0t?utm_source=share&utm_medium=member_desktop&rcm=ACoAACUdvW4BBa3ATIuge9hSDmKIqvKcv_v3t3I

On 2025-10-02 06:19:23, user Wolfgang Graier wrote:

This is a thrilling work! Thank you for sharing it with the community. Did you had a chance to test for differences in mitochondrial dynamics and motility upon various ratios of L-MFN2 and S-MFN2? Good luck with publishing.<br /> Best, Wolfgang

On 2025-10-01 15:50:28, user Sean C wrote:

Amazing work Vikash! Did you happen to try and add Fen1 (Flap Endonuclease 1) into the mix at all. Fen1 is thought to degrade 5' flaps, perhaps that might increase editing rates?

On 2025-10-01 12:46:23, user Richard Mott wrote:

The published version of this manuscript is now available from Genome Biology: https://doi.org/10.1186/s13059-025-03741-0

In due course the BioRXiv MS will be linked to the published version, which is similar but contains a number of additional analyses.

On 2025-09-29 17:58:18, user Evan Saitta wrote:

Correction: Acknowledgements should include "D. Vidal receives funding from a Marie Sklodowska Curie Actions grant (EvoSaurAF 101068861)"

On 2025-09-29 14:52:13, user Olavo Amaral wrote:

Warning note by the authors:<br /> In following up with the project by discussing replications individually with participant labs, we have detected a few errors in the data that were included in this preprint. The ones found up to now change some replication rates slightly (i.e. from 15-45% to 19-45% in the primary analysis) but do not make a meaningful difference in the article's conclusions.<br /> This, however, has raised concerns that our original data checking process (which was performed asynchronously) was likely suboptimal. We have thus decided to hold off revision of the preprint and submission to a journal until we have discussed each experiment individually with the labs that replicated it, in order to check the original data and clear up any doubts. In the meantime, we will also add this warning note to the first page of the preprint.<br /> This process should be concluded in early December, when a revised preprint should be expected. In the meantime, keep in mind that the results in the final version are bound to present minor changes when compared to those in this version.

On 2025-08-12 14:00:19, user Gil Benard wrote:

I really appreciated this work — an in-depth analysis of replicability in science. It presents many important and interesting findings that contribute to the current discussion on reproducibility “crisis” and science integrity. I was particularly impressed by their report of sizable heterogeneity among the results of the replicating labs, despite the fact that (a) they were theoretically under neither publication pressure nor any other pressure from the science environment, (b) protocols and reagents were carefully checked and standardized among the labs, and (c) the experiments were conducted under very controlled conditions.<br /> Could there be intrinsic limitations to replicability in biomedical science?

On 2025-09-29 13:06:57, user Mohd Sufiyan Khan wrote:

This is a landmark study in the evolution of generative biology. The team demonstrates, for the first time, that genome language models (Evo 1 & Evo 2) can transcend protein- or circuit-level design and generate complete, viable phage genomes with evolutionary novelty. What stands out to me as someone deeply engaged in AI-driven drug discovery and computational biology is the rigorous integration of multi-layered constraints: from sequence quality, gene architecture, and tropism specificity, to evolutionary diversification. Such hierarchical filtering mirrors the multi-stage AI pipelines we build for therapeutic design, where generative creativity must always be balanced by biological feasibility.

Equally impressive is the experimental validation: 16 fully functional phages synthesized from ∼300 AI-generated genomes, some with faster lysis kinetics and higher competitive fitness than the natural ΦX174. The ability of a cocktail of these phages to rapidly overcome bacterial resistance underscores how AI-guided genomic exploration can access adaptive solutions beyond natural evolution—a principle that could redefine both antimicrobial strategies and synthetic biology at large

.

On a broader horizon, this work offers a blueprint for steerable, constraint-driven genome design. Much like our own efforts in applying generative AI to novel small-molecule scaffolds for resistant cancer targets, this study exemplifies how language-model-based frameworks can capture the hidden rules of evolution and recompose them into functional biological systems. The implications span from phage therapy to programmable synthetic organisms.

Congratulations to the authors for pushing the frontier of generative biology—this is not just phage design, but a proof-of-principle for the next era of genome-scale AI-driven engineering.

On 2025-09-22 15:29:33, user Fraser Lab wrote:

James S Fraser, Joseph Bondy-Denomy

This manuscript describes very cool molecular biology with careful engineering and a convincing structural and functional assessment of a reconstructed ΦX174-like phage. The build-test workflow is clearly described, and the recovery of infectious particles provides a strong experimental anchor for the computational design story. This is a great demonstration of what is currently feasible for whole-genome phage engineering.

However, it misses a major opportunity to contextualize the generative model performance with explicit baselines. This makes it difficult to assess whether there is a true advance due to the ability of the generative model to move in sequence space with success rates (~16/300) above alternative (lightweight) models. Moreover, the sequence space explored here appears to stay close to known natural sequences, so it is hard to appreciate how the generative model adds novelty beyond proximity to existing genomes. Side-by-side comparisons against simple alternatives (for example a consensus design, frame-preserving randomization under GC and codon constraints, ancestral reconstruction, or family statistical models such as direct coupling analysis) would clarify whether the model improves assembly success or shifts resistance outcomes. Recent studies provide useful templates for such “null model” panels, see OpenCRISPR Figure 3E ( https://pmc.ncbi.nlm.nih.gov/articles/PMC12422970/) , and ProGen Lysozymes Figure S10 ( https://pmc.ncbi.nlm.nih.gov/articles/PMC10400306/) for evaluating LLM-generation of proteins. Including analogous baselines here, with success rates and resistance readouts reported for each, would make the impact of this contribution and generality of this approach unmistakable.

J.S.F. receives consulting fees from and has an equity interest in Profluent Bio, a company using generative AI. J.B.-D. is a scientific advisory board member of SNIPR Biome, Excision Biotherapeutics, LeapFrog Bio, and Acrigen Biosciences and is a co-founder of Acrigen Biosciences and ePhective Therapeutics.

On 2025-09-29 10:48:53, user Martin R. Smith wrote:

Thanks for conducting this very interesting study!

If we need ~200 characters for a good estimate of the relationships between 20 tips, this makes me wonder whether we should be using a 10:1 ratio as a rule of thumb. If we’re interested in the relationships between 20 taxa, and we can only score 100 characters, my intuition says that including more taxa would still improve our estimate of the relationships between the 20 taxa of interest, even if the placement of the ‘extra’ taxa is uncertain. I’d be interested to know whether your study has any suggestions here.

I also wonder whether you tried using the Clustering Information Distance alongside the Robinson–Foulds, as the biases with the RF distance seem like they could be particularly problematic in a study such as this, where the misplacement of a single taxon could be a likely event (see Smith 2020, https://doi.org/10.1093/bioinformatics/btaa614) . On a similar vein, I wonder whether it would be more meaningful to take the mean distance between trees in the posterior distribution and the true tree, rather than selecting a single tree – there is no guarantee that the MAP tree is representative of the posterior distribution (though potentially some tree space analysis could support the assumption that it is). You might also consider verifying that the ESS of your tree topologies is at an acceptable level (e.g. with https://github.com/afmagee/treess) , to be confident that the topologies – not just the estimates of other model parameters – have converged.

On 2025-09-29 09:33:08, user Flemming Damgaard Nielsen wrote:

Interesting article, but table S3 does not seem to be uploaded, I would very much like to see this to compare your findings to my own.

On 2025-09-28 22:14:48, user Justin McCormick wrote:

Great contribution to endothelial cell biology + Raman microscopy!

On 2025-09-28 17:45:38, user Mario E. Valdes-Tresanco wrote:

Great work! Are there plans to extend this to Gram-negative bacteria? I tried to access the GitHub repository ( https://github.com/lupiochi/ppIRIS) , but it is currently unavailable.

On 2025-09-28 12:30:01, user Arijit Mukherjee wrote:

This work is now published - https://doi.org/10.1016/j.chom.2025.09.007

On 2025-09-27 18:39:01, user Gözde Merve Türksoy wrote:

AArticle Published https://apsjournals.apsnet.org/doi/10.1094/MPMI-09-25-0125-R

https://doi.org/10.1094/MPMI-09-25-0125-R

On 2025-09-26 18:30:37, user Hexx wrote:

this tells me nothing about the possible risks. 1/10

On 2025-09-26 16:52:17, user Elzi wrote:

To clarify, the sample pool consisted of all males, no women?

On 2025-09-25 18:29:14, user Yun Hwa Choi wrote:

This pre-print is published. https://rupress.org/jem/article/222/2/e20231816/277154/Dual-role-of-vascular-endothelial-growth-factor-C

On 2025-09-25 15:06:09, user Anestis Gkanogiannis wrote:

Could you please update the record to indicate the published version?

Published article: Plants (2025), <br /> doi: https://doi.org/10.3390/plants14192970

Thanks

On 2025-09-25 07:54:20, user Jukka Kallijärvi wrote:

Now published in Nucleic Acids Research. <br /> Nucleic Acids Res. 2024 Jan 11;52(1):e6. doi: 10.1093/nar/gkad1091.

On 2025-09-25 07:20:24, user Joanna Groom wrote:

This pre-print does not appear in pubmed searchers - can this be fixed?

Also, this article is now published in Nature Immunology <br /> https://www.nature.com/articles/s41590-025-02258-9

please edit to add this information and link

On 2025-09-24 19:16:13, user Giovanni A. wrote:

Extremely interesting!

On 2025-09-24 13:20:02, user Andries Peeters wrote:

Could it be that the reference given for the example of coral reef restoration (van Elsas et al., 2012) is not correct?

On 2025-09-24 07:06:09, user Fabrizio Vacca wrote:

Comments, suggestions and constructive criticism are welcome!

Fabrizio

On 2025-09-23 20:28:30, user Gennady Gorin wrote:

I read through this with some interest. If you are not yet aware of it, you may be interested in our paper from several years ago: https://www.cell.com/cell-systems/fulltext/S2405-4712(23)00244-2 . Basically, although the moment-based estimates are useful (and almost mandatory for inference over non-iid samples), it is also relatively straightforward to compute the full likelihoods by quadrature. As the cells within a cell type are assumed iid here, this is more or less compatible with the framework.

The low-beta regime can also be represented as a two-state model with bursty (and potentially leaky) expression in the active state, slightly more straightforward to solve numerically.

For the connection to ATAC, please see https://journals.aps.org/pre/abstract/10.1103/PhysRevE.110.064405 . The same model is straightforward to generalize to some latent regulator or a coupled series thereof (although the best way to define such coupling is still obscure).

The joint modeling of these quantities is a somewhat underexplored problem. One key issue we raise in the PRE paper is that noise modeling for ATAC is unusually challenging. There are, of course, other challenges, like the slightly non-iid nature of measurements across supposedly homogeneous cells ("cell size" effects) and the usual DNA/gene mapping issues: how should one map a peak to a particular gene? How should one represent the relationship between multiple peaks that all ostensibly control or overlap a single gene?

It is exciting to see people pursuing mechanistic approaches for this problem, and I look forward to future work.

On 2025-09-23 17:23:55, user RFD Enjoyer wrote:

Fantastic, super exciting work. Would it be correct to infer from the lack of protein binder experiments in this paper that another paper is in the works that more comprehensively benchmarks RFD3’s experimental success on protein/small molecule ligand binder design? Or will these experiments be added to a later version of this paper? I’d be very interested in particular to see how binder design conditioned only on target peptide sequence works with RFD3, analogous to the DNA binder designs shown here that were conditioned only on DNA sequence.

On 2025-09-23 16:02:03, user Prof. T. K. Wood wrote:

Eyes wide shut: if you only use DefenseFinder, then you miss nearly all of the toxin/antitoxin systems; i.e., the most prevalent phage defense system is missing to a large degree. I recommend running TAFinder or TADB. etc., too, to search for TAs, which likely are anti-phage systems.

On 2025-09-23 15:30:45, user Hari Shankar Gadri wrote:

This is strong research; however, some sentences lack smooth flow and appear fragmented. In particular, the genome assembly results section has breaks in continuity that affect the overall readability. The linkage map results section also contains grammatical errors and some sentences that disrupt the flow. The methodology section is overly detailed and could be made more concise, while the discussion section is poorly structured and requires significant improvement.

On 2025-09-23 07:51:08, user Leclercq Sebastien wrote:

Dear Mateusz and co-workers,<br /> thanks for this new method of metagenomics processing, with a very ambitious aim. To fully appreciate the efficiency of using ARGs alleles to predict host pathogens, I think it would be useful to add as suppl. data a table summarizing for each ARG in your database, how many allele are unique to a single pathogenic species, or shared by 2, 3, 4, and let's say 5 or more species.<br /> Indeed, when looking at the unclassified kmers from your validation test in your suppl. data, it seems that most mobile genes of public health concern are in the list (most ESBL and carbapenemases, mcr1, all tetracycline genes, etc.).

Second, it is not clear, when a gene is categorized as 'Genomic origin', whether it is a mutation of an intrinsic gene or whether it is located on integrative elements ICE or IMEs. It makes a huge difference in terms of dissemination potential.

On 2025-09-22 23:33:52, user Siyuan Wu wrote:

Publication Update: This preprint has been accepted and published in Genome Biology (DOI: 10.1186/s13059-025-03758-5). The published version is available at: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-025-03758-5

On 2025-09-22 13:17:23, user Takanori Nakane wrote:

I am impressed by this work. I have a few comments, questions and suggestions. Although I used the imperative mood in some sentences, never feel obliged to do them. They are just some ideas. (Being a non-native speaker of English, my comments seem to sound too harsh sometimes)

1. You mentioned "solely using data at high spatial frequencies" and "use data only at high spatial frequencies". This sounds as if you did not use low frequency signals but I guess CryoSPARC's initial resolution and maximum resolution parameters only set the high resolution cutoff, not the low resolution cutoff. In other words, if you use "Initial resolution 5 Å, maximum resolution 2.3 Å", it uses DC to 5 Å in early iterations and gradually increases the high resolution cutoff until it reaches DC to 2.3 Å in the last iterations. Am I wrong?

2. You mentioned that "other approaches that we and others have tried produced maps with no interpretable high resolution structural features" and "other parameters often converged on false minima". Could you please describe what was tried and unsuccessful? For example, in all cases, you set "center structures in real space OFF". Was this essential? "Enforce non-negativity" was TRUE for calmodulin, FALSE for Hb-dimer and Aca2-RNA and not mentioned for iPKAc. Again, were these choices critical? You used "a beta version of CryoSPARC v5, which has an option to apply a soft spherical mask to the ab initio volume at each iteration". Was this new masking feature essential for success?

3. Your "final resolution" in the ab initio jobs (but why is it called "maximum resolution" for iPKAc?) is higher (i.e. numerically smaller) than the final, refined resolution. Was this necessary? What is the minimum (i.e. numerically largest) resolution necessary for success in each case? If you can solve the structure using lower resolutions for alignment, you can get reasonable resolution estimates without gold-standard (GS) splits in cisTEM's way.

4. In general, refinement fails when the starting (reference) model is too bad beyond the radius of convergence and/or when individual particles are too noisy to be aligned. Of course, these two are not independent; noisier particles can be aligned with a better reference. Regularization (e.g. using GSFSC or non-uniform refinement) and injection of prior information (e.g. Blush) improve the radius of convergence by making the energy landscape of the target function smoother. It would be interesting to see which problem (bad initial model or noisy alignment) hampered structure solution in these cases.

4.1. You briefly mentioned refinement trials using HR-HAIR maps as the initial reference. It would be interesting to do this more systematically. Take a HA-HAIR map, low pass filter it at various resolutions and perform refinement with global search. When does it succeed? Is the resolution different from the minimum resolution required for ab initio jobs (point 3 above). If they are very different, regularization by GSFSC might be too strong. Another possibility is that the loss of SNR in half 1 reference and half 2 reference relative to the full reference made alignment too hard. When GSFSC was proposed, this effect was said to be negligible (and overcome by a better regularization), but it might make a critical difference for extremely difficult cases like this.

4.2. It is also worth testing marginalization over poses. Does it help, or make it worse?

4.3. For some datasets, you used only a subset of the EMPIAR entry. This gives you a good chance to perform the test 4.1 more rigorously. Perform HA-HAIR on particles from the first X movies. Then perform refinement tests on particles from the remaining movies. If ab initio succeds but global search refinement fails with N particles, doesglobal search refinement succeed with 2N particles? If so, the SNR reduction due to GS split was the culprit.

1. Another idea to assess the resolution when the GSFSC is unavailable is as follows. Take a non-GS HR-HAIR map and low pass filter it to say 3.3 Å. Refine an atomic model against it. Calculate the map-model FSC on the original, non-low pass filtered map. Because an atomic model refined to 3.3 Å has predictive power beyond 3.3 Å, it can be used to gauge whether the map contains chemically sensible information beyond 3.3 Å. (You should first verify this strategy on maps with known GS resolution)

Sorry for my lengthy and scattered comment. I am still thinking a better way to investigate this interesting observation and validate the resolution and haven't consolidated my thoughts fully. I just wrote what I have so far. I hope at least some of the above is useful for your investigation.

On 2025-09-22 08:24:30, user najib wrote:

the study look intersting, is it possible to know if there was any confirmation for the antiviral binders in vitro?

On 2025-09-21 20:54:32, user Nikolai Slavov wrote:

Links to RAW and processed data are available at: https://scp.slavovlab.net/Leduc_et_al_2025

On 2025-09-20 11:45:15, user Chris Redfern wrote:

This paper has now been peer-reviewed and published online in Ibis: https://doi.org/10.1111/ibi.13444

On 2025-09-19 18:20:53, user Samantha “Pixie” Piszkiewicz wrote:

Hi! Very cool work. Have you considered pre-conditioning your algae with salt stress in addition to osmolytes like trehalose to increase uptake of the compatible solute into the cytosol? You could add the salt and osmolyte as a bolus feed after fermentation and remove most of the salt in your pelleting step before formulation for freezing.

On 2025-09-19 09:29:01, user Alice Risely wrote:

Thank you for submitting this preprint. I used this paper to practice peer review with my MSc Wildlife Conservation students. I thought the peer review process could be helpful, therefore I have copied the review we created below:

This study aimed to quantify the temporal dynamics of chytrid infection in Yosemite toads, with a particular focus on changes in infection dynamics before and after the first hibernation period in metamorphs. The authors hypothesized that changes in physiology and/or exposure associated with hibernation could alter pathogen infection rates and growth. To test this, they sampled first-year metamorphs across six alpine meadows before and after their first hibernation. They also sampled tadpoles, juveniles, and adults during the same pre-hibernation period to explore infection patterns across age classes, although sample sizes for tadpoles and adults were relatively small, and these cohorts were not sampled after hibernation.

The authors found that tadpoles were free of infection, whereas infection prevalence increased to about 20% in first-year metamorphs before hibernation and then rose sharply to over 90% after hibernation. Infection prevalence and intensity remained high the following summer. They suggest this striking increase is linked to hibernation itself, though the underlying mechanisms remain unclear. Two hypotheses are brought up in the discussion: (1) metamorphs may already be exposed prior to hibernation, but immune suppression during hibernation allows pathogen proliferation to detectable levels, or (2) metamorphs enter hibernation chytrid-free and become infected during hibernation. Notably, no effects of infection on morbidity were detected, suggesting Yosemite toads may act as asymptomatic carriers.

Overall, I found the study nicely written, interesting and relatively well designed, with some exceptions. The methods are largely appropriate for the central question of whether hibernation in first-year metamorphs contributes to chytrid persistence in this population. The inclusion of a conceptual figure of the study design was particularly helpful. I have no major concerns regarding the methodology.

That said, I did find parts of the manuscript confusing and have several suggestions for improvement. My primary conceptual criticism is that the results apply only to first-year metamorphs. To test more robustly whether hibernation itself drives changes in infection dynamics, it would have been valuable to examine adults or even other nearby species that do not hibernate (although I understand that these would be subject to different climates). Including these comparisons would strengthen the case that hibernation per se influences infection prevalence. As it stands, the study convincingly demonstrates a link between dormancy and infection dynamics in metamorphs, but not necessarily beyond this cohort. I encourage the authors to specify this in the abstract and to emphasize that the mechanisms remain unresolved.

I also found the introdiction quite confusingly structured and did not fully outline what is known from previous studies or provdie enough information on the ecology of the study species and why it makes a good model system to test these questions. I have more detailed comments on the introduction below.

Section-Specific Comments:

Introduction<br /> The introduction clearly states the study’s aims but would benefit from more detailed context on the current state of knowledge. Summarizing prior studies in greater depth - for example, Kasler et al. (2023), which is mentioned in the discussion but not the introduction - would help the reader understand the state of the knowledge and where the gap or conflicting evidence arises. At present, the introduction focuses too heavily on highlighting the results, while the rationale, state of the field, and knowledge gaps are underdeveloped.

A bit more information on the ecology of Yosemite toads would also be helpful. For instance: Can chytrid transmission occur during hibernation? Do toads hibernate individually or communally? Why is this species particularly interesting for examining infection dynamics beyond the fact that it hibernates? The authors also assume that everyone knows about chytrid – the fact that is causes huge population declines and extinctions, and any known factors on its spread and persistence, are not even mentioned.

The structure of the introduction is somewhat disjointed, with results and knowledge gaps raised in multiple places rather than in a logical sequence. I suggest beginning the introduction with chytrid fungus (or emerging fungal pathogens more generally), followed by discussion of seasonal and life-history drivers of infection. Dormancy could then be introduced as a potentially important factor early on – but starting with dormancy when this paper is fundamentally about understanding drivers of chytrid infection and maintenance is possibly confusing to some. Notably, the authors argue that their results “challenge current thinking about seasons,” yet the introduction contains little background on known seasonal effects on chytrid dynamics. Providing this context would make their justifications and interpretation more compelling.

Finally, sample sizes are difficult to find in the text. Including these in Figure 1 and briefly outlining the study design at the end of the introduction would improve clarity on methods.

Overall, a thorough restructuring and expansion of the introduction would make it more informative and logically ordered.

Methods<br /> I had questions about the modelling framework. Why were pre- and post-hibernation cohorts analyzed separately? Would a zero-inflated model have been more appropriate for intensity data, and were these approaches considered? Presenting model outputs and explanatory power in a supplementary table would improve transparency.

Additionally, it would help to explain why juveniles and adults were only sampled at a single meadow and time point, rather than following the same design as metamorphs. Clarifying these decisions would improve understanding of the study’s scope and limitations.

Results<br /> It is unclear why only two figures are presented in the main text, with the remainder in the Supplementary Materials. Some of the supplementary data would be valuable in the main body, ideally integrated into a single figure that allows direct comparison across life stages and survey periods.

Figure 3 should include sample sizes, as in Figure 2. It is also unclear why tadpoles are not represented in this figure, given their relevance to the infection trajectory.

Discussion<br /> Much of the contextual information in the discussion would be better placed in the introduction (e.g., references such as Kasler et al. that address similar questions). This shift would allow the discussion to focus more sharply on how this study advances understanding and to highlight its limitations (e.g., small adult sample sizes, lack of non-hibernating species for comparison).

Supp Materials<br /> Note that zeros are missing in one of the intensity figures, and that the order of the meadow sites is not always matching across figures.

On 2025-09-19 09:03:31, user Karthik Raman wrote:

https://academic.oup.com/nargab/article/7/3/lqaf121/8246936 - now published

On 2025-09-19 09:02:25, user Karthik Raman wrote:

https://biosignaling.biomedcentral.com/articles/10.1186/s12964-025-02306-9 - pls. add this link

On 2025-09-19 06:42:52, user Iannis Talianidis wrote:

From Iannis Talianidis: This paper has been published in July 11, 2024 in Nature Communication.<br /> doi: 10.1038/s41467-024-50259-3. PMID: 38992049; PMCID: PMC11239883.

On 2025-09-18 23:17:30, user Kristopher Kilian wrote:

This preprint has now been published in Nature Communications:

Giulia Silvani, Chantal Kopecky, Sara Romanazzo, Vanina Rodríguez, Ayan Das, Elvis Pandzic, John G. Lock, Christine L. Chaffer, Kate Poole & Kristopher A. Kilian, Nature Communications, 16, 8160 (2025)

https://doi.org/10.1038/s41467-025-63374-6

On 2025-09-18 12:27:13, user Volker Boehm wrote:

The final version of this study is now published in Molecular Cell: https://doi.org/10.1016/j.molcel.2025.08.015

On 2025-09-18 07:43:42, user Prof. T. K. Wood wrote:

Results are similar as 2013 study with CCCP and E. coli; again, please compare with this paper which pioneered the use of CCCP to induce persistence.

On 2025-09-18 07:38:14, user Prof. T. K. Wood wrote:

Chemical induction of persistence by reducing protein production was discovered in 2013 and used now by over 50 labs; please cite the appropriate work: https://journals.asm.org/doi/10.1128/aac.02135-12

On 2025-09-17 19:03:07, user Arthur Erauw wrote:

Interesting!

On 2025-09-17 14:02:20, user Zeyuan (Johnson) Chen wrote:

Dear Biorxiv,

Could you update this article so that it links to the final journal version: https://www.nature.com/articles/s41588-025-02300-4

Thanks

On 2025-09-17 12:32:29, user manez wrote:

Information for readers: this preprint has been published and the link will be available soon.

On 2025-09-16 21:55:02, user disqus_FNcjH0JLKv wrote:

This paper has been published in the Computational and Structural Biotechnology Journal: https://doi.org/10.1016/j.csbj.2025.09.018

On 2025-09-16 14:34:39, user Hailemariam Amsalu wrote:

This is an interesting preprint that makes a valuable contribution to reproductive biology research on ovary. The application of membrane inserts which provides a natural like environment for ovaries and whole mount antibody staining that helps to detect proteins in intact tissues/organs are interesting. <br /> The Chromatin and Reproductive Aging Research Lab (CARL) of Hebrew university, led by Professor Michael Klustein, has discussed the manuscript on its regular lab meeting session and compiled the following comments. <br /> Major Comments <br /> In the introduction part, the paper doesn’t explain the objective/s of the protocol and its advantages over the pre-established protocols and the improvements it has. <br /> For intact and big tissues, like ovaries, light sheet microscopy is a good choice to evaluate the 3D structures of the ovary and its content. Therefore, the paper would be more complete if it evaluates the ovaries with light sheet microscopy. <br /> The discussion is not referred to and compared with previous studies in which it is difficult to validate and understand the protocol novelty and variability.<br /> Minor comments <br /> On the protocol section, Subsection 2, it says “All reagents used in this study are listed in Supplementary Table 1.” However, the table is not found anywhere. It is also better to use different numbering systems for different headings (e.g. Roman numbering system for the subheadings, Arabic for the details.). In addition, it would be clearer if the experiment design (control and treatment groups) is clearly presented in the protocol section, ethics and mice subsection. <br /> Figure 2, (C-C’-C’’) needs more clarification about what it represents. On graphs, significance bars are missed on the graphs E and G. <br /> There are some grammatical and typo errors. Especially, the Ovary culture and the Result part should be written in a very simple and concise way for better understanding.<br /> Overall, this work will be very useful for researchers studying reproductive biology involving ovary.

On 2025-09-16 14:07:03, user Brett Pike wrote:

Is there a short answer as to why SyRI was not included?

On 2025-09-15 22:00:46, user Raphael Sandoz wrote:

Hi, I can't see the supplementary tables. Could you upload them?

On 2025-09-15 15:03:52, user overmind wrote:

Great name. Just so you know, there's a naming conflict in the Bioinformatics space. https://academic.oup.com/bioinformatics/article/21/5/676/220389

On 2025-09-15 09:21:59, user Ross Mounce wrote:

Where's the model?

A major product of this research is the "fine-tuned a BERT machine learning model". I read through the paper a couple of times but could not seem to find a link to be able to access the model e.g. on GitHub, GitLab, or Codeberg. What licence is the model available under and where can I obtain it from?

Secondly, is it not a possible conflict of interest if "Our model is currently integrated into the online submission systems of three journals from a major publisher and is being used to screen cancer-related manuscripts"

especially if you're choosing not to name the journals, the publisher, or supply the source code of fine-tuned machine learning model you're using. Is the major publisher paying money to the authors, or an institution the authors are associated with, to integrate this into its submission system?

On 2025-09-15 07:51:30, user Thorsten Schnurbusch wrote:

Figures are missing in the text!?

On 2025-09-14 10:51:13, user Val wrote:

Very interesting indeed. I wonder what this will mean for us going forward. Was this always here or post Covid? Something to consider?

On 2025-09-13 18:19:11, user BeForeverYoung wrote:

To combat aging, a comprehensive therapy is required, the efficacy of which should be tested on models of isolated human organs. As an intermediate goal, it is essential to address the creation of a restored organ that is resistant to pathological signaling. Subsequently, this technology should be translated for application in humans.

On 2025-09-13 04:10:37, user duckman wrote:

Using the score_interface function in BindCraft (with binder_chain set to A), I tried to reproduce the AF3 Rosetta metrics for several binders, but the values did not match those in final_data.csv (e.g., Motif0030_ems_3hC_714_0001_0003_6877_0001 and Pdl1_binder_AF2_54). However, I did succeed in reproducing the af3_ipSAE_max and af3_ipSAE_min metrics for these binders. Are there any caveats I should be aware of when generating AF3 Rosetta metrics?

On 2025-09-11 22:01:56, user Stanislav Dikiy wrote:

Hello, this study has now been published: https://www.nature.com/articles/s41590-024-02075-6 PMID: 39905200

On 2025-09-11 14:56:10, user Christoph Jüschke wrote:

Congratulations on your very interesting study! It represents a valuable contribution to the field of therapeutic splicing correction.

We would, however, like to kindly suggest that the manuscript could benefit from the inclusion of additional references to previous work in the field. Specifically, in vivo studies involving intravitreal injection of AAVs to deliver U1 snRNA to the mouse retina have already been conducted and evaluated for efficacy, safety, and potential off-target effects. We believe it would strengthen your manuscript to reference and discuss this study alongside citations of your own previous work (e.g., Balestra et al):<br /> • Swirski S, May O, Ahlers M, Wissinger B, Greschner M, Jüschke C, Neidhardt J. In Vivo Efficacy and Safety Evaluations of Therapeutic Splicing Correction Using U1 snRNA in the Mouse Retina. Cells. 2023 Mar 21;12(6):955.

In addition, the following statements from your manuscript:<br /> “By targeting the 5′ splice site downstream of specific exons, ExSpeU1s can correct aberrant splicing in various cellular and mouse models” and<br /> “To restore defective splicing, ExSpeU1s have been created with sequence changes that permit targeted binding to intronic sequences downstream of the mutant 5’ ss”<br /> could benefit from referencing a related U1 snRNA splice-correction study in optic atrophy—a condition that, like familial dysautonomia, is characterized by retinal ganglion cell degeneration:<br /> • Jüschke C, Klopstock T, Catarino CB, Owczarek-Lipska M, Wissinger B, Neidhardt J. Autosomal dominant optic atrophy: A novel treatment for OPA1 splice defects using U1 snRNA adaption. Mol Ther Nucleic Acids. 2021 Oct 21;26:1186-1197.

Furthermore, to provide a more comprehensive overview of foundational work in the field, including patient-derived cell lines, and acknowledge earlier contributions toward the development of in vivo U1 snRNA-based therapies, the following publications may also be relevant:<br /> • Tanner G, Glaus E, Barthelmes D, Ader M, Fleischhauer J, Pagani F, Berger W, Neidhardt J. Therapeutic strategy to rescue mutation-induced exon skipping in rhodopsin by adaptation of U1 snRNA. Hum Mutat. 2009;30:255–263.<br /> • Glaus E, Schmid F, Da Costa R, Berger W, Neidhardt J. Gene therapeutic approach using mutation-adapted U1 snRNA to correct a RPGR splice defect in patient-derived cells. Mol Ther. 2011;19:936–941.<br /> • Schmid F, Glaus E, Barthelmes D, Fliegauf M, Gaspar H, Nürnberg G, Nürnberg P, Omran H, Berger W, Neidhardt J. U1 snRNA-mediated gene therapeutic correction of splice defects caused by an exceptionally mild BBS mutation. Hum Mutat. 2011;32:815–824.

Finally, we would like to highlight two recent publications that might have implications toward potential risks of U1 therapy. Kim et al. showed that U1 snRNP may affect promoter activity by inhibiting premature polyadenylation, and Nadeu et al. identified recurrent tumour-specific U1 snRNA mutations in mature B-cell neoplasms:<br /> • Kim G, Carroll CL, Wakefield ZP, Tuncay M, Fiszbein A. U1 snRNP regulates alternative promoter activity by inhibiting premature polyadenylation. Mol Cell. 2025;85(10):1968-1981.<br /> • Nadeu F, Shuai S, Clot G, Hilton LK, Diaz-Navarro A, Martín S, Royo R, Baumann T, Kulis M, López-Oreja I, Cossio M, Lu J, Ljungström V, Young E, Plevova K, Knisbacher BA, Lin Z, Hahn CK, Bousquets P, Alcoceba M, González M, Colado E, Payer ÁR, Aymerich M, Terol MJ, Rivas-Delgado A, Enjuanes A, Ruiz-Gaspà S, Chatzikonstantinou T, Hägerstrand D, Jylhä C, Skaftason A, Mansouri L, Stranska K, Doubek M, van Gastel-Mol EJ, Davis Z, Walewska R, Scarfò L, Trentin L, Visentin A, Parikh SA, Rabe KG, Moia R, Armand M, Rossi D, Davi F, Gaidano G, Kay NE, Shanafelt TD, Ghia P, Oscier D, Langerak AW, Beà S, López-Guillermo A, Neuberg D, Wu CJ, Getz G, Pospisilova S, Stamatopoulos K, Rosenquist R, Huber W, Zenz T, Colomer D, Martín-Subero JI, Delgado J, Morin RD, Stein LD, Puente XS, Campo E. Disease-specific U1 spliceosomal RNA mutations in mature B-cell neoplasms. Leukemia. 2025;39(9):2076-2086.

Of course, we understand that not every study in the field can be cited. Our suggestions are intended to help ensure a balanced and comprehensive presentation of the field’s progress, particularly as it clearly relates to U1 snRNA-based splicing correction strategies.

Thank you for your thoughtful work, and we look forward to seeing this research further advance the field.

On 2025-09-10 21:49:42, user Venkat Subramanian wrote:

Read this Paper as a layman, not trained in Biological as well as Computer Sciences, with an objective of judging how much of the paper I could understand.

i must confess with a little ' search assistance' I could understand the emphasis of the Paper. I am confident that more enlightened Users from the Medical fraternity will derive greater benefits from the paper.

Eventually, if the Model leads to more meaningful diagnosis and devising of treatment methods, the end beneficiaries will be the Cancer Patients.

Many Congratulations for Aarti Venkat and her Team and for all those who inspired them to come up with this Outstanding effort. A Special pat on their backs for presenting the paper in a language and lucid style that even a layman like me could understand.

Well done!

On 2025-09-10 17:53:41, user J. Gillary wrote:

I noticed in the publication in Nature Communications it stated, "In a mouse model of CM brain endothelial cell-specific ablation of H-2Kb or H-2Db was found to reduce CD8+ T cell interaction with the brain vasculature which prevented BBB breakdown and death of the animals37. In CM, brain endothelial cells take up parasite Ags from their luminal side to subsequently cross-present these Ags again on their luminal surface to circulating CD8+ T cells. Our present study shows, however, that inflamed brain microvascular endothelial cells can also process exogenous protein Ags provided from their abluminal side and present it on MHC class I molecules to naïve CD8+ T cells on their luminal side leading to their activation and proliferation and differentiation to effector CD8+ T cells inducing brain endothelial apoptosis." I just wanted to point out that you state your study shows abluminal presentation and the other study doesn't and then you cite the study (37), however the study you cited does in fact shows microvascular endothelial cells can process Ags from abluminal side and present it in a supplementary figure. They had also presented that data in abstracts since 2018. I just thought it was important to mention they were inaccurately cited and did show this first in their paper.

On 2025-09-10 12:08:18, user Bettina Böttcher wrote:

The work is now peer-reviewed and has been published. The published work can be found here:<br /> Flegler, V.J., Rasmussen, A., Hedrich, R. et al. Mechanosensitive channel engineering: A study on the mixing and matching of YnaI and MscS sensor paddles and pores. Nat Commun 16, 7881 (2025). https://doi.org/10.1038/s41467-025-63253-0

On 2025-09-10 09:27:51, user David Curtis wrote:

Pathogenicity of nonsynonymous variants has previously been systematically assessed for a wide variety of predictors, including AlphaMissense and GPN-MSA

David Curtis<br /> UCL Genetics Institute

Of relevance to this topic are two recent reports assessing the relative performance of a range of different software tools to predict the pathogenicity of nonsynonymous coding variants (Curtis, 2024, 2025). It would be appropriate to discuss the findings of these previous studies and their relevance to the novel results reported here.

References

Curtis, D. (2024). Assessment of ability of AlphaMissense to identify variants affecting susceptibility to common disease. European Journal of Human Genetics 2024, 1–9. https://doi.org/10.1038/s41431-024-01675-y <br /> Curtis, D. (2025). Assessment of ability of a DNA language model to predict pathogenicity of rare coding variants. Journal of Human Genetics. https://doi.org/10.1038/S10038-025-01385-3

On 2025-09-10 09:07:46, user Vladislav Temkin wrote:

Dear authors,

I am wondering if described fenomena is involved in synthesis of proinflammatory proteins such as IL-6 and TNF.

Thank you.<br /> Vladislav Temkin

On 2025-09-10 07:44:10, user Thibaud Decaens wrote:

Now published in Oecologia, 207, 151<br /> https://doi.org/10.1007/s00442-025-05788-z

On 2025-09-09 23:42:39, user Emil Kriukov wrote:

The preprint has been published: https://doi.org/10.1016/j.ydbio.2025.08.025

On 2025-09-09 15:40:33, user Gat Rauner wrote:

This paper is now published in the Journal "Development" under the title: "Single-cell organogenesis captures complex breast tissue formation in three dimensions"<br /> Link: https://doi.org/10.1242/dev.204813

On 2025-09-09 15:07:14, user JH wrote:

Dear Évora et al. Thank you for this interesting piece of research. It is really exciting.<br /> It is, however, important that you cite the work of people from the palaeoproteomic field:<br /> - As a minimum cite "Buckley, M., Collins, M., Thomas-Oates, J., Wilson, J.C., 2009. Species identification by analysis of bone collagen using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry. Rapid Commun. Mass Spectrom. 23, 3843–3854. https://doi.org/10.1002/rcm.4316 " for the ZooMS method in general.

• As a minimum cite "Kirby, D.P., Manick, A., Newman, R., 2020. Minimally Invasive Sampling of Surface Coatings for Protein Identification by Peptide Mass Fingerprinting: A Case Study with Photographs. J. Amer. Inst. Conserv. 59, 235–245. https://doi.org/10.1080/01971360.2019.1656446 "<br /> for the usage of polishing film, grit size 30 µm.

• cite "Niedermeyer, T.H.J., Strohalm, M., 2012. mMass as a software tool for the annotation of cyclic peptide tandem mass spectra. PLoS One 7, e44913. https://doi.org/10.1371/journal.pone.0044913 " for the usage of Mmass.

• Another paper combining traceology and ZooMS on osseous bone material has been conducted prior to this study, though on Early Neolithic material. It shows similar patterns. Maybe include this as a part of the research history of the field: "Hansen, J., Sierra, A., Mata, S., Gassiot Ballbè, E., Rey Lanaspa, J., Welker, F., Saña Seguí, M., Clemente Conte, I., 2024. Combining traceological analysis and ZooMS on Early Neolithic bone artefacts from the cave of Coro Trasito, NE Iberian Peninsula: Cervidae used equally to Caprinae. PLoS One 19, e0306448. https://doi.org/10.1371/journal.pone.0306448 "

• When describing the peptide markers refer to "Brown, S., Douka, K., Collins, M.J., Richter, K.K., 2021. On the standardization of ZooMS nomenclature. J. Proteomics 235, 104041. https://doi.org/10.1016/j.jprot.2020.104041 "

• In relation to the domestic and wild form of the Caprinae species, I think you could mention that proteomically your "sheep" could be Ovis aries or Rupicapra rupicapra (sometimes written as Caprinae (not Capra sp.)) and your "goat" could be Capra hircus or Capra ibex/Capra pyrenaica (sometimes written as just Capra sp.)).

• You need to make your spectra publicly available on an online repository, e.g. Zenodo.org, so that the spectra can be assessed externally. It would be interesting to examine the glutamine deamidation rates to gain insight into the validity of the extracted protein.

I hope this is helpful,<br /> all the best.

On 2025-09-08 23:37:55, user Pedro wrote:

This article has been accepted at AISTATS 2025. An updated version of this article is available at https://proceedings.mlr.press/v258/seber25a.html . Because PMLR does not use DOIs, BioRxiv is unable to update the "this article is a preprint and has not been certified by peer review" message.

On 2025-09-08 11:51:40, user Bettina Hause wrote:

The manuscirpt has bee published in Nature Communications, volume 16, Article number: 6684 (2025), doi: /10.1038/s41467-025-61832-9. <br /> The title changed slightly to: "Transcriptomics and trans-organellar complementation reveal limited signaling of 12-cis-oxo-phytodienoic acid during early wound response in Arabidopsis".

On 2025-09-08 02:07:05, user Peter Hickey wrote:

Could you please add the Supplementary Material to the bioRxiv submission?

On 2025-09-07 13:22:26, user Earl Starks wrote:

Where can we get this test done? Thanks!

On 2025-09-07 02:03:17, user Genetix_Fan599 wrote:

It would be great to see the supplementary material referenced in the text!

On 2025-09-05 10:52:12, user Daniel Paluh wrote:

The peer-reviewed version of this manuscript is now available: https://royalsocietypublishing.org/doi/10.1098/rsos.251196

On 2025-09-05 07:58:55, user Jesus Murillo wrote:

This paper has now been published in Phytopathology, as:

Oliveira LOd, Rody HVS, Aguilera A, Murillo J 2025<br /> Integrative elements repeatedly captured the phaseolotoxin biosynthesis gene cluster and invaded Pseudomonas syringae multiple times<br /> Phytopathology https://doi.org/10.1094/PHYTO-01-25-0017-R

On 2025-09-05 05:33:47, user Jeziel Dener Damasceno wrote:

Please, could this preprint be linked to the peer reviewed publication:<br /> https://www.cell.com/cell-reports/fulltext/S2211-1247(25)00865-4?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS2211124725008654%3Fshowall%3Dtrue

On 2025-09-04 13:05:58, user Fernando de la Cuesta wrote:

This preprint has been published and is available in Open Access format here:<br /> https://isevjournals.onlinelibrary.wiley.com/doi/10.1002/jev2.70147

On 2025-09-04 12:50:30, user James Glazier wrote:

An impressive set of experiments measuring molecular dynamics and control inside individual cells at tissue scale coupled to a well validated mathematical model of the critical feedback mechanisms. While the general concept of the somitic clock has been considered for many years, this study breaks new ground in identifying its components and principles of operation and in providing rigorous development and application of a mathematical framework for its study.