From 8df2f025323fcbce94858b9e726ce8666fdad229 Mon Sep 17 00:00:00 2001 From: Bot <> Date: Tue, 14 Jan 2025 01:38:41 +0000 Subject: [PATCH] Run bot and update output --- output.csv | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/output.csv b/output.csv index 209eeca..7e61a40 100644 --- a/output.csv +++ b/output.csv @@ -1,4 +1,7 @@ comment url,comment forum,comment keywords,comment message,comment date,comment username,comment name,paper doi,paper title,paper authors,paper date +https://www.biorxiv.org/content/10.1101/2024.12.10.627842v1#comment-6628979382,biorxivstage,1,"Dear Authors, I enjoyed your excellent experimental prove that mu-opiods activate medial habenula. I am curious about your opinion. I wonder if optogenetic activation of MHb can test my idea that MOR and some anesthetics activate the MHb→IPN→ MRN→ serotonin→claustrum →cortical SWA circuit ? These agents can through slow-wave activity (SWA) in in cortical and striatal neurons induces loss of awareness. I also predicted that MOR activation in MHb causes opioid-induced respiratory depression OIRD. Thus targeted MOR activation speficaly in MHb should cause respiratory depression OIRD, with high morphine dose even respiratory arrest. I am very interested for your feedback. Best regards, Karin Vadovičová DOI: 10.13140/RG.2.2.29677.97766 Circuits for anesthesia, unawareness, OIRD, sleep and memory replay: MHb→IPN→PAG + DRN + MRN→claustrum→cortex. December 2023",2025-01-13T10:10:26,karinvadovicova,Karin Vadovicova,10.1101/2024.12.10.627842,Mu-opioid receptor activation potentiates excitatory transmission at the habenulo-peduncular synapse,"Sarthak M. Singhal, Agata Szlaga, Yen-Chu Chen, William S. Conrad, Thomas S. Hnasko",2024-12-16 +https://www.biorxiv.org/content/10.1101/2023.02.13.528102v1#comment-6628851473,biorxivstage,0,"The expansion distance varies by Xenium Analyzer version. In XOA v2.0 and later versions, the expansion distance is 5 µm, while in XOA v1.0-1.9, the default expansion distance was 15 µm. Therefore, it is important to specify which version of the Xenium Analyzer is being used. https://www.10xgenomics.com/support/software/xenium-onboard-analysis/latest/algorithms-overview/segmentation#seg-nucleus-expansion",2025-01-13T02:26:25,harimchun,Harim Chun,10.1101/2023.02.13.528102,Optimizing Xenium In Situ data utility by quality assessment and best practice analysis workflows,"Sergio Marco Salas, Paulo Czarnewski, Louis B. Kuemmerle, Saga Helgadottir, Christoffer Matsson-Langseth, Sebastian Tismeyer, Christophe Avenel, Habib Rehman, Katarina Tiklova, Axel Andersson, Maria Chatzinikolaou, Fabian J. Theis, Malte D. Luecken, Carolina Wählby, Naveed Ishaque, Mats Nilsson",2023-02-14 +https://www.biorxiv.org/content/10.1101/2024.12.10.627701v1#comment-6627663022,biorxivstage,0,"Here's a courtesy review from xPeerd.com Summary The manuscript titled ""E-cadherin endocytosis promotes non-canonical EGFR:STAT signalling to induce cell death and inhibit heterochromatinisation"" studies the impact of E-cadherin endocytosis on EGFR and STAT signalling pathways in Drosophila wing discs. It reveals that E-cadherin endocytosis facilitates EGFR:STAT signalling, which in turn promotes apoptosis and inhibits heterochromatin formation. Potential Major Revisions 1. Research Design and Methodology: - While the methodology is sound, there are areas that need more clarity. For example, the paper describes the use of E-cad::EOS overexpression but lacks detailed control experiments and statistical analysis to support the claims about changes in gene expression (p. 4, Section 1). - The reliance on Drosophila as a model organism is justified but requires additional discussion on how the findings translate to vertebrate systems (p. 1, Summary). 2. Clear Contribution to the Field: - The potential tumor-suppressive mechanism proposed is intriguing, but the manuscript needs to more clearly define its novel contributions against existing literature. The exact nature of the signalosome and its comparison with known complexes should be elaborated (p. 17, Discussion). - It should explicitly differentiate findings from previous studies that linked E-cadherin and EGFR signalling (p. 11). Potential Minor Revisions 1. Typographic and Grammatical Errors: - Typographic errors, such as “the transcriptional reporter” should be “transcriptional reporter” (p. 14, Line 20). - Ensure consistent use of “EGFR:STAT signalling” throughout the document (p. 12, Line 1). 2. Formatting Issues: - Ensure all figure legends and references are consistently formatted (Figures on pages 12-16). - Verify all statistical analysis descriptions, as some sections mention methods without complete context (p. 16-17). 3. AI Content Analysis: - The document appears to be human-authored, as the writing style and the depth of content are consistent with academic rigor. There are no substantial indicators of AI-generated content. Recommendations 1. Enhanced Clarity: - Enhance clarity by adding detailed flow diagrams for the signalling pathways discussed, particularly the role of endocytic trafficking of E-cadherin and its intersection with EGFR and STAT signalling pathways. - Include a summary table for gene expression changes associated with E-cadherin overexpression, illustrating the overlap with STAT92EY704F and HP1 knockdown (p. 4). 2. Control Experiments: - Additional control experiments are essential, particularly targeting the specificity of STAT92E interactions with Heterochromatin Protein 1 (HP1) and EGFR (p. 3-4). 3. Linking to Human Context: - Increase the discussion of how these findings might translate to human epithelial cancers, supporting the relevance of these mechanisms with references to similar studies in mammalian cells (p. 11-12). Conclusion The manuscript offers valuable insights into the non-canonical roles of STAT and EGFR signalling regulated by E-cadherin endocytosis. Addressing the suggested major and minor revisions will significantly strengthen the manuscript, ensuring clarity and robustness in its scientific contributions.",2025-01-11T04:47:52,xpeerd,xPeer,10.1101/2024.12.10.627701,E-cadherin endocytosis promotes non-canonical EGFR:STAT signalling to induce cell death and inhibit heterochromatinisation,"Miguel Ramirez-Moreno, Amy Quinton, Eleanor Jacobsen, Przemyslaw A. Stempor, Martin P. Zeidler, Natalia A. Bulgakova",2024-12-12 https://www.biorxiv.org/content/10.1101/2024.01.31.578241v1#comment-6626707565,biorxivstage,0,Revised version published: https://www.nature.com/articles/s41564-024-01910-8,2025-01-09T19:54:36,jpgerdt,J. P. Gerdt,10.1101/2024.01.31.578241,Bacterium secretes chemical inhibitor that sensitizes competitor to bacteriophage infection,"Zhiyu Zang, Chengqian Zhang, Kyoung Jin Park, Daniel A. Schwartz, Ram Podicheti, Jay T. Lennon, Joseph P. Gerdt",2024-01-31 https://www.biorxiv.org/content/10.1101/2025.01.04.631256v1#comment-6626589422,biorxivstage,0,"Thank you for engaging with our research! Our work acknowledges that signal leakage / volume conduction / common source artefacts comprise a large proportion of measured zero-phase-lag connectivity. However, we find that in interactions between brain-regions where such artefacts are likely negligible, functional connectivity at zero- / near-zero-phase-lag still constitutes most interactions. To address your point, a key piece of evidence from our study demonstrates that zero-phase-inclusive methods capture not just neural activity, but functional connectivity (which, by definition, is the statistical interdependency between activity). Specifically, we observe that the proportion of zero- / near-zero-phase-lag (or pi-phase-lag) functional connectivity between homotopic interhemispheric brain regions (~85%) is significantly higher than that between heterotopic interhemispheric regions. This cannot be explained by the artefacts that you mention. Consistently, we find that zero-phase-inclusive functional connectivity methods capture homotopic interhemispheric functional connectivity better than zero-phase-exclusive methods.",2025-01-09T17:06:07,disqus_nWKKDRH05H,Chirag Mehra,10.1101/2025.01.04.631256,"Zero-phase-delay connectivity increases the reliability, concordance with structure, and prognostic ability of functional connectivity metrics","Chirag Mehra, Ahmad Beyh, Petroula Laiou, Pilar Garces, Declan Murphy, Eva Loth, Flavio Dell’Acqua, Joshua B Ewen, Mark P Richardson, Jonathan O’Muircheartaigh",2025-01-04 https://www.biorxiv.org/content/10.1101/2022.03.18.484870v1#comment-6626348870,biorxivstage,0,"Dear community, this article was published under the title “STIC2 selectively binds ribosome-nascent chain complexes in the cotranslational sorting of Arabidopsis thylakoid proteins” ( https://doi.org/10.1038/s44318-024-00211-4) in The EMBO Journal in August 2024. Best regards Dominique Stolle",2025-01-09T09:22:25,dominiquesebastianstolle,Dominique Sebastian Stolle,10.1101/2022.03.18.484870,Proteomic identification of the interactome of stalled ribosome nascent chain complexes translating the thylakoid membrane protein D1,"Dominique S. Stolle, Paul Treimer, Jan Lambertz, Lena Osterhoff, Annika Bischoff, Beatrix Dünschede, Anja Rödiger, Christian Herrmann, Sacha Baginsky, Marc M. Nowaczyk, Danja Schünemann",2022-03-19 @@ -95,7 +98,4 @@ https://www.biorxiv.org/content/10.1101/2023.08.06.552164v1#comment-6606135757,b https://www.biorxiv.org/content/10.1101/2024.11.11.622837v1#comment-6606113171,biorxivstage,0,"Huang et al have developed impressive ELOVL1 inhibitors for the treatment of ALD. Their lead compound effectively reduces the putative pathological substance, VLCFAs, in the CNS. There are a few points to address in this paper that could significantly improve an otherwise strong piece of work. In figure 2 Huang et al report C26:0/C22:0 levels. These probably slightly underestimate the potency of their compound, as ELOVL1 inhibition reduces not only C26:0, but also C22:0, the denominator. Through most of the paper, they report absolute concentrations, so this is a minor concern. Huang et al incorrectly report that our studies (Come et al, 2021) ""measured only Lysophosphotidylcholine C26:0 levels"", and were thus less complete. We report reductions in C26:0 LPCs, acyl carnitines and total VLCFAs in the CNS in our paper. Huang et al report that ""treatment with the ELOVL1 inhibitor unexpectedly led to profound transcriptional changes beyond correction of pathways altered by the loss of ABCD1."" Almost all small molecule drugs have off target activities and clinically apparent side effects that are accompanied by transcriptional changes. The most parsimonious explanation for the ""off target"" transcriptional changes is ""off target"" activity of the compound. If they were to test multiple structurally distinct ELOVL1 inhibitors and find a shared pattern across scaffolds, that could support the argument that this is somehow a general property of small molecule ELOVL1 inhibition. The conclusion to their abstract that ""ELOVL1 inhibition may have broader consequences . . . than the correction of lipid homeostasis"" goes beyond the data they have in hand. In the face of a disease that is fatal in one third of boys who carry mutations, such changes in transcriptional profiles, without any associated findings in traditional in vivo toxicology studies, should not be a reason to discontinue development. Indeed, an absence of ""off-target"" or unexpected transcriptional changes is a barrier that even most currently prescribed safe and effective drugs could not meet. This paper presents an impressive chemistry campaign and biological characterization of novel ELOVL1 inhibitors.",2024-12-06T16:11:31,sanjaymagavi,Sanjay Magavi,10.1101/2024.11.11.622837,Elovl1 inhibition reduced very long chain fatty acids in a mouse model of adrenoleukodystrophy,"Jeremy Y. Huang, Brian Freed, Martin Hanus, Kelly Keefe, Ming Sum Ruby Chiang, Alexander Brezzani, Yongyi Luo, Yihang Li, Becky Lam, Stephanie Holley, Joseph Gans, Zuzana Dostalova, Buyun Tang, Clifford Phaneuf, Erin Teeple, Laura Parisi, Lilu Guo, Zhonglin Zhao, Sofia N. Kinton, Jacquelyn Dwyer, Sandrine Teixeira, Hong Ma, Gary Asmussen, Rajashree McLaren, Donghui Wang, Ann Baker, Craig S. Siegel, David Fink, Kristen Randall, Alexei Belenky, Suchitra Venugopal, Giorgio Gaglia, Jennifer Johnson, Dinesh S. Bangari, S. Pablo Sardi, Bailin Zhang, Alexander Michel, Jonathan D. Proto, Alla Kloss, Tatiana Gladysheva, Can Kayatekin",2024-11-11 https://www.biorxiv.org/content/10.1101/2024.12.04.626764v2#comment-6606070801,biorxivstage,0,"In examining a few of the top hits from this work, it becomes apparent that most of them are due to contamination during sequencing. Here are two examples: https://www.ncbi.nlm.nih.gov/nuccore/NZ_BMOE01000030.1?report=fasta https://www.ncbi.nlm.nih.gov/nuccore/NZ_WWEN01000019.1?report=fasta Both of these are small contigs, and therefore may not actually be part of a contiguous bacteria genome. When we BLAST them with BLASTN, they match very closely known synthetic vectors for working with HIV. Therefore, in these two cases (and all others I've spot checked, although I have not been comprehensive), there is no evidence that these genes are present in the bacterial genome. Rather, these are evidently cases where someone was sequencing an HIV-related sample on the same lane as a bacterial genome, and cross-contamination occurred (either index hopping or well to well). The HIV-vector sequence was then assembled as part of the bacterial genome, and missed in contamination screening (both by the authors and by NCBI!). Thus I was not able to identify any cases of genomic evidence for the claims of the authors, although I did not look at every hit because the pattern above quickly emerged. If the authors of this work want to provide sufficient evidence for the claim that there are close homologs of HIV related proteins in bacterial genomes, I would suggest taking a close manual inspection of all hits in their table. They should be able to show that these hits are integrated into bacterial chromosomes, and not always on separate contigs. They should show the raw reads then support those integrations. If the claim is that ""some bacteria can acquire HIV-1 genetic material"", they should then also do a comparison at the nucleotide level, not at the translated amino acid level. Finally, if that claim was tree, they should construct a phylogenetic tree to identify the nearest HIV relative and estimate time since divergence. However, it is simply dubious that this is biologically true, because it would be an extraordinary claim if true, with no evidence yet emerged. It is essential to always closely inspect genomic results. Genomic results are not a black box, and manual inspection can quickly shed light on them.",2024-12-06T15:02:38,acc_account,Alex Crits-Christoph,10.1101/2024.12.04.626764,HIV-1 protein coding sequences are present in relevant bacteria,"Hector F. Pelaez-Prestel, Juan Mozas-Gutierrez, Esther M. Lafuente, Pedro A. Reche",2024-12-05 https://www.biorxiv.org/content/10.1101/2023.04.24.538108v1#comment-6605795247,biorxivstage,1,"Courtesy review from xPeerd.com Summary The preprint ""Cell based dATP delivery as a therapy for chronic heart failure"" proposes using genetically modified human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) to deliver deoxy-ATP (dATP) to improve contractility in chronic heart failure. This strategy involves overexpressing ribonucleotide reductase in hPSC-CMs, enhancing their dATP production. Key outcomes include increased left ventricular function, greater exercise capacity, improved cardiac metabolism, and reduced symptoms of heart failure in animal models. The approach combines remuscularization with enhanced contractility, offering a novel therapeutic direction for chronic heart failure. Major Revisions 1. Validation of Claims: - Doubts: The study's claims about the efficacy of dATP-producing CMs in treating chronic heart failure need stronger validation in larger and diverse animal models and human studies. - Critique: The majority of the experimental data involve rodent models. Larger animal studies and eventual human trials are crucial for assessing translative potential and variability in responses (e.g., differences in metabolism and immune reactions). - Example: ""Our goal was to improve regenerative strategies by genome-editing hPSC to make dATP-donor cells… Our results indicate that dATP donor CMs can… persistently improve the function of the chronically injured heart"". 2. Mechanisms of dATP Delivery: - Clarification: Detailed mechanisms on how dATP is transported through gap junctions from donor to host cardiomyocytes require further elucidation and quantification. - Critique: While the study mentions gap junction-mediated dATP transfer, the precise dynamic and extent of this transfer across intercellular connections in vivo are not fully described. - Example: ""In vivo, dATP-producing CMs formed new myocardium that transferred dATP to host cardiomyocytes via gap junctions, increasing their dATP levels"". 3. Long-Term Safety and Efficacy: - Skepticism: Long-term safety data and potential adverse effects of continuous dATP elevation, such as risks of arrhythmogenicity, were not adequately addressed. - Critique: Although the study indicates beneficial dATP effects, continuous high levels of dATP need further investigation to rule out chronic side effects including arrhythmias or maladaptive cardiac remodeling. - Example: Concerns: ""Interventions to increase the contractility of the failing heart have been sought for decades…our novel strategy of cell therapy…"". Minor Revisions 1. Typos and Errors: - There are a few minor typographical errors and ambiguities in phrasing that can be corrected for better readability. 2. Figures and Diagrams Consistency: - The figures and diagrams should uniformly represent the data and should be referenced consistently within the text (e.g., Fig 6 referenced properly with aligned legends and labels). 3. Formatting and Style: - Standardize font sizes and alignments, particularly in figures and tables. Ensure that equation formatting, subscripts, and superscripts are consistently applied. Recommendations 1. Include Larger Animal Studies: - Conduct larger and more diverse animal studies to establish translational efficacy and safety across different species. This would bridge the existing gap between rodent models and potential human applications. 2. Detailed Mechanistic Studies: - Expand mechanistic studies on the biophysics of dATP transfer and integration into host cells, detailing the kinetics of dATP movement and concentration gradients across different heart zones. 3. Extended Safety Profiles: - Investigate long-term safety profiles of dATP elevation in vivo, focusing on electrical stability of myocardial tissues and potential non-target effects on other tissues/organs跨链接。 4. Human Trials: - Initiate phase I clinical trials after thorough preclinical validations to evaluate safety, dosage, efficacy, and delivery mechanisms in human heart failure patients. 5. Data Sharing and Reproducibility: - Provide access to raw data and methodological details to enhance reproducibility and allow independent verification of results. In conclusion, the preprint presents a promising approach to treating chronic heart failure using genetically engineered hPSC-CMs. Nonetheless, further work on validation, safety, and translational studies is essential to move toward clinical applications.",2024-12-06T01:19:33,xpeerd,xPeer,10.1101/2023.04.24.538108,Cell based dATP delivery as a therapy for chronic heart failure,"Ketaki N Mhatre, Julie Mathieu, Amy Martinson, Galina Flint, Leslie P. Blakley, Arash Tabesh, Hans Reinecke, Xiulan Yang, Xuan Guan, Eesha Murali, Jordan M Klaiman, Guy L Odom, Mary Beth Brown, Rong Tian, Stephen D Hauschka, Daniel Raftery, Farid Moussavi-Harami, Michael Regnier, Charles E Murry",2023-04-28 -https://www.biorxiv.org/content/10.1101/2023.10.27.564195v2#comment-6605738895,biorxivstage,1,"There is increasing evidence suggesting an interplay between DNA damage response (DDR) and cellular metabolism pathways, specifically regarding the regulatory role of the DDR kinase Ataxia Telangiectasia and Rad3-related protein (ATR) and the metabolic regulator mechanistic Target of Rapamycin Complex 1 (mTORC1) in p16-low cancer cells. However, the mechanism by which ATR regulates mTORC1 activity remains poorly understood. To address these knowledge gaps, the authors of the Tangudu et al. manuscript investigated the role of ATR in activating mTORC1 in both unperturbed and p16 knockdown cell models. The findings of this study unveiled several key novelties including the role of ATR in modulating mTORC1 activity via de novo cholesterol synthesis under both low p16 expression and basal conditions. Additionally, lanosterol synthase (LSS), an enzyme that regulates the biosynthesis of cholesterol, is regulated by ATR, and ATR's regulation of mTORC1 is independent of the Checkpoint Kinase 1 (CHK1) and Tuberous Sclerosis Complex (TSC) pathways. Several innovative experimental techniques were employed within the course of the study, including the simultaneous proteomic and transcriptomic profiling used to identify transcriptional and post-translational changes in ATR signaling and the use of phospho-specific antibodies to monitor the effects of ATR modulation on mTORC1 activation at specific time points. However, we have identified one major concern that we believe should be addressed prior to the publication of the paper. The major concern that was found in the paper was that the mechanism of action for the ATR-mTORC1 pathway was not fully represented in all the broad ranges of cells in the data shown in the figures. The issue is that while in Figure 1 the expression of ATR and mTORC1 was shown through a broad range of cell lines, the latter portion of the paper focused primarily on SKMEL28 cells, a melanoma cell line, which does not fully represent the broad spectrum of the cellular model that the ATR-mTORC1 pathway has a role in general cell metabolism and proliferation. An experiment that could be done to address this major issue of underrepresentation of the ATR-mTORC1 expression in unperturbed cells, as well as diseased cells, is to repeat the experiments done from Figure 2 to Figure 4 in all cells that were used in Figure 1 (HeLa, HEK293, MEFs). There are also some minor concerns we identified with the manuscript. One small issue is the lack of quantification or statistical analysis included in Figure 1. This would allow for a better understanding of the content of the figure. Another minor concern is the coloration of the fluorescence images in Figure 4. The chosen colors make it difficult to make out the overlaps in the merged images, especially in Figure 4B. This could be fixed by changing the colors to ones that are more distinct when merged. The final minor concern identified is the absence of GTPase Rheb in the working model. GTPase Rheb is included in the introduction as it plays a role in the activation of mTORC1 after localization to the lysosome. While the paper is focused on the localization of mTORC1, its activation by GTPase Rheb may also be affected by this mechanism.",2024-12-05T23:03:05,dinasarsam,Dina Sarsam,10.1101/2023.10.27.564195,ATR promotes mTORC1 activity via de novo cholesterol synthesis,"Naveen Kumar Tangudu, Alexandra N. Grumet, Richard Fang, Raquel Buj, Aidan R. Cole, Apoorva Uboveja, Amandine Amalric, Baixue Yang, Zhentai Huang, Cassandra Happe, Mai Sun, Stacy L. Gelhaus, Matthew L. MacDonald, Nadine Hempel, Nathaniel W. Snyder, Katarzyna M. Kedziora, Alexander J. Valvezan, Katherine M. Aird",2024-10-24 -https://www.biorxiv.org/content/10.1101/2023.10.27.564195v2#comment-6605727179,biorxivstage,0,"The DNA Damage Repair (DDR) pathway, including ATR/ATM, have previously been linked to metabolism and mTORC1 regulation, however the key players and mechanisms, especially in unperturbed cells, in this downstream signaling pathway are currently unknown. This manuscript, authored by Aird et al., demonstrates that in both p16 knockdown cells and unperturbed cells, ATR increases lanosterol synthesis through de novo cholesterol synthesis, which promotes mTORC1 activity by lysosomal localization. They determined that this pathway consists of ATR, not ATM, and is independent of the CHK2 and TSC2 processes. The paper contains several major concerns, detailed below, that must be addressed before the data can be properly evaluated. Until these concerns are resolved the findings within the paper are unable to be thoroughly assessed, delaying our understanding of how ATR influences mTORC1 during DDR. The first major concern identified within the paper is the lack of orthogonal validation, specifically for Figure 4 A & B. This is a concern because, in order to validate the findings, different methods should be applied to confirm reliability, reproducibility and robustness. In Figure 4 A & B we are specifically relying on the visual trends to make a conclusion, which could be misinterpreted. The authors should perform Radiolabeled Cholesterol Uptake Assays which measure cell cholesterol absorbance by labeling cholesterol compounds with radioactive isotopes which would contribute to their orthogonal validation and help support their results. The second major concern is that phosphorylation of S6K is not limited to mTORC1. This is a major concern because S6K is a direct substrate for other kinases, such as JNK1 and PKC. Multiple validations are required to show that mTORC1 activity leads to the decrease in phosphorylation of S6K. One validation that can be conducted is to overexpress ATR to observe if phosphorylation of S6K increases, which would further support the direct link between mTORC1 activity and S6K phosphorylation. An additional validation is to conduct an in vitro kinase assay with mTORC1 and S6K to eliminate the possibility of confounding variables. The third major concern is that p16 expression can vary significantly between cell types. This is a major concern because HeLa cells have high levels of p16 expression, HEK293 cells have low levels of p16 expression unless under stress and MEFs have significantly higher levels of p16 as cells approach senescence. Quantifying the basal levels of p16 in each type of cell line is crucial since the focus is on ATR’s effect under basal conditions. This can be done by quantifying the levels of p16 through western blotting and testing if ATR affects mTORC1 similarly across varying expression levels. The first minor concern is that the quantification of the western blots is needed throughout the paper in order to substantially improve the clarity of the figures. The second minor concern would be to provide justification about the selection of the specific cell lines which would provide clarity on the major concerns related to varying p16 expression. The third minor concern is that the verbiage of mTORC1 should be consistent throughout the whole paper to increase readability and reduce confusion. The final minor concern is that Figure 3’s title is unclear; replacing “decreases” with “knockdown” would be more effective.",2024-12-05T22:37:23,cecyliaolivo,Cecylia Olivo,10.1101/2023.10.27.564195,ATR promotes mTORC1 activity via de novo cholesterol synthesis,"Naveen Kumar Tangudu, Alexandra N. Grumet, Richard Fang, Raquel Buj, Aidan R. Cole, Apoorva Uboveja, Amandine Amalric, Baixue Yang, Zhentai Huang, Cassandra Happe, Mai Sun, Stacy L. Gelhaus, Matthew L. MacDonald, Nadine Hempel, Nathaniel W. Snyder, Katarzyna M. Kedziora, Alexander J. Valvezan, Katherine M. Aird",2024-10-24 -https://www.biorxiv.org/content/10.1101/2023.10.17.562733v3#comment-6605454677,biorxivstage,0,"Please note that this manuscript is now published (with slightly different title, and updates) at npj Systems Biology and Applications: https://doi.org/10.1038/s41540-024-00472-z",2024-12-05T15:09:17,disqus_OdEGrOAwVx,Paul Macklin,10.1101/2023.10.17.562733,A multiscale model of immune surveillance in micrometastases: towards cancer patient digital twins,"Heber L. Rocha, Boris Aguilar, Michael Getz, Ilya Shmulevich, Paul Macklin",2024-08-10 -https://www.biorxiv.org/content/10.1101/2024.11.27.625595v1#comment-6605413816,biorxivstage,1,"Hi Eric, thanks for your comment. I am not familiar with DoRothEA. Could you please point us to the paper? CellOracle and GAGER have different goals. CellOracle can simulate the effect of perturbation or knockout of a TF given a list of TFs whereas our method (GAGER) can identify a set of TFs to restore gene expressions from levels of a source (diseased for example ) state to levels of a desired (healthy) state. -Atif",2024-12-05T14:01:13,disqus_TXyqmkXA6W,Atif Rahman,10.1101/2024.11.27.625595,GAGER: gene regulatory network assisted gene expression restoration,"Md Zarzees Uddin Shah Chowdhury, Sumaiya Sultana Any, Md. Abul Hasan Samee, Atif Rahman",2024-12-02 \ No newline at end of file +https://www.biorxiv.org/content/10.1101/2023.10.27.564195v2#comment-6605738895,biorxivstage,1,"There is increasing evidence suggesting an interplay between DNA damage response (DDR) and cellular metabolism pathways, specifically regarding the regulatory role of the DDR kinase Ataxia Telangiectasia and Rad3-related protein (ATR) and the metabolic regulator mechanistic Target of Rapamycin Complex 1 (mTORC1) in p16-low cancer cells. However, the mechanism by which ATR regulates mTORC1 activity remains poorly understood. To address these knowledge gaps, the authors of the Tangudu et al. manuscript investigated the role of ATR in activating mTORC1 in both unperturbed and p16 knockdown cell models. The findings of this study unveiled several key novelties including the role of ATR in modulating mTORC1 activity via de novo cholesterol synthesis under both low p16 expression and basal conditions. Additionally, lanosterol synthase (LSS), an enzyme that regulates the biosynthesis of cholesterol, is regulated by ATR, and ATR's regulation of mTORC1 is independent of the Checkpoint Kinase 1 (CHK1) and Tuberous Sclerosis Complex (TSC) pathways. Several innovative experimental techniques were employed within the course of the study, including the simultaneous proteomic and transcriptomic profiling used to identify transcriptional and post-translational changes in ATR signaling and the use of phospho-specific antibodies to monitor the effects of ATR modulation on mTORC1 activation at specific time points. However, we have identified one major concern that we believe should be addressed prior to the publication of the paper. The major concern that was found in the paper was that the mechanism of action for the ATR-mTORC1 pathway was not fully represented in all the broad ranges of cells in the data shown in the figures. The issue is that while in Figure 1 the expression of ATR and mTORC1 was shown through a broad range of cell lines, the latter portion of the paper focused primarily on SKMEL28 cells, a melanoma cell line, which does not fully represent the broad spectrum of the cellular model that the ATR-mTORC1 pathway has a role in general cell metabolism and proliferation. An experiment that could be done to address this major issue of underrepresentation of the ATR-mTORC1 expression in unperturbed cells, as well as diseased cells, is to repeat the experiments done from Figure 2 to Figure 4 in all cells that were used in Figure 1 (HeLa, HEK293, MEFs). There are also some minor concerns we identified with the manuscript. One small issue is the lack of quantification or statistical analysis included in Figure 1. This would allow for a better understanding of the content of the figure. Another minor concern is the coloration of the fluorescence images in Figure 4. The chosen colors make it difficult to make out the overlaps in the merged images, especially in Figure 4B. This could be fixed by changing the colors to ones that are more distinct when merged. The final minor concern identified is the absence of GTPase Rheb in the working model. GTPase Rheb is included in the introduction as it plays a role in the activation of mTORC1 after localization to the lysosome. While the paper is focused on the localization of mTORC1, its activation by GTPase Rheb may also be affected by this mechanism.",2024-12-05T23:03:05,dinasarsam,Dina Sarsam,10.1101/2023.10.27.564195,ATR promotes mTORC1 activity via de novo cholesterol synthesis,"Naveen Kumar Tangudu, Alexandra N. Grumet, Richard Fang, Raquel Buj, Aidan R. Cole, Apoorva Uboveja, Amandine Amalric, Baixue Yang, Zhentai Huang, Cassandra Happe, Mai Sun, Stacy L. Gelhaus, Matthew L. MacDonald, Nadine Hempel, Nathaniel W. Snyder, Katarzyna M. Kedziora, Alexander J. Valvezan, Katherine M. Aird",2024-10-24 \ No newline at end of file