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arXiv 2024年

作者： Bajcsy, Peter Bhattiprolu, Sreenivas Börner, Katy Cimini, Beth A. Collinson, Lucy Ellenberg, Jan Fiolka, Reto Giger, Maryellen Goscinski, Wojtek Hartley, Matthew Hotaling, Nathan Horwitz, Rick Jug, Florian Kreshuk, Anna Lundberg, Emma Mathur, Aastha Narayan, Kedar Onami, Shuichi Plant, Anne L. Prior, Fred Swedlow, Jason Taylor, Adam Keppler, Antje National Institute of Standards and Technology GaithersburgMD20899 United States ZEISS Microscopy Customer Center DublinCA94568 United States Intelligent Systems Engineering Indiana University BloomingtonIN47408 United States Imaging Platform Broad Institute CambridgeMA United States Electron Microscopy Science Technology Platform Francis Crick Institute 1 Midland Road LondonNW1 1AT United Kingdom European Molecular Biology Laboratory Heidelberg Germany Lyda Hill Department of Bioinformatics UT Southwestern Medical Center DallasTX United States Department of Radiology and Committee on Medical Physics University of Chicago ChicagoIL United States National Imaging Facility Brisbane Australia European Molecular Biology Laboratory European Bioinformatics Institute Hinxton Cambridge United Kingdom National Center for Advancing Translational Science National Institutes of Health Rockville United States Allen Institute for Cell Science Seattle United States Fondazione Human Technopole Milan Italy Stanford University California US and SciLifeLab KTH Royal Institute of Technology Stockholm Sweden Center for Molecular Microscopy Center for Cancer Research National Cancer Institute National Institutes of Health BethesdaMD20892 United States Cancer Research Technology Program Frederick National Laboratory for Cancer Research FrederickMD21702 United States RIKEN Center for Biosystems Dynamics Research Kobe Japan Department of Biomedical Informatics Department of Radiology University of Arkansas for Medical Sciences Little RockAR72205 United States Divisions of Computational Biology and Molecular Cell and Developmental Biology University of Dundee United Kingdom Sage Bionetworks SeattleWA United States

Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we take a first step at presenting some of the most common use cases as well as critical/emerging use cases of (including the use of artificial intelligence for) biological and medical image data, which would benefit tremendously from better frameworks for sharing (including technical, resourcing, legal, and ethical aspects). In the second half of this paper, we paint an ‘ideal world scenario’ for how global image data sharing could work and benefit all life sciences and beyond. As this is still a long way off, we conclude by suggesting several concrete measures directed toward our institutions, existing imaging communities and data initiatives, and national funders, as well as publishers. Our vision is that within the next ten years, most researchers in the world will be able to make their datasets openly available and use quality image data of interest to them for their research and benefit. This paper is published in parallel with a companion White Paper entitled "Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data", which addresses challenges and opportunities related to producing well-documented and high-quality image data that is ready to be shared. The driving goal is to address remaining challenges and democratize access to everyday practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location. © 2024, CC BY-NC-SA.

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Multiplex gene editing models of del(7q) reveal combined CUX1 and EZH2 loss drives clonal expansion and drug resistance

Blood neoplasia

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Blood neoplasia 2025年第2期2卷 100083页

作者： Matthew R M Jotte Angela Stoddart Tanner C Martinez Raven Moten Yuqing Xue Molly K Imgruet Hunter Blaylock Henna S Nam Bonnie Hu Jermaine Austin Ningfei An Saira Khan Sandeep K Gurbuxani Megan E McNerney Interdisciplinary Scientist Training Program The University of Chicago Medicine Chicago IL. Department of Pathology The University of Chicago Medicine Chicago IL. Committee on Cancer Biology The University of Chicago Medicine Chicago IL. Department of Pediatrics The University of Chicago Medicine Chicago IL. Committee on Immunology The University of Chicago Medicine Chicago IL. Committee on Genetics Genomics and Systems Biology The University of Chicago Medicine Chicago IL. The University of Chicago Medicine Comprehensive Cancer Center The University of Chicago Medicine Chicago IL.

Loss of all or part of chromosome 7 [-7/del(7q)] is recurrent in myeloid neoplasms and associated with a poor response to chemotherapy. Chromosome 7-encoded genes driving drug resistance and the consequences of combinatorial 7q tumor suppressor gene loss have remained unclear, the latter question largely because of the challenges of modeling aneuploidy. Here, we use in silico data mining to uncover 7q genes involved in chemotherapy resistance. We establish murine models of del(7q) clonal hematopoiesis and drug resistance with multiplex CRISPR-Cas9 (CRISPR-associated protein 9)-mediated inactivation of 4 genes, , , , and . Postgenotoxic exposure, combined deficiency of and preferentially promotes clonal myeloid expansion in vivo, with compounding defects in DNA damage recognition and repair. Human acute myeloid leukemia cell lines similarly illustrate central roles for and loss in survival and DNA damage resolution after chemotherapy exposure. Transcriptome analysis reveals combined and loss recapitulates gene signatures of -7 patients and defective DNA damage response pathways, to a greater extent than single gene loss. This work reveals a genetic interaction between and , and sheds light on how -7/del(7q) contributes to leukemogenesis and drug resistance characteristic of these adverse-risk neoplasms. These data support the concept of 7q as a contiguous gene syndrome region, in which combined loss of multiple gene drives pathogenesis. Furthermore, our CRISPR-based approach may serve as a framework for interrogating other recurrent aneuploid events in cancer.

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Compromised nonsense-mediated RNA decay results in truncated RNA-binding protein production upon DUX4 expression

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Cell Reports 2023年第6期42卷 112642页

作者： Campbell, Amy E. Dyle, Michael C. Albanese, Roberto Matheny, Tyler Sudheendran, Kavitha Cortázar, Michael A. Forman, Thomas Fu, Rui Gillen, Austin E. Caruthers, Marvin H. Floor, Stephen N. Calviello, Lorenzo Jagannathan, Sujatha Department of Biochemistry and Molecular Genetics University of Colorado Anschutz Medical Campus Aurora 80045 CO United States Functional Genomics Research Centre Human Technopole Milan 20157 Italy Computational Biology Research Centre Human Technopole Milan 20157 Italy RNA Bioscience Initiative University of Colorado Anschutz Medical Campus Aurora 80045 CO United States Department of Biochemistry University of Colorado Boulder Boulder 80309 CO United States Department of Craniofacial Biology University of Colorado Anschutz Medical Campus Aurora 80045 CO United States Medical Scientist Training Program University of Colorado Anschutz Medical Campus Aurora 80045 CO United States Department of Cell and Tissue Biology University of California San Francisco San Francisco 94143 CA United States Helen Diller Family Comprehensive Cancer Center University of California San Francisco San Francisco 94143 CA United States

Nonsense-mediated RNA decay (NMD) degrades transcripts carrying premature termination codons. NMD is thought to prevent the synthesis of toxic truncated proteins. However, whether loss of NMD results in widespread production of truncated proteins is unclear. A human genetic disease, facioscapulohumeral muscular dystrophy (FSHD), features acute inhibition of NMD upon expression of the disease-causing transcription factor, DUX4. Using a cell-based model of FSHD, we show production of truncated proteins from physiological NMD targets and find that RNA-binding proteins are enriched for aberrant truncations. The NMD isoform of one RNA-binding protein, SRSF3, is translated to produce a stable truncated protein, which is detected in FSHD patient-derived myotubes. Ectopic expression of truncated SRSF3 confers toxicity, and its downregulation is cytoprotective. Our results delineate the genome-scale impact of NMD loss. This widespread production of potentially deleterious truncated proteins has implications for FSHD biology as well as other genetic diseases where NMD is therapeutically modulated. © 2023 The Author(s)

关键词： CP: Molecular biology DUX4 dystrophy FSHD muscular NMD quality control RNA decay splicing translation

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Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq

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Nature communications 2025年第1期16卷 4785页

作者： Hefei Zhang Xuhang Li Dongyuan Song Onur Yukselen Shivani Nanda Alper Kucukural Jingyi Jessica Li Manuel Garber Albertha J M Walhout Department of Systems Biology University of Massachusetts Chan Medical School Worcester MA USA. Bioinformatics Interdepartmental Ph.D. Program University of California Los Angeles CA USA. Department of Genetics and Genome Sciences University of Connecticut Health Center Farmington CT USA. Via Scientific Inc. Cambridge MA USA. Pathology Beth Israel Deaconess Medical Center Harvard Medical School Boston MA USA. Department of Genomics and Computational Biology University of Massachusetts Chan Medical School Worcester MA USA. Department of Statistics and Data Science Department of Biostatistics Department of Computational Medicine and Department of Human Genetics University of California Los Angeles CA USA. Department of Genomics and Computational Biology University of Massachusetts Chan Medical School Worcester MA USA. manuel.garber@umassmed.edu. Department of Systems Biology University of Massachusetts Chan Medical School Worcester MA USA. marian.walhout@umassmed.edu.

Transcriptomes provide highly informative molecular phenotypes that, combined with gene perturbation, can connect genotype to phenotype. An ultimate goal is to perturb every gene and measure transcriptome changes, however, this is challenging, especially in whole animals. Here, we present 'Worm Perturb-Seq (WPS)', a method that provides high-resolution RNA-sequencing profiles for hundreds of replicate perturbations at a time in living animals. WPS introduces multiple experimental advances combining strengths of Caenhorhabditis elegans genetics and multiplexed RNA-sequencing with a novel analytical framework, EmpirDE. EmpirDE leverages the unique power of large transcriptomic datasets and improves statistical rigor by using gene-specific empirical null distributions to identify DEGs. We apply WPS to 103 nuclear hormone receptors (NHRs) and find a striking 'pairwise modularity' in which pairs of NHRs regulate shared target genes. We envision the advances of WPS to be useful not only for C. elegans, but broadly for other models, including human cells.

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FOXA2 promotes metastatic competence in small cell lung cancer

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Nature communications 2025年第1期16卷 4865页

作者： Kenta Kawasaki Sohrab Salehi Yingqian A Zhan Kevin Chen Jun Ho Lee Eralda Salataj Hong Zhong Parvathy Manoj Dennis Kinyua Barbara P Mello Harsha Sridhar Sam E Tischfield Irina Linkov Nicholas Ceglia Matthew Zatzman Eliyahu Havasov Neil J Shah Fanli Meng Brian Loomis Umesh K Bhanot Esther Redin Elisa de Stanchina Pierre-Jacques Hamard Richard P Koche Andrew McPherson Álvaro Quintanal-Villalonga Sohrab P Shah Joan Massagué Charles M Rudin Cancer Biology and Genetics Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New York NY USA. Department of Medicine Memorial Sloan Kettering Cancer Center New York NY USA. Computational Oncology Program Memorial Sloan Kettering Cancer Center New York NY USA. Center for Epigenetics Research Memorial Sloan Kettering Cancer Center New York NY USA. Antitumor Assessment Core Memorial Sloan Kettering Cancer Center New York NY USA. Marie-Josée and Henry R. Kravis Center for Molecular Oncology Memorial Sloan Kettering Cancer Center New York NY USA. Pathology Core Facility Memorial Sloan Kettering Cancer Center New York NY USA. Cancer Biology and Genetics Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New York NY USA. massaguj@***. Weill Cornell Medicine Graduate School of Medical Sciences New York NY USA. massaguj@***. Department of Medicine Memorial Sloan Kettering Cancer Center New York NY USA. rudinc@***. Weill Cornell Medicine Graduate School of Medical Sciences New York NY USA. rudinc@***.

Small cell lung cancer (SCLC) is known for its high metastatic potential, with most patients demonstrating clinically evident metastases in multiple organs at diagnosis. The factors contributing to this exceptional metastatic capacity have not been defined. To bridge this gap, we compare gene expression in SCLC patient samples who never experienced metastasis or relapse throughout their clinical course, versus primary SCLC patient samples from more typical patients who had metastatic disease at diagnosis. This analysis identifies FOXA2 as a transcription factor strongly associated with SCLC metastasis. Subsequent analyses in experimental models demonstrates that FOXA2 induces a fetal neuroendocrine gene expression program and promotes multi-site metastasis. Moreover, we identify ASCL1, a transcription factor known for its initiating role in SCLC tumorigenesis, as a direct binder of the FOXA2 promoter and regulator of FOXA2 expression. Taken together, these data define the ASCL1-FOXA2 axis as a critical driver of multiorgan SCLC metastasis.

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313P Association of CDK12 mutations and trastuzumab deruxtecan (TDxd) administration with overall survival (mOS) in HER2+ mBC

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ESMO Open 2025年 10卷 104885-104885页

作者： E. Alvarez S. Escrivá-De-Romaní M. Arumi de Dios M.A. Rezqallah Aron L. Ollé L.R. Joval M. Malumbres J.A. Seoane C. Saura Manich Oncology Vall d'Hebron Institute of Oncology Barcelona Spain Medical Oncology Dept. Vall d'Hebron University Hospital Barcelona Spain Oncology Department Vall d'Hebron University Hospital Barcelona Spain Oncología Médica Dept. Vall d'Hebron University Hospital Barcelona Spain Cancer Computational Biology VHIO - Vall d'Hebron Institute of Oncology Barcelona Spain Biostatistics VHIO Valle de Hebrón Instituto de Oncología Barcelona Spain Systems Oncology VHIO - Vall d'Hebron Institute of Oncology Barcelona Spain Cancer Computational Biology Vall d'Hebron Institute of Oncology Barcelona Spain Breast Cancer Program Vall d'Hebron University Hospital Barcelona Spain

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GENETICALLY DISTINCT PATHOGENESIS OF EPSTEIN-BARR VIRUS (EBV)-POSITIVE VERSUS EBV-NEGATIVE CLASSICAL HODGKIN (CHL) LYMPHOMA

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Hematological Oncology 2023年第S2期41卷

作者： K. Gomez G. Schiavoni Y. Nam J. Reynier C. Khamnei M. Aitken C. van Noesel B. Falini L. Pasqualucci R. Rabadan E. Tiacci Program for Mathematical Genomics and Department of Systems Biology Columbia University New York New York USA Institute of Hematology and Center for Hemato-Oncology Research Department of Medicine and Surgery University and Hospital of Perugia Perugia Italy Amsterdam University Medical Centers Department of Pathology University of Amsterdam Amsterdam Netherlands Institute for Cancer Genetics and Department of Pathology and Cell Biology Columbia University New York New York USA

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End-to-End Differentiable Molecular Mechanics Force Field Construction

arXiv

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arXiv 2020年

作者： Wang, Yuanqing Fass, Josh Kaminow, Benjamin Herr, John E. Rufa, Dominic Zhang, Ivy Pulido, Iván Henry, Mike Chodera, John D. Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Tri-Institutional PhD Program in Computational Biology and Medicine Weill Cornell Medical College Cornell University New YorkNY10065 United States Physiology Biophysics and System Biology Ph.D. Program Weill Cornell Medical College Cornell University New YorkNY10065 United States Program in Creative Writing Division of Humanities and Arts The City College of New York The City University of New York New YorkNY10031 United States

Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecular modeling and drug discovery, from rapid virtual screening to detailed free energy calculations. Traditionally, MM potentials have relied on human-curated, inflexible, and poorly extensible discrete chemical perception rules (atom types) for applying parameters to small molecules or biopolymers, making it difficult to optimize both types and parameters to fit quantum chemical or physical property data. Here, we propose an alternative approach that uses graph neural networks to perceive chemical environments, producing continuous atom embeddings from which valence and nonbonded parameters can be predicted using invariance-preserving layers. Since all stages are built from smooth neural functions, the entire process-spanning chemical perception to parameter assignment-is modular and end-to-end differentiable with respect to model parameters, allowing new force fields to be easily constructed, extended, and applied to arbitrary molecules. We show that this approach is not only sufficiently expressive to reproduce legacy atom types, but that it can learn to accurately reproduce and extend existing molecular mechanics force fields. Trained with arbitrary loss functions, it can construct entirely new force fields self-consistently applicable to both biopolymers and small molecules directly from quantum chemical calculations, with superior fidelity than traditional atom or parameter typing schemes. When adapted to simultaneously fit partial charge models, espaloma delivers high-quality partial atomic charges orders of magnitude faster than current best-practices with little inaccuracy. When trained on the same quantum chemical small molecule dataset used to parameterize the Open Force Field ("Parsley") openff-1.2.0 small molecule force field augmented with a peptide dataset

关键词： Atoms

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Acquired Mutations within the JAK2 Kinase Domain Confer Resistance to JAK Inhibitors in an in Vitro model of a High-Risk Acute Lymphoblastic Leukemia

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BLOOD 2020年 136卷 5-6页

作者： Downes, Charlotte E. J. McClure, Barbara J. Rehn, Jacqueline Breen, James Bruning, John B. Yeung, David T. White, Deborah L. Cancer Program Precision Medicine Theme South Australian Health and Medical Research Institute (SAHMRI) Adelaide Australia School of Biological Sciences The University of Adelaide Adelaide Australia Adelaide Medical School The University of Adelaide Adelaide Australia Computational and Systems Biology Program Precision Medicine Theme South Australian Health and Medical Research Institute (SAHMRI) Adelaide Australia Robinson Research Institute The University of Adelaide Adelaide Australia Precision Medicine Theme South Australian Health and Medical Research Institute Adelaide Australia Department of Haematology Royal Adelaide Hospital and SA Pathology Adelaide Australia Australian and New Zealand Childrens Haematology/Oncology Group (ANZCHOG) Melbourne Australia

Introduction Philadelphia chromosome-like acute lymphoblastic leukemia (Ph-like ALL) is a high-risk subtype of ALL associated with high relapse rates and poor survival. Rearrangements of Janus kinase 2 ( JAK2 r) are present in approximately 5% and 14% of pediatric and young adult Ph-like ALL cases respectively. The resultant JAK2 gene fusions drive leukemogenesis through constitutive activation of the JAK/STAT signaling pathway and are associated with very poor outcomes in patients with Ph-like ALL. All JAK inhibitors in clinical development are type I inhibitors, which bind in the ATP-binding site of JAK2. A phase II clinical trial is currently assessing the only FDA-approved JAK1/2 inhibitor, ruxolitinib in high-risk B-cell ALL cases harboring JAK2 alterations. The development of treatment resistance to targeted inhibitors in other diseases is well documented and often results in disease relapse. Elucidating mechanisms of ruxolitinib resistance in JAK2 r ALL will inform approaches to monitor the emergence of resistance in ongoing clinical trials and enable the development of therapeutic strategies to overcome or avert resistance. Methods JAK2 r B-ALL was modelled in the pro-B cell line, Ba/F3, by expressing the high-risk B-ALL fusion, ATF7IP-JAK2 . Ruxolitinib resistance in three independent ATF7IP-JAK2 Ba/F3 cell lines was achieved following dose escalation to a clinically relevant dose of 1 μM ruxolitinib. Sanger sequencing of the RT-PCR amplified JAK2 fusion revealed each resistant line had acquired a different mutation within the JAK2 kinase domain. Therapeutic sensitives were assessed by staining with Fixable Aqua Dead Cell Stain (Invitrogen) and Annexin V, and analysis by flow cytometry. Alterations in signaling pathways were determined using phosphoflow cytometry and western blot analysis. computational modelling of acquired JAK2 mutations and subsequent influence on ruxolitinib binding was performed using ICM-Pro (Molsoft L.C.C.). Results In addition to th

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