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Investigation of RNA-RNA Interactions Using the RISE Database

Current Protocols in bioinformatics 2018年第1期64卷 e58/1-e58/1页

作者： Ju, Yanyan Gong, Jing Yang, Yucheng T. Zhang, Qiangfeng Cliff MOE Key Laboratory of Bioinformatics Beijing Advanced Innovation Center for Structural Biology Center for Synthetic and Systems Biology Tsinghua-Peking Joint Center for Life Sciences School of Life Sciences Tsinghua University Beijing China Department of Molecular Biophysics and Biochemistry Yale University New Haven CT United States Program in Computational Biology and Bioinformatics Yale University New Haven CT United States

RNA-RNA interactions (RRIs) are essential to understanding the regulatory mechanisms of RNAs. Mapping RRIs in vivo in a transcriptome-wide manner remained challenging until the recent development of several sequencing-based technologies. However, RRIs generated from large-scale studies had not been systematically collected and analyzed before. This article introduces RISE, a database of the RNA Interactome from Sequencing Experiments. RISE provides a comprehensive collection of RRIs in human, mouse, and yeast, derived from transcriptome-wide sequencing experiments, as well as targeted sequencing studies and other public databases/datasets. To facilitate better understanding of the biological roles of these RRIs, RISE also offers rich functional annotations involving RNAs, and an interactive interface to explore the analysis results. Here, we provide a brief description of the RISE website and a step-by-step protocol for using RISE to study RRIs. © 2018 by John Wiley & Sons, Inc. © 2018 John Wiley & Sons, Inc.

关键词： interaction RISE RNA RNA structure RNA-RNA sequencing experiments

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Cell2Sentence: teaching large language models the language of biology 24

Cell2Sentence: teaching large language models the language o...

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Proceedings of the 41st International Conference on Machine Learning

作者： Daniel Levine Syed Asad Rizvi Sacha Lévy Nazreen Pallikkavaliyaveetil David Zhang Xingyu Chen Sina Ghadermarzi Ruiming Wu Zihe Zheng Ivan Vrkic Anna Zhong Daphne Raskin Insu Han Antonio Henrique De Oliveira Fonseca Josue Ortega Caro Amin Karbasi Rahul M. Dhodapkar David Van Dijk Department of Computer Science Yale University New Haven CT School of Engineering Applied Science University of Pennsylvania Philadelphia PA School of Computer and Communication Sciences Swiss Federal Institute of Technology Lausanne Lausanne Switzerland Department of Computer Science Yale University New Haven CT and Department of Neuroscience Yale School of Medicine New Haven CT Department of Computer Science Yale University New Haven CT and Department of Neuroscience Yale School of Medicine New Haven CT and Wu Tsai Institute Yale University New Haven CT Google and Yale Institute for Foundations of Data Science New Haven CT and Department of Computer Science Yale University New Haven CT and Yale School of Engineering and Applied Science New Haven CT Roski Eye Institute University of Southern California Los Angeles CA and Department of Internal Medicine (Cardiology) Yale School of Medicine New Haven CT Department of Computer Science Yale University New Haven CT and Yale Institute for Foundations of Data Science New Haven CT and Wu Tsai Institute Yale University New Haven CT and Cardiovascular Research Center Yale School of Medicine New Haven CT and Interdepartmental Program in Computational Biology & Bioinformatics Yale University New Haven CT and Department of Internal Medicine (Cardiology) Yale School of Medicine New Haven CT

We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into "cell sentences," C2S bridges the gap between natural language processing and biology. We demonstrate cell sentences enable the fine-tuning of language models for diverse tasks in biology, including cell generation, complex cell-type annotation, and direct data-driven text generation. Our experiments reveal that GPT-2, when fine-tuned with C2S, can generate biologically valid cells based on cell type inputs, and accurately predict cell types from cell sentences. This illustrates that language models, through C2S fine-tuning, can acquire a significant understanding of single-cell biology while maintaining robust text generation capabilities. C2S offers a flexible, accessible framework to integrate natural language processing with transcriptomics, utilizing existing models and libraries for a wide range of biological applications.

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Mechanistic Insights into Enhanced Reactivation of Organophosphate-Inhibited Enzymes by Methyl-Substituted 2-Pralidoxime Analogs

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The journal of physical chemistry. B 2025年第23期129卷 2025 May 27页

作者： Phiphob Naweephattana Nalinee Kongkaew Panida Surawatanawong Nawee Kungwan Yin Fang Peter Wolschann Phornphimon Maitarad Kowit Hengphasatporn Yasuteru Shigeta Thanyada Rungrotmongkol Alisa S Vangnai Center of Excellence in Biocatalyst and Sustainable Biotechnology Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand. Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok 10330 Thailand. Department of Chemistry and Center of Excellence for Innovation in Chemistry Faculty of Science Mahidol University Bangkok 10400 Thailand. Department of Chemistry Faculty of Science Chiang Mai University Chiang Mai 50200 Thailand. Research Center for Translational Medicine Shanghai East Hospital Affiliated to Tongji University Shanghai 200120 China. Institute of Theoretical Chemistry University of Vienna Vienna 1010 Austria. Research Center of Nano Science and Technology Department of chemistry College of Science Shanghai University Shanghai 200444 China. Center for Computational Sciences University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba 305-8577 Japan.

Organophosphate (OP) compounds, such as paraoxon (POX), inhibit enzymes critical for neurotransmission, causing severe neurotoxic effects. Pralidoxime (2-pyridine aldoxime methyl chloride) or 2-PAM is commonly employed to reverse this inhibition, but its reactivation efficiency is limited. This study computationally explores the reactivation mechanisms of 2-PAM and its methyl-substituted analogs, 4-methyl-2-PAM (4-Met-2-PAM), and 4,6-dimethyl-2-PAM (4,6-Dimet-2-PAM). The reactivation process involves several key steps, such as hydrogen transfer and nucleophilic substitution (). Introducing methyl groups at positions 4 and 6 increases the negative charge on the oxime oxygen, improving nucleophilicity and reactivity. Both 4-Met-2-PAM and 4,6-Dimet-2-PAM show better reactivity than 2-PAM, with 4,6-Dimet-2-PAM demonstrating the greatest improvement. This enhanced reactivity shifts the rate-determining step from nucleophilic substitution to the initial hydrogen transfer. These findings offer valuable insights for designing more effective oxime-based antidotes for organophosphate poisoning.

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An Open-Source Knowledge Graph Ecosystem for the Life Sciences

arXiv

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arXiv 2023年

作者： Callahan, Tiffany J. Tripodi, Ignacio J. Stefanski, Adrianne L. Cappelletti, Luca Taneja, Sanya B. Wyrwa, Jordan M. Casiraghi, Elena Matentzoglu, Nicolas A. Reese, Justin Silverstein, Jonathan C. Hoyt, Charles Tapley Boyce, Richard D. Malec, Scott A. Unni, Deepak R. Joachimiak, Marcin P. Robinson, Peter N. Mungall, Christopher J. Cavalleri, Emanuele Fontana, Tommaso Valentini, Giorgio Mesiti, Marco Gillenwater, Lucas A. Santangelo, Brook Vasilevsky, Nicole A. Hoehndorf, Robert Bennett, Tellen D. Ryan, Patrick B. Hripcsak, George Kahn, Michael G. Bada, Michael Baumgartner, William A. Hunter, Lawrence E. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Computer Science Department Interdisciplinary Quantitative Biology University of Colorado Boulder BoulderCO80301 United States AnacletoLab Computer Science Department University of Milan 20122 Italy Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Semanticly Ltd Athens Greece Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15206 United States Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Division of Translational Informatics University of New Mexico School of Medicine AlbuquerqueNM87131 United States SIB Swiss Institute of Bioinformatics Basel Switzerland Berlin Institute of Health at Charité-Universitatsmedizin Berlin10117 Germany ELLIS European Laboratory for Learning and Intelligent Systems Germany Department of Biomedical Informatics University of Colorado School of Medicine AuroraCO80045 United States Data Collaboration Center Critical Path Institute 1840 E River Rd. Suite 100 TucsonAZ85718 United States Computer Electrical and Mathematical Sciences & Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Janssen Research and Development RaritanNJ08869 United States Division of General Internal Medicine University of Colorado School of Medicine A

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability. © 2023, CC BY.

关键词： Ecosystems

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IBEX: An open and extensible method for high content multiplex imaging of diverse tissues

arXiv

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arXiv 2021年

作者： Radtke, Andrea J. Chu, Colin J. Yaniv, Ziv Yao, Li Marr, James Beuschel, Rebecca T. Ichise, Hiroshi Gola, Anita Kabat, Juraj Lowekamp, Bradley Speranza, Emily Croteau, Joshua Thakur, Nishant Jonigk, Danny Davis, Jeremy Hernandez, Jonathan M. Germain, Ronald N. Center for Advanced Tissue Imaging Laboratory of Immune System Biology NIAID NIH BethesdaMD United States Lymphocyte Biology Section Laboratory of Immune System Biology NIAID NIH BethesdaMD United States Translational Health Sciences Bristol Medical School University of Bristol United Kingdom Bioinformatics and Computational Bioscience Branch NIAID NIH BethesdaMD United States Howard Hughes Medical Institute Chevy ChaseMD United States Leica Microsystems Inc. Wetzlar Germany Howard Hughes Medical Institute Robin Neustein Laboratory of Mammalian Cell Biology and Development The Rockefeller University New YorkNY United States Biological Imaging Section Research Technologies Branch NIAID NIH BethesdaMD United States Innate Immunity and Pathogenesis Section Laboratory of Virology NIAID NIH HamiltonMT United States Department of Business Development BioLegend Inc. San DiegoCA United States Institute of Pathology Hannover Medical School Hannover Germany Surgical Oncology Program Metastasis Biology Section Center for Cancer Research National Cancer Institute NIH BethesdaMD United States

High content imaging is needed to catalogue the variety of cellular phenotypes and multi-cellular ecosystems present in metazoan tissues. We recently developed Iterative Bleaching Extends multi-pleXity (IBEX), an iterative immunolabeling and chemical bleaching method that enables multiplexed imaging (>65 parameters) in diverse tissues, including human organs relevant for international consortia efforts. IBEX is compatible with over 250 commercially available antibodies, 16 unique fluorophores, and can be easily adopted to different imaging platforms using slides and non-proprietary imaging chambers. The overall protocol consists of iterative cycles of antibody labelling, imaging, and chemical bleaching that can be completed at relatively low cost in 2-5 days by biologists with basic laboratory skills. To support widespread adoption, we provide extensive details on tissue processing, curated lists of validated antibodies, and tissue-specific panels for multiplex imaging. Furthermore, instructions are included on how to automate the method using competitively priced instruments and reagents. Finally, we present a software solution for image alignment that can be executed by individuals without programming experience using open source software and freeware. In summary, IBEX is an open and versatile method that can be readily implemented by academic laboratories and scaled to achieve high content mapping of diverse tissues in support of a Human Reference Atlas or other such applications. © 2021, CC BY.

关键词： Bleaching

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Expanding the computational Toolbox for Single-Particle Cryo-Electron Microscopy

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Microscopy and Microanalysis 2019年第S2期25卷 2686-2687页

作者： Alberto Bartesaghi Department of Computer Science Trinity College of Arts & Sciences Department of Biochemistry Duke University School of Medicine Department of Electrical and Computer Engineering Pratt School of Engineering Duke Institute for Brain Sciences Computational Biology and Bioinformatics Program Duke University Durham NC USA.

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Distinct adipose progenitor cells emerging with age drive active adipogenesis

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Science (New York, N.Y.) 2025年第6745期388卷 eadj0430页

作者： Wang, Guan Li, Gaoyan Song, Anying Zhao, Yutian Yu, Jiayu Wang, Yifan Dai, Wenting Salas, Martha Qin, Hanjun Medrano, Leonard Dow, Joan Li, Aimin Armstrong, Brian Fueger, Patrick T. Yu, Hua Zhu, Yi Shao, Mengle Wu, Xiwei Jiang, Lei Campisi, Judith Yang, Xia Wang, Qiong A. Department of Molecular and Cellular Endocrinology Arthur Riggs Diabetes and Metabolism Research Institute City of Hope Medical Center Duarte CA USA Department of Integrative Biology and Physiology University of California Los Angeles CA United States Light Microscopy Core City of Hope Medical Center Duarte CA USA The Integrative Genomics Core City of Hope Medical Center Duarte CA USA Division of Developmental and Translational Diabetes and Endocrinology Research City of Hope Medical Center Duarte CA USA Comprehensive Metabolic Phenotyping Core City of Hope Medical Center Duarte CA USA Pathology Core of Shared Resources City of Hope Medical Center Duarte CA USA Comprehensive Cancer Center Beckman Research Institute City of Hope Medical Center Duarte CA USA Children's Nutrition Research Center Department of Pediatrics Baylor College of Medicine Houston TX United States Key Laboratory of Immune Response and Immunotherapy Shanghai Institute of Immunity and Infection Chinese Academy of Sciences Shanghai China Buck Institute for Research on Aging Novato CA United States Bioinformatics Interdepartmental Program University of California Los Angeles CA United States Institute for Quantitative and Computational Biosciences University of California Los Angeles CA United States Molecular and Medical Pharmacology University of California Los Angeles CA United States

Starting at middle age, adults often suffer from visceral adiposity and associated adverse metabolic disorders. Lineage tracing in mice revealed that adipose progenitor cells (APCs) in visceral fat undergo extensive adipogenesis during middle age. Thus, despite the low turnover rate of adipocytes in young adults, adipogenesis is unlocked during middle age. Transplantations quantitatively showed that APCs in middle-aged mice exhibited high adipogenic capacity cell-autonomously. Single-cell RNA sequencing identified a distinct APC population, the committed preadipocyte, age-enriched (CP-A), emerging at this age. CP-As demonstrated elevated proliferation and adipogenesis activity. Pharmacological and genetic manipulations indicated that leukemia inhibitory factor receptor signaling was indispensable for CP-A adipogenesis and visceral fat expansion. These findings uncover a fundamental mechanism of age-dependent adipose remodeling, offering critical insights into age-related metabolic diseases.

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Tegavivint triggers TECR-dependent nonapoptotic cancer cell death

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Nature chemical biology 2025年 2025 May 26页

作者： Logan Leak Ziwei Wang Alby J Joseph Brianna Johnson Alyssa A Chan Cassandra M Decosto Leslie Magtanong Pin-Joe Ko Weaverly Colleen Lee Joan Ritho Sophia Manukian Alec Millner Shweta Chitkara Jennifer J Salinas Rachid Skouta Matthew G Rees Melissa M Ronan Jennifer A Roth Chad L Myers Jason Moffat Charles Boone Steven J Bensinger David A Nathanson G Ekin Atilla-Gokcumen Everett J Moding Scott J Dixon Department of Biology Stanford University Stanford CA USA. Department of Radiation Oncology Stanford University School of Medicine Stanford CA USA. Department of Chemistry University at Buffalo The State University of New York Buffalo NY USA. Department of Molecular and Medical Pharmacology University of California Los Angeles Los Angeles CA USA. Department of Chemistry University of Massachusetts Amherst Amherst MA USA. Department of Biology University of Massachusetts Amherst Amherst MA USA. Broad Institute of MIT and Harvard Cambridge MA USA. Department of Computer Science and Engineering Bioinformatics and Computational Biology Graduate Program University of Minnesota-Twin Cities Minneapolis MN USA. Program in Genetics & Genome Biology The Hospital for Sick Children Toronto Ontario Canada. Department of Molecular Genetics University of Toronto Toronto Ontario Canada. Donnelly Centre University of Toronto Toronto Ontario Canada. RIKEN Center for Sustainable Resource Science Saitama Japan. Department of Microbiology Immunology and Molecular Genetics University of California Los Angeles Los Angeles CA USA. UCLA Lipidomics Laboratory University of California Los Angeles Los Angeles CA USA. Jonsson Comprehensive Cancer Center David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA. Department of Biology Stanford University Stanford CA USA. sjdixon@stanford.edu.

Small molecules that induce nonapoptotic cell death are of fundamental mechanistic interest and may be useful to treat certain cancers. Here we report that tegavivint, a drug candidate undergoing human clinical trials, can activate a unique mechanism of nonapoptotic cell death in sarcomas and other cancer cells. This lethal mechanism is distinct from ferroptosis, necroptosis and pyroptosis and requires the lipid metabolic enzyme trans-2,3-enoyl-CoA reductase (TECR). TECR is canonically involved in the synthesis of very-long-chain fatty acids but appears to promote nonapoptotic cell death in response to CIL56 and tegavivint via the synthesis of the saturated long-chain fatty acid palmitate. These findings outline a lipid-dependent nonapoptotic cell death mechanism that can be induced by a drug candidate currently being tested in humans.

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Author Correction: Single-nucleus genomics in outbred rats with divergent cocaine addiction-like behaviors reveals changes in amygdala GABAergic inhibition

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Nature neuroscience 2023年第11期26卷 2035页

作者： Jessica L Zhou Giordano de Guglielmo Aaron J Ho Marsida Kallupi Narayan Pokhrel Hai-Ri Li Apurva S Chitre Daniel Munro Pejman Mohammadi Lieselot L G Carrette Olivier George Abraham A Palmer Graham McVicker Francesca Telese Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA USA. Integrative Biology Laboratory Salk Institute for Biological Studies La Jolla CA USA. Department of Psychiatry University of California San Diego La Jolla CA USA. Department of Medicine University of California San Diego La Jolla CA USA. Department of Integrative Structural and Computational Biology The Scripps Research Institute La Jolla CA USA. Center for Immunity and Immunotherapies Seattle Children's Research Institute Seattle WA USA. Department of Pediatrics University of Washington School of Medicine Seattle WA USA. Department of Genome Sciences University of Washington Seattle WA USA. Institute for Genomic Medicine University of California San Diego La Jolla CA USA. Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA USA. gmcvicker@salk.edu. Integrative Biology Laboratory Salk Institute for Biological Studies La Jolla CA USA. gmcvicker@salk.edu. Department of Psychiatry University of California San Diego La Jolla CA USA. ftelese@health.ucsd.edu. Department of Medicine University of California San Diego La Jolla CA USA. ftelese@health.ucsd.edu.

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Serotonin transporter inhibits antitumor immunity through regulating the intratumoral serotonin axis

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Cell 2025年

作者： Li, Bo Elsten-Brown, James Li, Miao Zhu, Enbo Li, Zhe Chen, Yuning Kang, Elliot Ma, Feiyang Chiang, Jennifer Li, Yan-Ruide Zhu, Yichen Huang, Jie Fung, Audrey Scarborough, Quentin Cadd, Robin Zhou, Jin J. Chin, Arnold I. Pellegrini, Matteo Yang, Lili Department of Microbiology Immunology and Molecular Genetics University of California Los Angeles Los Angeles 90095 CA United States Department of Materials Science and Engineering University of California Los Angeles Los Angeles 90095 CA United States Department of Molecular Cell and Developmental Biology University of California Los Angeles Los Angeles 90095 CA United States Department of Biostatistics Fielding School of Public Health University of California Los Angeles Los Angeles CA United States Department of Urology University of California Los Angeles Los Angeles 90095 CA United States Eli & Edythe Broad Center of Regenerative Medicine and Stem Cell Research University of California Los Angeles Los Angeles 90095 CA United States Jonsson Comprehensive Cancer Center David Geffen School of Medicine University of California Los Angeles Los Angeles 90095 CA United States Bioinformatics Interdepartmental Program University of California Los Angeles Los Angeles 90095 CA United States Institute for Quantitative and Computational Biosciences-The Collaboratory University of California Los Angeles Los Angeles 90095 CA United States Department of Bioengineering University of California Los Angeles Los Angeles 90095 CA United States Goodman-Luskin Microbiome Center University of California Los Angeles Los Angeles 90095 CA United States Molecular Biology Institute University of California Los Angeles Los Angeles 90095 CA United States Parker Institute for Cancer Immunotherapy University of California Los Angeles Los Angeles 90095 CA United States

Identifying additional immune checkpoints hindering antitumor T cell responses is key to the development of next-generation cancer immunotherapies. Here, we report the induction of serotonin transporter (SERT), a regulator of serotonin levels and physiological functions in the brain and peripheral tissues, in tumor-infiltrating CD8 T cells. Inhibition of SERT using selective serotonin reuptake inhibitors (SSRIs), the most widely prescribed antidepressants, significantly suppressed tumor growth and enhanced T cell antitumor immunity in various mouse syngeneic and human xenograft tumor models. Importantly, SSRI treatment exhibited significant therapeutic synergy with programmed cell death protein 1 (PD-1) blockade, and clinical data correlation studies negatively associated intratumoral SERT expression with patient survival in a range of cancers. Mechanistically, SERT functions as a negative-feedback regulator inhibiting CD8 T cell reactivities by depleting intratumoral T cell-autocrine serotonin. These findings highlight the significance of the intratumoral serotonin axis and identify SERT as an immune checkpoint, positioning SSRIs as promising candidates for cancer immunotherapy. © 2025 The Authors

关键词： antidepressants cancer immunotherapy immune checkpoint intratumoral serotonin axis PD-1 blockade serotonin serotonin transporter SSRIs T cell antitumor immunity tumor microenvironment

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