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Sex differences in proteomics of cardiovascular disease – Results from the Yale-CMD registry

IJC Heart and Vasculature 2025年 58卷 101667-101667页

作者： Liu, Yihan Wang, Zuoheng Collins, Sean P. Testani, Jeffery Safdar, Basmah Program in Computational Biology & Biomedical informatics Yale University New Haven CT United States Department of Biostatistics Yale University School of Public Health New Haven CT United States Department of Biomedical Informatics & Data Science Yale University School of Medicine New Haven CT United States Department of Emergency Medicine Vanderbilt University Medical Center Veterans Affairs Tennessee Valley Healthcare System Geriatric Research Education and Clinical Center (GRECC) Nashville TN United States Section of Cardiovascular Medicine Department of Internal Medicine Yale University School of Medicine New Haven CT United States Department of Emergency Medicine Yale University School of Medicine New Haven CT United States

Aims This study assessed sex-specific proteomic profiles by cardiovascular disease (CVD) phenotype (coronary artery disease [CAD] vs coronary microvascular dysfunction [CMD]) and describe their role in sex-specific pathways. Methods: In a secondary biobank analysis of the Yale-CMD registry, adults with ischemic symptoms who underwent cardiac positron emission test/computed tomography were categorized as a) controls (normal coronary flow reserve (CFR) > 2 without perfusion defect or coronary calcification), b) having CMD (CFR < 2 without defect or calcification), or c) having CAD (known CAD or new perfusion defect). Using proximity extension assays (Olink® Explore 3072), we examined 2944 proteins. Differential protein expression was assessed using linear regression models, adjusting for age, race, body mass index, diabetes, dyslipidemia, hypertension, or smoking. Results: Of 190 patients, 91 provided blood samples (mean age, 56 years;66 %, females;48 %, controls;24 %, CAD;27 %, CMD). Among controls, 15 proteins showed sex differences (5 proteins upregulated in females, 10 in males;false discovery rate [FDR < 0.05]). Upregulated in CAD patients were FSHB in females and INSL3 and EDDM3B in males (FDR < 0.05). Among CMD patients, SCGB3A1 and HGFAC were higher in females;INSL3, SPINT3, EDDM3B, and KLK3 were higher in males (FDR < 0.05). Per pathway analysis, females showed upregulation of immune pathways in CAD and lipid and glucose metabolism pathways in CMD. Males showed upregulated endothelial regulation of blood flow in CAD and increased angiogenesis in CMD. Conclusions: Sex differences exist in the proteomic profiles of CAD and CMD patients, highlighting a need for precision medicine. © 2025

关键词： Coronary artery disease Coronary microvascular dysfunction Olink Precision medicine Proteomics Sex differences

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Mapping Emergency Medicine Data to the Observational Medical Outcomes Partnership Common Data Model: A Gap Analysis of the American College of Emergency Physicians Clinical Emergency Data Registry

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JACEP Open 2025年第1期6卷 100016-100016页

作者： Cohen, Inessa Diao, Zihan Goyal, Pawan Gupta, Aarti Hawk, Kathryn Malcom, Bill Malicki, Caitlin Sharma, Dhruv Sweeney, Brian Weiner, Scott G. Venkatesh, Arjun Taylor, R. Andrew Department of Emergency Medicine Yale School of Medicine New Haven CT United States Section for Biomedical Informatics and Data Science Yale University School of Medicine New Haven CT United States Program of Computational Biology and Bioinformatics Yale University New Haven CT United States American College of Emergency Physicians Washington DC United States Department of Emergency Medicine Brigham and Women's Hospital Boston MA United States Center for Outcomes Research and Evaluation (CORE) Section of Cardiovascular Medicine Yale School of Medicine New Haven CT United States

Objectives: This study aims to conduct a gap analysis to determine the feasibility of mapping electronic health record data from the Clinical Emergency Data Registry (CEDR) to the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM). Methods: We employed a structured approach using a custom-built comparison matrix. This matrix facilitated the alignment of CEDR data fields with the corresponding elements in the OMOP-CDM schema. Each field was evaluated for compatibility, with categorization into 3 distinct types: direct matches, fields requiring transformation, and fields with no OMOP-CDM equivalent. The mapping process was informed by consultations with the Observational Health Data Sciences and informatics community forums and was guided by existing documentation and best practices in data harmonization. We performed descriptive analyses, quantifying the extent of direct matches and identifying the specific transformations needed for each CEDR-CDM field to ensure compliance with the OMOP-CDM model. Results: Our analysis indicates a high degree of compatibility between CEDR and OMOP, with over 90% (244/269) of CEDR fields being successfully mapped. Specifically, 173 fields had direct matches, whereas 71 required transformations. Challenges identified include addressing fields unique to CEDR with no OMOP-CDM equivalent and managing the transformations required for proper alignment. Conclusion: The OMOP-CDM presents a promising framework for standardizing emergency medicine data, thereby enhancing future query automation, analytics, and cross-institutional collaboration. Despite the potential challenges in capturing unique CEDR fields and addressing necessary transformations, most emergency department data can be standardized within the OMOP-CDM, fostering broader insights and applications in research and public health. © 2024 The Author(s)

关键词： databases electronic health records emergency service informatics opioid/adverse effects quality of health care registries

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Protein Folding as a Jamming Transition

PRX Life

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PRX Life 2025年第1期3卷 013018-013018页

作者： Alex T. Grigas Zhuoyi Liu Jack A. Logan Mark D. Shattuck Corey S. O'Hern Graduate Program in Computational Biology and Biomedical Informatics Yale University New Haven Connecticut 06520 USA Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven Connecticut 06520 USA Department of Mechanical Engineering Yale University New Haven Connecticut 06520 USA Benjamin Levich Institute and Physics Department The City College of New York New York New York 10031 USA Department of Physics Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA

Proteins fold to a specific functional conformation with a densely packed core that controls their stability. Despite their importance, we lack a quantitative explanation for why all protein cores, regardless of their overall fold, possess the same average packing fraction 〈ϕ〉≈0.55. However, important developments in the physics of jamming in particulate systems can shed light on the packing of protein cores. Here, we extend the framework of jamming to describe core packing in collapsed polymers, as well as in all-atom models of folded proteins. First, we show in a spherical bead-spring polymer model (with and without bond-angle constraints) that as the hydrophobic interactions increase relative to thermal fluctuations, a jamming-like transition occurs when the core packing fraction exceeds ϕc with the same power-law scaling behavior for the potential energy Vr, excess contact number ΔN, and characteristic frequency of the vibrational density of states ω* versus Δϕ=ϕ−ϕc as that for jammed particulate systems. Then, we develop an all-atom model for proteins and find that, above ϕc∼0.55, protein cores undergo a jamming-like transition, but with anomalous power-law scaling for Vr, ΔN, and ω* versus Δϕ. The all-atom protein model remains close to the native protein structure during jamming and accurately refolds from partially unfolded states.

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Multimodal AI predicts clinical outcomes of drug combinations from preclinical data

arXiv

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arXiv 2025年

作者： Huang, Yepeng Su, Xiaorui Ullanat, Varun Liang, Ivy Clegg, Lindsay Olabode, Damilola Ho, Nicholas John, Bino Gibbs, Megan Zitnik, Marinka Department of Biomedical Informatics Harvard Medical School BostonMA United States Program in Biological and Biomedical Sciences Harvard Medical School BostonMA United States Harvard College CambridgeMA United States Clinical Pharmacology and Quantitative Pharmacology Clinical Pharmacology & Safety Sciences R&D AstraZeneca GaithersburgMD United States Clinical Pharmacology and Quantitative Pharmacology Clinical Pharmacology & Safety Sciences R&D AstraZeneca WalthamMA United States Program in Computational Biology Carnegie Mellon University PittsburghPA United States Imaging and Data Analytics Clinical Pharmacology & Safety Sciences R&D AstraZeneca WalthamMA United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University AllstonMA United States Broad Institute of MIT and Harvard CambridgeMA United States Harvard Data Science Initiative CambridgeMA United States

Predicting clinical outcomes from preclinical data is essential for identifying safe and effective drug combinations, reducing late-stage clinical failures, and accelerating the development of precision therapies. Current models rely on structural or target-based features to identify high-efficacy, low-toxicity drug combinations. However, these approaches fail to incorporate the multimodal data necessary for accurate, clinically-relevant predictions. Here, we introduce MADRIGAL, a multimodal AI model that learns from structural, pathway, cell viability, and transcriptomic data to predict drug combination effects across 953 clinical outcomes and 21,842 compounds, including combinations of approved drugs and novel compounds in development. MADRIGAL uses a transformer bottleneck module to unify preclinical drug data modalities while handling missing data during training and inference—a major challenge in multimodal learning. It outperforms single-modality methods and state-of-the-art models in predicting adverse drug interactions. MADRIGAL performs virtual screening of anticancer drug combinations and supports polypharmacy management for type II diabetes and metabolic dysfunction-associated steatohepatitis (MASH). It identifies transporter-mediated drug interactions. MADRIGAL predicts resmetirom, the first and only FDA-approved drug for MASH, among therapies with the most favorable safety profile. It supports personalized cancer therapy by integrating genomic profiles from cancer patients. Using primary acute myeloid leukemia samples and patient-derived xenograft models, it predicts the efficacy of personalized drug combinations. Integrating MADRIGAL with a large language model allows users to describe clinical outcomes in natural language, improving safety assessment by identifying potential adverse interactions and toxicity risks. MADRIGAL provides a multimodal approach for designing combination therapies with improved predictive accuracy and clinical relevance. Copy

关键词： Diseases

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BPS2025 - Biophysical approaches to study viral proteins at the cellular membranes

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Biophysical Journal 2025年第3期124卷 547a-547a页

作者： Elka R. Georgieva Md Majharul Islam Adeyemi Ogunbowale Saman Majeed Lan Dang Peter P. Borbat Olamide Ishola Elaheh Hadadianpour Arvin S. Delkhosh Juan C. Rueda Amador Steven Ludtke Department of Chemistry and Biochemistry Texas Tech University Lubbock TX USA Graduate Program in Quantitative and Computational Biosciences Graduate School of Biomedical Sciences Baylor College of Medicine Houston TX USA Department of Chemistry and Chemical Biology Cornell University Ithaca NY USA Department of Biochemistry and Molecular Pharmacology Baylor College of Medicine Houston TX USA

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biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Uncertainty abounds, what now?

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Science 2025年第6740期387卷 1261页

作者： Dov Greenbaum Mark Gerstein The reviewer is at the Zvi Meitar Institute for Legal Implications of Emerging Technologies The reviewer is at the Harry Radzyner Law School The reviewer is at the Dina Recanati School of Medicine Reichman University Herzliya Israel The reviewer is at the Department of Biomedical Informatics and Data Science The reviewer is at the Program in Computational Biology and Bioinformatics The reviewer is at the Department of Computer Science Yale University New Haven CT USA.

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Semi-automated literature mining to identify putative biomarkers of disease from multiple biofluids

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Journal of Clinical Bioinformatics 2014年第1期4卷 1-9页

作者： Jordan, Rick Visweswaran, Shyam Gopalakrishnan, Vanathi Department of Biomedical Informatics University of Pittsburgh Pittsburgh PA United States Intelligent Systems Program University of Pittsburgh Pittsburgh PA United States Department of Computational and Systems Biology University of Pittsburgh Pittsburgh PA United States

Background: computational methods for mining of biomedical literature can be useful in augmenting manual searches of the literature using keywords for disease-specific biomarker discovery from biofluids. In this work, we develop and apply a semi-automated literature mining method to mine abstracts obtained from PubMed to discover putative biomarkers of breast and lung cancers in specific biofluids. Methodology: A positive set of abstracts was defined by the terms 'breast cancer' and 'lung cancer' in conjunction with 14 separate 'biofluids' (bile, blood, breastmilk, cerebrospinal fluid, mucus, plasma, saliva, semen, serum, synovial fluid, stool, sweat, tears, and urine), while a negative set of abstracts was defined by the terms '(biofluid) NOT breast cancer' or '(biofluid) NOT lung cancer.' More than 5.3 million total abstracts were obtained from PubMed and examined for biomarker-disease-biofluid associations (34,296 positive and 2,653,396 negative for breast cancer;28,355 positive and 2,595,034 negative for lung cancer). Biological entities such as genes and proteins were tagged using ABNER, and processed using Python scripts to produce a list of putative biomarkers. Z-scores were calculated, ranked, and used to determine significance of putative biomarkers found. Manual verification of relevant abstracts was performed to assess our method's performance. Results: Biofluid-specific markers were identified from the literature, assigned relevance scores based on frequency of occurrence, and validated using known biomarker lists and/or databases for lung and breast cancer [NCBI's On-line Mendelian Inheritance in Man (OMIM), Cancer Gene annotation server for cancer genomics (CAGE), NCBI's Genes & Disease, NCI's Early Detection Research Network (EDRN), and others]. The specificity of each marker for a given biofluid was calculated, and the performance of our semi-automated literature mining method assessed for breast and lung cancer. Conclusions: We developed a semi-auto

关键词： Biofluid Biomarker Breast cancer Literature mining Lung cancer Text mining

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Signaling Pathways Differentially Affect RNA Polymerase II Initiation, Pausing, and Elongation Rate in Cells (vol 50, pg 212, 2013)

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MOLECULAR CELL 2013年第5期50卷 778-778页

作者： Danko, Charles G. Hah, Nasun Luo, Xin Martins, Andre L. Core, Leighton Lis, John T. Siepel, Adam Kraus, W. Lee Department of Molecular Biology and Genetics Cornell University Ithaca NY 14853 USA Department of Biological Statistics and Computational Biology Cornell University Ithaca NY 14853 USA Graduate Field of Biochemistry Molecular and Cell Biology Cornell University Ithaca NY 14853 USA Graduate Field of Computational Biology Cornell University Ithaca NY 14853 USA Graduate School of Biomedical Sciences Program in Genetics and Development University of Texas Southwestern Medical Center Dallas TX 75390 USA Cecil H. and Ida Green Center for Reproductive Biology Sciences and Division of Basic Reproductive Biology Research Department of Obstetrics and Gynecology University of Texas Southwestern Medical Center Dallas TX 75390 USA

RNA polymerase II (Pol II) transcribes hundreds of kilobases of DNA, limiting the production of mRNAs and lncRNAs. We used global run-on sequencing (GRO-seq) to measure the rates of transcription by Pol II following gene activation. Elongation rates vary as much as 4-fold at different genomic loci and in response to two distinct cellular signaling pathways (i.e., 17β-estradiol [E2] and TNF-α). The rates are slowest near the promoter and increase during the first ~15 kb transcribed. Gene body elongation rates correlate with Pol II density, resulting in systematically higher rates of transcript production at genes with higher Pol II density. Pol II dynamics following short inductions indicate that E2 stimulates gene expression by increasing Pol II initiation, whereas TNF-α reduces Pol II residence time at pause sites. Collectively, our results identify previously uncharacterized variation in the rate of transcription and highlight elongation as an important, variable, and regulated rate-limiting step during transcription.◆ (Copyright © 2013 Elsevier Inc. All rights reserved.)

关键词： CELLULAR signal transduction RNA polymerases GENETIC transcription MESSENGER RNA GENETIC regulation PROMOTERS (Genetics) TUMOR necrosis factor

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Innovative computational advances for disease outbreaks

Innovative computational advances for disease outbreaks

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World Congress on Engineering 2016, WCE 2016

作者： Gebreyesus, Kidane Desta Chang, Chuan-Hsiung Institute of Information Science Bioinformatics Program Taiwan International Graduate Program Academia Sinica Taipei 115 Taiwan Center for Systems and Synthetic Biology Institute of Biomedical Informatics National Yang-Ming University Taipei 112 Taiwan

ISBN: (纸本)9789881925305

SIR (susceptible, infectious, removed) epidemic models are commonly used in infectious disease study. These models use systems of differential equations to estimate epidemiological parameters. However, differential equations often suffer from numerical challenges. Here, we proposed simple, effective and robust innovative methods for estimating key infectious disease parameters. Our methods are reformed from the SIR models, by adjusting components of model compartments. We illustrated the methods using the recent large disease outbreaks, Ebola virus disease and Middle East respiratory syndrome Coronavirus.

关键词： Coronavirus

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