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Targeting DNA junction sites by bis-intercalators induces topological changes with potent antitumor effects

Nucleic acids research 2024年第15期52卷 9303-9316页

作者： Shih-Chun Huang Chia-Wei Chen Roshan Satange Chang-Chih Hsieh Chih-Chun Chang Shun-Ching Wang Chi-Li Peng Tai-Lin Chen Ming-Hsi Chiang Yih-Chern Horng Ming-Hon Hou Doctoral Program in Medical Biotechnology National Chung Hsing University Taichung 402 Taiwan. Graduate Institute of Genomics and Bioinformatics National Chung Hsing University Taichung 402 Taiwan. Department of Chemistry National Changhua University of Education Changhua 50058 Taiwan. Institute of Chemistry Academia Sinica Taipei 11528 Taiwan. Graduate Institute of Biotechnology National Chung Hsing University Taichung 402 Taiwan. Post Baccalaureate Medicine School of Medicine National Chung Hsing University Taichung 402 Taiwan. Biotechnology Center National Chung Hsing University Taichung 402 Taiwan.

Targeting inter-duplex junctions in catenated DNA with bidirectional bis-intercalators is a potential strategy for enhancing anticancer effects. In this study, we used d(CGTATACG)2, which forms a tetraplex base-pair junction that resembles the DNA-DNA contact structure, as a model target for two alkyl-linked diaminoacridine bis-intercalators, DA4 and DA5. Cross-linking of the junction site by the bis-intercalators induced substantial structural changes in the DNA, transforming it from a B-form helical end-to-end junction to an over-wounded side-by-side inter-duplex conformation with A-DNA characteristics and curvature. These structural perturbations facilitated the angled intercalation of DA4 and DA5 with propeller geometry into two adjacent duplexes. The addition of a single carbon to the DA5 linker caused a bend that aligned its chromophores with CpG sites, enabling continuous stacking and specific water-mediated interactions at the inter-duplex contacts. Furthermore, we have shown that the different topological changes induced by DA4 and DA5 lead to the inhibition of topoisomerase 2 activities, which may account for their antitumor effects. Thus, this study lays the foundations for bis-intercalators targeting biologically relevant DNA-DNA contact structures for anticancer drug development.

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Comparing the prediction performance of machine learning and deep learning methods for 3c-like protease cleavage sites 23

Comparing the prediction performance of machine learning and...

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7th International Conference on Medical and Health Informatics, ICMHI 2023

作者： Huang, Lan-Ying Chen, Chi-Wei Chang, Kai-Po Chu, Yen-Wei Medical Biotechnology National Chung Hsing University Taichung402 Taiwan Department of Pathology School of Medicine China Medical University Taichung402 Taiwan Department of Pathology China Medical University Hospital Taichung402 Taiwan Graduate Degree Program of Smart Healthcare and Bioinformatics I-Shou University Kaohsiung82445 Taiwan Department of Biomedical Engineering I-Shou University Kaohsiung82445 Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University Taichung402 Taiwan Institute of Molecular Biology National Chung Hsing University Taichung402 Taiwan Agricultural Biotechnology Center National Chung Hsing University Taichung402 Taiwan Biotechnology Center National Chung Hsing University Taichung402 Taiwan Translational Medicine National Chung Hsing University Taichung402 Taiwan Rong Hsing Research Center for Translational Medicine National Chung Hsing University Taichung402 Taiwan Taichung402 Taiwan

ISBN: (纸本)9798400700712

The impact of COVID-19 on the human body after infection requires further investigation. The protease carried by the virus may interact with cellular proteins, thereby affecting the regulatory pathways. Machine learning can provide predictive information to aid the research design in such studies. This study examined the effectiveness of different encoding methods for training models using traditional machine learning and deep learning methods. Using the advantages of deep learning methods for high-dimensional data analysis, amino acid properties were extracted and classified. Viral polyprotein data were used as the training set and human protein substrates were collected from the literature as the independent test set. In the one-dimensional convolutional neural network model, the accuracy of the independent test set was the highest, at 0.832. © 2023 ACM.

关键词： Coronavirus

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Global epidemiology and antimicrobial resistance of Enterobacterales harbouring genes encoding OXA-48-like carbapenemases: insights from the results of the Antimicrobial Testing Leadership and Surveillance (ATLAS) programme 2018-2021

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The Journal of antimicrobial chemotherapy 2024年第7期79卷 1581-1589页

作者： Yu-Lin Lee Wei-Yao Wang Wen-Chien Ko Po-Ren Hsueh Division of Infectious Disease Department of Internal Medicine Chung Shan Medical University Hospital Taichung Taiwan. PhD Program in Medical Biotechnology Institute of Genomics and Bioinformatics National Chung-Hsing University Taichung Taiwan. School of Medicine Chung Shan Medical University Taichung Taiwan. Department of Medicine College of Medicine National Cheng Kung University Tainan Taiwan. Departments of Laboratory Medicine and Internal Medicine China Medical University Hospital Chin Medical University Taichung Taiwan. School of Medicine China Medical University Taichung Taiwan. PhD Program for Ageing School of Medicine China Medical University Taichung Taiwan. Departments of Laboratory Medicine and Internal Medicine National Taiwan University Hospital National Taiwan University College of Medicine Taipei Taiwan.

OBJECTIVES:The recent emergence of carbapenem-resistant Enterobacterales poses a major and escalating threat to global public health. This study aimed to analyse the global distribution and antimicrobial resistance of Enterobacterales harbouring variant OXA-48-like carbapenemase-related genes. METHODS:Enterobacterales isolates were collected from the Antimicrobial Testing Leadership and Surveillance (ATLAS) programme during 2018-2021. Comprehensive antimicrobial susceptibility testing and β-lactamase gene detection were also conducted, along with statistical analysis of the collected data. RESULTS:Among the 72 244 isolates, 1934 Enterobacterales isolates were identified to harbour blaOXA-48-like genes, predominantly Klebsiella spp. (86.9%). High rates of multidrug resistance were observed, with only ceftazidime/avibactam and tigecycline showing favourable susceptibility. A discrepancy between the genotype and phenotype of carbapenem resistance was evident: 16.8% (233 out of 1384) of the Enterobacterales isolates with blaOXA-48-like genes exhibited susceptibility to meropenem. Specifically, 37.4% (64/95) of Escherichia coli strains with blaOXA-48-like genes displayed meropenem susceptibility, while the corresponding percentages for Klebsiella pneumoniae and Enterobacter cloacae complex were 25.2% (160/1184) and 0% (0/36), respectively (P < 0.05). Geographical analysis revealed that the highest prevalence of blaOXA-48-like genes occurred in Asia, the Middle East and Eastern Europe. The proportion of K. pneumoniae isolates harbouring blaOXA-232 increased from 23.9% in 2018 to 56.0% in 2021. By contrast, the proportion of blaOXA-48 decreased among K. pneumoniae isolates during 2018-2021. CONCLUSIONS:This study underscores the widespread and increasing prevalence of blaOXA-48-like genes in Enterobacterales and emphasizes the need for enhanced surveillance, improved diagnostic methods and tailored antibiotic stewardship to combat the spread of these resistant pathogens.

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Current Progress of COPD Early Detection: Key Points and Novel Strategies

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International Journal of COPD 2023年 18卷 1511-1524页

作者： Lin, Ching-Hsiung Cheng, Shih-Lung Chen, Chiung-Zuei Chen, Chia-Hung Lin, Sheng-Hao Wang, Hao-Chien Division of Chest Medicine Changhua Christian Hospital Changhua 500 Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University Taichung 402 Taiwan Ph.D. Program in Translational Medicine National Chung Hsing University Taichung Taiwan Department of Recreation and Holistic Wellness MingDao University Changhua Taiwan Department of Internal Medicine Far Eastern Memorial Hospital Taipei 220 Taiwan Department of Chemical Engineering and Materials Science Yuan Ze University Taoyuan 320 Taiwan Division of Pulmonary Medicine Department of Internal Medicine National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan Taiwan Division of Pulmonary and Critical Care Medicine Department of Internal Medicine China Medical University Hospital Taichung 404 Taiwan Department of Internal Medicine National Taiwan University Hospital Taipei 100 Taiwan

Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity and mortality worldwide, with approxi-mately 70% to 80% of adults with COPD being undiagnosed. Patients with undiagnosed COPD are at increased risk of poor outcomes and a worsened quality of life, making early detection a crucial strategy to mitigate the impact of COPD and reduce the burden on healthcare systems. In the past decade, increased interest has been focused on the development of effective strategies and instrument for COPD early detection. However, identifying undiagnosed cases of COPD is still challenging. Both screening and case-finding approaches have been adopted to identify undiagnosed COPD, with case-finding being recommended by the 2023 Global Initiative for Chronic Obstructive Lung Disease (GOLD) guideline and the updated United States Preventive Services Task Force (USPTF) recommendation. Nonetheless, the approaches, criteria, and instruments used for early detection of COPD are varied. However, advances in the taxonomy and risk factors of COPD are continuously being investigated. It is important to continuously assess the current state of knowledge on COPD early detection, given the challenges associated with identifying undiagnosed COPD. This review aims to highlight recent advances in early detection of COPD. To discuss the current challenge and opportunity in COPD early detection, providing an overview of existing literature on COPD case-finding strategies, including the approaches, criteria for subjects, and instruments. The review also summarizes the current progress in COPD case-findings and proposes a COPD case-finding flowchart as an efficient method for identifying at risk COPD patients. © 2023 Lin et al.

关键词： at-risk case-finding COPD early detection undiagnosed

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Machine learning approaches to identify systemic lupus erythematosus in anti-nuclear antibody-positive patients using genomic data and electronic health records

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BioData Mining 2024年第1期17卷 1页

作者： Chung, Chih-Wei Chou, Seng-Cho Hsiao, Tzu-Hung Zhang, Grace Joyce Chung, Yu-Fang Chen, Yi-Ming Department of Information Management National Taiwan University Taipei Taiwan Department of Medical Research Taichung Veterans General Hospital Taichung Taiwan Department of Public Health Fu Jen Catholic University New Taipei Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University Taichung Taiwan Department of Cellular and Physiological Sciences The University of British Columbia Vancouver BC Canada Department of Electrical Engineering Tunghai University Taichung Taiwan Division of Allergy Immunology and Rheumatology Department of Internal Medicine Taichung Veterans General Hospital 1650 Section 4 Boulevard Xitun Dist. Taichung 407 Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan School of Medicine College of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Rong Hsing Research Center for Translational Medicine & Ph.D. Program in Translational Medicine National Chung Hsing University Taichung Taiwan Precision Medicine Research Center College of Medicine National Chung Hsing University Taichung Taiwan

Background: Although the 2019 EULAR/ACR classification criteria for systemic lupus erythematosus (SLE) has required at least a positive anti-nuclear antibody (ANA) titer (≥ 1:80), it remains challenging for clinicians to identify patients with SLE. This study aimed to develop a machine learning (ML) approach to assist in the detection of SLE patients using genomic data and electronic health records. Methods: Participants with a positive ANA (≥ 1:80) were enrolled from the Taiwan Precision Medicine Initiative cohort. The Taiwan Biobank version 2 array was used to detect single nucleotide polymorphism (SNP) data. Six ML models, Logistic Regression, Random Forest (RF), Support Vector Machine, Light Gradient Boosting Machine, Gradient Tree Boosting, and Extreme Gradient Boosting (XGB), were used to identify SLE patients. The importance of the clinical and genetic features was determined by Shapley Additive Explanation (SHAP) values. A logistic regression model was applied to identify genetic variations associated with SLE in the subset of patients with an ANA equal to or exceeding 1:640. Results: A total of 946 SLE and 1,892 non-SLE controls were included in this analysis. Among the six ML models, RF and XGB demonstrated superior performance in the differentiation of SLE from non-SLE. The leading features in the SHAP diagram were anti-double strand DNA antibodies, ANA titers, AC4 ANA pattern, polygenic risk scores, complement levels, and SNPs. Additionally, in the subgroup with a high ANA titer (≥ 1:640), six SNPs positively associated with SLE and five SNPs negatively correlated with SLE were discovered. Conclusions: ML approaches offer the potential to assist in diagnosing SLE and uncovering novel SNPs in a group of patients with autoimmunity. © 2024, The Author(s).

关键词： Anti-nuclear antibody Machine learning Polygenic risk score Single nucleotide polymorphism Systemic lupus erythematosus

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Targeting human mitochondrial NAD(P)+-dependent malic enzyme (ME2) impairs energy metabolism and redox state and exhibits antileukemic activity in acute myeloid leukemia

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Cellular Oncology 2023年第5期46卷 1301-1316页

作者： Chen, Kun-Chi Hsiao, I-Hsin Huang, Yu-Nan Chou, Yu-Tung Lin, Yi-Chun Hsieh, Ju-Yi Chang, Yung-Lung Wu, Kang-Hsi Liu, Guang-Yaw Hung, Hui-Chih Department of Life Sciences National Chung Hsing University Taichung Taiwan Ph.D. Program in Tissue Engineering and Regenerative Medicine National Chung Hsing University Taichung Taiwan Department of Biochemistry National Defense Medical Center Taipei Taiwan School of Medicine Chung Shan Medical University Taichung Taiwan Department Pediatrics Chung Shan Medical University Hospital Taichung Taiwan Institute of Medicine College of Medicine Chung Shan Medical University Taichung Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University Taichung Taiwan EGG & Animal Biotechnology Center National Chung Hsing University Taichung Taiwan

Acute myeloid leukemia (AML) is a fast-growing and highly fatal blood cancer, and recent research has shown that targeting metabolism may be a promising therapeutic approach for treating AML. One promising target is the human mitochondrial NAD(P)+-dependent malic enzyme (ME2), which is involved in the production of pyruvate and NAD(P)H and the regulation of the NAD+/NADH redox balance. Inhibition of ME2 via silencing ME2 or utilizing its allosteric inhibitor disodium embonate (Na2EA) causes a decrease in pyruvate and NADH, leading to a decrease in producing ATP via cellular respiration and oxidative phosphorylation. ME2 inhibition also decreases NADPH levels, resulting in an increase in reactive oxygen species (ROS) and oxidative stress, which ultimately leads to cellular apoptosis. Additionally, ME2 inhibition reduces pyruvate metabolism and the biosynthetic pathway. ME2 silencing inhibits the growth of xenotransplanted human AML cells, and the allosteric ME2 inhibitor Na2EA demonstrates antileukemic activity against immune-deficient mice with disseminated AML. Both of these effects are a result of impaired energy metabolism in mitochondria. These findings suggest that the targeting ME2 may be an effective strategy for treating AML. Overall, ME2 plays an essential role in energy metabolism of AML cells, and its inhibition may offer a promising approach for AML treatment.

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Bile acid synthesis impedes tumor-specific T cell responses during liver cancer

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Science (New York, N.Y.) 2025年第6730期387卷 192-201页

作者： Varanasi, Siva Karthik Chen, Dan Liu, Yingluo Johnson, Melissa A. Miller, Cayla M. Ganguly, Souradipta Lande, Kathryn LaPorta, Michael A. Hoffmann, Filipe Araujo Mann, Thomas H. Teneche, Marcos G. Casillas, Eduardo Mangalhara, Kailash C. Mathew, Varsha Sun, Ming Jensen, Isaac J. Farsakoglu, Yagmur Chen, Timothy Parisi, Bianca Deota, Shaunak Havas, Aaron Lee, Jin Chung, H Kay Schietinger, Andrea Panda, Satchidananda Williams, April E. Farber, Donna L. Dhar, Debanjan Adams, Peter D. Feng, Gen-Sheng Shadel, Gerald S. Sundrud, Mark S. Kaech, Susan M. NOMIS Center for Immunobiology and Microbial Pathogenesis Salk Institute for Biological Studies La Jolla CA United States Department of Pathology School of Medicine University of California San Diego La Jolla CA United States Sanford Burnham Prebys Medical Discovery Institute La Jolla CA United States Department of Medicine School of Medicine University of California San Diego CA United States Razavi Newman Integrative Genomics and Bioinformatics Core Facility Salk Institute for Biological Studies La Jolla CA United States Department of Microbiology and Immunology Columbia University Irving Medical Center New York NY USA Department of Surgery Columbia University Irving Medical Center New York NY USA Regulatory Biology Laboratory Salk Institute for Biological Studies La Jolla CA United States Immunology Program Memorial Sloan Kettering Cancer Center New York NY USA Immunology and Microbial Pathogenesis Program Weill Cornell Graduate School of Medical Sciences New York NY USA Parker Institute for Cancer Immunotherapy New York NY USA Department of Microbiology and Immunology Geisel School of Medicine at Dartmouth Hanover NH USA Department of Medicine Geisel School of Medicine at Dartmouth Hanover NH USA Center for Digestive Health Dartmouth Health NH United States Dartmouth Cancer Center Dartmouth Health NH United States

The metabolic landscape of cancer greatly influences antitumor immunity, yet it remains unclear how organ-specific metabolites in the tumor microenvironment influence immunosurveillance. We found that accumulation of primary conjugated and secondary bile acids (BAs) are metabolic features of human hepatocellular carcinoma and experimental liver cancer models. Inhibiting conjugated BA synthesis in hepatocytes through deletion of the BA-conjugating enzyme bile acid-CoA:amino acid N-acyltransferase (BAAT) enhanced tumor-specific T cell responses, reduced tumor growth, and sensitized tumors to anti-programmed cell death protein 1 (anti-PD-1) immunotherapy. Furthermore, different BAs regulated CD8+ T cells differently;primary BAs induced oxidative stress, whereas the secondary BA lithocholic acid inhibited T cell function through endoplasmic reticulum stress, which was countered by ursodeoxycholic acid. We demonstrate that modifying BA synthesis or dietary intake of ursodeoxycholic acid could improve tumor immunotherapy in liver cancer model systems.

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Repurposed Nrf2 activator dimethyl fumarate rescues muscle inflammation and fibrosis in an aggravated mdx mouse model of Duchenne muscular dystrophy

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Redox Biology 2025年 84卷 103676页

作者： Kourakis, Stephanie Timpani, Cara A. Bagaric, Ryan M. Qi, Bo Ali, Benazir A. Boyer, Rebecca Spiesberger, Guinevere Kandhari, Nitika Yan, Xu Kuang, Jujiao Tulangekar, Ankita de Haan, Judy B. Deveson-Lucas, Deanna Stupka, Nicole Fischer, Dirk Rybalka, Emma Institute for Health and Sport (IHeS) Victoria University Melbourne VIC Australia Inherited and Acquired Myopathies Program Australian Institute for Musculoskeletal Science (AIMSS) St Albans VIC Australia Department of Medicine – Western Health Melbourne Medical School The University of Melbourne St Albans VIC Australia Monash Genomics and Bioinformatics Platform Biomedical Discovery Institute Monash University Clayton VIC Australia Sarcopenia Research Program Australian Institute for Musculoskeletal Science (AIMSS) St Albans VIC Australia Basic Science Domain Oxidative Stress Laboratory Baker Heart and Diabetes Institute Melbourne VIC Australia Baker Department of Cardiometabolic Health University of Melbourne Parkville VIC Australia Division of Neuropediatric and Developmental Medicine University Children's Hospital of Basel (UKBB) Basel Switzerland

In inherited neuromuscular disease, Duchenne muscular dystrophy (DMD), glucocorticoids significantly slow disease progression yet impart side effects severe enough to preclude use in a significant proportion of patients. Extending our findings that acute treatment with FDA approved multiple sclerosis drug, dimethyl fumarate (DMF), rescues muscle pathology in juvenile mdx mice, we aimed to conduct tiered pre-clinical testing toward translation. To aggravate disease phenotype in adult mdx muscles that usually lack human equivalent muscle pathology, we used bi-weekly treadmill running for 4 weeks which increased plasma DMD biomarker, creatine kinase, by 2-fold and quadriceps fibrosis by ∼30 %. Using this model, we screened DMF for 5 weeks in a head-to-head comparison, and in combination, with standard-of-care prednisone (PRED), to model the most likely clinical trial scenario. We show comparable efficacy between DMF and PRED at reducing inflammation via NF-κB suppression and CD68⁺ macrophage infiltration. Moderate term DMF monotherapy had additional anti-fibrotic and anti-lipogenic effects on skeletal and cardiac muscle beyond those seen with PRED treatment, although combination therapy exacerbated fibrosis in quadriceps. Our study supports DMF as a repurposing candidate for DMD, especially for patients who cannot tolerate chronic glucocorticoid treatment. We also highlight the importance of evaluating combination therapy to identify potential off-target effects between emerging therapeutics and glucocorticoids towards better designed clinical trials. © 2025

关键词： Dimethyl fumarate Duchenne muscular dystrophy Muscle pathology Therapeutics

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SCALE RELIANT INFERENCE

arXiv

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arXiv 2022年

作者： Nixon, Michelle Pistner McGovern, Kyle C. Letourneau, Jeffrey David, Lawrence A. Lazar, Nicole A. Mukherjee, Sayan Silverman, Justin D. College of Information Sciences and Technology The Pennsylvania State University United States Program in Bioinformatics and Genomics The Pennsylvania State University United States Department of Molecular Genetics and Microbiology Duke University United States Department of Statistics The Pennsylvania State University United States Huck Institutes for the Life Sciences The Pennsylvania State University United States Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University United States Center for Scalable Data Analytics and Artificial Intelligence University of Leipzig Germany Max Planck Institute for Mathematics in the Natural Sciences Germany Department of Medicine The Pennsylvania State University United States

Many scientific fields, including human gut microbiome science, collect multivariate count data where the sum of the counts is unrelated to the scale of the underlying system being measured (e.g., total microbial load in a subject’s colon). This disconnect complicates downstream analyses such as differential analysis in case-control studies. This article is motivated by a novel study of in vitro human gut microbiome models. Popular tools for analyzing these data led to dramatically elevated rates of both false positives and false negatives. To understand those failures, we provide a formal problem statement that frames these challenges of scale in terms of the classical theory of identifiability. We call this the problem of Scale Reliant Inference (SRI). We use this formulation to prove fundamental limits on SRI in terms of criteria such as consistency and type-I error control. We show that the failures of existing methods stem from a fundamental failure to properly quantify uncertainty in the system scale. We demonstrate that a particular type of Bayesian model called a Bayesian Partially Identified Model (PIMs) can correctly quantify uncertainty in SRI. We introduce Scale Simulation Random Variables (SSRVs) as a flexible and efficient approach to specifying and inferring Bayesian PIMs. In the context of both real and simulated data, we find SSRVs drastically decrease type-I and type-II error rates. © 2022, CC BY.

关键词： Bayesian networks

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An atlas of single-cell eQTLs dissects autoimmune disease genes and identifies novel drug classes for treatment

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Cell genomics 2025年第4期5卷 100820-100820页

作者： Wang, Lida Markus, Havell Chen, Dieyi Chen, Siyuan Zhang, Fan Gao, Shuang Khunsriraksakul, Chachrit Chen, Fang Olsen, Nancy Foulke, Galen Jiang, Bibo Carrel, Laura Liu, Dajiang J. Department of Public Health Sciences Pennsylvania State University College of Medicine Hershey 17033 PA United States Bioinformatics and Genomics PhD Program Pennsylvania State University College of Medicine Hershey 17033 PA United States Institute for Personalized Medicine Pennsylvania State University College of Medicine Hershey 17033 PA United States Department of Biochemistry and Molecular Biology Pennsylvania State University College of Medicine Hershey 17033 PA United States Department of Medicine Penn State University College of Medicine Hershey 17033 PA United States Department of Dermatology Penn State University College of Medicine Hershey 17033 PA United States

Most variants identified from genome-wide association studies (GWASs) are non-coding and regulate gene expression. However, many risk loci fail to colocalize with expression quantitative trait loci (eQTLs), potentially due to limited GWAS and eQTL analysis power or cellular heterogeneity. Population-scale single-cell RNA-sequencing (scRNA-seq) datasets are emerging, enabling mapping of eQTLs in different cell types (sc-eQTLs). Compared to eQTL data from bulk tissues (bk-eQTLs), sc-eQTL datasets are smaller. We propose a joint model of bk-eQTLs as a weighted sum of sc-eQTLs (JOBS) from constituent cell types to improve power. Applying JOBS to One1K1K and eQTLGen data, we identify 586% more eQTLs, matching the power of 4× the sample sizes of OneK1K. Integrating sc-eQTLs with GWAS data creates an atlas for 14 immune-mediated disorders, colocalizing 29.9% or 32.2% more loci than using sc-eQTL or bk-eQTL alone. Extending JOBS, we develop a drug-repurposing pipeline and identify novel drugs validated by real-world data. © 2025 The Author(s)

关键词： autoimmune disease bulk eQTL colocalization drug repurposing GWAS scRNAseq sequencing design single cell eQTL transcriptome

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