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Fan J, Li X, Yang J, Zhang S, Qu HQ, Ji D, Glessner JT, Hao J, Ding Z, Wang N, Meng X, Xia Q, Hakonarson H, Wei W, Li J. Revealing novel genomic insights and therapeutic targets for juvenile idiopathic arthritis through omics. Rheumatology (Oxford) 2024; 63:SI249-SI259. [PMID: 38317060 DOI: 10.1093/rheumatology/keae078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 12/22/2023] [Accepted: 01/10/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND The genetic architecture of JIA remains only partially comprehended. There is a clear imperative for continued endeavours to uncover insights into the underlying causes of JIA. METHODS This study encompassed a comprehensive spectrum of endeavours, including conducting a JIA genome-wide association study (GWAS) meta-analysis that incorporated data from 4550 JIA cases and 18 446 controls. We employed in silico and genome-editing approaches to prioritizing target genes. To investigate pleiotropic effects, we conducted phenome-wide association studies. Cell-type enrichment analyses were performed by integrating bulk and single-cell sequencing data. Finally, we delved into potential druggable targets for JIA. RESULTS Fourteen genome-wide significant non-HLA loci were identified, including four novel loci, each exhibiting pleiotropic associations with other autoimmune diseases or musculoskeletal traits. We uncovered strong genetic correlation between JIA and BMD traits at 52 genomic regions, including three GWAS loci for JIA. Candidate genes with immune functions were captured by in silico analyses at each novel locus, with additional findings identified through our experimental approach. Cell-type enrichment analysis revealed 21 specific immune cell types crucial for the affected organs in JIA, indicating their potential contribution to the disease. Finally, 24 known or candidate druggable target genes were prioritized. CONCLUSIONS Our identification of four novel JIA-associated genes, CD247, RHOH, COLEC10 and IRF8, broadens the novel potential drug repositioning opportunities. We established a new genetic link between COLEC10, TNFRSF11B and JIA/BMD. Additionally, the identification of RHOH underscores its role in positive thymocyte selection, thereby illuminating a critical facet of JIA's underlying biological mechanisms.
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Affiliation(s)
- Jingxian Fan
- Department of Cell Biology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), Tianjin Key Laboratory of Medical Epigenetics, Tianjin Institute of Immunology, Tianjin Key Laboratory of Birth Defects for Prevention and Treatment, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
- Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Xiumei Li
- Department of Cell Biology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), Tianjin Key Laboratory of Medical Epigenetics, Tianjin Institute of Immunology, Tianjin Key Laboratory of Birth Defects for Prevention and Treatment, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Jie Yang
- Department of Cell Biology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), Tianjin Key Laboratory of Medical Epigenetics, Tianjin Institute of Immunology, Tianjin Key Laboratory of Birth Defects for Prevention and Treatment, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Sipeng Zhang
- Department of Cell Biology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), Tianjin Key Laboratory of Medical Epigenetics, Tianjin Institute of Immunology, Tianjin Key Laboratory of Birth Defects for Prevention and Treatment, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Hui-Qi Qu
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Dandan Ji
- Department of Cell Biology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), Tianjin Key Laboratory of Medical Epigenetics, Tianjin Institute of Immunology, Tianjin Key Laboratory of Birth Defects for Prevention and Treatment, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Joseph T Glessner
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jian Hao
- Department of Rheumatology and Immunology, Tianjin Medical University General Hospital, Tianjin, China
| | - Zhiyong Ding
- Mills Institute for Personalized Cancer Care, Fynn Biotechnologies Ltd., Jinan, China
| | - Nan Wang
- Mills Institute for Personalized Cancer Care, Fynn Biotechnologies Ltd., Jinan, China
| | - Xinyi Meng
- Department of Cell Biology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), Tianjin Key Laboratory of Medical Epigenetics, Tianjin Institute of Immunology, Tianjin Key Laboratory of Birth Defects for Prevention and Treatment, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Qianghua Xia
- Department of Cell Biology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), Tianjin Key Laboratory of Medical Epigenetics, Tianjin Institute of Immunology, Tianjin Key Laboratory of Birth Defects for Prevention and Treatment, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
- Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Wei Wei
- Department of Rheumatology and Immunology, Tianjin Medical University General Hospital, Tianjin, China
| | - Jin Li
- Department of Cell Biology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), Tianjin Key Laboratory of Medical Epigenetics, Tianjin Institute of Immunology, Tianjin Key Laboratory of Birth Defects for Prevention and Treatment, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
- Department of Rheumatology and Immunology, Tianjin Medical University General Hospital, Tianjin, China
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Wang S, Bao C, Yang S, Gao C, Lu C, Jiang L, Chen L, Wang Z, Fang H. XGRm: A Web Server for Interpreting Mouse Summary-level Genomic Data. J Mol Biol 2024; 436:168705. [PMID: 39237194 DOI: 10.1016/j.jmb.2024.168705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 06/30/2024] [Accepted: 07/09/2024] [Indexed: 09/07/2024]
Abstract
We introduce XGR-model (or XGRm), a web server made accessible at http://www.xgrm.pro, with the aim of meeting the increasing demand for effectively interpreting summary-level genomic data in model organisms. Currently, it hosts two enrichment analysers and two subnetwork analysers to support enrichment and subnetwork analyses for user-input mouse genomic data, whether gene-centric or genomic region-centric. The enrichment analysers identify ontology term enrichments for input genes (GElyser) or for genes linked from input genomic regions (RElyser). The subnetwork analysers rely on our previously established network algorithm to identify gene subnetworks from input gene-centric summary data (GSlyser) or from input region-centric summary data (RSlyser), leveraging network information about either functional interactions or pathway-derived interactions. Collectively, XGRm offers an all-in-one solution for gaining systems biology insights into summary-level genomic data in mice, underpinned by our commitment to regular updates as well as natural extensions to other model organisms.
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Affiliation(s)
- Shan Wang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Chaohui Bao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Siyue Yang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Faculty of Medical Laboratory Science, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Chenxu Gao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Chang Lu
- MRC Laboratory of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, London W12 0HS, UK
| | - Lulu Jiang
- Translational Health Sciences, University of Bristol, Bristol BS1 3NY, UK
| | - Liye Chen
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Zheng Wang
- Medical Center of Hematology, Xinqiao Hospital of Army Medical University, State Key Laboratory of Trauma and Chemical Poisoning, Chongqing Key Laboratory of Hematology and Microenvironment, Chongqing 400037, China; Jinfeng Laboratory, Chongqing 401329, China; Bio-Med Informatics Research Center & Clinical Research Center, The Second Affiliated Hospital, Army Medical University, Chongqing 400037, China.
| | - Hai Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
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Wu J, Chen X, Li R, Lu Q, Ba Y, Fang J, Liu Y, Li R, Liu Y, Wang Y, Chen J, Li Y, Huang Y. Identifying genetic determinants of sarcopenia-related traits: a Mendelian randomization study of druggable genes. Metabolism 2024; 160:155994. [PMID: 39117060 DOI: 10.1016/j.metabol.2024.155994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 07/27/2024] [Accepted: 08/03/2024] [Indexed: 08/10/2024]
Abstract
BACKGROUND Sarcopenia, characterized by progressive muscle mass and function loss, particularly affects the elderly, and leads to severe consequences such as falls and mortality. Despite its prevalence, targeted pharmacotherapies for sarcopenia are lacking. Utilizing large-sample genome-wide association studies (GWAS) data is crucial for cost-effective drug discovery. METHODS Herein, we conducted four studies to understand the putative causal effects of genetic components on muscle mass and function. Study 1 employed a two-sample Mendelian randomization (MR) on 15,944 potential druggable genes, investigating their potential causality with muscle quantity and quality in a European population (N up to 461,089). Study 2 validated MR results through sensitivity analyses and colocalization analyses. Study 3 extended validation across other European cohorts, and study 4 conducted quantitative in vivo verification. RESULTS MR analysis revealed significant causality between four genes (BLOC-1 related complex subunit 7, BORCS7; peptidase m20 domain containing 1, PM20D1; nuclear casein kinase and cyclin dependent kinase substrate 1, NUCKS1 and ubiquinol-cytochrome c reductase complex assembly factor 1, UQCC1) and muscle mass and function (p-values range 5.98 × 10-6 to 9.26 × 10-55). To be specific, BORCS7 and UQCC1 negatively regulated muscle quantity and quality, whereas enhancing PM20D1 and NUCKS1 expression showed promise in promoting muscle mass and function. Causal relationships remained robust across sensitivity analyses, with UQCC1 exhibiting notable colocalization effects (PP·H4 93.4 % to 95.8 %). Further validation and in vivo replication verified the potential causality between these genes and muscle mass as well as function. CONCLUSIONS Our druggable genome-wide MR analysis identifies BORCS7, PM20D1, NUCKS1, and UQCC1 as causally associated with muscle mass and function. These findings offer insights into the genetic basis of sarcopenia, paving the way for these genes to become promising drug targets in mitigating this debilitating condition.
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Affiliation(s)
- Jihao Wu
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China; Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-Sen University, Guangzhou 510080, China
| | - Xiong Chen
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Ruijun Li
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-Sen University, Guangzhou 510080, China
| | - Qiying Lu
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-Sen University, Guangzhou 510080, China; Department of Rehabilitation Medicine, The Third Affiliated Hospital, Sun Yat-sen University, 600 Tianhe Road, Guangzhou 510630, China
| | - Yucheng Ba
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-Sen University, Guangzhou 510080, China
| | - Jiayun Fang
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-Sen University, Guangzhou 510080, China
| | - Yilin Liu
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-Sen University, Guangzhou 510080, China
| | - Ruijie Li
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-Sen University, Guangzhou 510080, China
| | - Yixuan Liu
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-Sen University, Guangzhou 510080, China
| | - Yiling Wang
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-Sen University, Guangzhou 510080, China
| | - Jinsi Chen
- Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-Sen University, Guangzhou 510080, China
| | - Yanbing Li
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China
| | - Yinong Huang
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China; Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-Sen University, Guangzhou 510080, China.
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Qi T, Song L, Guo Y, Chen C, Yang J. From genetic associations to genes: methods, applications, and challenges. Trends Genet 2024; 40:642-667. [PMID: 38734482 DOI: 10.1016/j.tig.2024.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/13/2024]
Abstract
Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.
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Affiliation(s)
- Ting Qi
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
| | - Liyang Song
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Yazhou Guo
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Chang Chen
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Jian Yang
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
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Lee PC, Jung IH, Thussu S, Patel V, Wagoner R, Burks KH, Amrute J, Elenbaas JS, Kang CJ, Young EP, Scherer PE, Stitziel NO. Instrumental variable and colocalization analyses identify endotrophin and HTRA1 as potential therapeutic targets for coronary artery disease. iScience 2024; 27:110104. [PMID: 38989470 PMCID: PMC11233907 DOI: 10.1016/j.isci.2024.110104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 03/26/2024] [Accepted: 05/22/2024] [Indexed: 07/12/2024] Open
Abstract
Coronary artery disease (CAD) remains a leading cause of disease burden globally, and there is a persistent need for new therapeutic targets. Instrumental variable (IV) and genetic colocalization analyses can help identify novel therapeutic targets for human disease by nominating causal genes in genome-wide association study (GWAS) loci. We conducted cis-IV analyses for 20,125 genes and 1,746 plasma proteins with CAD using molecular trait quantitative trait loci variant (QTLs) data from three different studies. 19 proteins and 119 genes were significantly associated with CAD risk by IV analyses and demonstrated evidence of genetic colocalization. Notably, our analyses validated well-established targets such as PCSK9 and ANGPTL4 while also identifying HTRA1 and endotrophin (a cleavage product of COL6A3) as proteins whose levels are causally associated with CAD risk. Further experimental studies are needed to confirm the causal role of the genes and proteins identified through our multiomic cis-IV analyses on human disease.
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Affiliation(s)
- Paul C. Lee
- Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - In-Hyuk Jung
- Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Shreeya Thussu
- Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Ved Patel
- Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Ryan Wagoner
- Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Kendall H. Burks
- Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Junedh Amrute
- Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Jared S. Elenbaas
- Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Chul Joo Kang
- McDonnell Genome Institute, Washington University School of Medicine, Saint Louis, MO 63108, USA
| | - Erica P. Young
- Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA
- McDonnell Genome Institute, Washington University School of Medicine, Saint Louis, MO 63108, USA
| | - Philipp E. Scherer
- Touchstone Diabetes Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Nathan O. Stitziel
- Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO 63110, USA
- McDonnell Genome Institute, Washington University School of Medicine, Saint Louis, MO 63108, USA
- Department of Genetics, Washington University School of Medicine, Saint Louis, MO 63110, USA
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Chen Y, Wang G, Chen J, Wang C, Dong X, Chang HM, Yuan S, Zhao Y, Mu L. Genetic and Epigenetic Landscape for Drug Development in Polycystic Ovary Syndrome. Endocr Rev 2024; 45:437-459. [PMID: 38298137 DOI: 10.1210/endrev/bnae002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/26/2023] [Accepted: 01/23/2024] [Indexed: 02/02/2024]
Abstract
The treatment of polycystic ovary syndrome (PCOS) faces challenges as all known treatments are merely symptomatic. The US Food and Drug Administration has not approved any drug specifically for treating PCOS. As the significance of genetics and epigenetics rises in drug development, their pivotal insights have greatly enhanced the efficacy and success of drug target discovery and validation, offering promise for guiding the advancement of PCOS treatments. In this context, we outline the genetic and epigenetic advancement in PCOS, which provide novel insights into the pathogenesis of this complex disease. We also delve into the prospective method for harnessing genetic and epigenetic strategies to identify potential drug targets and ensure target safety. Additionally, we shed light on the preliminary evidence and distinctive challenges associated with gene and epigenetic therapies in the context of PCOS.
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Affiliation(s)
- Yi Chen
- Reproductive Medicine Center, Zhongshan Hospital, Fudan University, Shanghai 200032, China
- The First School of Medicine, Wenzhou Medical University, Wenzhou 325035, China
| | - Guiquan Wang
- Department of Reproductive Medicine, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen 361003, China
- Xiamen Key Laboratory of Reproduction and Genetics, Xiamen University, Xiamen 361023, China
| | - Jingqiao Chen
- The First School of Medicine, Wenzhou Medical University, Wenzhou 325035, China
| | - Congying Wang
- The Department of Cardiology, The Fourth Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang 322000, China
| | - Xi Dong
- Reproductive Medicine Center, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Hsun-Ming Chang
- Department of Obstetrics and Gynecology, China Medical University Hospital, Taichung 40400, Taiwan
| | - Shuai Yuan
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institute, Stockholm 171 65, Sweden
| | - Yue Zhao
- State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, China
- National Clinical Research Center for Obstetrics and Gynecology, Beijing 100007, China
- Key Laboratory of Assisted Reproduction, Ministry of Education, Peking University, Beijing 100191, China
- Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Peking University, Beijing 100191, China
| | - Liangshan Mu
- Reproductive Medicine Center, Zhongshan Hospital, Fudan University, Shanghai 200032, China
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Sharma SD, Leung SH, Viatte S. Genetics of rheumatoid arthritis. Best Pract Res Clin Rheumatol 2024:101968. [PMID: 38955657 DOI: 10.1016/j.berh.2024.101968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 06/17/2024] [Accepted: 06/24/2024] [Indexed: 07/04/2024]
Abstract
In the past four decades, a plethora of genetic association studies have been carried out in cohorts of patients with rheumatoid arthritis. These studies have highlighted key aspects of disease pathogenesis and suggested causal mechanisms. In this review, we discuss major advances in our understanding of the genetic architecture of rheumatoid arthritis susceptibility, severity and treatment response and explain how genetics supports current models of disease pathogenesis and outcome. We outline future research directions, like Mendelian randomisation, and present a number of potential avenues for clinical translation, including risk and outcome prediction, patient stratification into treatment response groups and pharmacological applications.
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Affiliation(s)
- Seema D Sharma
- Versus Arthritis Centre for Genetics and Genomics, Centre for Musculoskeletal Research, The University of Manchester, Oxford Road, Manchester, M13 9PT, UK; NIHR Manchester Musculoskeletal Biomedical Research Centre, Central Manchester NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK.
| | - Shek H Leung
- Versus Arthritis Centre for Genetics and Genomics, Centre for Musculoskeletal Research, The University of Manchester, Oxford Road, Manchester, M13 9PT, UK.
| | - Sebastien Viatte
- Versus Arthritis Centre for Genetics and Genomics, Centre for Musculoskeletal Research, The University of Manchester, Oxford Road, Manchester, M13 9PT, UK; NIHR Manchester Musculoskeletal Biomedical Research Centre, Central Manchester NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK; Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
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Chen X, Shen R, Zhu D, Luo S, You G, Li R, Hong X, Li R, Wu J, Huang Y, Lin T. Drug repurposing opportunities for chronic kidney disease. iScience 2024; 27:109953. [PMID: 38947510 PMCID: PMC11214293 DOI: 10.1016/j.isci.2024.109953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 02/08/2024] [Accepted: 05/08/2024] [Indexed: 07/02/2024] Open
Abstract
The development of targeted drugs for the early prevention and management of chronic kidney disease (CKD) is of great importance. However, the success rates and cost-effectiveness of traditional drug development approaches are extremely low. Utilizing large sample genome-wide association study data for drug repurposing has shown promise in many diseases but has not yet been explored in CKD. Herein, we investigated actionable druggable targets to improve renal function using large-scale Mendelian randomization and colocalization analyses. We combined two population-scale independent genetic datasets and validated findings with cell-type-dependent eQTL data of kidney tubular and glomerular samples. We ultimately prioritized two drug targets, opioid receptor-like 1 and F12, with potential genetic support for restoring renal function and subsequent treatment of CKD. Our findings explore the potential pathological mechanisms of CKD, bridge the gap between the molecular mechanisms of pathogenesis and clinical intervention, and provide new strategies in future clinical trials of CKD.
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Affiliation(s)
- Xiong Chen
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Runnan Shen
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Dongxi Zhu
- School of Medicine, Sun Yat-Sen University, Guangzhou, China
| | - Shulu Luo
- Hospital of Stomatology, Guanghua School of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Guangzhou, P.R. China
| | - Guochang You
- School of Medicine, Sun Yat-Sen University, Guangzhou, China
| | - Ruijie Li
- School of Medicine, Sun Yat-Sen University, Guangzhou, China
| | - Xiaosi Hong
- Department of Endocrinology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Ruijun Li
- Hospital of Stomatology, Guanghua School of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Guangzhou, P.R. China
| | - Jihao Wu
- Hospital of Stomatology, Guanghua School of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-sen University, Guangzhou, P.R. China
| | - Yinong Huang
- Faculty of Medicine, Macau University of Science and Technology, Macau, China
| | - Tianxin Lin
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Clinical Research Center for Urological Diseases, Guangzhou, Guangdong, China
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Jiang J, Hiron TK, Agbaedeng TA, Malhotra Y, Drydale E, Bancroft J, Ng E, Reschen ME, Davison LJ, O’Callaghan CA. A Novel Macrophage Subpopulation Conveys Increased Genetic Risk of Coronary Artery Disease. Circ Res 2024; 135:6-25. [PMID: 38747151 PMCID: PMC11191562 DOI: 10.1161/circresaha.123.324172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 04/26/2024] [Accepted: 05/01/2024] [Indexed: 06/22/2024]
Abstract
BACKGROUND Coronary artery disease (CAD), the leading cause of death worldwide, is influenced by both environmental and genetic factors. Although over 250 genetic risk loci have been identified through genome-wide association studies, the specific causal variants and their regulatory mechanisms are still largely unknown, particularly in disease-relevant cell types such as macrophages. METHODS We utilized single-cell RNA-seq and single-cell multiomics approaches in primary human monocyte-derived macrophages to explore the transcriptional regulatory network involved in a critical pathogenic event of coronary atherosclerosis-the formation of lipid-laden foam cells. The relative genetic contribution to CAD was assessed by partitioning disease heritability across different macrophage subpopulations. Meta-analysis of single-cell RNA-seq data sets from 38 human atherosclerotic samples was conducted to provide high-resolution cross-referencing to macrophage subpopulations in vivo. RESULTS We identified 18 782 cis-regulatory elements by jointly profiling the gene expression and chromatin accessibility of >5000 macrophages. Integration with CAD genome-wide association study data prioritized 121 CAD-related genetic variants and 56 candidate causal genes. We showed that CAD heritability was not uniformly distributed and was particularly enriched in the gene programs of a novel CD52-hi lipid-handling macrophage subpopulation. These CD52-hi macrophages displayed significantly less lipoprotein accumulation and were also found in human atherosclerotic plaques. We investigated the cis-regulatory effect of a risk variant rs10488763 on FDX1, implicating the recruitment of AP-1 and C/EBP-β in the causal mechanisms at this locus. CONCLUSIONS Our results provide genetic evidence of the divergent roles of macrophage subsets in atherogenesis and highlight lipid-handling macrophages as a key subpopulation through which genetic variants operate to influence disease. These findings provide an unbiased framework for functional fine-mapping of genome-wide association study results using single-cell multiomics and offer new insights into the genotype-environment interactions underlying atherosclerotic disease.
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Affiliation(s)
- Jiahao Jiang
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
| | - Thomas K. Hiron
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
| | - Thomas A. Agbaedeng
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
| | - Yashaswat Malhotra
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
| | - Edward Drydale
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
| | - James Bancroft
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
| | - Esther Ng
- Nuffield Department of Orthopaedics, Kennedy Institute of Rheumatology, Rheumatology and Musculoskeletal Sciences (E.N.), University of Oxford, United Kingdom
| | - Michael E. Reschen
- Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, United Kingdom (M.E.R.)
| | - Lucy J. Davison
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
- Department of Clinical Science and Services, Royal Veterinary College, Hatfield, United Kingdom (L.J.D.)
| | - Chris A. O’Callaghan
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
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10
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Rosean S, Sosa EA, O'Shea D, Raj SM, Seoighe C, Greally JM. Regulatory landscape enrichment analysis (RLEA): a computational toolkit for non-coding variant enrichment and cell type prioritization. BMC Bioinformatics 2024; 25:179. [PMID: 38714913 PMCID: PMC11075237 DOI: 10.1186/s12859-024-05794-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 04/22/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND As genomic studies continue to implicate non-coding sequences in disease, testing the roles of these variants requires insights into the cell type(s) in which they are likely to be mediating their effects. Prior methods for associating non-coding variants with cell types have involved approaches using linkage disequilibrium or ontological associations, incurring significant processing requirements. GaiaAssociation is a freely available, open-source software that enables thousands of genomic loci implicated in a phenotype to be tested for enrichment at regulatory loci of multiple cell types in minutes, permitting insights into the cell type(s) mediating the studied phenotype. RESULTS In this work, we present Regulatory Landscape Enrichment Analysis (RLEA) by GaiaAssociation and demonstrate its capability to test the enrichment of 12,133 variants across the cis-regulatory regions of 44 cell types. This analysis was completed in 134.0 ± 2.3 s, highlighting the efficient processing provided by GaiaAssociation. The intuitive interface requires only four inputs, offers a collection of customizable functions, and visualizes variant enrichment in cell-type regulatory regions through a heatmap matrix. GaiaAssociation is available on PyPi for download as a command line tool or Python package and the source code can also be installed from GitHub at https://github.com/GreallyLab/gaiaAssociation . CONCLUSIONS GaiaAssociation is a novel package that provides an intuitive and efficient resource to understand the enrichment of non-coding variants across the cis-regulatory regions of different cells, empowering studies seeking to identify disease-mediating cell types.
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Affiliation(s)
- Samuel Rosean
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Eric A Sosa
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Dónal O'Shea
- School of Mathematics, Statistics & Applied Mathematics, National University of Ireland Galway, Galway, H91 TK33, Ireland
| | - Srilakshmi M Raj
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Cathal Seoighe
- School of Mathematics, Statistics & Applied Mathematics, National University of Ireland Galway, Galway, H91 TK33, Ireland
| | - John M Greally
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
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11
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Bao C, Tan T, Wang S, Gao C, Lu C, Yang S, Diao Y, Jiang L, Jing D, Chen L, Lv H, Fang H. A cross-disease, pleiotropy-driven approach for therapeutic target prioritization and evaluation. CELL REPORTS METHODS 2024; 4:100757. [PMID: 38631345 PMCID: PMC11046034 DOI: 10.1016/j.crmeth.2024.100757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 01/08/2024] [Accepted: 03/22/2024] [Indexed: 04/19/2024]
Abstract
Cross-disease genome-wide association studies (GWASs) unveil pleiotropic loci, mostly situated within the non-coding genome, each of which exerts pleiotropic effects across multiple diseases. However, the challenge "W-H-W" (namely, whether, how, and in which specific diseases pleiotropy can inform clinical therapeutics) calls for effective and integrative approaches and tools. We here introduce a pleiotropy-driven approach specifically designed for therapeutic target prioritization and evaluation from cross-disease GWAS summary data, with its validity demonstrated through applications to two systems of disorders (neuropsychiatric and inflammatory). We illustrate its improved performance in recovering clinical proof-of-concept therapeutic targets. Importantly, it identifies specific diseases where pleiotropy informs clinical therapeutics. Furthermore, we illustrate its versatility in accomplishing advanced tasks, including pathway crosstalk identification and downstream crosstalk-based analyses. To conclude, our integrated solution helps bridge the gap between pleiotropy studies and therapeutics discovery.
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Affiliation(s)
- Chaohui Bao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Tingting Tan
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Shan Wang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Chenxu Gao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Chang Lu
- MRC London Institute of Medical Sciences, Imperial College London, W12 0HS London, UK
| | - Siyue Yang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Faculty of Medical Laboratory Science, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yizhu Diao
- College of Finance and Statistics, Hunan University, Changsha, Hunan 410079, China
| | - Lulu Jiang
- Translational Health Sciences, University of Bristol, BS1 3NY Bristol, UK
| | - Duohui Jing
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Liye Chen
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, OX3 7LD Oxford, UK.
| | - Haitao Lv
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; School of Chinese Medicine, State Key Laboratory of Environmental and Biological Analysis, Hong Kong Chinese Medicine Phenome Research Center, Hong Kong Baptist University, Hong Kong 999077, China.
| | - Hai Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
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12
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Zhao W, Fang P, Lai C, Xu X, Wang Y, Liu H, Jiang H, Liu X, Liu J. Proteome-wide Mendelian randomization identifies therapeutic targets for ankylosing spondylitis. Front Immunol 2024; 15:1366736. [PMID: 38566994 PMCID: PMC10985162 DOI: 10.3389/fimmu.2024.1366736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 03/05/2024] [Indexed: 04/04/2024] Open
Abstract
Background Ankylosing Spondylitis (AS) is a chronic inflammatory disorder which can lead to considerable pain and disability. Mendelian randomization (MR) has been extensively applied for repurposing licensed drugs and uncovering new therapeutic targets. Our objective is to pinpoint innovative therapeutic protein targets for AS and assess the potential adverse effects of druggable proteins. Methods We conducted a comprehensive proteome-wide MR study to assess the causal relationships between plasma proteins and the risk of AS. The plasma proteins were sourced from the UK Biobank Pharma Proteomics Project (UKB-PPP) database, encompassing GWAS data for 2,940 plasma proteins. Additionally, GWAS data for AS were extracted from the R9 version of the Finnish database, including 2,860 patients and 270,964 controls. The colocalization analysis was executed to identify shared causal variants between plasma proteins and AS. Finally, we examined the potential adverse effects of druggable proteins for AS therapy by conducting a phenome-wide association study (PheWAS) utilizing the extensive Finnish database in version R9, encompassing 2,272 phenotypes categorized into 46 groups. Results The findings revealed a positive genetic association between the predicted plasma levels of six proteins and an elevated risk of AS, while two proteins exhibited an inverse association with AS risk (P fdr < 0.05). Among these eight plasma proteins, colocalization analysis identified AIF1, TNF, FKBPL, AGER, ALDH5A1, and ACOT13 as shared variation with AS(PPH3+PPH4>0.8), suggesting that they represent potential direct targets for AS intervention. Further phenotype-wide association studies have shown some potential side effects of these six targets (P fdr < 0.05). Conclusion Our investigation examined the causal connections between six plasma proteins and AS, providing a comprehensive understanding of potential therapeutic targets.
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Affiliation(s)
- Wenlong Zhao
- Department of Orthopedics, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Department of Orthopedics, The Affiliated Jinling Hospital of Nanjing Medical University, Nanjing, China
| | - Peng Fang
- Department of Orthopedics, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Chengteng Lai
- Department of Orthopedics, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Xiaoyu Xu
- Department of Biology, Wake Forest University, North Carolina, NC, United States
| | - Yang Wang
- Department of Orthopedics, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Hao Liu
- Department of Orthopedics, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Hui Jiang
- Department of Orthopedics, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Xiaozhou Liu
- Department of Orthopedics, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Jun Liu
- Department of Orthopedics, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
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13
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Ma Y, Zhou Y, Jiang D, Dai W, Li J, Deng C, Chen C, Zheng G, Zhang Y, Qiu F, Sun H, Xing S, Han H, Qu J, Wu N, Yao Y, Su J. Integration of human organoids single-cell transcriptomic profiles and human genetics repurposes critical cell type-specific drug targets for severe COVID-19. Cell Prolif 2024; 57:e13558. [PMID: 37807299 PMCID: PMC10905359 DOI: 10.1111/cpr.13558] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/31/2023] [Accepted: 09/18/2023] [Indexed: 10/10/2023] Open
Abstract
Human organoids recapitulate the cell type diversity and function of their primary organs holding tremendous potentials for basic and translational research. Advances in single-cell RNA sequencing (scRNA-seq) technology and genome-wide association study (GWAS) have accelerated the biological and therapeutic interpretation of trait-relevant cell types or states. Here, we constructed a computational framework to integrate atlas-level organoid scRNA-seq data, GWAS summary statistics, expression quantitative trait loci, and gene-drug interaction data for distinguishing critical cell populations and drug targets relevant to coronavirus disease 2019 (COVID-19) severity. We found that 39 cell types across eight kinds of organoids were significantly associated with COVID-19 outcomes. Notably, subset of lung mesenchymal stem cells increased proximity with fibroblasts predisposed to repair COVID-19-damaged lung tissue. Brain endothelial cell subset exhibited significant associations with severe COVID-19, and this cell subset showed a notable increase in cell-to-cell interactions with other brain cell types, including microglia. We repurposed 33 druggable genes, including IFNAR2, TYK2, and VIPR2, and their interacting drugs for COVID-19 in a cell-type-specific manner. Overall, our results showcase that host genetic determinants have cellular-specific contribution to COVID-19 severity, and identification of cell type-specific drug targets may facilitate to develop effective therapeutics for treating severe COVID-19 and its complications.
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Affiliation(s)
- Yunlong Ma
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
| | - Yijun Zhou
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Dingping Jiang
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
| | - Wei Dai
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, China
| | - Jingjing Li
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Chunyu Deng
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Cheng Chen
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Gongwei Zheng
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Yaru Zhang
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
| | - Fei Qiu
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Haojun Sun
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Shilai Xing
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
| | - Haijun Han
- School of Medicine, Hangzhou City University, Hangzhou, China
| | - Jia Qu
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Nan Wu
- Beijing Key Laboratory for Genetic Research of Skeletal Deformity, Key Laboratory of Big Data for Spinal Deformities, Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Yinghao Yao
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
| | - Jianzhong Su
- National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Biomedical Informatics, Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Zhejiang, China
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Zhang Z, Wang S, Jiang L, Wei J, Lu C, Li S, Diao Y, Fang Z, He S, Tan T, Yang Y, Zou K, Shi J, Lin J, Chen L, Bao C, Fei J, Fang H. Priority index for critical Covid-19 identifies clinically actionable targets and drugs. Commun Biol 2024; 7:189. [PMID: 38366110 PMCID: PMC10873402 DOI: 10.1038/s42003-024-05897-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 02/07/2024] [Indexed: 02/18/2024] Open
Abstract
While genome-wide studies have identified genomic loci in hosts associated with life-threatening Covid-19 (critical Covid-19), the challenge of resolving these loci hinders further identification of clinically actionable targets and drugs. Building upon our previous success, we here present a priority index solution designed to address this challenge, generating the target and drug resource that consists of two indexes: the target index and the drug index. The primary purpose of the target index is to identify clinically actionable targets by prioritising genes associated with Covid-19. We illustrate the validity of the target index by demonstrating its ability to identify pre-existing Covid-19 phase-III drug targets, with the majority of these targets being found at the leading prioritisation (leading targets). These leading targets have their evolutionary origins in Amniota ('four-leg vertebrates') and are predominantly involved in cytokine-cytokine receptor interactions and JAK-STAT signaling. The drug index highlights opportunities for repurposing clinically approved JAK-STAT inhibitors, either individually or in combination. This proposed strategic focus on the JAK-STAT pathway is supported by the active pursuit of therapeutic agents targeting this pathway in ongoing phase-II/III clinical trials for Covid-19.
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Affiliation(s)
- Zhiqiang Zhang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Shan Wang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Lulu Jiang
- Translational Health Sciences, University of Bristol, Bristol, BS1 3NY, UK
| | - Jianwen Wei
- Network and Information Center, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Chang Lu
- MRC London Institute of Medical Sciences, Imperial College London, London, W12 0HS, UK
| | - Shengli Li
- Precision Research Center for Refractory Diseases, Institute for Clinical Research, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201620, China
| | - Yizhu Diao
- College of Finance and Statistics, Hunan University, Changsha, 410079, Hunan, China
| | - Zhongcheng Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Shuo He
- College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Tingting Tan
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yisheng Yang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Kexin Zou
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Jiantao Shi
- Key Laboratory of RNA Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, 200031, China
| | - James Lin
- Network and Information Center, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Liye Chen
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
| | - Chaohui Bao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- Department of General Surgery, Ruijin Hospital Luwan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, 200020, China.
| | - Jian Fei
- Department of General Surgery, Ruijin Hospital Luwan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, 200020, China.
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Hai Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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15
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Su D, Guo Y, Yang R, Fang Z, Lu X, Xu Q, Teng Y, Sun H, Yang C, Dong J, Yu H, Mao J, Yu L, Zhao H, Wang X. Identifying a panel of nine genes as novel specific model in endometriosis noninvasive diagnosis. Fertil Steril 2024; 121:323-333. [PMID: 37995798 DOI: 10.1016/j.fertnstert.2023.11.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 11/10/2023] [Accepted: 11/15/2023] [Indexed: 11/25/2023]
Abstract
OBJECTIVE To study biomarkers to develop a novel diagnosis model for endometriosis and validate it using clinical samples. DESIGN We used publicly available data sets and weighted gene coexpression network analysis to identify differentially expressed genes. Ten machine learning algorithms were used to develop an integrative model for predicting endometriosis. The accuracy and robustness of the model were validated using data sets and clinical samples. SETTING Department of Obstetrics and Gynecology, Tangdu Hospital, Air Force Medical University, Xi'an, Shaanxi, China. PATIENT(S) The study included clinical patients between the ages of 20 and 40 years who required laparoscopic surgery and who had not undergone hormone therapy within the previous 3 months. All the healthy individuals had given birth to a child at least once in their lives. Patients with inflammatory conditions, malignant diseases, immune diseases, myoma, or adenomyosis were excluded. Paraffin blocks of the samples were collected (case, n = 5; control, n = 5). Blood samples of 58 individuals were collected (case, n = 28; control, n = 30). INTERVENTION(S) None. MAIN OUTCOME MEASURE(S) The areas under the receiver operator characteristic curve of our diagnostic model were measured for data sets and clinical samples. Multiplex immunohistochemical staining and real-time quantitative polymerase chain reaction assays were used for the validation of the model from tissue slides and peripheral blood samples. RESULT(S) A nine-gene panel endometriosis messenger RNA score (EMScore), was constructed to distinguish the patients with endometriosis from healthy individuals using algorithms. The EMScore accurately predicted endometriosis, and the areas under the receiver operator characteristic curve of our diagnostic model were 0.920, and 0.942 for tissue and blood samples, respectively. Moreover, the EMScore outperformed other acknowledged signatures for predicting endometriosis across seven clinical cohorts. Overall, the EMScore constitutes a sensitive and specific noninvasive diagnostic method for endometriosis. CONCLUSION(S) We developed the EMScore, a novel model that can aid in the diagnosis of endometriosis using peripheral blood samples. This study will contribute to the development of improved clinical noninvasive and sensitive diagnostic tools for endometriosis. These nine genes might be potential target molecules for treating endometriosis.
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Affiliation(s)
- Danjie Su
- Department of Obstetrics and Gynecology, Tangdu Hospital, Air Force Medical University, Xi'an, Shaanxi, People's Republic of China
| | - Yongdong Guo
- Department of Oncology, Tangdu Hospital, Air Force Medical University, Xi'an, Shaanxi, People's Republic of China
| | - Rui Yang
- Department of Oncology, Tangdu Hospital, Air Force Medical University, Xi'an, Shaanxi, People's Republic of China
| | - Zheng Fang
- Department of Obstetrics and Gynecology, Tangdu Hospital, Air Force Medical University, Xi'an, Shaanxi, People's Republic of China
| | - Xueyan Lu
- Department of Obstetrics and Gynecology, Tangdu Hospital, Air Force Medical University, Xi'an, Shaanxi, People's Republic of China
| | - Qian Xu
- Department of Obstetrics and Gynecology, Tangdu Hospital, Air Force Medical University, Xi'an, Shaanxi, People's Republic of China
| | - Yunfei Teng
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Xi'an Jiaotong University (Xibei Hospital), Xi'an, Shaanxi, People's Republic of China
| | - Huijun Sun
- Department of Obstetrics and Gynecology, Tangdu Hospital, Air Force Medical University, Xi'an, Shaanxi, People's Republic of China
| | - Cheng Yang
- Department of Oncology, Tangdu Hospital, Air Force Medical University, Xi'an, Shaanxi, People's Republic of China
| | - Jie Dong
- Department of Obstetrics and Gynecology, Tangdu Hospital, Air Force Medical University, Xi'an, Shaanxi, People's Republic of China
| | - Haikun Yu
- Department of Obstetrics and Gynecology, Tangdu Hospital, Air Force Medical University, Xi'an, Shaanxi, People's Republic of China
| | - Jiaqin Mao
- Department of Obstetrics and Gynecology, Tangdu Hospital, Air Force Medical University, Xi'an, Shaanxi, People's Republic of China
| | - Liangcheng Yu
- Department of Obstetrics and Gynecology, Tangdu Hospital, Air Force Medical University, Xi'an, Shaanxi, People's Republic of China
| | - Hongxi Zhao
- Department of Obstetrics and Gynecology, Tangdu Hospital, Air Force Medical University, Xi'an, Shaanxi, People's Republic of China
| | - Xiaohong Wang
- Department of Obstetrics and Gynecology, Tangdu Hospital, Air Force Medical University, Xi'an, Shaanxi, People's Republic of China.
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16
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Alamad B, Elliott K, Knight JC. Cross-population applications of genomics to understand the risk of multifactorial traits involving inflammation and immunity. CAMBRIDGE PRISMS. PRECISION MEDICINE 2024; 2:e3. [PMID: 38549844 PMCID: PMC10953767 DOI: 10.1017/pcm.2023.25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 11/15/2023] [Accepted: 12/18/2023] [Indexed: 04/26/2024]
Abstract
The interplay between genetic and environmental factors plays a significant role in interindividual variation in immune and inflammatory responses. The availability of high-throughput low-cost genotyping and next-generation sequencing has revolutionized our ability to identify human genetic variation and understand how this varies within and between populations, and the relationship with disease. In this review, we explore the potential of genomics for patient benefit, specifically in the diagnosis, prognosis and treatment of inflammatory and immune-related diseases. We summarize the knowledge arising from genetic and functional genomic approaches, and the opportunity for personalized medicine. The review covers applications in infectious diseases, rare immunodeficiencies and autoimmune diseases, illustrating advances in diagnosis and understanding risk including use of polygenic risk scores. We further explore the application for patient stratification and drug target prioritization. The review highlights a key challenge to the field arising from the lack of sufficient representation of genetically diverse populations in genomic studies. This currently limits the clinical utility of genetic-based diagnostic and risk-based applications in non-Caucasian populations. We highlight current genome projects, initiatives and biobanks from diverse populations and how this is being used to improve healthcare globally by improving our understanding of genetic susceptibility to diseases and regional pathogens such as malaria and tuberculosis. Future directions and opportunities for personalized medicine and wider application of genomics in health care are described, for the benefit of individual patients and populations worldwide.
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Affiliation(s)
- Bana Alamad
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Kate Elliott
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Julian C. Knight
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Chinese Academy of Medical Science Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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17
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Jung J, Lu Z, de Smith A, Mancuso N. Novel insight into the etiology of ischemic stroke gained by integrative multiome-wide association study. Hum Mol Genet 2024; 33:170-181. [PMID: 37824084 PMCID: PMC10772041 DOI: 10.1093/hmg/ddad174] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 09/14/2023] [Accepted: 10/09/2023] [Indexed: 10/13/2023] Open
Abstract
Stroke, characterized by sudden neurological deficits, is the second leading cause of death worldwide. Although genome-wide association studies (GWAS) have successfully identified many genomic regions associated with ischemic stroke (IS), the genes underlying risk and their regulatory mechanisms remain elusive. Here, we integrate a large-scale GWAS (N = 1 296 908) for IS together with molecular QTLs data, including mRNA, splicing, enhancer RNA (eRNA), and protein expression data from up to 50 tissues (total N = 11 588). We identify 136 genes/eRNA/proteins associated with IS risk across 60 independent genomic regions and find IS risk is most enriched for eQTLs in arterial and brain-related tissues. Focusing on IS-relevant tissues, we prioritize 9 genes/proteins using probabilistic fine-mapping TWAS analyses. In addition, we discover that blood cell traits, particularly reticulocyte cells, have shared genetic contributions with IS using TWAS-based pheWAS and genetic correlation analysis. Lastly, we integrate our findings with a large-scale pharmacological database and identify a secondary bile acid, deoxycholic acid, as a potential therapeutic component. Our work highlights IS risk genes/splicing-sites/enhancer activity/proteins with their phenotypic consequences using relevant tissues as well as identify potential therapeutic candidates for IS.
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Affiliation(s)
- Junghyun Jung
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, United States
| | - Zeyun Lu
- Biostatistics Division, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 2001 North Soto Street, Los Angeles, CA 90033, United States
| | - Adam de Smith
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, United States
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, United States
- Biostatistics Division, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 2001 North Soto Street, Los Angeles, CA 90033, United States
- Department of Quantitative and Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, United States
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18
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Duffy Á, Petrazzini BO, Stein D, Park JK, Forrest IS, Gibson K, Vy HM, Chen R, Márquez-Luna C, Mort M, Verbanck M, Schlessinger A, Itan Y, Cooper DN, Rocheleau G, Jordan DM, Do R. Development of a human genetics-guided priority score for 19,365 genes and 399 drug indications. Nat Genet 2024; 56:51-59. [PMID: 38172303 DOI: 10.1038/s41588-023-01609-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 11/13/2023] [Indexed: 01/05/2024]
Abstract
Studies have shown that drug targets with human genetic support are more likely to succeed in clinical trials. Hence, a tool integrating genetic evidence to prioritize drug target genes is beneficial for drug discovery. We built a genetic priority score (GPS) by integrating eight genetic features with drug indications from the Open Targets and SIDER databases. The top 0.83%, 0.28% and 0.19% of the GPS conferred a 5.3-, 9.9- and 11.0-fold increased effect of having an indication, respectively. In addition, we observed that targets in the top 0.28% of the score were 1.7-, 3.7- and 8.8-fold more likely to advance from phase I to phases II, III and IV, respectively. Complementary to the GPS, we incorporated the direction of genetic effect and drug mechanism into a directional version of the score called the GPS with direction of effect. We applied our method to 19,365 protein-coding genes and 399 drug indications and made all results available through a web portal.
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Affiliation(s)
- Áine Duffy
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Ben Omega Petrazzini
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - David Stein
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Joshua K Park
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Iain S Forrest
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Kyle Gibson
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Ha My Vy
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Robert Chen
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Carla Márquez-Luna
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Matthew Mort
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK
| | | | - Avner Schlessinger
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Yuval Itan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - David N Cooper
- Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK
| | - Ghislain Rocheleau
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Daniel M Jordan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
- Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
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19
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Kaya S, Alliston T, Evans DS. Genetic and Gene Expression Resources for Osteoporosis and Bone Biology Research. Curr Osteoporos Rep 2023; 21:637-649. [PMID: 37831357 PMCID: PMC11098148 DOI: 10.1007/s11914-023-00821-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/11/2023] [Indexed: 10/14/2023]
Abstract
PURPOSE OF REVIEW The integration of data from multiple genomic assays from humans and non-human model organisms is an effective approach to identify genes involved in skeletal fragility and fracture risk due to osteoporosis and other conditions. This review summarizes genome-wide genetic variation and gene expression data resources relevant to the discovery of genes contributing to skeletal fragility and fracture risk. RECENT FINDINGS Genome-wide association studies (GWAS) of osteoporosis-related traits are summarized, in addition to gene expression in bone tissues in humans and non-human organisms, with a focus on rodent models related to skeletal fragility and fracture risk. Gene discovery approaches using these genomic data resources are described. We also describe the Musculoskeletal Knowledge Portal (MSKKP) that integrates much of the available genomic data relevant to fracture risk. The available genomic resources provide a wealth of knowledge and can be analyzed to identify genes related to fracture risk. Genomic resources that would fill particular scientific gaps are discussed.
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Affiliation(s)
- Serra Kaya
- Department of Orthopedic Surgery, University of California, San Francisco, CA, USA
| | - Tamara Alliston
- Department of Orthopedic Surgery, University of California, San Francisco, CA, USA
| | - Daniel S Evans
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA.
- California Pacific Medical Center Research Institute, San Francisco, CA, USA.
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20
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Dobbs Spendlove M, M. Gibson T, McCain S, Stone BC, Gill T, Pickett BE. Pathway2Targets: an open-source pathway-based approach to repurpose therapeutic drugs and prioritize human targets. PeerJ 2023; 11:e16088. [PMID: 37790614 PMCID: PMC10544355 DOI: 10.7717/peerj.16088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 08/22/2023] [Indexed: 10/05/2023] Open
Abstract
Background Recent efforts to repurpose existing drugs to different indications have been accompanied by a number of computational methods, which incorporate protein-protein interaction networks and signaling pathways, to aid with prioritizing existing targets and/or drugs. However, many of these existing methods are focused on integrating additional data that are only available for a small subset of diseases or conditions. Methods We have designed and implemented a new R-based open-source target prioritization and repurposing method that integrates both canonical intracellular signaling information from five public pathway databases and target information from public sources including OpenTargets.org. The Pathway2Targets algorithm takes a list of significant pathways as input, then retrieves and integrates public data for all targets within those pathways for a given condition. It also incorporates a weighting scheme that is customizable by the user to support a variety of use cases including target prioritization, drug repurposing, and identifying novel targets that are biologically relevant for a different indication. Results As a proof of concept, we applied this algorithm to a public colorectal cancer RNA-sequencing dataset with 144 case and control samples. Our analysis identified 430 targets and ~700 unique drugs based on differential gene expression and signaling pathway enrichment. We found that our highest-ranked predicted targets were significantly enriched in targets with FDA-approved therapeutics for colorectal cancer (p-value < 0.025) that included EGFR, VEGFA, and PTGS2. Interestingly, there was no statistically significant enrichment of targets for other cancers in this same list suggesting high specificity of the results. We also adjusted the weighting scheme to prioritize more novel targets for CRC. This second analysis revealed epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase (PI3K), and two mitogen-activated protein kinases (MAPK14 and MAPK3). These observations suggest that our open-source method with a customizable weighting scheme can accurately prioritize targets that are specific and relevant to the disease or condition of interest, as well as targets that are at earlier stages of development. We anticipate that this method will complement other approaches to repurpose drugs for a variety of indications, which can contribute to the improvement of the quality of life and overall health of such patients.
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Affiliation(s)
- Mauri Dobbs Spendlove
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
| | - Trenton M. Gibson
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
| | - Shaney McCain
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
| | - Benjamin C. Stone
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
| | | | - Brett E. Pickett
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
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21
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Kim MS, Song M, Kim B, Shim I, Kim DS, Natarajan P, Do R, Won HH. Prioritization of therapeutic targets for dyslipidemia using integrative multi-omics and multi-trait analysis. Cell Rep Med 2023; 4:101112. [PMID: 37582372 PMCID: PMC10518515 DOI: 10.1016/j.xcrm.2023.101112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 12/22/2022] [Accepted: 06/19/2023] [Indexed: 08/17/2023]
Abstract
Drug targets with genetic support are several-fold more likely to succeed in clinical trials. We introduce a genetic-driven approach based on causal inferences that can inform drug target prioritization, repurposing, and adverse effects of using lipid-lowering agents. Given that a multi-trait approach increases the power to detect meaningful variants/genes, we conduct multi-omics and multi-trait analyses, followed by network connectivity investigations, and prioritize 30 potential therapeutic targets for dyslipidemia, including SORT1, PSRC1, CELSR2, PCSK9, HMGCR, APOB, GRN, HFE2, FJX1, C1QTNF1, and SLC5A8. 20% (6/30) of prioritized targets from our hypothesis-free drug target search are either approved or under investigation for dyslipidemia. The prioritized targets are 22-fold higher in likelihood of being approved or under investigation in clinical trials than genome-wide association study (GWAS)-curated targets. Our results demonstrate that the genetic-driven approach used in this study is a promising strategy for prioritizing targets while informing about the potential adverse effects and repurposing opportunities.
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Affiliation(s)
- Min Seo Kim
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Minku Song
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Beomsu Kim
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Injeong Shim
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Dan Say Kim
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Pradeep Natarajan
- Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hong-Hee Won
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea; Samsung Genome Institute, Samsung Medical Center, Seoul, Republic of Korea.
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22
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Bao C, Gu L, Wang S, Zou K, Zhang Z, Jiang L, Chen L, Fang H. Priority index for asthma (PIA): In silico discovery of shared and distinct drug targets for adult- and childhood-onset disease. Comput Biol Med 2023; 162:107095. [PMID: 37285660 DOI: 10.1016/j.compbiomed.2023.107095] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 04/30/2023] [Accepted: 05/27/2023] [Indexed: 06/09/2023]
Abstract
Asthma is a chronic disease that is caused by a combination of genetic risks and environmental triggers and can affect both adults and children. Genome-wide association studies have revealed partly distinct genetic architectures for its two age-of-onset subtypes (namely, adult-onset and childhood-onset). We reason that identifying shared and distinct drug targets between these subtypes may inform the development of subtype-specific therapeutic strategies. In attempting this, we here introduce Priority Index for Asthma or PIA, a genetics-led and network-driven drug target prioritisation tool for asthma. We demonstrate the validity of the tool in improving drug target prioritisation for asthma compared to the status quo methods, as well as in capturing the underlying etiology and existing therapeutics for the disease. We also illustrate how PIA can be used to prioritise drug targets for adult- and childhood-onset asthma, as well as to identify shared and distinct pathway crosstalk genes. Shared crosstalk genes are mostly involved in JAK-STAT signaling, with clinical evidence supporting that targeting this pathway may be a promising drug repurposing opportunity for both subtypes. Crosstalk genes specific to childhood-onset asthma are enriched for PI3K-AKT-mTOR signaling, and we identify genes that are already targeted by licensed medications as repurposed drug candidates for this subtype. We make all our results accessible and reproducible at http://www.genetictargets.com/PIA. Collectively, our study has significant implications for asthma computational medicine research and can guide the future development of subtype-specific therapeutic strategies for the disease.
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Affiliation(s)
- Chaohui Bao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Leyao Gu
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Faculty of Medical Laboratory Science, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shan Wang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kexin Zou
- School of Life Sciences, Central South University, Hunan, China
| | - Zhiqiang Zhang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Lulu Jiang
- Translational Health Sciences, University of Bristol, Bristol, UK
| | - Liye Chen
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Hai Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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23
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Bao C, Lu C, Lin J, Gough J, Fang H. The dcGO Domain-Centric Ontology Database in 2023: New Website and Extended Annotations for Protein Structural Domains. J Mol Biol 2023; 435:168093. [PMID: 37061086 PMCID: PMC7614987 DOI: 10.1016/j.jmb.2023.168093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 03/24/2023] [Accepted: 04/06/2023] [Indexed: 04/17/2023]
Abstract
Protein structural domains have been less studied than full-length proteins in terms of ontology annotations. The dcGO database has filled this gap by providing mappings from protein domains to ontologies. The dcGO update in 2023 extends annotations for protein domains of multiple definitions (SCOP, Pfam, and InterPro) with commonly used ontologies that are categorised into functions, phenotypes, diseases, drugs, pathways, regulators, and hallmarks. This update adds new dimensions to the utility of both ontology and protein domain resources. A newly designed website at http://www.protdomainonto.pro/dcGO offers a more centralised and user-friendly way to access the dcGO database, with enhanced faceted search returning term- and domain-specific information pages. Users can navigate both ontology terms and annotated domains through improved ontology hierarchy browsing. A newly added facility enables domain-based ontology enrichment analysis.
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Affiliation(s)
- Chaohui Bao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Chang Lu
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge CB2 0QH, UK; MRC London Institute of Medical Sciences, Imperial College London, London W12 0HS, UK
| | - James Lin
- High Performance Computing Center, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Julian Gough
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge CB2 0QH, UK
| | - Hai Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
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24
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Pinakhina D, Loboda A, Sergushichev A, Artomov M. Gene, cell type, and drug prioritization analysis suggest genetic basis for the utility of diuretics in treating Alzheimer disease. HGG ADVANCES 2023; 4:100203. [PMID: 37250495 PMCID: PMC10209737 DOI: 10.1016/j.xhgg.2023.100203] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 04/25/2023] [Indexed: 05/31/2023] Open
Abstract
We introduce a user-friendly tool for risk gene, cell type, and drug prioritization for complex traits: GCDPipe. It uses gene-level GWAS-derived data and gene expression data to train a model for the identification of disease risk genes and relevant cell types. Gene prioritization information is then coupled with known drug target data to search for applicable drug agents based on their estimated functional effects on the identified risk genes. We illustrate the utility of our approach in different settings: identification of the cell types, implicated in disease pathogenesis, was tested in inflammatory bowel disease (IBD) and Alzheimer disease (AD); gene target and drug prioritization was tested in IBD and schizophrenia. The analysis of phenotypes with known disease-affected cell types and/or existing drug candidates shows that GCDPipe is an effective tool to unify genetic risk factors with cellular context and known drug targets. Next, analysis of the AD data with GCDPipe suggested that gene targets of diuretics, as an Anatomical Therapeutic Chemical drug subgroup, are significantly enriched among the genes prioritized by GCDPipe, indicating their possible effect on the course of the disease.
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Affiliation(s)
- Daria Pinakhina
- ITMO University, 197101 Saint Petersburg, Russia
- Bekhterev National Medical Research Center, 192019 Saint Petersburg, Russia
| | - Alexander Loboda
- ITMO University, 197101 Saint Petersburg, Russia
- Almazov National Medical Research Center, 191014 Saint Petersburg, Russia
| | | | - Mykyta Artomov
- ITMO University, 197101 Saint Petersburg, Russia
- Broad Institute, Cambridge, MA 02142, USA
- Massachusetts General Hospital, Boston, MA 02114, USA
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43210, USA
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25
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Sadler MC, Auwerx C, Deelen P, Kutalik Z. Multi-layered genetic approaches to identify approved drug targets. CELL GENOMICS 2023; 3:100341. [PMID: 37492104 PMCID: PMC10363916 DOI: 10.1016/j.xgen.2023.100341] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/25/2023] [Accepted: 05/16/2023] [Indexed: 07/27/2023]
Abstract
Drugs targeting genes linked to disease via evidence from human genetics have increased odds of approval. Approaches to prioritize such genes include genome-wide association studies (GWASs), rare variant burden tests in exome sequencing studies (Exome), or integration of a GWAS with expression/protein quantitative trait loci (eQTL/pQTL-GWAS). Here, we compare gene-prioritization approaches on 30 clinically relevant traits and benchmark their ability to recover drug targets. Across traits, prioritized genes were enriched for drug targets with odds ratios (ORs) of 2.17, 2.04, 1.81, and 1.31 for the GWAS, eQTL-GWAS, Exome, and pQTL-GWAS methods, respectively. Adjusting for differences in testable genes and sample sizes, GWAS outperforms e/pQTL-GWAS, but not the Exome approach. Furthermore, performance increased through gene network diffusion, although the node degree, being the best predictor (OR = 8.7), revealed strong bias in literature-curated networks. In conclusion, we systematically assessed strategies to prioritize drug target genes, highlighting the promises and pitfalls of current approaches.
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Affiliation(s)
- Marie C. Sadler
- University Center for Primary Care and Public Health, Route de Berne 113, 1010 Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland
| | - Chiara Auwerx
- University Center for Primary Care and Public Health, Route de Berne 113, 1010 Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland
- Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland
| | - Patrick Deelen
- Department of Genetics, University of Groningen, 9700 Groningen, the Netherlands
- Oncode Institute, 3521 Utrecht, the Netherlands
| | - Zoltán Kutalik
- University Center for Primary Care and Public Health, Route de Berne 113, 1010 Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Quartier Sorge, 1015 Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland
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26
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Liu D, Shi Y, Chen H, Nisar MA, Jabara N, Langwinski N, Mattson S, Nagaoka K, Bai X, Lu S, Huang CK. Molecular profiling reveals potential targets in cholangiocarcinoma. World J Gastroenterol 2023; 29:4053-4071. [PMID: 37476584 PMCID: PMC10354586 DOI: 10.3748/wjg.v29.i25.4053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 02/16/2023] [Accepted: 05/23/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND Cholangiocarcinoma (CCA) is a devastating malignancy and has a very poor prognosis if tumors spread outside the liver. Understanding the molecular mechanisms underlying the CCA progression will likely yield therapeutic approaches toward treating this deadly disease. AIM To determine the molecular pathogenesis in CCA progression. METHODS In silico analysis, in vitro cell culture, CCA transgenic animals, histological, and molecular assays were adopted to determine the molecular pathogenesis. RESULTS The transcriptomic data of human CCA samples were retrieved from The Cancer Genome Atlas (TGCA, CHOL), European Bioinformatics Institute (EBI, GAD00001001076), and Gene Expression Omnibus (GEO, GSE107943) databases. Using Gene set enrichment analysis, the cell cycle and Notch related pathways were demonstrated to be significantly activated in CCA in TCGA and GEO datasets. We, through differentially expressed genes, found several cell cycle and notch associated genes were significantly up-regulated in cancer tissues when compared with the non-cancerous control samples. The associated genes, via quantitative real-time PCR and western blotting assays, were further examined in normal human cholangiocytes, CCA cell lines, mouse normal bile ducts, and mouse CCA tumors established by specifically depleting P53 and expressing KrasG12D mutation in the liver. Consistently, we validated that the cell cycle and Notch pathways are up-regulated in CCA cell lines and mouse CCA tumors. Interestingly, targeting cell cycle and notch pathways using small molecules also exhibited significant beneficial effects in controlling tumor malignancy. More importantly, we demonstrated that several cell cycle and Notch associated genes are significantly associated with poor overall survival and disease-free survival using the Log-Rank test. CONCLUSION In summary, our study comprehensively analyzed the gene expression pattern of CCA samples using publicly available datasets and identified the cell cycle and Notch pathways are potential therapeutic targets in this deadly disease.
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Affiliation(s)
- Dan Liu
- Department of Gastroenterology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
| | - Yang Shi
- Department of Gastroenterology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
| | - Hongze Chen
- Pathology and Laboratory Medicine, Tulane University School of Medicine, New Orleans, LA 70112, United States
| | - Muhammad Azhar Nisar
- Pathology and Laboratory Medicine, Tulane University School of Medicine, New Orleans, LA 70112, United States
| | - Nicholas Jabara
- Pathology and Laboratory Medicine, Tulane University School of Medicine, New Orleans, LA 70112, United States
| | - Noah Langwinski
- Pathology and Laboratory Medicine, Tulane University School of Medicine, New Orleans, LA 70112, United States
| | - Sophia Mattson
- Pathology and Laboratory Medicine, Tulane University School of Medicine, New Orleans, LA 70112, United States
| | - Katsuya Nagaoka
- Department of Medicine, Rhode Island Hospital and the Alpert Medical School of Brown University, Providence, RI 02903, United States
| | - Xuewei Bai
- Department of Medicine, Rhode Island Hospital and the Alpert Medical School of Brown University, Providence, RI 02903, United States
| | - Shaolei Lu
- Department of Pathology, Alpert Medical School of Brown University, Providence, RI 02903, United States
| | - Chiung-Kuei Huang
- Pathology and Laboratory Medicine, Tulane University School of Medicine, New Orleans, LA 70112, United States
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27
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Kiryluk K, Sanchez-Rodriguez E, Zhou XJ, Zanoni F, Liu L, Mladkova N, Khan A, Marasa M, Zhang JY, Balderes O, Sanna-Cherchi S, Bomback AS, Canetta PA, Appel GB, Radhakrishnan J, Trimarchi H, Sprangers B, Cattran DC, Reich H, Pei Y, Ravani P, Galesic K, Maixnerova D, Tesar V, Stengel B, Metzger M, Canaud G, Maillard N, Berthoux F, Berthelot L, Pillebout E, Monteiro R, Nelson R, Wyatt RJ, Smoyer W, Mahan J, Samhar AA, Hidalgo G, Quiroga A, Weng P, Sreedharan R, Selewski D, Davis K, Kallash M, Vasylyeva TL, Rheault M, Chishti A, Ranch D, Wenderfer SE, Samsonov D, Claes DJ, Akchurin O, Goumenos D, Stangou M, Nagy J, Kovacs T, Fiaccadori E, Amoroso A, Barlassina C, Cusi D, Del Vecchio L, Battaglia GG, Bodria M, Boer E, Bono L, Boscutti G, Caridi G, Lugani F, Ghiggeri G, Coppo R, Peruzzi L, Esposito V, Esposito C, Feriozzi S, Polci R, Frasca G, Galliani M, Garozzo M, Mitrotti A, Gesualdo L, Granata S, Zaza G, Londrino F, Magistroni R, Pisani I, Magnano A, Marcantoni C, Messa P, Mignani R, Pani A, Ponticelli C, Roccatello D, Salvadori M, Salvi E, Santoro D, Gembillo G, Savoldi S, Spotti D, Zamboli P, Izzi C, Alberici F, Delbarba E, Florczak M, Krata N, Mucha K, Pączek L, Niemczyk S, Moszczuk B, Pańczyk-Tomaszewska M, Mizerska-Wasiak M, Perkowska-Ptasińska A, Bączkowska T, Durlik M, Pawlaczyk K, Sikora P, Zaniew M, Kaminska D, Krajewska M, Kuzmiuk-Glembin I, Heleniak Z, Bullo-Piontecka B, Liberek T, Dębska-Slizien A, Hryszko T, Materna-Kiryluk A, Miklaszewska M, Szczepańska M, Dyga K, Machura E, Siniewicz-Luzeńczyk K, Pawlak-Bratkowska M, Tkaczyk M, Runowski D, Kwella N, Drożdż D, Habura I, Kronenberg F, Prikhodina L, van Heel D, Fontaine B, Cotsapas C, Wijmenga C, Franke A, Annese V, Gregersen PK, Parameswaran S, Weirauch M, Kottyan L, Harley JB, Suzuki H, Narita I, Goto S, Lee H, Kim DK, Kim YS, Park JH, Cho B, Choi M, Van Wijk A, Huerta A, Ars E, Ballarin J, Lundberg S, Vogt B, Mani LY, Caliskan Y, Barratt J, Abeygunaratne T, Kalra PA, Gale DP, Panzer U, Rauen T, Floege J, Schlosser P, Ekici AB, Eckardt KU, Chen N, Xie J, Lifton RP, Loos RJF, Kenny EE, Ionita-Laza I, Köttgen A, Julian BA, Novak J, Scolari F, Zhang H, Gharavi AG. Genome-wide association analyses define pathogenic signaling pathways and prioritize drug targets for IgA nephropathy. Nat Genet 2023; 55:1091-1105. [PMID: 37337107 DOI: 10.1038/s41588-023-01422-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 05/05/2023] [Indexed: 06/21/2023]
Abstract
IgA nephropathy (IgAN) is a progressive form of kidney disease defined by glomerular deposition of IgA. Here we performed a genome-wide association study of 10,146 kidney-biopsy-diagnosed IgAN cases and 28,751 controls across 17 international cohorts. We defined 30 genome-wide significant risk loci explaining 11% of disease risk. A total of 16 loci were new, including TNFSF4/TNFSF18, REL, CD28, PF4V1, LY86, LYN, ANXA3, TNFSF8/TNFSF15, REEP3, ZMIZ1, OVOL1/RELA, ETS1, IGH, IRF8, TNFRSF13B and FCAR. The risk loci were enriched in gene orthologs causing abnormal IgA levels when genetically manipulated in mice. We also observed a positive genetic correlation between IgAN and serum IgA levels. High polygenic score for IgAN was associated with earlier onset of kidney failure. In a comprehensive functional annotation analysis of candidate causal genes, we observed convergence of biological candidates on a common set of inflammatory signaling pathways and cytokine ligand-receptor pairs, prioritizing potential new drug targets.
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Affiliation(s)
- Krzysztof Kiryluk
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA.
- Institute for Genomic Medicine, Columbia University, New York City, NY, USA.
| | - Elena Sanchez-Rodriguez
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Xu-Jie Zhou
- Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China
| | - Francesca Zanoni
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Lili Liu
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Nikol Mladkova
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Atlas Khan
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Maddalena Marasa
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Jun Y Zhang
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Olivia Balderes
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Simone Sanna-Cherchi
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
- Institute for Genomic Medicine, Columbia University, New York City, NY, USA
| | - Andrew S Bomback
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Pietro A Canetta
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Gerald B Appel
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Jai Radhakrishnan
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA
| | - Hernan Trimarchi
- Nephrology Service, Hospital Británico de Buenos Aires, Buenos Aires, Argentina
| | - Ben Sprangers
- Department of Microbiology and Immunology, Laboratory of Molecular Immunology, KU Leuven, Leuven, Belgium
- Division of Nephrology, University Hospitals Leuven, Leuven, Belgium
| | - Daniel C Cattran
- Department of Nephrology, University of Toronto, Toronto General Hospital, Toronto, Ontario, Canada
| | - Heather Reich
- Department of Nephrology, University of Toronto, Toronto General Hospital, Toronto, Ontario, Canada
| | - York Pei
- Department of Nephrology, University of Toronto, Toronto General Hospital, Toronto, Ontario, Canada
| | - Pietro Ravani
- Division of Nephrology, Department of Internal Medicine, University of Calgary, Calgary, Alberta, Canada
| | | | - Dita Maixnerova
- 1st Faculty of Medicine and General University Hospital, Charles University, Prague, Czech Republic
| | - Vladimir Tesar
- 1st Faculty of Medicine and General University Hospital, Charles University, Prague, Czech Republic
| | - Benedicte Stengel
- Centre for Research in Epidemiology and Population Health (CESP), Paris-Saclay University, Versailles Saint Quentin University, INSERM Clinical Epidemiology Team, Villejuif, France
| | - Marie Metzger
- Centre for Research in Epidemiology and Population Health (CESP), Paris-Saclay University, Versailles Saint Quentin University, INSERM Clinical Epidemiology Team, Villejuif, France
| | - Guillaume Canaud
- Université de Paris, Hôpital Necker-Enfants Malades, Paris, France
| | - Nicolas Maillard
- Nephrology, Dialysis, and Renal Transplantation Department, University North Hospital, Saint Etienne, France
| | - Francois Berthoux
- Nephrology, Dialysis, and Renal Transplantation Department, University North Hospital, Saint Etienne, France
| | | | - Evangeline Pillebout
- Center for Research on Inflammation, University of Paris, INSERM and CNRS, Paris, France
| | - Renato Monteiro
- Center for Research on Inflammation, University of Paris, INSERM and CNRS, Paris, France
| | - Raoul Nelson
- Division of Pediatric Nephrology, Department of Pediatrics, University of Utah, Salt Lake City, UT, USA
| | - Robert J Wyatt
- Division of Pediatric Nephrology, University of Tennessee Health Sciences Center, Memphis, TN, USA
- Children's Foundation Research Center, Le Bonheur Children's Hospital, Memphis, TN, USA
| | - William Smoyer
- Division of Pediatric Nephrology, Nationwide Children's Hospital, Columbus, OH, USA
| | - John Mahan
- Division of Pediatric Nephrology, Nationwide Children's Hospital, Columbus, OH, USA
| | - Al-Akash Samhar
- Division of Pediatric Nephrology, Driscoll Children's Hospital, Corpus Christi, TX, USA
| | - Guillermo Hidalgo
- Division of Pediatric Nephrology, Department of Pediatrics, HMH Hackensack University Medical Center, Hackensack, NJ, USA
| | - Alejandro Quiroga
- Division of Pediatric Nephrology, Helen DeVos Children's Hospital, Grand Rapids, MI, USA
| | - Patricia Weng
- Division of Pediatric Nephrology, Mattel Children's Hospital, Los Angeles, CA, USA
| | - Raji Sreedharan
- Division of Pediatric Nephrology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - David Selewski
- Division of Pediatric Nephrology, Mott Children's Hospital, Ann Arbor, MI, USA
| | - Keefe Davis
- Division of Pediatric Nephrology, Department of Pediatrics, The Medical University of South Carolina (MUSC), Charleston, SC, USA
| | - Mahmoud Kallash
- Division of Pediatric Nephrology, SUNY Buffalo, Buffalo, NY, USA
| | - Tetyana L Vasylyeva
- Division of Pediatric Nephrology, Department of Pediatrics, Nationwide Children's Hospital, Columbus, OH, USA
| | - Michelle Rheault
- Division of Pediatric Nephrology, University of Minnesota, Minneapolis, MN, USA
| | - Aftab Chishti
- Division of Pediatric Nephrology, University of Kentucky, Lexington, KY, USA
| | - Daniel Ranch
- Division of Pediatric Nephrology, Department of Pediatrics, University of Kentucky, Lexington, KY, USA
| | - Scott E Wenderfer
- Division of Pediatric Nephrology, Baylor College of Medicine/Texas Children's Hospital, Houston, TX, USA
| | - Dmitry Samsonov
- Division of Pediatric Nephrology, Boston Children's Hospital, Boston, MA, USA
| | - Donna J Claes
- Division of Pediatric Nephrology, Department of Pediatrics, New York Medical College, New York City, NY, USA
| | - Oleh Akchurin
- Division of Pediatric Nephrology, Department of Pediatrics, Weill Cornell Medical College, New York City, NY, USA
| | | | - Maria Stangou
- The Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Judit Nagy
- 2nd Department of Internal Medicine, Nephrological and Diabetological Center, University of Pécs, Pécs, Hungary
| | - Tibor Kovacs
- 2nd Department of Internal Medicine, Nephrological and Diabetological Center, University of Pécs, Pécs, Hungary
| | - Enrico Fiaccadori
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Antonio Amoroso
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Cristina Barlassina
- Renal Division, Dipartimento di Medicina, Chirurgia e Odontoiatria, San Paolo Hospital, School of Medicine, University of Milan, Milan, Italy
| | - Daniele Cusi
- Renal Division, Dipartimento di Medicina, Chirurgia e Odontoiatria, San Paolo Hospital, School of Medicine, University of Milan, Milan, Italy
| | | | | | | | - Emanuela Boer
- Division of Nephrology and Dialysis, Gorizia Hospital, Gorizia, Italy
| | - Luisa Bono
- Nephrology and Dialysis, A.R.N.A.S. Civico and Benfratelli, Palermo, Italy
| | - Giuliano Boscutti
- Nephrology, Dialysis and Renal Transplant Unit, S. Maria della Misericordia Hospital, ASUFC, Udine, Italy
| | - Gianluca Caridi
- Division of Nephrology, Dialysis and Transplantation, IRCCS Giannina Gaslini Institute, Genova, Italy
| | - Francesca Lugani
- Division of Nephrology, Dialysis and Transplantation, IRCCS Giannina Gaslini Institute, Genova, Italy
| | - GianMarco Ghiggeri
- Division of Nephrology, Dialysis and Transplantation, IRCCS Giannina Gaslini Institute, Genova, Italy
| | - Rosanna Coppo
- Regina Margherita Children's Hospital, Torino, Italy
| | - Licia Peruzzi
- Regina Margherita Children's Hospital, Torino, Italy
| | | | | | | | | | - Giovanni Frasca
- Division of Nephrology, Dialysis and Renal Transplantation, Riuniti Hospital, Ancona, Italy
| | | | - Maurizio Garozzo
- Unità Operativa di Nefrologia e Dialisi, Ospedale di Acireale, Acireale, Italy
| | - Adele Mitrotti
- Nephrology, Dialysis and Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari Aldo Moro, Bari, Italy
| | - Loreto Gesualdo
- Nephrology, Dialysis and Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari Aldo Moro, Bari, Italy
| | - Simona Granata
- Renal Unit, Department of Medicine, University of Verona, Verona, Italy
| | - Gianluigi Zaza
- Renal Unit, Department of Medicine, University of Verona, Verona, Italy
| | | | - Riccardo Magistroni
- Department of Surgical, Medical, Dental, Oncologic and Regenerative Medicine, University of Modena and Reggio Emilia, Modena, Italy
| | - Isabella Pisani
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Andrea Magnano
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | | | - Piergiorgio Messa
- Nephrology Dialysis and Kidney Transplant Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Università degli Studi di Milano, Milan, Italy
| | - Renzo Mignani
- Azienda Unità Sanitaria Locale Rimini, Rimini, Italy
| | - Antonello Pani
- Department of Nephrology and Dialysis, G. Brotzu Hospital, Cagliari, Italy
| | | | - Dario Roccatello
- Nephrology and Dialysis Unit, G. Bosco Hub Hospital (ERK-net Member) and University of Torino, Torino, Italy
| | - Maurizio Salvadori
- Division of Nephrology and Renal Transplantation, Carreggi Hospital, Florence, Italy
| | - Erica Salvi
- Renal Division, DMCO (Dipartimento di Medicina, Chirurgia e Odontoiatria), San Paolo Hospital, School of Medicine, University of Milan, Milan, Italy
| | - Domenico Santoro
- Unit of Nephrology and Dialysis, AOU G Martino, University of Messina, Messina, Italy
| | - Guido Gembillo
- Unit of Nephrology and Dialysis, AOU G Martino, University of Messina, Messina, Italy
| | - Silvana Savoldi
- Unit of Nephrology and Dialysis, ASL TO4-Consultorio Cirié, Turin, Italy
| | | | | | - Claudia Izzi
- Department of Medical and Surgical Specialties and Nephrology Unit, University of Brescia-ASST Spedali Civili, Brescia, Italy
| | - Federico Alberici
- Department of Medical and Surgical Specialties and Nephrology Unit, University of Brescia-ASST Spedali Civili, Brescia, Italy
| | - Elisa Delbarba
- Department of Medical and Surgical Specialties and Nephrology Unit, University of Brescia-ASST Spedali Civili, Brescia, Italy
| | - Michał Florczak
- Department of Immunology, Transplantology and Internal Diseases, Medical University of Warsaw, Warsaw, Poland
| | - Natalia Krata
- Department of Immunology, Transplantology and Internal Diseases, Medical University of Warsaw, Warsaw, Poland
| | - Krzysztof Mucha
- Department of Immunology, Transplantology and Internal Diseases, Medical University of Warsaw, Warsaw, Poland
- Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland
| | - Leszek Pączek
- Department of Immunology, Transplantology and Internal Diseases, Medical University of Warsaw, Warsaw, Poland
- Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland
| | - Stanisław Niemczyk
- Department of Internal Disease, Nephrology and Dialysotherapy, Military Institute of Medicine, Warsaw, Poland
| | - Barbara Moszczuk
- Department of Immunology, Transplantology and Internal Diseases, Medical University of Warsaw, Warsaw, Poland
- Department of Clinical Immunology, Medical University of Warsaw, Warsaw, Poland
| | | | | | | | - Teresa Bączkowska
- Department of Transplantation Medicine, Nephrology and Internal Diseases, Medical University of Warsaw, Warsaw, Poland
| | - Magdalena Durlik
- Department of Transplantation Medicine, Nephrology and Internal Diseases, Medical University of Warsaw, Warsaw, Poland
| | - Krzysztof Pawlaczyk
- Department of Nephrology, Transplantology and Internal Medicine, Poznan Medical University, Poznan, Poland
| | - Przemyslaw Sikora
- Department of Pediatric Nephrology, Medical University of Lublin, Lublin, Poland
| | - Marcin Zaniew
- Department of Pediatrics, University of Zielona Góra, Zielona Góra, Poland
| | - Dorota Kaminska
- Clinical Department of Nephrology and Transplantation Medicine, Wroclaw Medical University, Wroclaw, Poland
| | - Magdalena Krajewska
- Clinical Department of Nephrology and Transplantation Medicine, Wroclaw Medical University, Wroclaw, Poland
| | - Izabella Kuzmiuk-Glembin
- Department of Nephrology, Transplantology and Internal Diseases, Medical University of Gdansk, Gdansk, Poland
| | - Zbigniew Heleniak
- Department of Nephrology, Transplantology and Internal Diseases, Medical University of Gdansk, Gdansk, Poland
| | - Barbara Bullo-Piontecka
- Department of Nephrology, Transplantology and Internal Diseases, Medical University of Gdansk, Gdansk, Poland
| | - Tomasz Liberek
- Department of Nephrology, Transplantology and Internal Diseases, Medical University of Gdansk, Gdansk, Poland
| | - Alicja Dębska-Slizien
- Department of Nephrology, Transplantology and Internal Diseases, Medical University of Gdansk, Gdansk, Poland
| | - Tomasz Hryszko
- 2nd Department of Nephrology and Hypertension with Dialysis Unit, Medical University of Bialystok, Bialystok, Poland
| | | | - Monika Miklaszewska
- Department of Pediatric Nephrology and Hypertension, Jagiellonian University Medical College, Krakow, Poland
| | - Maria Szczepańska
- Department of Pediatrics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice, Katowice, Poland
| | - Katarzyna Dyga
- Department of Pediatrics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice, Katowice, Poland
| | - Edyta Machura
- Department of Pediatrics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice, Katowice, Poland
| | - Katarzyna Siniewicz-Luzeńczyk
- Department of Pediatrics, Immunology and Nephrology, Polish Mother's Memorial Hospital Research Institute, Lodz, Poland
| | - Monika Pawlak-Bratkowska
- Department of Pediatrics, Immunology and Nephrology, Polish Mother's Memorial Hospital Research Institute, Lodz, Poland
| | - Marcin Tkaczyk
- Department of Pediatrics, Immunology and Nephrology, Polish Mother's Memorial Hospital Research Institute, Lodz, Poland
| | - Dariusz Runowski
- Department of Nephrology, Kidney Transplantation and Hypertension, Children's Memorial Health Institute, Warsaw, Poland
| | - Norbert Kwella
- Department of Nephrology, Hypertension and Internal Medicine, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland
| | - Dorota Drożdż
- Department of Pediatric Nephrology and Hypertension, Jagiellonian University Medical College, Krakow, Poland
| | - Ireneusz Habura
- Department of Nephrology, Karol Marcinkowski Hospital, Zielona Góra, Poland
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Larisa Prikhodina
- Division of Inherited and Acquired Kidney Diseases, Veltischev Research and Clinical Institute for Pediatrics of the Pirogov Russian National Research Medical University, Moscow, Russia
| | - David van Heel
- Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Bertrand Fontaine
- Sorbonne University, INSERM, Center of Research in Myology, Institute of Myology, University Hospital Pitie-Salpetriere, Paris, France
- Assistance Publique-Hôpitaux de Paris (AP-HP), Service of Neuro-Myology, University Hospital Pitie-Salpetriere, Paris, France
| | - Chris Cotsapas
- Departments of Neurology and Genetics, Yale University, New Haven, CT, USA
| | | | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Vito Annese
- CBP American Hospital, Dubai, United Arab Emirates
| | - Peter K Gregersen
- Robert S. Boas Center for Genomics and Human Genetics, Feinstein Institutes for Medical Research, North Shore LIJ Health System, New York City, NY, USA
| | | | - Matthew Weirauch
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Leah Kottyan
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - John B Harley
- US Department of Veterans Affairs Medical Center and Cincinnati Education and Research for Veterans Foundation, Cincinnati, OH, USA
| | - Hitoshi Suzuki
- Department of Nephrology, Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Ichiei Narita
- Division of Clinical Nephrology and Rheumatology, Kidney Research Center, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Shin Goto
- Division of Clinical Nephrology and Rheumatology, Kidney Research Center, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Hajeong Lee
- Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Dong Ki Kim
- Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Yon Su Kim
- Biomedical Science, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jin-Ho Park
- Department of Family Medicine, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea
| | - BeLong Cho
- Department of Family Medicine, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea
- Institute on Aging, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Murim Choi
- Biomedical Science, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ans Van Wijk
- Amsterdam University Medical Centre, VU University Medical Center (VUMC), Amsterdam, the Netherlands
| | - Ana Huerta
- Hospital Universitario Puerta del Hierro Majadahonda, REDINREN, IISCIII, Madrid, Spain
| | - Elisabet Ars
- Molecular Biology Laboratory and Nephrology Department, Fundació Puigvert, Instituto de Investigaciones Biomédicas Sant Pau, Universitat Autònoma de Barcelona, REDINREN, IISCIII, Barcelona, Spain
| | - Jose Ballarin
- Molecular Biology Laboratory and Nephrology Department, Fundació Puigvert, Instituto de Investigaciones Biomédicas Sant Pau, Universitat Autònoma de Barcelona, REDINREN, IISCIII, Barcelona, Spain
| | - Sigrid Lundberg
- Department of Nephrology, Danderyd University Hospital, and Department of Clinical Sciences, Karolinska Institutet, Stockholm, Sweden
| | - Bruno Vogt
- Department of Nephrology and Hypertension, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Laila-Yasmin Mani
- Department of Nephrology and Hypertension, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Yasar Caliskan
- Division of Nephrology, Saint Louis University, Saint Louis, MO, USA
| | - Jonathan Barratt
- John Walls Renal Unit, University Hospitals of Leicester, Leicester, UK
| | | | | | - Daniel P Gale
- Department of Renal Medicine, University College London, London, UK
| | | | - Thomas Rauen
- Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany
| | - Jürgen Floege
- Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany
| | - Pascal Schlosser
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Arif B Ekici
- Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Kai-Uwe Eckardt
- Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Department of Nephrology and Hypertension, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Nan Chen
- Department of Nephrology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Jingyuan Xie
- Department of Nephrology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Richard P Lifton
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
- Laboratory of Human Genetics and Genomics, The Rockefeller University, New York City, NY, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Department of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Eimear E Kenny
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York City, NY, USA
- Center for Population Genomic Health, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Iuliana Ionita-Laza
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York City, NY, USA
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Bruce A Julian
- Departments of Microbiology and Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jan Novak
- Departments of Microbiology and Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Francesco Scolari
- Department of Medical and Surgical Specialties and Nephrology Unit, University of Brescia-ASST Spedali Civili, Brescia, Italy
| | - Hong Zhang
- Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China
| | - Ali G Gharavi
- Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA.
- Institute for Genomic Medicine, Columbia University, New York City, NY, USA.
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28
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Szebényi K, Barrio-Hernandez I, Gibbons GM, Biasetti L, Troakes C, Beltrao P, Lakatos A. A human proteogenomic-cellular framework identifies KIF5A as a modulator of astrocyte process integrity with relevance to ALS. Commun Biol 2023; 6:678. [PMID: 37386082 PMCID: PMC10310856 DOI: 10.1038/s42003-023-05041-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 06/13/2023] [Indexed: 07/01/2023] Open
Abstract
Genome-wide association studies identified several disease-causing mutations in neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). However, the contribution of genetic variants to pathway disturbances and their cell type-specific variations, especially in glia, is poorly understood. We integrated ALS GWAS-linked gene networks with human astrocyte-specific multi-omics datasets to elucidate pathognomonic signatures. It predicts that KIF5A, a motor protein kinesin-1 heavy-chain isoform, previously detected only in neurons, can also potentiate disease pathways in astrocytes. Using postmortem tissue and super-resolution structured illumination microscopy in cell-based perturbation platforms, we provide evidence that KIF5A is present in astrocyte processes and its deficiency disrupts structural integrity and mitochondrial transport. We show that this may underly cytoskeletal and trafficking changes in SOD1 ALS astrocytes characterised by low KIF5A levels, which can be rescued by c-Jun N-terminal Kinase-1 (JNK1), a kinesin transport regulator. Altogether, our pipeline reveals a mechanism controlling astrocyte process integrity, a pre-requisite for synapse maintenance and suggests a targetable loss-of-function in ALS.
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Affiliation(s)
- Kornélia Szebényi
- John van Geest Centre for Brain Repair, Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0PY, UK
- Institute of Enzymology, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | | | - George M Gibbons
- John van Geest Centre for Brain Repair, Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0PY, UK
| | - Luca Biasetti
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Claire Troakes
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Pedro Beltrao
- European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, UK.
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, 8093, Switzerland.
| | - András Lakatos
- John van Geest Centre for Brain Repair, Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0PY, UK.
- Wellcome Trust-MRC Cambridge Stem Cell Institute, Cambridge Biomedical Campus, Cambridge, CB2 0AW, UK.
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29
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Brown AC, Cohen CJ, Mielczarek O, Migliorini G, Costantino F, Allcock A, Davidson C, Elliott KS, Fang H, Lledó Lara A, Martin AC, Osgood JA, Sanniti A, Scozzafava G, Vecellio M, Zhang P, Black MH, Li S, Truong D, Molineros J, Howe T, Wordsworth BP, Bowness P, Knight JC. Comprehensive epigenomic profiling reveals the extent of disease-specific chromatin states and informs target discovery in ankylosing spondylitis. CELL GENOMICS 2023; 3:100306. [PMID: 37388915 PMCID: PMC10300554 DOI: 10.1016/j.xgen.2023.100306] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 01/30/2023] [Accepted: 03/27/2023] [Indexed: 07/01/2023]
Abstract
Ankylosing spondylitis (AS) is a common, highly heritable inflammatory arthritis characterized by enthesitis of the spine and sacroiliac joints. Genome-wide association studies (GWASs) have revealed more than 100 genetic associations whose functional effects remain largely unresolved. Here, we present a comprehensive transcriptomic and epigenomic map of disease-relevant blood immune cell subsets from AS patients and healthy controls. We find that, while CD14+ monocytes and CD4+ and CD8+ T cells show disease-specific differences at the RNA level, epigenomic differences are only apparent upon multi-omics integration. The latter reveals enrichment at disease-associated loci in monocytes. We link putative functional SNPs to genes using high-resolution Capture-C at 10 loci, including PTGER4 and ETS1, and show how disease-specific functional genomic data can be integrated with GWASs to enhance therapeutic target discovery. This study combines epigenetic and transcriptional analysis with GWASs to identify disease-relevant cell types and gene regulation of likely pathogenic relevance and prioritize drug targets.
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Affiliation(s)
- Andrew C. Brown
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Carla J. Cohen
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DS, UK
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Olga Mielczarek
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- Horizon Discovery (PerkinElmer) Cambridge Research Park, 8100 Beach Dr., Waterbeach, Cambridge CB25 9TL, UK
| | - Gabriele Migliorini
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, UK
| | - Félicie Costantino
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- UVSQ, INSERM UMR1173, Infection et Inflammation, Laboratory of Excellence INFLAMEX, Université Paris-Saclay, Paris, France
- Rheumatology Department, AP-HP, Ambroise Paré Hospital, Paris, France
| | - Alice Allcock
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Connor Davidson
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | | | - Hai Fang
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Centre for Translational Medicine at Shanghai, Ruijin Hospital affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Alicia Lledó Lara
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Alice C. Martin
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Julie A. Osgood
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Anna Sanniti
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Giuseppe Scozzafava
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Matteo Vecellio
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
- Centro Ricerche Fondazione Italiana Ricerca sull’Artrite (FIRA), Fondazione Pisana per la Scienza ONLUS, Via Ferruccio Giovannini 13, 56017 San Giuliano Terme (Pisa), Italy
| | - Ping Zhang
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | - Mary Helen Black
- Data Science, Population Analytics, Janssen R&D, Spring House, PA 19002, USA
| | - Shuwei Li
- Data Science, Population Analytics, Janssen R&D, Spring House, PA 19002, USA
| | - Dongnhu Truong
- Data Science, Population Analytics, Janssen R&D, Spring House, PA 19002, USA
| | - Julio Molineros
- Data Science, Population Analytics, Janssen R&D, Spring House, PA 19002, USA
| | - Trevor Howe
- Data Science, External Innovation, Janssen R&D, London W1G 0BG, UK
| | - B. Paul Wordsworth
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
- National Institute for Health Research, Comprehensive Biomedical Research Centre, Oxford OX4 2PG, UK
| | - Paul Bowness
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
- National Institute for Health Research, Comprehensive Biomedical Research Centre, Oxford OX4 2PG, UK
| | - Julian C. Knight
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
- National Institute for Health Research, Comprehensive Biomedical Research Centre, Oxford OX4 2PG, UK
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30
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Xu Z, Biswas B, Li L, Amzal B. AI/ML in Precision Medicine: A Look Beyond the Hype. Ther Innov Regul Sci 2023:10.1007/s43441-023-00541-1. [PMID: 37310669 DOI: 10.1007/s43441-023-00541-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 05/26/2023] [Indexed: 06/14/2023]
Abstract
Artificial Intelligence (AI) and Machine Learning (ML) are making headlines in medical research, especially in drug discovery, digital imaging, disease diagnostics, genetic testing, and optimal care pathway (personalized care). However, the potential uses and benefits of AI/ML applications need to be distinguished from hype. In the 2022 American Statistical Association Biopharmaceutical Section Regulatory-Industry Statistical Workshop, we convened a panel of experts from FDA and industry to talk about the challenges of successfully applying AI/ML in precision medicine and how to overcome those challenges. This paper provides a summary and expansion on the topics discussed in the panel: the application of AI/ML, bias, and data quality.
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Affiliation(s)
- Zhiheng Xu
- U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA.
| | - Bipasa Biswas
- U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA
| | - Lin Li
- PharmaLex, Inc, 5280 Corporate Dr, Frederick, MD, 21703, USA
| | - Billy Amzal
- Quinten Health, Inc, 8, Rue Vernier, 75017, Paris, France
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31
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Narganes-Carlón D, Crowther DJ, Pearson ER. A publication-wide association study (PWAS), historical language models to prioritise novel therapeutic drug targets. Sci Rep 2023; 13:8366. [PMID: 37225853 DOI: 10.1038/s41598-023-35597-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 05/20/2023] [Indexed: 05/26/2023] Open
Abstract
Most biomedical knowledge is published as text, making it challenging to analyse using traditional statistical methods. In contrast, machine-interpretable data primarily comes from structured property databases, which represent only a fraction of the knowledge present in the biomedical literature. Crucial insights and inferences can be drawn from these publications by the scientific community. We trained language models on literature from different time periods to evaluate their ranking of prospective gene-disease associations and protein-protein interactions. Using 28 distinct historical text corpora of abstracts published between 1995 and 2022, we trained independent Word2Vec models to prioritise associations that were likely to be reported in future years. This study demonstrates that biomedical knowledge can be encoded as word embeddings without the need for human labelling or supervision. Language models effectively capture drug discovery concepts such as clinical tractability, disease associations, and biochemical pathways. Additionally, these models can prioritise hypotheses years before their initial reporting. Our findings underscore the potential for extracting yet-to-be-discovered relationships through data-driven approaches, leading to generalised biomedical literature mining for potential therapeutic drug targets. The Publication-Wide Association Study (PWAS) enables the prioritisation of under-explored targets and provides a scalable system for accelerating early-stage target ranking, irrespective of the specific disease of interest.
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Affiliation(s)
- David Narganes-Carlón
- Division of Population Health and Genomics, Ninewells Hospital, School of Medicine, University of Dundee, Dundee, DD1 9SY, UK.
- Exscientia Ltd, Dundee One, River Court, 5 West Victoria Dock Road, Dundee, DD1 3JT, UK.
| | - Daniel J Crowther
- Exscientia Ltd, Dundee One, River Court, 5 West Victoria Dock Road, Dundee, DD1 3JT, UK
| | - Ewan R Pearson
- Division of Population Health and Genomics, Ninewells Hospital, School of Medicine, University of Dundee, Dundee, DD1 9SY, UK
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32
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Jung J, Lu Z, de Smith A, Mancuso N. Novel insight into the etiology of ischemic stroke gained by integrative transcriptome-wide association study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.30.23287918. [PMID: 37034585 PMCID: PMC10081428 DOI: 10.1101/2023.03.30.23287918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Stroke, characterized by sudden neurological deficits, is the second leading cause of death worldwide. Although genome-wide association studies (GWAS) have successfully identified many genomic regions associated with ischemic stroke (IS), the genes underlying risk and their regulatory mechanisms remain elusive. Here, we integrate a large-scale GWAS (N=1,296,908) for IS together with mRNA, splicing, enhancer RNA (eRNA) and protein expression data (N=11,588) from 50 tissues. We identify 136 genes/eRNA/proteins associated with IS risk across 54 independent genomic regions and find IS risk is most enriched for eQTLs in arterial and brain-related tissues. Focusing on IS-relevant tissues, we prioritize 9 genes/proteins using probabilistic fine-mapping TWAS analyses. In addition, we discover that blood cell traits, particularly reticulocyte cells, have shared genetic contributions with IS using TWAS-based pheWAS and genetic correlation analysis. Lastly, we integrate our findings with a large-scale pharmacological database and identify a secondary bile acid, deoxycholic acid, as a potential therapeutic component. Our work highlights IS risk genes/splicing-sites/enhancer activity/proteins with their phenotypic consequences using relevant tissues as well as identify potential therapeutic candidates for IS.
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Affiliation(s)
- Junghyun Jung
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Zeyun Lu
- Biostatistics Division, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Adam de Smith
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Biostatistics Division, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
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33
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Zhou J, Tian G, Quan Y, Kong Q, Huang F, Li J, Wu W, Tang Y, Zhou Z, Liu X. The long noncoding RNA THBS1-AS1 promotes cardiac fibroblast activation in cardiac fibrosis by regulating TGFBR1. JCI Insight 2023; 8:160745. [PMID: 36787190 PMCID: PMC10070117 DOI: 10.1172/jci.insight.160745] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 02/10/2023] [Indexed: 02/15/2023] Open
Abstract
Cardiac fibrosis is associated with an adverse prognosis in cardiovascular disease that results in a decreased cardiac compliance and, ultimately, heart failure. Recent studies have identified the role of long noncoding RNA (lncRNA) in cardiac fibrosis. However, the functions of many lncRNAs in cardiac fibrosis remain to be characterized. Through a whole-transcriptome sequencing and bioinformatics analysis on a mouse model of pressure overload-induced cardiac fibrosis, we screened a key lncRNA termed thrombospondin 1 antisense 1 (THBS1-AS1), which was positively associated with cardiac fibrosis. In vitro functional studies demonstrated that the silencing of THBS1-AS1 ameliorated TGF-β1 effects on cardiac fibroblast (CF) activation, and the overexpression of THBS1-AS1 displayed the opposite effect. A mechanistic study revealed that THBS1-AS1 could sponge miR-221/222 to regulate the expression of TGFBR1. Moreover, under TGF-β1 stimulation, the forced expression of miR-221/222 or the knockdown TGFBR1 significantly reversed the THBS1-AS1 overexpression induced by further CF activation. In vivo, specific knockdown of THBS1-AS1 in activated CFs significantly alleviated transverse aorta constriction-induced (TAC-induced) cardiac fibrosis in mice. Finally, we demonstrated that the human THBS1-AS1 can also affect the activation of CFs by regulating TGFBR1. In conclusion, this study reveals that lncRNA THBS1-AS1 is a potentially novel regulator of cardiac fibrosis and may serve as a target for the treatment of cardiac fibrosis.
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Affiliation(s)
- Junteng Zhou
- Laboratory of Cardiovascular Diseases, Regenerative Medicine Research Center
- Health Management Center, General Practice Medical Center, and
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China
| | - Geer Tian
- Laboratory of Cardiovascular Diseases, Regenerative Medicine Research Center
| | - Yue Quan
- Laboratory of Cardiovascular Diseases, Regenerative Medicine Research Center
| | - Qihang Kong
- Laboratory of Cardiovascular Diseases, Regenerative Medicine Research Center
| | - Fangyang Huang
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China
| | - Junli Li
- Laboratory of Cardiovascular Diseases, Regenerative Medicine Research Center
| | - Wenchao Wu
- Laboratory of Cardiovascular Diseases, Regenerative Medicine Research Center
| | - Yong Tang
- International Joint Research Centre on Purinergic Signaling, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Acupuncture & Chronobiology Key Laboratory of Sichuan Province, Chengdu, China
| | - Zhichao Zhou
- Division of Cardiology, Department of Medicine Solna, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Xiaojing Liu
- Laboratory of Cardiovascular Diseases, Regenerative Medicine Research Center
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China
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34
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Barrio-Hernandez I, Schwartzentruber J, Shrivastava A, Del-Toro N, Gonzalez A, Zhang Q, Mountjoy E, Suveges D, Ochoa D, Ghoussaini M, Bradley G, Hermjakob H, Orchard S, Dunham I, Anderson CA, Porras P, Beltrao P. Network expansion of genetic associations defines a pleiotropy map of human cell biology. Nat Genet 2023; 55:389-398. [PMID: 36823319 PMCID: PMC10011132 DOI: 10.1038/s41588-023-01327-9] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 01/30/2023] [Indexed: 02/25/2023]
Abstract
Interacting proteins tend to have similar functions, influencing the same organismal traits. Interaction networks can be used to expand the list of candidate trait-associated genes from genome-wide association studies. Here, we performed network-based expansion of trait-associated genes for 1,002 human traits showing that this recovers known disease genes or drug targets. The similarity of network expansion scores identifies groups of traits likely to share an underlying genetic and biological process. We identified 73 pleiotropic gene modules linked to multiple traits, enriched in genes involved in processes such as protein ubiquitination and RNA processing. In contrast to gene deletion studies, pleiotropy as defined here captures specifically multicellular-related processes. We show examples of modules linked to human diseases enriched in genes with known pathogenic variants that can be used to map targets of approved drugs for repurposing. Finally, we illustrate the use of network expansion scores to study genes at inflammatory bowel disease genome-wide association study loci, and implicate inflammatory bowel disease-relevant genes with strong functional and genetic support.
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Affiliation(s)
- Inigo Barrio-Hernandez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
| | - Jeremy Schwartzentruber
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
- Wellcome Sanger Institute, Cambridge, UK
| | - Anjali Shrivastava
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
| | - Noemi Del-Toro
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
| | - Asier Gonzalez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
| | - Qian Zhang
- Wellcome Sanger Institute, Cambridge, UK
| | - Edward Mountjoy
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
| | - Daniel Suveges
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
| | - David Ochoa
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
| | - Maya Ghoussaini
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
| | - Glyn Bradley
- Computational Biology, Genomic Sciences, GSK, Stevenage, UK
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
| | - Sandra Orchard
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
| | - Ian Dunham
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
- Wellcome Sanger Institute, Cambridge, UK
| | - Carl A Anderson
- Open Targets, Cambridge, UK
- Wellcome Sanger Institute, Cambridge, UK
| | - Pablo Porras
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
| | - Pedro Beltrao
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.
- Open Targets, Cambridge, UK.
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland.
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35
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Carss KJ, Deaton AM, Del Rio-Espinola A, Diogo D, Fielden M, Kulkarni DA, Moggs J, Newham P, Nelson MR, Sistare FD, Ward LD, Yuan J. Using human genetics to improve safety assessment of therapeutics. Nat Rev Drug Discov 2023; 22:145-162. [PMID: 36261593 DOI: 10.1038/s41573-022-00561-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/02/2022] [Indexed: 02/07/2023]
Abstract
Human genetics research has discovered thousands of proteins associated with complex and rare diseases. Genome-wide association studies (GWAS) and studies of Mendelian disease have resulted in an increased understanding of the role of gene function and regulation in human conditions. Although the application of human genetics has been explored primarily as a method to identify potential drug targets and support their relevance to disease in humans, there is increasing interest in using genetic data to identify potential safety liabilities of modulating a given target. Human genetic variants can be used as a model to anticipate the effect of lifelong modulation of therapeutic targets and identify the potential risk for on-target adverse events. This approach is particularly useful for non-clinical safety evaluation of novel therapeutics that lack pharmacologically relevant animal models and can contribute to the intrinsic safety profile of a drug target. This Review illustrates applications of human genetics to safety studies during drug discovery and development, including assessing the potential for on- and off-target associated adverse events, carcinogenicity risk assessment, and guiding translational safety study designs and monitoring strategies. A summary of available human genetic resources and recommended best practices is provided. The challenges and future perspectives of translating human genetic information to identify risks for potential drug effects in preclinical and clinical development are discussed.
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Affiliation(s)
| | - Aimee M Deaton
- Amgen, Cambridge, MA, USA.,Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Alberto Del Rio-Espinola
- Novartis Institutes for BioMedical Research, Basel, Switzerland.,GentiBio Inc., Cambridge, MA, USA
| | | | - Mark Fielden
- Amgen, Thousand Oaks, MA, USA.,Kate Therapeutics, San Diego, CA, USA
| | | | - Jonathan Moggs
- Novartis Institutes for BioMedical Research, Basel, Switzerland
| | | | | | - Frank D Sistare
- Merck & Co., West Point, PA, USA.,315 Meadowmont Ln, Chapel Hill, NC, USA
| | - Lucas D Ward
- Amgen, Cambridge, MA, USA. .,Alnylam Pharmaceuticals, Cambridge, MA, USA.
| | - Jing Yuan
- Amgen, Cambridge, MA, USA.,Pfizer, Cambridge, MA, USA
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Kong Q, Zhou J, Ma C, Wei Z, Chen Y, Cheng Y, Wu W, Zhou Z, Tang Y, Liu X. Inhibition of long noncoding RNA Gm41724 alleviates pressure overload-induced cardiac fibrosis by regulating lamina-associated polypeptide 2α. Pharmacol Res 2023; 188:106677. [PMID: 36702426 DOI: 10.1016/j.phrs.2023.106677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 01/15/2023] [Accepted: 01/22/2023] [Indexed: 01/24/2023]
Abstract
Cardiac fibrosis is a pathological process underlying myocardial remodeling and is characterized by excessive deposition of the myocardial extracellular matrix. Long noncoding RNAs (lncRNAs) have emerged as critical regulators of various biological processes. In this study, we investigated the role of a novel lncRNA, Gm41724, in cardiac fibrosis induced by pressure overload. High-throughput whole transcriptome sequencing analysis was performed to detect differentially expressed lncRNAs in cardiac fibroblasts (CFs) with or without TGF-β1 treatment. Differential expression analysis and gene set enrichment analysis identified Gm41724 as a potential molecule targeting fibrosis. Gm41724 positively regulated the activation of CFs induced by TGF-β1 and pressure overload. Knocking down Gm41724 could inhibit the differentiation of CFs into myofibroblasts and alleviate cardiac fibrosis induced by pressure overload. Mechanistically, comprehensive identification of RNA-binding proteins by mass spectrometry (CHIRP-MS) and RNA immunoprecipitation (RIP) assay combined with other methods of molecular biological revealed the important role of Gm41724 binding to lamina-associated polypeptide 2α (lap2α) for the activation of CFs. Further mechanistic studies indicated that the regulator of G protein signaling 4 (Rgs4), as the downstream effector of Gm41724/lap2α, regulated CFs activation. Our results implicated the involvement of Gm41724 in cardiac fibrosis induced by pressure overload and it is expected to be a promising target for anti-fibrotic therapy.
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Affiliation(s)
- Qihang Kong
- Laboratory of Cardiovascular Diseases, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Junteng Zhou
- Health Management Center, West China Hospital, Sichuan University, Chengdu 610041, China; Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Chi Ma
- Laboratory Animal Center, West China Second University Hospital, Sichuan University, Chengdu 610041, China
| | - Zisong Wei
- Laboratory of Cardiovascular Diseases, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yan Chen
- Laboratory of Cardiovascular Diseases, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yue Cheng
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Wenchao Wu
- Laboratory of Cardiovascular Diseases, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Zhichao Zhou
- Division of Cardiology, Department of Medicine Solna, Karolinska University Hospital, Karolinska Institutet, Stockholm 17176, Sweden
| | - Yong Tang
- School of Health and Rehabilitation, International Collaborative Centre on Big Science Plan for Purinergic Signalling, Chengdu University of Traditional Chinese Medicine, Acupuncture and Chronobiology Key Laboratory of Sichuan Province, Chengdu 610075, China.
| | - Xiaojing Liu
- Laboratory of Cardiovascular Diseases, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu 610041, China; Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China.
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Zhang Z, Bao C, Jiang L, Wang S, Wang K, Lu C, Fang H. When cancer drug resistance meets metabolomics (bulk, single-cell and/or spatial): Progress, potential, and perspective. Front Oncol 2023; 12:1054233. [PMID: 36686803 PMCID: PMC9854130 DOI: 10.3389/fonc.2022.1054233] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 12/20/2022] [Indexed: 01/07/2023] Open
Abstract
Resistance to drug treatment is a critical barrier in cancer therapy. There is an unmet need to explore cancer hallmarks that can be targeted to overcome this resistance for therapeutic gain. Over time, metabolic reprogramming has been recognised as one hallmark that can be used to prevent therapeutic resistance. With the advent of metabolomics, targeting metabolic alterations in cancer cells and host patients represents an emerging therapeutic strategy for overcoming cancer drug resistance. Driven by technological and methodological advances in mass spectrometry imaging, spatial metabolomics involves the profiling of all the metabolites (metabolomics) so that the spatial information is captured bona fide within the sample. Spatial metabolomics offers an opportunity to demonstrate the drug-resistant tumor profile with metabolic heterogeneity, and also poses a data-mining challenge to reveal meaningful insights from high-dimensional spatial information. In this review, we discuss the latest progress, with the focus on currently available bulk, single-cell and spatial metabolomics technologies and their successful applications in pre-clinical and translational studies on cancer drug resistance. We provide a summary of metabolic mechanisms underlying cancer drug resistance from different aspects; these include the Warburg effect, altered amino acid/lipid/drug metabolism, generation of drug-resistant cancer stem cells, and immunosuppressive metabolism. Furthermore, we propose solutions describing how to overcome cancer drug resistance; these include early detection during cancer initiation, monitoring of clinical drug response, novel anticancer drug and target metabolism, immunotherapy, and the emergence of spatial metabolomics. We conclude by describing the perspectives on how spatial omics approaches (integrating spatial metabolomics) could be further developed to improve the management of drug resistance in cancer patients.
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Affiliation(s)
- Zhiqiang Zhang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Chaohui Bao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lu Jiang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shan Wang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kankan Wang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chang Lu
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - Hai Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,*Correspondence: Hai Fang,
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Parolo S, Mariotti F, Bora P, Carboni L, Domenici E. Single-cell-led drug repurposing for Alzheimer's disease. Sci Rep 2023; 13:222. [PMID: 36604493 PMCID: PMC9816180 DOI: 10.1038/s41598-023-27420-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 12/29/2022] [Indexed: 01/06/2023] Open
Abstract
Alzheimer's disease is the most common form of dementia. Notwithstanding the huge investments in drug development, only one disease-modifying treatment has been recently approved. Here we present a single-cell-led systems biology pipeline for the identification of drug repurposing candidates. Using single-cell RNA sequencing data of brain tissues from patients with Alzheimer's disease, genome-wide association study results, and multiple gene annotation resources, we built a multi-cellular Alzheimer's disease molecular network that we leveraged for gaining cell-specific insights into Alzheimer's disease pathophysiology and for the identification of drug repurposing candidates. Our computational approach pointed out 54 candidate drugs, mainly targeting MAPK and IGF1R signaling pathways, which could be further evaluated for their potential as Alzheimer's disease therapy.
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Affiliation(s)
- Silvia Parolo
- Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), 38068, Rovereto, Italy.
| | - Federica Mariotti
- grid.491181.4Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy
| | - Pranami Bora
- grid.491181.4Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy
| | - Lucia Carboni
- grid.6292.f0000 0004 1757 1758Department of Pharmacy and Biotechnology, Alma Mater Studiorum University of Bologna, 40126 Bologna, Italy
| | - Enrico Domenici
- grid.491181.4Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Italy ,grid.11696.390000 0004 1937 0351Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, 38123 Trento, Italy
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Barrio-Hernandez I, Beltrao P. Network analysis of genome-wide association studies for drug target prioritisation. Curr Opin Chem Biol 2022; 71:102206. [PMID: 36087372 DOI: 10.1016/j.cbpa.2022.102206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 07/29/2022] [Accepted: 08/05/2022] [Indexed: 01/27/2023]
Abstract
Over the past decades, genome-wide association studies (GWAS) have led to a dramatic expansion of genetic variants implicated with human traits and diseases. These advances are expected to result in new drug targets but the identification of causal genes and the cell biology underlying human diseases from GWAS remains challenging. Here, we review protein interaction network-based methods to analyse GWAS data. These approaches can rank candidate drug targets at GWAS-associated loci or among interactors of disease genes without direct genetic support. These methods identify the cell biology affected in common across diseases, offering opportunities for drug repurposing, as well as be combined with expression data to identify focal tissues and cell types. Going forward, we expect that these methods will further improve from advances in the characterisation of context specific interaction networks and the joint analysis of rare and common genetic signals.
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Affiliation(s)
- Inigo Barrio-Hernandez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, CB10 1SD, UK; Open Targets, Wellcome Genome Campus, Cambridge, CB10 1SA, UK.
| | - Pedro Beltrao
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, CB10 1SD, UK; Open Targets, Wellcome Genome Campus, Cambridge, CB10 1SA, UK; Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, 8093, Switzerland.
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40
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Xu H, Zhang A, Fang C, Zhu Q, Wang W, Liu Y, Zhang Z, Wang X, Yuan L, Xu Y, Shao A, Lou M. SLC11A1 as a stratification indicator for immunotherapy or chemotherapy in patients with glioma. Front Immunol 2022; 13:980378. [PMID: 36531992 PMCID: PMC9748290 DOI: 10.3389/fimmu.2022.980378] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 11/15/2022] [Indexed: 12/05/2022] Open
Abstract
Background Glioma is a fatal tumor originating from the brain, which accounts for most intracranial malignancies. Currently, Immunotherapy has turned into a novel and promising treatment in glioma patients. however, there are still few effective biomarkers to mirror the reaction to immunotherapy in patients with glioma. Therefore, we intended to elucidate the evaluable efficacy of SLC11A1 in glioma patients. Methods In this study, samples from Shanghai General Hospital and data from TCGA, GEO, CGGA datasets were used to investigate and validate the relationship between SLC11A1 and the progression of glioma. We evaluated the predictive value of SLC11A1 on the prognosis of glioma with cox regression analysis. Then the relationship between immune infiltration and SLC11A1 was also analyzed. Ultimately, we performed the prediction on the immunotherapeutic response and therapeutic drugs according to the expression of SLC11A1. Results Expression of SLC11A1 increased with progression and predicted unfavorable prognosis for glioma patients. The hazard ratio for SLC11A1 expression was 2.33 with 95% CI (1.92-2.58) (P < 0.001) in cox analysis. And based on expression, we found SLC11A1 stratified glioma patients into subgroups with different immune activation statuses. Moreover, we observed that patients with higher SLC11A1 levels companied with better immunotherapeutic response, while those with lower SLC11A1 levels may respond better to temozolomide. Conclusion This study provided evidence that SLC11A1 was a novel prognostic marker and immunotherapy response indicator for gliomas. In some cases, SLC11A1 could be an effective marker for identifying patients who might benefit from immunotherapy or chemotherapy.
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Affiliation(s)
- Houshi Xu
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China,Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Anke Zhang
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang, China
| | - Chaoyou Fang
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qingwei Zhu
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Wang
- Department of Urology, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Yibo Liu
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang, China
| | - Zeyu Zhang
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang, China
| | - Xiaoyu Wang
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang, China
| | - Ling Yuan
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuanzhi Xu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China,*Correspondence: Meiqing Lou, ; Anwen Shao, ; Yuanzhi Xu,
| | - Anwen Shao
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang, China,*Correspondence: Meiqing Lou, ; Anwen Shao, ; Yuanzhi Xu,
| | - Meiqing Lou
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,*Correspondence: Meiqing Lou, ; Anwen Shao, ; Yuanzhi Xu,
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Huang D, Feng X, Yang H, Wang J, Zhang W, Fan X, Dong X, Chen K, Yu Y, Ma X, Yi X, Li M. QTLbase2: an enhanced catalog of human quantitative trait loci on extensive molecular phenotypes. Nucleic Acids Res 2022; 51:D1122-D1128. [PMID: 36330927 PMCID: PMC9825467 DOI: 10.1093/nar/gkac1020] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/17/2022] [Accepted: 10/21/2022] [Indexed: 11/06/2022] Open
Abstract
Deciphering the fine-scale molecular mechanisms that shape the genetic effects at disease-associated loci from genome-wide association studies (GWAS) remains challenging. The key avenue is to identify the essential molecular phenotypes that mediate the causal variant and disease under particular biological conditions. Therefore, integrating GWAS signals with context-specific quantitative trait loci (QTLs) (such as different tissue/cell types, disease states, and perturbations) from extensive molecular phenotypes would present important strategies for full understanding of disease genetics. Via persistent curation and systematic data processing of large-scale human molecular trait QTLs (xQTLs), we updated our previous QTLbase database (now QTLbase2, http://mulinlab.org/qtlbase) to comprehensively analyze and visualize context-specific QTLs across 22 molecular phenotypes and over 95 tissue/cell types. Overall, the resource features the following major updates and novel functions: (i) 960 more genome-wide QTL summary statistics from 146 independent studies; (ii) new data for 10 previously uncompiled QTL types; (iii) variant query scope expanded to fit 195 QTL datasets based on whole-genome sequencing; (iv) supports filtering and comparison of QTLs for different biological conditions, such as stimulation types and disease states; (v) a new linkage disequilibrium viewer to facilitate variant prioritization across tissue/cell types and QTL types.
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Affiliation(s)
| | | | - Hongxi Yang
- Department of Pharmacology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Jianhua Wang
- Department of Pharmacology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Wenwen Zhang
- Department of Pharmacology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Xutong Fan
- Department of Pharmacology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Xiaobao Dong
- Department of Bioinformatics, Tianjin Key Laboratory of Molecular Cancer Epidemiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Kexin Chen
- Department of Bioinformatics, Tianjin Key Laboratory of Molecular Cancer Epidemiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Ying Yu
- Department of Pharmacology, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Xin Ma
- Correspondence may also be addressed to Xin Ma.
| | - Xianfu Yi
- Correspondence may also be addressed to Xianfu Yi.
| | - Mulin Jun Li
- To whom correspondence should be addressed. Tel: +86 22 83336668; Fax: +86 22 83336668;
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Zhang L, Li G, Liang B, Su X, Xie H, Sun H, Wu G. Integrative analyses of immune-related biomarkers and associated mechanisms in coronary heart disease. BMC Med Genomics 2022; 15:219. [PMID: 36266609 PMCID: PMC9585797 DOI: 10.1186/s12920-022-01375-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 10/04/2022] [Indexed: 11/17/2022] Open
Abstract
Various studies showed that the effect of immune activation is pro-atherogenic and coronary heart disease (CHD) should therefore be considered an autoimmune disease. This study aimed to identify potential immune-related biomarkers, pathways, and the potential regulatory networks underlying CHD. Differentially expressed genes (DEGs) between CHD and control samples were determined by analyzing GSE71226 and GSE9128. The overlapping differential expression immune-related genes (DE-IRGs) for CHD were identified by analyzing the ImmPort database and two GEO databases. A total of 384 DE-IRGs were identified. Subsequently, comprehensive enrichment analyses suggested that DE-IRGs were enriched in immune-related pathways, including autoimmune thyroid disease, the intestinal immune network for IGA production, and downstream signaling events of B cell receptors. The signature of DE-IRGs was validated using an external independent dataset GSE20681 (AUC = 0.875). Furthermore, we conducted protein–protein interaction network analysis and identified eight hub genes, which were most enriched in regulation of defense response, NF-κB signaling pathway, regulation of JNK cascade, and regulation of cytokine production. Moreover, networks of miRNAs-mRNAs and transcription factors (TFs)-mRNA underlying the integrated data were established, involving eight miRNAs and 76 TF-targeting hub genes. Ultimately, 17 SNPs in miRNA-mediated gene networks were identified. We screened potential immune-related genes in CHD and constructed miRNA-mRNA-TF and SNP-miRNA networks, which not only provide inspired insights into the occurrence and the molecular mechanisms of CHD but also lay a foundation for targeting potential biomarkers using immunotherapy and for understanding the molecular mechanisms of CHD.
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Affiliation(s)
- Lianbo Zhang
- Department of Clinical Pharmacy, Jilin Province FAW General Hospital, Changchun, China
| | - Guibin Li
- Department of Orthopaedics, Jilin Province FAW General Hospital, Changchun, China
| | - Bo Liang
- Department of Cardiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xiaoli Su
- Department of Human Resources, Jilin Province FAW General Hospital, Changchun, China
| | - Haolin Xie
- Medical Association Office, Jilin Province FAW General Hospital, Changchun, China
| | - Hongxia Sun
- Department of Pharmacology, School of Pharmacy, Beihua University, Jilin, China
| | - Ge Wu
- Department of Clinical Pharmacy, Jilin Province FAW General Hospital, Changchun, China.
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Kuksa PP, Greenfest-Allen E, Cifello J, Ionita M, Wang H, Nicaretta H, Cheng PL, Lee WP, Wang LS, Leung YY. Scalable approaches for functional analyses of whole-genome sequencing non-coding variants. Hum Mol Genet 2022; 31:R62-R72. [PMID: 35943817 PMCID: PMC9585666 DOI: 10.1093/hmg/ddac191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 08/04/2022] [Accepted: 08/08/2022] [Indexed: 11/23/2022] Open
Abstract
Non-coding genetic variants outside of protein-coding genome regions play an important role in genetic and epigenetic regulation. It has become increasingly important to understand their roles, as non-coding variants often make up the majority of top findings of genome-wide association studies (GWAS). In addition, the growing popularity of disease-specific whole-genome sequencing (WGS) efforts expands the library of and offers unique opportunities for investigating both common and rare non-coding variants, which are typically not detected in more limited GWAS approaches. However, the sheer size and breadth of WGS data introduce additional challenges to predicting functional impacts in terms of data analysis and interpretation. This review focuses on the recent approaches developed for efficient, at-scale annotation and prioritization of non-coding variants uncovered in WGS analyses. In particular, we review the latest scalable annotation tools, databases and functional genomic resources for interpreting the variant findings from WGS based on both experimental data and in silico predictive annotations. We also review machine learning-based predictive models for variant scoring and prioritization. We conclude with a discussion of future research directions which will enhance the data and tools necessary for the effective functional analyses of variants identified by WGS to improve our understanding of disease etiology.
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Affiliation(s)
- Pavel P Kuksa
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Emily Greenfest-Allen
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jeffrey Cifello
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matei Ionita
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hui Wang
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Heather Nicaretta
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Po-Liang Cheng
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Wan-Ping Lee
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Li-San Wang
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yuk Yee Leung
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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Li F, Yin J, Lu M, Mou M, Li Z, Zeng Z, Tan Y, Wang S, Chu X, Dai H, Hou T, Zeng S, Chen Y, Zhu F. DrugMAP: molecular atlas and pharma-information of all drugs. Nucleic Acids Res 2022; 51:D1288-D1299. [PMID: 36243961 PMCID: PMC9825453 DOI: 10.1093/nar/gkac813] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/30/2022] [Accepted: 10/12/2022] [Indexed: 02/06/2023] Open
Abstract
The efficacy and safety of drugs are widely known to be determined by their interactions with multiple molecules of pharmacological importance, and it is therefore essential to systematically depict the molecular atlas and pharma-information of studied drugs. However, our understanding of such information is neither comprehensive nor precise, which necessitates the construction of a new database providing a network containing a large number of drugs and their interacting molecules. Here, a new database describing the molecular atlas and pharma-information of drugs (DrugMAP) was therefore constructed. It provides a comprehensive list of interacting molecules for >30 000 drugs/drug candidates, gives the differential expression patterns for >5000 interacting molecules among different disease sites, ADME (absorption, distribution, metabolism and excretion)-relevant organs and physiological tissues, and weaves a comprehensive and precise network containing >200 000 interactions among drugs and molecules. With the great efforts made to clarify the complex mechanism underlying drug pharmacokinetics and pharmacodynamics and rapidly emerging interests in artificial intelligence (AI)-based network analyses, DrugMAP is expected to become an indispensable supplement to existing databases to facilitate drug discovery. It is now fully and freely accessible at: https://idrblab.org/drugmap/.
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Affiliation(s)
| | | | - Mingkun Lu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Zhaorong Li
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba–Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Zhenyu Zeng
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba–Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Ying Tan
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
| | - Shanshan Wang
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Xinyi Chu
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Haibin Dai
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Su Zeng
- Correspondence may also be addressed to Su Zeng.
| | - Yuzong Chen
- Correspondence may also be addressed to Yuzong Chen.
| | - Feng Zhu
- To whom correspondence should be addressed.
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45
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Namba S, Konuma T, Wu KH, Zhou W, Okada Y. A practical guideline of genomics-driven drug discovery in the era of global biobank meta-analysis. CELL GENOMICS 2022; 2:100190. [PMID: 36778001 PMCID: PMC9903693 DOI: 10.1016/j.xgen.2022.100190] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 07/27/2022] [Accepted: 09/08/2022] [Indexed: 11/06/2022]
Abstract
Genomics-driven drug discovery is indispensable for accelerating the development of novel therapeutic targets. However, the drug discovery framework based on evidence from genome-wide association studies (GWASs) has not been established, especially for cross-population GWAS meta-analysis. Here, we introduce a practical guideline for genomics-driven drug discovery for cross-population meta-analysis, as lessons from the Global Biobank Meta-analysis Initiative (GBMI). Our drug discovery framework encompassed three methodologies and was applied to the 13 common diseases targeted by GBMI (N mean = 1,329,242). Individual methodologies complementarily prioritized drugs and drug targets, which were systematically validated by referring previously known drug-disease relationships. Integration of the three methodologies provided a comprehensive catalog of candidate drugs for repositioning, nominating promising drug candidates targeting the genes involved in the coagulation process for venous thromboembolism and the interleukin-4 and interleukin-13 signaling pathway for gout. Our study highlighted key factors for successful genomics-driven drug discovery using cross-population meta-analyses.
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Affiliation(s)
- Shinichi Namba
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - Takahiro Konuma
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan,Central Pharmaceutical Research Institute, Japan Tobacco Inc., Takatsuki 569-1125, Japan
| | - Kuan-Han Wu
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Wei Zhou
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan,Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8654, Japan,Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan,Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita 565-0871, Japan,Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita 565-0871, Japan,Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita 565-0871, Japan,Corresponding author
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46
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Identifying disease-critical cell types and cellular processes by integrating single-cell RNA-sequencing and human genetics. Nat Genet 2022; 54:1479-1492. [PMID: 36175791 PMCID: PMC9910198 DOI: 10.1038/s41588-022-01187-9] [Citation(s) in RCA: 63] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 08/18/2022] [Indexed: 12/13/2022]
Abstract
Genome-wide association studies provide a powerful means of identifying loci and genes contributing to disease, but in many cases, the related cell types/states through which genes confer disease risk remain unknown. Deciphering such relationships is important for identifying pathogenic processes and developing therapeutics. In the present study, we introduce sc-linker, a framework for integrating single-cell RNA-sequencing, epigenomic SNP-to-gene maps and genome-wide association study summary statistics to infer the underlying cell types and processes by which genetic variants influence disease. The inferred disease enrichments recapitulated known biology and highlighted notable cell-disease relationships, including γ-aminobutyric acid-ergic neurons in major depressive disorder, a disease-dependent M-cell program in ulcerative colitis and a disease-specific complement cascade process in multiple sclerosis. In autoimmune disease, both healthy and disease-dependent immune cell-type programs were associated, whereas only disease-dependent epithelial cell programs were prominent, suggesting a role in disease response rather than initiation. Our framework provides a powerful approach for identifying the cell types and cellular processes by which genetic variants influence disease.
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47
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Reply to Zhu et al.: Implications of CHRNB1 and ERBB2 in the pathobiology of myasthenia gravis. Proc Natl Acad Sci U S A 2022; 119:e2209096119. [PMID: 35969799 PMCID: PMC9459306 DOI: 10.1073/pnas.2209096119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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48
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Recent advances in elucidating the genetic basis of systemic sclerosis. Curr Opin Rheumatol 2022; 34:295-301. [PMID: 35979692 DOI: 10.1097/bor.0000000000000897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE OF REVIEW Systemic sclerosis (SSc) is a complex autoimmune disorder that affects the connective tissue and causes severe vascular damage and fibrosis of the skin and internal organs. There are recent advances in the field that apply novel methods to high throughput genotype information of thousands of patients with SSc and provide promising results towards the use of genomic data to help SSc diagnosis and clinical care. RECENT FINDINGS This review addresses the development of the first SSc genomic risk score, which can contribute to differentiating SSc patients from healthy controls and other immune-mediated diseases. Moreover, we explore the implementation of data mining strategies on the results of genome-wide association studies to highlight subtype-specific HLA class II associations and a strong association of the HLA class I locus with SSc for the first time. Finally, the combination of genomic data with transcriptomics informed drug repurposing and genetic association studies in well characterized SSc patient cohorts identified markers of severe complications of the disease. SUMMARY Early diagnosis and clinical management of SSc and SSc-related complications are still challenging for rheumatologists. The development of predictive models and tools using genotype data may help to finally deliver personalized clinical care and treatment for patients with SSc in the near future.
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49
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Dey KK, Gazal S, van de Geijn B, Kim SS, Nasser J, Engreitz JM, Price AL. SNP-to-gene linking strategies reveal contributions of enhancer-related and candidate master-regulator genes to autoimmune disease. CELL GENOMICS 2022; 2:100145. [PMID: 35873673 PMCID: PMC9306342 DOI: 10.1016/j.xgen.2022.100145] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
We assess contributions to autoimmune disease of genes whose regulation is driven by enhancer regions (enhancer-related) and genes that regulate other genes in trans (candidate master-regulator). We link these genes to SNPs using several SNP-to-gene (S2G) strategies and apply heritability analyses to draw three conclusions about 11 autoimmune/blood-related diseases/traits. First, several characterizations of enhancer-related genes using functional genomics data are informative for autoimmune disease heritability after conditioning on a broad set of regulatory annotations. Second, candidate master-regulator genes defined using trans-eQTL in blood are also conditionally informative for autoimmune disease heritability. Third, integrating enhancer-related and master-regulator gene sets with protein-protein interaction (PPI) network information magnified their disease signal. The resulting PPI-enhancer gene score produced >2-fold stronger heritability signal and >2-fold stronger enrichment for drug targets, compared with the recently proposed enhancer domain score. In each case, functionally informed S2G strategies produced 4.1- to 13-fold stronger disease signals than conventional window-based strategies.
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Affiliation(s)
- Kushal K. Dey
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Corresponding author
| | - Steven Gazal
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Bryce van de Geijn
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Genentech, South San Francisco, CA 94080, USA
| | - Samuel Sungil Kim
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Joseph Nasser
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jesse M. Engreitz
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
- BASE Initiative, Betty Irene Moore Children’s Heart Center, Lucile Packard Children’s Hospital, Stanford University School of Medicine, Stanford, CA 94304, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alkes L. Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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50
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Fang H. PiER: web-based facilities tailored for genetic target prioritisation harnessing human disease genetics, functional genomics and protein interactions. Nucleic Acids Res 2022; 50:W583-W592. [PMID: 35610036 PMCID: PMC9252812 DOI: 10.1093/nar/gkac379] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 04/19/2022] [Accepted: 05/02/2022] [Indexed: 11/23/2022] Open
Abstract
Integrative prioritisation promotes translational use of disease genetic findings in target discovery. I report 'PiER' (http://www.genetictargets.com/PiER), web-based facilities that support ab initio and real-time genetic target prioritisation through integrative use of human disease genetics, functional genomics and protein interactions. By design, the PiER features two facilities: elementary and combinatory. The elementary facility is designed to perform specific tasks, including three online tools: eV2CG, utilising functional genomics to link disease-associated variants (particularly located at the non-coding genome) to core genes likely responsible for genetic associations in disease; eCG2PG, using knowledge of protein interactions to 'network' core genes and additional peripheral genes, producing a ranked list of core and peripheral genes; and eCrosstalk, exploiting the information of pathway-derived interactions to identify highly-ranked genes mediating crosstalk between molecular pathways. Each of elementary tasks giving results is sequentially piped to the next one. By chaining together elementary tasks, the combinatory facility automates genetics-led and network-based integrative prioritisation for genetic targets at the gene level (cTGene) and at the crosstalk level (cTCrosstalk). Together with a tutorial-like booklet describing instructions on how to use, the PiER facilities meet multi-tasking needs to accelerate computational translational medicine that leverages human disease genetics and genomics for early-stage target discovery and drug repurposing.
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Affiliation(s)
- Hai Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
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