1
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Sakaue S, Weinand K, Isaac S, Dey KK, Jagadeesh K, Kanai M, Watts GFM, Zhu Z, Brenner MB, McDavid A, Donlin LT, Wei K, Price AL, Raychaudhuri S. Tissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles. Nat Genet 2024; 56:615-626. [PMID: 38594305 DOI: 10.1038/s41588-024-01682-1] [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: 03/07/2023] [Accepted: 02/07/2024] [Indexed: 04/11/2024]
Abstract
Translating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer-gene maps from disease-relevant tissues. Building enhancer-gene maps is essential but challenging with current experimental methods in primary human tissues. Here we developed a nonparametric statistical method, SCENT (single-cell enhancer target gene mapping), that models association between enhancer chromatin accessibility and gene expression in single-cell or nucleus multimodal RNA sequencing and ATAC sequencing data. We applied SCENT to 9 multimodal datasets including >120,000 single cells or nuclei and created 23 cell-type-specific enhancer-gene maps. These maps were highly enriched for causal variants in expression quantitative loci and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases and linked somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer-gene maps, essential for defining noncoding variant function.
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Affiliation(s)
- Saori Sakaue
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kathryn Weinand
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Shakson Isaac
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kushal K Dey
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Karthik Jagadeesh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Masahiro Kanai
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Gerald F M Watts
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Zhu Zhu
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael B Brenner
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrew McDavid
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
| | - Laura T Donlin
- Hospital for Special Surgery, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Kevin Wei
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alkes L Price
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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2
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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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3
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Lagattuta KA, Park HL, Rumker L, Ishigaki K, Nathan A, Raychaudhuri S. The genetic basis of autoimmunity seen through the lens of T cell functional traits. Nat Commun 2024; 15:1204. [PMID: 38331990 PMCID: PMC10853555 DOI: 10.1038/s41467-024-45170-w] [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: 08/16/2023] [Accepted: 01/15/2024] [Indexed: 02/10/2024] Open
Abstract
Autoimmune disease heritability is enriched in T cell-specific regulatory regions of the genome. Modern-day T cell datasets now enable association studies between single nucleotide polymorphisms (SNPs) and a myriad of molecular phenotypes, including chromatin accessibility, gene expression, transcriptional programs, T cell antigen receptor (TCR) amino acid usage, and cell state abundances. Such studies have identified hundreds of quantitative trait loci (QTLs) in T cells that colocalize with genetic risk for autoimmune disease. The key challenge facing immunologists today lies in synthesizing these results toward a unified understanding of the autoimmune T cell: which genes, cell states, and antigens drive tissue destruction?
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Affiliation(s)
- Kaitlyn A Lagattuta
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Hannah L Park
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Laurie Rumker
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kazuyoshi Ishigaki
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Laboratory for Human Immunogenetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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4
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Schnitzler GR, Kang H, Fang S, Angom RS, Lee-Kim VS, Ma XR, Zhou R, Zeng T, Guo K, Taylor MS, Vellarikkal SK, Barry AE, Sias-Garcia O, Bloemendal A, Munson G, Guckelberger P, Nguyen TH, Bergman DT, Hinshaw S, Cheng N, Cleary B, Aragam K, Lander ES, Finucane HK, Mukhopadhyay D, Gupta RM, Engreitz JM. Convergence of coronary artery disease genes onto endothelial cell programs. Nature 2024; 626:799-807. [PMID: 38326615 PMCID: PMC10921916 DOI: 10.1038/s41586-024-07022-x] [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: 10/17/2022] [Accepted: 01/03/2024] [Indexed: 02/09/2024]
Abstract
Linking variants from genome-wide association studies (GWAS) to underlying mechanisms of disease remains a challenge1-3. For some diseases, a successful strategy has been to look for cases in which multiple GWAS loci contain genes that act in the same biological pathway1-6. However, our knowledge of which genes act in which pathways is incomplete, particularly for cell-type-specific pathways or understudied genes. Here we introduce a method to connect GWAS variants to functions. This method links variants to genes using epigenomics data, links genes to pathways de novo using Perturb-seq and integrates these data to identify convergence of GWAS loci onto pathways. We apply this approach to study the role of endothelial cells in genetic risk for coronary artery disease (CAD), and discover 43 CAD GWAS signals that converge on the cerebral cavernous malformation (CCM) signalling pathway. Two regulators of this pathway, CCM2 and TLNRD1, are each linked to a CAD risk variant, regulate other CAD risk genes and affect atheroprotective processes in endothelial cells. These results suggest a model whereby CAD risk is driven in part by the convergence of causal genes onto a particular transcriptional pathway in endothelial cells. They highlight shared genes between common and rare vascular diseases (CAD and CCM), and identify TLNRD1 as a new, previously uncharacterized member of the CCM signalling pathway. This approach will be widely useful for linking variants to functions for other common polygenic diseases.
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Affiliation(s)
- Gavin R Schnitzler
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Helen Kang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Shi Fang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Ramcharan S Angom
- Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine and Science, Jacksonville, FL, USA
| | - Vivian S Lee-Kim
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - X Rosa Ma
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Ronghao Zhou
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Tony Zeng
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Katherine Guo
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Martin S Taylor
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Shamsudheen K Vellarikkal
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Aurelie E Barry
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Oscar Sias-Garcia
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Alex Bloemendal
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA
| | - Glen Munson
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Tung H Nguyen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Drew T Bergman
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Stephen Hinshaw
- Department of Chemical and Systems Biology, ChEM-H, and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nathan Cheng
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Brian Cleary
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Faculty of Computing and Data Sciences, Departments of Biology and Biomedical Engineering, Biological Design Center, and Program in Bioinformatics, Boston University, Boston, MA, USA
| | - Krishna Aragam
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Eric S Lander
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, MIT, Cambridge, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Hilary K Finucane
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Debabrata Mukhopadhyay
- Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine and Science, Jacksonville, FL, USA
| | - Rajat M Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA.
- Divisions of Genetics and Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
| | - Jesse M Engreitz
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA.
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
- Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA.
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA.
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5
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Armaos A, Serra F, Núñez-Carpintero I, Seo JH, Baca SC, Gustincich S, Valencia A, Freedman ML, Cirillo D, Giambartolomei C, Tartaglia GG. The PENGUIN approach to reconstruct protein interactions at enhancer-promoter regions and its application to prostate cancer. Nat Commun 2023; 14:8084. [PMID: 38057321 PMCID: PMC10700545 DOI: 10.1038/s41467-023-43767-1] [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: 11/08/2022] [Accepted: 11/18/2023] [Indexed: 12/08/2023] Open
Abstract
We introduce Promoter-Enhancer-Guided Interaction Networks (PENGUIN), a method for studying protein-protein interaction (PPI) networks within enhancer-promoter interactions. PENGUIN integrates H3K27ac-HiChIP data with tissue-specific PPIs to define enhancer-promoter PPI networks (EPINs). We validated PENGUIN using cancer (LNCaP) and benign (LHSAR) prostate cell lines. Our analysis detected EPIN clusters enriched with the architectural protein CTCF, a regulator of enhancer-promoter interactions. CTCF presence was coupled with the prevalence of prostate cancer (PrCa) single nucleotide polymorphisms (SNPs) within the same EPIN clusters, suggesting functional implications in PrCa. Within the EPINs displaying enrichments in both CTCF and PrCa SNPs, we also show enrichment in oncogenes. We substantiated our identified SNPs through CRISPR/Cas9 knockout and RNAi screens experiments. Here we show that PENGUIN provides insights into the intricate interplay between enhancer-promoter interactions and PPI networks, which are crucial for identifying key genes and potential intervention targets. A dedicated server is available at https://penguin.life.bsc.es/ .
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Affiliation(s)
- Alexandros Armaos
- Istituto Italiano di Tecnologia, CHT@Erzelli, Via Enrico Melen 83, Building B, 7th floor, 16152, Genova, Italy
| | - François Serra
- Barcelona Supercomputing Center, Plaça Eusebi Güell, 1-3, 08034, Barcelona, Spain
- Josep Carreras Leukaemia Research Institute, Ctra de Can Ruti, Camí de les Escoles, 08916, Badalona, Barcelona, Spain
| | | | - Ji-Heui Seo
- Department of Medical Oncology, The Center for Functional Cancer Epigenetics, Dana Farber Cancer Institute, Boston, MA, 02215, USA
| | - Sylvan C Baca
- Department of Medical Oncology, The Center for Functional Cancer Epigenetics, Dana Farber Cancer Institute, Boston, MA, 02215, USA
| | - Stefano Gustincich
- Istituto Italiano di Tecnologia, CHT@Erzelli, Via Enrico Melen 83, Building B, 7th floor, 16152, Genova, Italy
| | - Alfonso Valencia
- Barcelona Supercomputing Center, Plaça Eusebi Güell, 1-3, 08034, Barcelona, Spain
- ICREA - Institució Catalana de Recerca I Estudis Avançats, Pg. Lluís Companys 23, 08010, Barcelona, Spain
| | - Matthew L Freedman
- Department of Medical Oncology, The Center for Functional Cancer Epigenetics, Dana Farber Cancer Institute, Boston, MA, 02215, USA
- Eli and Edythe L. Broad Institute, 415 Main St., Cambridge, MA, 02142, USA
| | - Davide Cirillo
- Barcelona Supercomputing Center, Plaça Eusebi Güell, 1-3, 08034, Barcelona, Spain.
| | - Claudia Giambartolomei
- Istituto Italiano di Tecnologia, CHT@Erzelli, Via Enrico Melen 83, Building B, 7th floor, 16152, Genova, Italy.
- Health Data Science Centre, Human Technopole, Milan, Italy.
| | - Gian Gaetano Tartaglia
- Istituto Italiano di Tecnologia, CHT@Erzelli, Via Enrico Melen 83, Building B, 7th floor, 16152, Genova, Italy.
- ICREA - Institució Catalana de Recerca I Estudis Avançats, Pg. Lluís Companys 23, 08010, Barcelona, Spain.
- Istituto Italiano di Tecnologia, CNLS@Sapienza, Viale Regina Elena, 00161, Rome, Italy.
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6
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Mostafavi H, Spence JP, Naqvi S, Pritchard JK. Systematic differences in discovery of genetic effects on gene expression and complex traits. Nat Genet 2023; 55:1866-1875. [PMID: 37857933 DOI: 10.1038/s41588-023-01529-1] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 09/14/2023] [Indexed: 10/21/2023]
Abstract
Most signals in genome-wide association studies (GWAS) of complex traits implicate noncoding genetic variants with putative gene regulatory effects. However, currently identified regulatory variants, notably expression quantitative trait loci (eQTLs), explain only a small fraction of GWAS signals. Here, we show that GWAS and cis-eQTL hits are systematically different: eQTLs cluster strongly near transcription start sites, whereas GWAS hits do not. Genes near GWAS hits are enriched in key functional annotations, are under strong selective constraint and have complex regulatory landscapes across different tissue/cell types, whereas genes near eQTLs are depleted of most functional annotations, show relaxed constraint, and have simpler regulatory landscapes. We describe a model to understand these observations, including how natural selection on complex traits hinders discovery of functionally relevant eQTLs. Our results imply that GWAS and eQTL studies are systematically biased toward different types of variant, and support the use of complementary functional approaches alongside the next generation of eQTL studies.
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Affiliation(s)
| | | | - Sahin Naqvi
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA
| | - Jonathan K Pritchard
- Department of Genetics, Stanford University, Stanford, CA, USA.
- Department of Biology, Stanford University, Stanford, CA, USA.
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7
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Weinstock JS, Arce MM, Freimer JW, Ota M, Marson A, Battle A, Pritchard JK. Gene regulatory network inference from CRISPR perturbations in primary CD4+ T cells elucidates the genomic basis of immune disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.17.557749. [PMID: 37745614 PMCID: PMC10516010 DOI: 10.1101/2023.09.17.557749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The effects of genetic variation on complex traits act mainly through changes in gene regulation. Although many genetic variants have been linked to target genes in cis, the trans-regulatory cascade mediating their effects remains largely uncharacterized. Mapping trans-regulators based on natural genetic variation, including eQTL mapping, has been challenging due to small effects. Experimental perturbation approaches offer a complementary and powerful approach to mapping trans-regulators. We used CRISPR knockouts of 84 genes in primary CD4+ T cells to perturb an immune cell gene network, targeting both inborn error of immunity (IEI) disease transcription factors (TFs) and background TFs matched in constraint and expression level, but without a known immune disease association. We developed a novel Bayesian structure learning method called Linear Latent Causal Bayes (LLCB) to estimate the gene regulatory network from perturbation data and observed 211 directed edges among the genes which could not be detected in existing CD4+ trans-eQTL data. We used LLCB to characterize the differences between the IEI and background TFs, finding that the gene groups were highly interconnected, but that IEI TFs were much more likely to regulate immune cell specific pathways and immune GWAS genes. We further characterized nine coherent gene programs based on downstream effects of the TFs and linked these modules to regulation of GWAS genes, finding that canonical JAK-STAT family members are regulated by KMT2A, a global epigenetic regulator. These analyses reveal the trans-regulatory cascade from upstream epigenetic regulator to intermediate TFs to downstream effector cytokines and elucidate the logic linking immune GWAS genes to key signaling pathways.
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Affiliation(s)
- Joshua S. Weinstock
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
- Department of Genetics, Stanford University, Stanford, CA
| | - Maya M. Arce
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA
- Department of Medicine, University of California, San Francisco, San Francisco, CA
| | - Jacob W. Freimer
- Department of Genetics, Stanford University, Stanford, CA
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA
| | - Mineto Ota
- Department of Genetics, Stanford University, Stanford, CA
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA
| | - Alexander Marson
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA
- Department of Medicine, University of California, San Francisco, San Francisco, CA
- Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, 94720
- Institute for Human Genetics (IHG), University of California, San Francisco, San Francisco, CA, 94143
- Diabetes Center, University of California, San Francisco, San Francisco, CA, 94143
- Parker Institute for Cancer Immunotherapy, University of California, San Francisco, San Francisco, CA, 94129
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, 94143
- UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, 94158
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD
- Department of Computer Science, Johns Hopkins University, Baltimore, MD
- Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD
| | - Jonathan K. Pritchard
- Department of Genetics, Stanford University, Stanford, CA
- Department of Biology, Stanford University, Stanford, CA
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Ma Y, Deng C, Zhou Y, Zhang Y, Qiu F, Jiang D, Zheng G, Li J, Shuai J, Zhang Y, Yang J, Su J. Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data. CELL GENOMICS 2023; 3:100383. [PMID: 37719150 PMCID: PMC10504677 DOI: 10.1016/j.xgen.2023.100383] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 05/26/2023] [Accepted: 07/25/2023] [Indexed: 09/19/2023]
Abstract
Advances in single-cell RNA sequencing (scRNA-seq) techniques have accelerated functional interpretation of disease-associated variants discovered from genome-wide association studies (GWASs). However, identification of trait-relevant cell populations is often impeded by inherent technical noise and high sparsity in scRNA-seq data. Here, we developed scPagwas, a computational approach that uncovers trait-relevant cellular context by integrating pathway activation transformation of scRNA-seq data and GWAS summary statistics. scPagwas effectively prioritizes trait-relevant genes, which facilitates identification of trait-relevant cell types/populations with high accuracy in extensive simulated and real datasets. Cellular-level association results identified a novel subpopulation of naive CD8+ T cells related to COVID-19 severity and oligodendrocyte progenitor cell and microglia subsets with critical pathways by which genetic variants influence Alzheimer's disease. Overall, our approach provides new insights for the discovery of trait-relevant cell types and improves the mechanistic understanding of disease variants from a pathway perspective.
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Affiliation(s)
- Yunlong Ma
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
| | - Chunyu Deng
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150080, China
| | - Yijun Zhou
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
| | - Yaru Zhang
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
| | - Fei Qiu
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
| | - Dingping Jiang
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
| | - Gongwei Zheng
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
| | - Jingjing Li
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
| | - Jianwei Shuai
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
| | - Yan Zhang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150080, China
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310012, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Jianzhong Su
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
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Gaulton KJ, Preissl S, Ren B. Interpreting non-coding disease-associated human variants using single-cell epigenomics. Nat Rev Genet 2023; 24:516-534. [PMID: 37161089 PMCID: PMC10629587 DOI: 10.1038/s41576-023-00598-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/27/2023] [Indexed: 05/11/2023]
Abstract
Genome-wide association studies (GWAS) have linked hundreds of thousands of sequence variants in the human genome to common traits and diseases. However, translating this knowledge into a mechanistic understanding of disease-relevant biology remains challenging, largely because such variants are predominantly in non-protein-coding sequences that still lack functional annotation at cell-type resolution. Recent advances in single-cell epigenomics assays have enabled the generation of cell type-, subtype- and state-resolved maps of the epigenome in heterogeneous human tissues. These maps have facilitated cell type-specific annotation of candidate cis-regulatory elements and their gene targets in the human genome, enhancing our ability to interpret the genetic basis of common traits and diseases.
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Affiliation(s)
- Kyle J Gaulton
- Department of Paediatrics, Paediatric Diabetes Research Center, University of California San Diego School of Medicine, La Jolla, CA, USA.
| | - Sebastian Preissl
- Center for Epigenomics, University of California San Diego School of Medicine, La Jolla, CA, USA.
- Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Bing Ren
- Center for Epigenomics, University of California San Diego School of Medicine, La Jolla, CA, USA.
- Department of Cellular and Molecular Medicine, University of California San Diego School of Medicine, La Jolla, CA, USA.
- Ludwig Institute for Cancer Research, La Jolla, CA, USA.
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Zhu C, Baumgarten N, Wu M, Wang Y, Das AP, Kaur J, Ardakani FB, Duong TT, Pham MD, Duda M, Dimmeler S, Yuan T, Schulz MH, Krishnan J. CVD-associated SNPs with regulatory potential reveal novel non-coding disease genes. Hum Genomics 2023; 17:69. [PMID: 37491351 PMCID: PMC10369730 DOI: 10.1186/s40246-023-00513-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 07/12/2023] [Indexed: 07/27/2023] Open
Abstract
BACKGROUND Cardiovascular diseases (CVDs) are the leading cause of death worldwide. Genome-wide association studies (GWAS) have identified many single nucleotide polymorphisms (SNPs) appearing in non-coding genomic regions in CVDs. The SNPs may alter gene expression by modifying transcription factor (TF) binding sites and lead to functional consequences in cardiovascular traits or diseases. To understand the underlying molecular mechanisms, it is crucial to identify which variations are involved and how they affect TF binding. METHODS The SNEEP (SNP exploration and analysis using epigenomics data) pipeline was used to identify regulatory SNPs, which alter the binding behavior of TFs and link GWAS SNPs to their potential target genes for six CVDs. The human-induced pluripotent stem cells derived cardiomyocytes (hiPSC-CMs), monoculture cardiac organoids (MCOs) and self-organized cardiac organoids (SCOs) were used in the study. Gene expression, cardiomyocyte size and cardiac contractility were assessed. RESULTS By using our integrative computational pipeline, we identified 1905 regulatory SNPs in CVD GWAS data. These were associated with hundreds of genes, half of them non-coding RNAs (ncRNAs), suggesting novel CVD genes. We experimentally tested 40 CVD-associated non-coding RNAs, among them RP11-98F14.11, RPL23AP92, IGBP1P1, and CTD-2383I20.1, which were upregulated in hiPSC-CMs, MCOs and SCOs under hypoxic conditions. Further experiments showed that IGBP1P1 depletion rescued expression of hypertrophic marker genes, reduced hypoxia-induced cardiomyocyte size and improved hypoxia-reduced cardiac contractility in hiPSC-CMs and MCOs. CONCLUSIONS IGBP1P1 is a novel ncRNA with key regulatory functions in modulating cardiomyocyte size and cardiac function in our disease models. Our data suggest ncRNA IGBP1P1 as a potential therapeutic target to improve cardiac function in CVDs.
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Affiliation(s)
- Chaonan Zhu
- Institute for Cardiovascular Regeneration, Goethe University, 60590, Frankfurt Am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, 60590, Frankfurt Am Main, Germany
| | - Nina Baumgarten
- Institute for Cardiovascular Regeneration, Goethe University, 60590, Frankfurt Am Main, Germany
- German Center for Cardiovascular Research, Partner Site Rhein-Main, 60590, Frankfurt Am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, 60590, Frankfurt Am Main, Germany
| | - Meiqian Wu
- Institute for Cardiovascular Regeneration, Goethe University, 60590, Frankfurt Am Main, Germany
| | - Yue Wang
- Institute for Cardiovascular Regeneration, Goethe University, 60590, Frankfurt Am Main, Germany
| | - Arka Provo Das
- Institute for Cardiovascular Regeneration, Goethe University, 60590, Frankfurt Am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, 60590, Frankfurt Am Main, Germany
| | - Jaskiran Kaur
- Institute for Cardiovascular Regeneration, Goethe University, 60590, Frankfurt Am Main, Germany
| | - Fatemeh Behjati Ardakani
- Institute for Cardiovascular Regeneration, Goethe University, 60590, Frankfurt Am Main, Germany
- German Center for Cardiovascular Research, Partner Site Rhein-Main, 60590, Frankfurt Am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, 60590, Frankfurt Am Main, Germany
| | - Thanh Thuy Duong
- Genome Biologics, Theodor-Stern-Kai 7, 60590, Frankfurt Am Main, Germany
| | - Minh Duc Pham
- Institute for Cardiovascular Regeneration, Goethe University, 60590, Frankfurt Am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, 60590, Frankfurt Am Main, Germany
- Department of Medicine III, Cardiology/Angiology/ Nephrology, Goethe University Hospital, Frankfurt, Germany
- Genome Biologics, Theodor-Stern-Kai 7, 60590, Frankfurt Am Main, Germany
| | - Maria Duda
- Genome Biologics, Theodor-Stern-Kai 7, 60590, Frankfurt Am Main, Germany
| | - Stefanie Dimmeler
- Institute for Cardiovascular Regeneration, Goethe University, 60590, Frankfurt Am Main, Germany
- German Center for Cardiovascular Research, Partner Site Rhein-Main, 60590, Frankfurt Am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, 60590, Frankfurt Am Main, Germany
| | - Ting Yuan
- Institute for Cardiovascular Regeneration, Goethe University, 60590, Frankfurt Am Main, Germany.
- Cardio-Pulmonary Institute, Goethe University Hospital, 60590, Frankfurt Am Main, Germany.
- Department of Medicine III, Cardiology/Angiology/ Nephrology, Goethe University Hospital, Frankfurt, Germany.
| | - Marcel H Schulz
- Institute for Cardiovascular Regeneration, Goethe University, 60590, Frankfurt Am Main, Germany.
- German Center for Cardiovascular Research, Partner Site Rhein-Main, 60590, Frankfurt Am Main, Germany.
- Cardio-Pulmonary Institute, Goethe University Hospital, 60590, Frankfurt Am Main, Germany.
| | - Jaya Krishnan
- Institute for Cardiovascular Regeneration, Goethe University, 60590, Frankfurt Am Main, Germany.
- German Center for Cardiovascular Research, Partner Site Rhein-Main, 60590, Frankfurt Am Main, Germany.
- Cardio-Pulmonary Institute, Goethe University Hospital, 60590, Frankfurt Am Main, Germany.
- Department of Medicine III, Cardiology/Angiology/ Nephrology, Goethe University Hospital, Frankfurt, Germany.
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11
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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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12
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Orozco G, Schoenfelder S, Walker N, Eyre S, Fraser P. 3D genome organization links non-coding disease-associated variants to genes. Front Cell Dev Biol 2022; 10:995388. [PMID: 36340032 PMCID: PMC9631826 DOI: 10.3389/fcell.2022.995388] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 09/27/2022] [Indexed: 11/13/2022] Open
Abstract
Genome sequencing has revealed over 300 million genetic variations in human populations. Over 90% of variants are single nucleotide polymorphisms (SNPs), the remainder include short deletions or insertions, and small numbers of structural variants. Hundreds of thousands of these variants have been associated with specific phenotypic traits and diseases through genome wide association studies which link significant differences in variant frequencies with specific phenotypes among large groups of individuals. Only 5% of disease-associated SNPs are located in gene coding sequences, with the potential to disrupt gene expression or alter of the function of encoded proteins. The remaining 95% of disease-associated SNPs are located in non-coding DNA sequences which make up 98% of the genome. The role of non-coding, disease-associated SNPs, many of which are located at considerable distances from any gene, was at first a mystery until the discovery that gene promoters regularly interact with distal regulatory elements to control gene expression. Disease-associated SNPs are enriched at the millions of gene regulatory elements that are dispersed throughout the non-coding sequences of the genome, suggesting they function as gene regulation variants. Assigning specific regulatory elements to the genes they control is not straightforward since they can be millions of base pairs apart. In this review we describe how understanding 3D genome organization can identify specific interactions between gene promoters and distal regulatory elements and how 3D genomics can link disease-associated SNPs to their target genes. Understanding which gene or genes contribute to a specific disease is the first step in designing rational therapeutic interventions.
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Affiliation(s)
- Gisela Orozco
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom.,NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, United Kingdom
| | - Stefan Schoenfelder
- Enhanc3D Genomics Ltd., Cambridge, United Kingdom.,Epigenetics Programme, The Babraham Institute, Babraham Research Campus, CB22 3AT Cambridge, Cambridge, United Kingdom
| | | | - Stephan Eyre
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom.,NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, United Kingdom
| | - Peter Fraser
- Enhanc3D Genomics Ltd., Cambridge, United Kingdom.,Department of Biological Science, Florida State University, Tallahassee, FL, United States
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