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Leblanc FJA, Jin X, Kang K, Lee CJM, Xu J, Xuan L, Ma W, Belhaj H, Benzaki M, Mehta N, Foo RSY, Reilly S, Anene-Nzelu CG, Pan Z, Nattel S, Yang B, Lettre G. Atrial fibrillation variant-to-gene prioritization through cross-ancestry eQTL and single-nucleus multiomic analyses. iScience 2024; 27:110660. [PMID: 39262787 PMCID: PMC11388022 DOI: 10.1016/j.isci.2024.110660] [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: 12/14/2023] [Revised: 03/28/2024] [Accepted: 07/31/2024] [Indexed: 09/13/2024] Open
Abstract
Atrial fibrillation (AF) is the most common arrhythmia in the world. Human genetics can provide strong AF therapeutic candidates, but the identification of the causal genes and their functions remains challenging. Here, we applied an AF fine-mapping strategy that leverages results from a previously published cross-ancestry genome-wide association study (GWAS), expression quantitative trait loci (eQTLs) from left atrial appendages (LAAs) obtained from two cohorts with distinct ancestry, and a paired RNA sequencing (RNA-seq) and ATAC sequencing (ATAC-seq) LAA single-nucleus assay (sn-multiome). At nine AF loci, our co-localization and fine-mapping analyses implicated 14 genes. Data integration identified several candidate causal AF variants, including rs7612445 at GNB4 and rs242557 at MAPT. Finally, we showed that the repression of the strongest AF-associated eQTL gene, LINC01629, in human embryonic stem cell-derived cardiomyocytes using CRISPR inhibition results in the dysregulation of pathways linked to genes involved in the development of atrial tissue and the cardiac conduction system.
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Affiliation(s)
- Francis J A Leblanc
- Montreal Heart Institute, Montreal, QC, Canada
- Department of Medicine, Université de Montréal, Montréal, QC, Canada
| | - Xuexin Jin
- Department of Pharmacology (State Key Laboratory of Frigid Zone Cardiovascular Disease, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin, Heilongjiang 150086, P.R. China
- Department of Cardiology, The First Affiliated Hospital, Harbin Medical University, Harbin 150001, China
| | - Kai Kang
- Department of Cardiovascular Surgery, The First Affiliated Hospital, Harbin Medical University, Harbin 150001, China
| | - Chang Jie Mick Lee
- Cardiovascular Disease Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Lina Xuan
- Department of Pharmacology (State Key Laboratory of Frigid Zone Cardiovascular Disease, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin, Heilongjiang 150086, P.R. China
| | - Wenbo Ma
- Department of Pharmacology (State Key Laboratory of Frigid Zone Cardiovascular Disease, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin, Heilongjiang 150086, P.R. China
| | | | - Marouane Benzaki
- Montreal Heart Institute, Montreal, QC, Canada
- Department of Medicine, Université de Montréal, Montréal, QC, Canada
| | - Neelam Mehta
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Roger Sik Yin Foo
- Cardiovascular Disease Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Svetlana Reilly
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Chukwuemeka George Anene-Nzelu
- Montreal Heart Institute, Montreal, QC, Canada
- Department of Medicine, Université de Montréal, Montréal, QC, Canada
- Cardiovascular Disease Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Zhenwei Pan
- Department of Pharmacology (State Key Laboratory of Frigid Zone Cardiovascular Disease, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin, Heilongjiang 150086, P.R. China
| | - Stanley Nattel
- Montreal Heart Institute, Montreal, QC, Canada
- Department of Medicine, Université de Montréal, Montréal, QC, Canada
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
- IHU Liryc and Fondation Bordeaux Université, Bordeaux, France
- Institute of Pharmacology, West German Heart and Vascular Center, Faculty of Medicine, University Duisburg-Essen, Essen, Germany
| | - Baofeng Yang
- Department of Pharmacology (State Key Laboratory of Frigid Zone Cardiovascular Disease, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin, Heilongjiang 150086, P.R. China
| | - Guillaume Lettre
- Montreal Heart Institute, Montreal, QC, Canada
- Department of Medicine, Université de Montréal, Montréal, QC, Canada
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2
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Shore CJ, Villicaña S, El-Sayed Moustafa JS, Roberts AL, Gunn DA, Bataille V, Deloukas P, Spector TD, Small KS, Bell JT. Genetic effects on the skin methylome in healthy older twins. Am J Hum Genet 2024; 111:1932-1952. [PMID: 39137780 DOI: 10.1016/j.ajhg.2024.07.010] [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: 12/05/2023] [Revised: 05/22/2024] [Accepted: 07/15/2024] [Indexed: 08/15/2024] Open
Abstract
Whole-skin DNA methylation variation has been implicated in several diseases, including melanoma, but its genetic basis has not yet been fully characterized. Using bulk skin tissue samples from 414 healthy female UK twins, we performed twin-based heritability and methylation quantitative trait loci (meQTL) analyses for >400,000 DNA methylation sites. We find that the human skin DNA methylome is on average less heritable than previously estimated in blood and other tissues (mean heritability: 10.02%). meQTL analysis identified local genetic effects influencing DNA methylation at 18.8% (76,442) of tested CpG sites, as well as 1,775 CpG sites associated with at least one distal genetic variant. As a functional follow-up, we performed skin expression QTL (eQTL) analyses in a partially overlapping sample of 604 female twins. Colocalization analysis identified over 3,500 shared genetic effects affecting thousands of CpG sites (10,067) and genes (4,475). Mediation analysis of putative colocalized gene-CpG pairs identified 114 genes with evidence for eQTL effects being mediated by DNA methylation in skin, including in genes implicating skin disease such as ALOX12 and CSPG4. We further explored the relevance of skin meQTLs to skin disease and found that skin meQTLs and CpGs under genetic influence were enriched for multiple skin-related genome-wide and epigenome-wide association signals, including for melanoma and psoriasis. Our findings give insights into the regulatory landscape of epigenomic variation in skin.
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Affiliation(s)
- Christopher J Shore
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
| | - Sergio Villicaña
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | | | - Amy L Roberts
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | | | - Veronique Bataille
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Panos Deloukas
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
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3
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Crone B, Boyle AP. Enhancing portability of trans-ancestral polygenic risk scores through tissue-specific functional genomic data integration. PLoS Genet 2024; 20:e1011356. [PMID: 39110742 PMCID: PMC11333000 DOI: 10.1371/journal.pgen.1011356] [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: 03/05/2024] [Revised: 08/19/2024] [Accepted: 06/27/2024] [Indexed: 08/21/2024] Open
Abstract
Portability of trans-ancestral polygenic risk scores is often confounded by differences in linkage disequilibrium and genetic architecture between ancestries. Recent literature has shown that prioritizing GWAS SNPs with functional genomic evidence over strong association signals can improve model portability. We leveraged three RegulomeDB-derived functional regulatory annotations-SURF, TURF, and TLand-to construct polygenic risk models across a set of quantitative and binary traits highlighting functional mutations tagged by trait-associated tissue annotations. Tissue-specific prioritization by TURF and TLand provide a significant improvement in model accuracy over standard polygenic risk score (PRS) models across all traits. We developed the Trans-ancestral Iterative Tissue Refinement (TITR) algorithm to construct PRS models that prioritize functional mutations across multiple trait-implicated tissues. TITR-constructed PRS models show increased predictive accuracy over single tissue prioritization. This indicates our TITR approach captures a more comprehensive view of regulatory systems across implicated tissues that contribute to variance in trait expression.
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Affiliation(s)
- Bradley Crone
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Alan P. Boyle
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, United States of America
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4
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Westergaard D, Steinthorsdottir V, Stefansdottir L, Rohde PD, Wu X, Geller F, Tyrmi J, Havulinna AS, Solé-Navais P, Flatley C, Ostrowski SR, Pedersen OB, Erikstrup C, Sørensen E, Mikkelsen C, Bruun MT, Aagaard Jensen B, Brodersen T, Ullum H, Magnus P, Andreassen OA, Njolstad PR, Kolte AM, Krebs L, Nyegaard M, Hansen TF, Feenstra B, Daly M, Lindgren CM, Thorleifsson G, Stefansson OA, Sveinbjornsson G, Gudbjartsson DF, Thorsteinsdottir U, Banasik K, Jacobsson B, Laisk T, Laivuori H, Stefansson K, Brunak S, Nielsen HS. Genome-wide association meta-analysis identifies five loci associated with postpartum hemorrhage. Nat Genet 2024; 56:1597-1603. [PMID: 39039282 PMCID: PMC11319197 DOI: 10.1038/s41588-024-01839-y] [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: 08/24/2023] [Accepted: 06/21/2024] [Indexed: 07/24/2024]
Abstract
Bleeding in early pregnancy and postpartum hemorrhage (PPH) bear substantial risks, with the former closely associated with pregnancy loss and the latter being the foremost cause of maternal death, underscoring the severe impact on maternal-fetal health. We identified five genetic loci linked to PPH in a meta-analysis. Functional annotation analysis indicated candidate genes HAND2, TBX3 and RAP2C/FRMD7 at three loci and showed that at each locus, associated variants were located within binding sites for progesterone receptors. There were strong genetic correlations with birth weight, gestational duration and uterine fibroids. Bleeding in early pregnancy yielded no genome-wide association signals but showed strong genetic correlation with various human traits, suggesting a potentially complex, polygenic etiology. Our results suggest that PPH is related to progesterone signaling dysregulation, whereas early bleeding is a complex trait associated with underlying health and possibly socioeconomic status and may include genetic factors that have not yet been identified.
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Affiliation(s)
- David Westergaard
- Department of Obstetrics and Gynaecology, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Methods and Analysis, Statistics Denmark, Copenhagen, Denmark
| | | | | | - Palle Duun Rohde
- Department of Health Science and Technology, Aalborg University, Gistrup, Denmark
| | - Xiaoping Wu
- Department of Clinical immunology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Frank Geller
- Department of Clinical immunology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Jaakko Tyrmi
- Centre for Child, Adolescent, and Maternal Health Research, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Aki S Havulinna
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Finnish Institute for Health and Welfare - THL, Helsinki, Finland
| | - Pol Solé-Navais
- Department of Obstetrics and Gynaecology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Christopher Flatley
- Department of Obstetrics and Gynaecology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Sisse Rye Ostrowski
- Department of Clinical immunology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ole Birger Pedersen
- Department of Clinical medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical immunology, Zealand University Hospital, Køge, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Erik Sørensen
- Department of Clinical immunology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Christina Mikkelsen
- Department of Clinical immunology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Mie Topholm Bruun
- Clinical Immunological Research Unit, Department of Clinical Immunology, Odense University Hospital, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | | | - Thorsten Brodersen
- Department of Clinical immunology, Zealand University Hospital, Køge, Denmark
| | - Henrik Ullum
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Per Magnus
- Department of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, Oslo, Norway
| | - Ole A Andreassen
- NORMENT Centre, University of Oslo, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Pål R Njolstad
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
| | - Astrid Marie Kolte
- Department of Obstetrics and Gynaecology, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Lone Krebs
- Department of Obstetrics and Gynaecology, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
- Department of Clinical medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mette Nyegaard
- Department of Health Science and Technology, Aalborg University, Gistrup, Denmark
| | - Thomas Folkmann Hansen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Danish Headache Center, Department of neurology, Copenhagen University Hospital, Glostrup, Denmark
| | - Bjarke Feenstra
- Department of Clinical immunology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Mark Daly
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Cecilia M Lindgren
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Big Data Institute Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Wellcome Trust Centre Human Genetics, University of Oxford, Oxford, UK
| | | | | | | | - Daniel F Gudbjartsson
- deCODE genetics/Amgen, Reykjavik, Iceland
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
| | - Unnur Thorsteinsdottir
- deCODE genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, Reykjavik University, Reykjavik, Iceland
| | - Karina Banasik
- Department of Obstetrics and Gynaecology, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Bo Jacobsson
- Department of Obstetrics and Gynaecology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, Oslo, Norway
| | - Triin Laisk
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Hannele Laivuori
- Centre for Child, Adolescent, and Maternal Health Research, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Obstetrics and Gynaecology, Tampere University Hospital, Tampere, Finland
| | - Kari Stefansson
- deCODE genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, Reykjavik University, Reykjavik, Iceland
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Henriette Svarre Nielsen
- Department of Obstetrics and Gynaecology, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark.
- Department of Clinical medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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5
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Mondal AK, Gaur M, Advani J, Swaroop A. Epigenome-metabolism nexus in the retina: implications for aging and disease. Trends Genet 2024; 40:718-729. [PMID: 38782642 PMCID: PMC11303112 DOI: 10.1016/j.tig.2024.04.012] [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: 03/18/2024] [Revised: 04/22/2024] [Accepted: 04/23/2024] [Indexed: 05/25/2024]
Abstract
Intimate links between epigenome modifications and metabolites allude to a crucial role of cellular metabolism in transcriptional regulation. Retina, being a highly metabolic tissue, adapts by integrating inputs from genetic, epigenetic, and extracellular signals. Precise global epigenomic signatures guide development and homeostasis of the intricate retinal structure and function. Epigenomic and metabolic realignment are hallmarks of aging and highlight a link of the epigenome-metabolism nexus with aging-associated multifactorial traits affecting the retina, including age-related macular degeneration and glaucoma. Here, we focus on emerging principles of epigenomic and metabolic control of retinal gene regulation, with emphasis on their contribution to human disease. In addition, we discuss potential mitigation strategies involving lifestyle changes that target the epigenome-metabolome relationship for maintaining retinal function.
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Affiliation(s)
- Anupam K Mondal
- Neurobiology, Neurodegeneration, and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Mohita Gaur
- Neurobiology, Neurodegeneration, and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Jayshree Advani
- Neurobiology, Neurodegeneration, and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Anand Swaroop
- Neurobiology, Neurodegeneration, and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD 20892, USA.
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6
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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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7
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Pushkarev O, van Mierlo G, Kribelbauer JF, Saelens W, Gardeux V, Deplancke B. Non-coding variants impact cis-regulatory coordination in a cell type-specific manner. Genome Biol 2024; 25:190. [PMID: 39026229 PMCID: PMC11256678 DOI: 10.1186/s13059-024-03333-4] [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: 10/09/2023] [Accepted: 07/09/2024] [Indexed: 07/20/2024] Open
Abstract
BACKGROUND Interactions among cis-regulatory elements (CREs) play a crucial role in gene regulation. Various approaches have been developed to map these interactions genome-wide, including those relying on interindividual epigenomic variation to identify groups of covariable regulatory elements, referred to as chromatin modules (CMs). While CM mapping allows to investigate the relationship between chromatin modularity and gene expression, the computational principles used for CM identification vary in their application and outcomes. RESULTS We comprehensively evaluate and streamline existing CM mapping tools and present guidelines for optimal utilization of epigenome data from a diverse population of individuals to assess regulatory coordination across the human genome. We showcase the effectiveness of our recommended practices by analyzing distinct cell types and demonstrate cell type specificity of CRE interactions in CMs and their relevance for gene expression. Integration of genotype information revealed that many non-coding disease-associated variants affect the activity of CMs in a cell type-specific manner by affecting the binding of cell type-specific transcription factors. We provide example cases that illustrate in detail how CMs can be used to deconstruct GWAS loci, assess variable expression of cell surface receptors in immune cells, and reveal how genetic variation can impact the expression of prognostic markers in chronic lymphocytic leukemia. CONCLUSIONS Our study presents an optimal strategy for CM mapping and reveals how CMs capture the coordination of CREs and its impact on gene expression. Non-coding genetic variants can disrupt this coordination, and we highlight how this may lead to disease predisposition in a cell type-specific manner.
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Affiliation(s)
- Olga Pushkarev
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Guido van Mierlo
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
| | - Judith Franziska Kribelbauer
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Wouter Saelens
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Vincent Gardeux
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Bart Deplancke
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
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8
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Liang Q, Abraham A, Capra JA, Kostka D. Disease-specific prioritization of non-coding GWAS variants based on chromatin accessibility. HGG ADVANCES 2024; 5:100310. [PMID: 38773771 PMCID: PMC11259938 DOI: 10.1016/j.xhgg.2024.100310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 05/15/2024] [Accepted: 05/16/2024] [Indexed: 05/24/2024] Open
Abstract
Non-protein-coding genetic variants are a major driver of the genetic risk for human disease; however, identifying which non-coding variants contribute to diseases and their mechanisms remains challenging. In silico variant prioritization methods quantify a variant's severity, but for most methods, the specific phenotype and disease context of the prediction remain poorly defined. For example, many commonly used methods provide a single, organism-wide score for each variant, while other methods summarize a variant's impact in certain tissues and/or cell types. Here, we propose a complementary disease-specific variant prioritization scheme, which is motivated by the observation that variants contributing to disease often operate through specific biological mechanisms. We combine tissue/cell-type-specific variant scores (e.g., GenoSkyline, FitCons2, DNA accessibility) into disease-specific scores with a logistic regression approach and apply it to ∼25,000 non-coding variants spanning 111 diseases. We show that this disease-specific aggregation significantly improves the association of common non-coding genetic variants with disease (average precision: 0.151, baseline = 0.09), compared with organism-wide scores (GenoCanyon, LINSIGHT, GWAVA, Eigen, CADD; average precision: 0.129, baseline = 0.09). Further on, disease similarities based on data-driven aggregation weights highlight meaningful disease groups, and it provides information about tissues and cell types that drive these similarities. We also show that so-learned similarities are complementary to genetic similarities as quantified by genetic correlation. Overall, our approach demonstrates the strengths of disease-specific variant prioritization, leads to improvement in non-coding variant prioritization, and enables interpretable models that link variants to disease via specific tissues and/or cell types.
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Affiliation(s)
- Qianqian Liang
- Department of Computational & Systems Biology and Center for Evolutionary Biology and Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Human Genetics, University of Pittsburgh School of Public Health, Pittsburgh, PA, USA
| | - Abin Abraham
- Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - John A Capra
- Department of Epidemiology & Biostatistics and Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Dennis Kostka
- Department of Computational & Systems Biology and Center for Evolutionary Biology and Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
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9
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Hartl C, Zhuang J, Tyler A, Zhou B, Wong E, Merberg D, Farrell B, DeBoever C, Bryant J, Diogo D. CREdb: A comprehensive database of Cis-Regulatory Elements and their activity in human cells and tissues. Epigenetics Chromatin 2024; 17:21. [PMID: 39014503 PMCID: PMC11253421 DOI: 10.1186/s13072-024-00545-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 06/08/2024] [Indexed: 07/18/2024] Open
Abstract
BACKGROUND Cis-regulatory elements (CREs) play a pivotal role in gene expression regulation, allowing cells to serve diverse functions and respond to external stimuli. Understanding CREs is essential for personalized medicine and disease research, as an increasing number of genetic variants associated with phenotypes and diseases overlap with CREs. However, existing databases often focus on subsets of regulatory elements and present each identified instance of element individually, confounding the effort to obtain a comprehensive view. To address this gap, we have created CREdb, a comprehensive database with over 10 million human regulatory elements across 1,058 cell types and 315 tissues harmonized from different data sources. We curated and aligned the cell types and tissues to standard ontologies for efficient data query. RESULTS Data from 11 sources were curated and mapped to standard ontological terms. 11,223,434 combined elements are present in the final database, and these were merged into 5,666,240 consensus elements representing the combined ranges of the individual elements informed by their overlap. Each consensus element contains curated metadata including the number of elements supporting it and a hash linking to the source databases. The inferred activity of each consensus element in various cell-type and tissue context is also provided. Examples presented here show the potential utility of CREdb in annotating non-coding genetic variants and informing chromatin accessibility profiling analysis. CONCLUSIONS We developed CREdb, a comprehensive database of CREs, to simplify the analysis of CREs by providing a unified framework for researchers. CREdb compiles consensus ranges for each element by integrating the information from all instances identified across various source databases. This unified database facilitates the functional annotation of non-coding genetic variants and complements chromatin accessibility profiling analysis. CREdb will serve as an important resource in expanding our knowledge of the epigenome and its role in human diseases.
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Affiliation(s)
- Chris Hartl
- Rancho BioSciences LLC, San Diego, California, USA
| | - Jiali Zhuang
- Genetics and Systems Biology, Takeda Development Center Americas, Inc, San Diego, CA, 92121, USA
| | - Aaron Tyler
- Rancho BioSciences LLC, San Diego, California, USA
| | - Bing Zhou
- Rancho BioSciences LLC, San Diego, California, USA
| | - Emily Wong
- Genetics and Systems Biology, Takeda Development Center Americas, Inc, San Diego, CA, 92121, USA
- Data Science and Operations, Vir Biotechnology Inc, San Francisco, CA, 94158, USA
| | - David Merberg
- Genetics and Systems Biology, Takeda Development Center Americas, Inc, Cambridge, MA, 02139, USA
| | - Brad Farrell
- Rancho BioSciences LLC, San Diego, California, USA
| | - Chris DeBoever
- Genetics and Systems Biology, Takeda Development Center Americas, Inc, San Diego, CA, 92121, USA
| | - Julie Bryant
- Rancho BioSciences LLC, San Diego, California, USA
| | - Dorothée Diogo
- Genetics and Systems Biology, Takeda Development Center Americas, Inc, Cambridge, MA, 02139, USA.
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10
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He J, Perera D, Wen W, Ping J, Li Q, Lyu L, Chen Z, Shu X, Long J, Cai Q, Shu XO, Zheng W, Long Q, Guo X. Enhancing Disease Risk Gene Discovery by Integrating Transcription Factor-Linked Trans-located Variants into Transcriptome-Wide Association Analyses. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.10.10.23295443. [PMID: 37873299 PMCID: PMC10593059 DOI: 10.1101/2023.10.10.23295443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Transcriptome-wide association studies (TWAS) have been successful in identifying disease susceptibility genes by integrating cis-variants predicted gene expression with genome-wide association studies (GWAS) data. However, trans-located variants for predicting gene expression remain largely unexplored. Here, we introduce transTF-TWAS, which incorporates transcription factor (TF)-linked trans-located variants to enhance model building. Using data from the Genotype-Tissue Expression project, we predict gene expression and alternative splicing and applied these models to large GWAS datasets for breast, prostate, and lung cancers. We demonstrate that transTF-TWAS outperforms other existing TWAS approaches in both constructing gene prediction models and identifying disease-associated genes, as evidenced by simulations and real data analysis. Our transTF-TWAS approach significantly contributes to the discovery of disease risk genes. Findings from this study have shed new light on several genetically driven key regulators and their associated regulatory networks underlying disease susceptibility.
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11
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Oguchi A, Suzuki A, Komatsu S, Yoshitomi H, Bhagat S, Son R, Bonnal RJP, Kojima S, Koido M, Takeuchi K, Myouzen K, Inoue G, Hirai T, Sano H, Takegami Y, Kanemaru A, Yamaguchi I, Ishikawa Y, Tanaka N, Hirabayashi S, Konishi R, Sekito S, Inoue T, Kere J, Takeda S, Takaori-Kondo A, Endo I, Kawaoka S, Kawaji H, Ishigaki K, Ueno H, Hayashizaki Y, Pagani M, Carninci P, Yanagita M, Parrish N, Terao C, Yamamoto K, Murakawa Y. An atlas of transcribed enhancers across helper T cell diversity for decoding human diseases. Science 2024; 385:eadd8394. [PMID: 38963856 DOI: 10.1126/science.add8394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 05/01/2024] [Indexed: 07/06/2024]
Abstract
Transcribed enhancer maps can reveal nuclear interactions underpinning each cell type and connect specific cell types to diseases. Using a 5' single-cell RNA sequencing approach, we defined transcription start sites of enhancer RNAs and other classes of coding and noncoding RNAs in human CD4+ T cells, revealing cellular heterogeneity and differentiation trajectories. Integration of these datasets with single-cell chromatin profiles showed that active enhancers with bidirectional RNA transcription are highly cell type-specific and that disease heritability is strongly enriched in these enhancers. The resulting cell type-resolved multimodal atlas of bidirectionally transcribed enhancers, which we linked with promoters using fine-scale chromatin contact maps, enabled us to systematically interpret genetic variants associated with a range of immune-mediated diseases.
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Affiliation(s)
- Akiko Oguchi
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Akari Suzuki
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Shuichiro Komatsu
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- IFOM ETS - the AIRC Institute of Molecular Oncology, Milan, Italy
| | - Hiroyuki Yoshitomi
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
- Department of Immunology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Shruti Bhagat
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
| | - Raku Son
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | | | - Shohei Kojima
- Genome Immunobiology RIKEN Hakubi Research Team, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Masaru Koido
- Division of Molecular Pathology, Department of Cancer Biology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kazuhiro Takeuchi
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
- Department of Medical Systems Genomics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Keiko Myouzen
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Gyo Inoue
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Tomoya Hirai
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Gastroenterological Surgery, Yokohama City University Graduate School of Medicine, Yokohama City University, Yokohama, Japan
| | - Hiromi Sano
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | | | | | | | - Yuki Ishikawa
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Nao Tanaka
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Shigeki Hirabayashi
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Hematology and Oncology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Division of Precision Medicine, Kyushu University Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Riyo Konishi
- Inter-Organ Communication Research Team, Institute for Life and Medical Sciences, Kyoto University, Kyoto, Japan
| | - Sho Sekito
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
- Department of Nephro-Urologic Surgery and Andrology, Mie University Graduate School of Medicine, Mie University, Tsu, Japan
| | - Takahiro Inoue
- Department of Nephro-Urologic Surgery and Andrology, Mie University Graduate School of Medicine, Mie University, Tsu, Japan
| | - Juha Kere
- Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden
- Stem Cells and Metabolism Research Program, University of Helsinki, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
| | - Shunichi Takeda
- Department of Radiation Genetics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Shenzhen University School of Medicine, Shenzhen, Guangdong, China
| | - Akifumi Takaori-Kondo
- Department of Hematology and Oncology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Itaru Endo
- Department of Gastroenterological Surgery, Yokohama City University Graduate School of Medicine, Yokohama City University, Yokohama, Japan
| | - Shinpei Kawaoka
- Inter-Organ Communication Research Team, Institute for Life and Medical Sciences, Kyoto University, Kyoto, Japan
- Department of Integrative Bioanalytics, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Hideya Kawaji
- Research Center for Genome & Medical Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
- Preventive Medicine and Applied Genomics Unit, RIKEN Center for Integrative Medical Science, Yokohama, Japan
- RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Japan
| | - Kazuyoshi Ishigaki
- Laboratory for Human Immunogenetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Hideki Ueno
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
- Department of Immunology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yoshihide Hayashizaki
- K.K. DNAFORM, Yokohama, Japan
- RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Japan
| | - Massimiliano Pagani
- IFOM ETS - the AIRC Institute of Molecular Oncology, Milan, Italy
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi, Milan, Italy
| | - Piero Carninci
- Laboratory for Transcriptome Technology, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Human Technopole, Milan, Italy
| | - Motoko Yanagita
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Nicholas Parrish
- Genome Immunobiology RIKEN Hakubi Research Team, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Kazuhiko Yamamoto
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yasuhiro Murakawa
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
- IFOM ETS - the AIRC Institute of Molecular Oncology, Milan, Italy
- Department of Medical Systems Genomics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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12
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Zhu M, Xu R, Yuan J, Wang J, Ren X, Cong T, You Y, Ju A, Xu L, Wang H, Zheng P, Tao H, Lin C, Yu H, Du J, Lin X, Xie W, Li Y, Lan X. Tracking-seq reveals the heterogeneity of off-target effects in CRISPR-Cas9-mediated genome editing. Nat Biotechnol 2024:10.1038/s41587-024-02307-y. [PMID: 38956324 DOI: 10.1038/s41587-024-02307-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 06/06/2024] [Indexed: 07/04/2024]
Abstract
The continued development of novel genome editors calls for a universal method to analyze their off-target effects. Here we describe a versatile method, called Tracking-seq, for in situ identification of off-target effects that is broadly applicable to common genome-editing tools, including Cas9, base editors and prime editors. Through tracking replication protein A (RPA)-bound single-stranded DNA followed by strand-specific library construction, Tracking-seq requires a low cell input and is suitable for in vitro, ex vivo and in vivo genome editing, providing a sensitive and practical genome-wide approach for off-target detection in various scenarios. We show, using the same guide RNA, that Tracking-seq detects heterogeneity in off-target effects between different editor modalities and between different cell types, underscoring the necessity of direct measurement in the original system.
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Affiliation(s)
- Ming Zhu
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing, China.
- Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China.
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, Tsinghua University, Beijing, China.
| | - Runda Xu
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing, China
- Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, Tsinghua University, Beijing, China
| | - Junsong Yuan
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing, China
- IDG-McGovern Institute for Brain Research, Center for Synthetic and Systems Biology, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Jiacheng Wang
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing, China
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, Tsinghua University, Beijing, China
- School of Life Sciences, Tsinghua University, Beijing, China
| | - Xiaoyu Ren
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing, China
- IDG-McGovern Institute for Brain Research, Center for Synthetic and Systems Biology, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Tingting Cong
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing, China
- IDG-McGovern Institute for Brain Research, Center for Synthetic and Systems Biology, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Yaxian You
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing, China
- Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, Tsinghua University, Beijing, China
| | - Anji Ju
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing, China
- Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, Tsinghua University, Beijing, China
| | - Longchen Xu
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing, China
- School of Life Sciences, Tsinghua University, Beijing, China
| | - Huimin Wang
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing, China
- Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China
| | - Peiyuan Zheng
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing, China
- IDG-McGovern Institute for Brain Research, Center for Synthetic and Systems Biology, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Huiying Tao
- Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China
- Department of Urology, Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Chunhua Lin
- Department of Urology, Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Honghao Yu
- Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China
- Key Laboratory of Medical Biotechnology and Translational Medicine, Guilin Medical University, Guilin, China
| | - Juanjuan Du
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing, China
- IDG-McGovern Institute for Brain Research, Center for Synthetic and Systems Biology, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Xin Lin
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing, China
- Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China
| | - Wei Xie
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing, China
- School of Life Sciences, Tsinghua University, Beijing, China
| | - Yinqing Li
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, Tsinghua University, Beijing, China.
- IDG-McGovern Institute for Brain Research, Center for Synthetic and Systems Biology, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China.
| | - Xun Lan
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing, China.
- Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China.
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, Tsinghua University, Beijing, China.
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13
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Linna-Kuosmanen S, Vuori M, Kiviniemi T, Palmu J, Niiranen T. Genetics, transcriptomics, metagenomics, and metabolomics in the pathogenesis and prediction of atrial fibrillation. Eur Heart J Suppl 2024; 26:iv33-iv40. [PMID: 39099578 PMCID: PMC11292413 DOI: 10.1093/eurheartjsupp/suae072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/06/2024]
Abstract
The primary cellular substrates of atrial fibrillation (AF) and the mechanisms underlying AF onset remain poorly characterized and therefore, its risk assessment lacks precision. While the use of omics may enable discovery of novel AF risk factors and narrow down the cellular pathways involved in AF pathogenesis, the work is far from complete. Large-scale genome-wide association studies and transcriptomic analyses that allow an unbiased, non-candidate-gene-based delineation of molecular changes associated with AF in humans have identified at least 150 genetic loci associated with AF. However, only few of these loci have been thoroughly mechanistically dissected, indicating that much remains to be discovered for targeted diagnostics and therapeutics. Metabolomics and metagenomics, on the other hand, add to the understanding of AF downstream of the primary substrate and integrate the signalling of environmental and host factors, respectively. These two rapidly developing fields have already provided several correlates of prevalent and incident AF that require additional validation in external cohorts and experimental studies. In this review, we take a look at the recent developments in genetics, transcriptomics, metagenomics, and metabolomics and how they may aid in improving the discovery of AF risk factors and shed light into the molecular mechanisms leading to AF onset.
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Affiliation(s)
- Suvi Linna-Kuosmanen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70211 Kuopio, Finland
| | - Matti Vuori
- Division of Medicine, Turku University Hospital, Turku, Finland
- Department of Internal Medicine, University of Turku, Turku, Finland
| | - Tuomas Kiviniemi
- Department of Internal Medicine, University of Turku, Turku, Finland
- Heart Center, Turku University Hospital, Turku, Finland
| | - Joonatan Palmu
- Department of Internal Medicine, University of Turku, Turku, Finland
| | - Teemu Niiranen
- Division of Medicine, Turku University Hospital, Turku, Finland
- Department of Internal Medicine, University of Turku, Turku, Finland
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Turku, Finland
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14
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Salignon J, Millan-Ariño L, Garcia MU, Riedel CG. Cactus: A user-friendly and reproducible ATAC-Seq and mRNA-Seq analysis pipeline for data preprocessing, differential analysis, and enrichment analysis. Genomics 2024; 116:110858. [PMID: 38735595 DOI: 10.1016/j.ygeno.2024.110858] [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: 02/09/2024] [Revised: 05/06/2024] [Accepted: 05/09/2024] [Indexed: 05/14/2024]
Abstract
The ever decreasing cost of Next-Generation Sequencing coupled with the emergence of efficient and reproducible analysis pipelines has rendered genomic methods more accessible. However, downstream analyses are basic or missing in most workflows, creating a significant barrier for non-bioinformaticians. To help close this gap, we developed Cactus, an end-to-end pipeline for analyzing ATAC-Seq and mRNA-Seq data, either separately or jointly. Its Nextflow-, container-, and virtual environment-based architecture ensures efficient and reproducible analyses. Cactus preprocesses raw reads, conducts differential analyses between conditions, and performs enrichment analyses in various databases, including DNA-binding motifs, ChIP-Seq binding sites, chromatin states, and ontologies. We demonstrate the utility of Cactus in a multi-modal and multi-species case study as well as by showcasing its unique capabilities as compared to other ATAC-Seq pipelines. In conclusion, Cactus can assist researchers in gaining comprehensive insights from chromatin accessibility and gene expression data in a quick, user-friendly, and reproducible manner.
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Affiliation(s)
- Jérôme Salignon
- Department of Bioscience and Nutrition, Karolinska Institute, Blickagången 16, Huddinge SE-141 83, Sweden.
| | - Lluís Millan-Ariño
- Department of Bioscience and Nutrition, Karolinska Institute, Blickagången 16, Huddinge SE-141 83, Sweden
| | - Maxime U Garcia
- National Genomics Infrastructure, Science for Life Laboratory, Tomtebodavägen 23A, Solna SE-171 65, Sweden; Department of Oncology-Pathology, Karolinska Institute, Visionsgatan 4, Solna SE-171 64, Sweden
| | - Christian G Riedel
- Department of Bioscience and Nutrition, Karolinska Institute, Blickagången 16, Huddinge SE-141 83, Sweden.
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15
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García-González J, Garcia-Gonzalez S, Liou L, O'Reilly PF. The Gene Expression Landscape of Disease Genes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.20.24309121. [PMID: 38947033 PMCID: PMC11213058 DOI: 10.1101/2024.06.20.24309121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Fine-mapping and gene-prioritisation techniques applied to the latest Genome-Wide Association Study (GWAS) results have prioritised hundreds of genes as causally associated with disease. Here we leverage these recently compiled lists of high-confidence causal genes to interrogate where in the body disease genes operate. Specifically, we combine GWAS summary statistics, gene prioritisation results and gene expression RNA-seq data from 46 tissues and 204 cell types in relation to 16 major diseases (including 8 cancers). In tissues and cell types with well-established relevance to the disease, the prioritised genes typically have higher absolute and relative (i.e. tissue/cell specific) expression compared to non-prioritised 'control' genes. Examples include brain tissues in psychiatric disorders (P-value < 1×10-7), microglia cells in Alzheimer's Disease (P-value = 9.8×10-3) and colon mucosa in colorectal cancer (P-value < 1×10-3). We also observe significantly higher expression for disease genes in multiple tissues and cell types with no established links to the corresponding disease. While some of these results may be explained by cell types that span multiple tissues, such as macrophages in brain, blood, lung and spleen in relation to Alzheimer's disease (P-values < 1×10-3), the cause for others is unclear and motivates further investigation that may provide novel insights into disease etiology. For example, mammary tissue in Type 2 Diabetes (P-value < 1×10-7); reproductive tissues such as breast, uterus, vagina, and prostate in Coronary Artery Disease (P-value < 1×10-4); and motor neurons in psychiatric disorders (P-value < 3×10-4). In the GTEx dataset, tissue type is the major predictor of gene expression but the contribution of each predictor (tissue, sample, subject, batch) varies widely among disease-associated genes. Finally, we highlight genes with the highest levels of gene expression in relevant tissues to guide functional follow-up studies. Our results could offer novel insights into the tissues and cells involved in disease initiation, inform drug target and delivery strategies, highlighting potential off-target effects, and exemplify the relative performance of different statistical tests for linking disease genes with tissue and cell type gene expression.
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Affiliation(s)
- Judit García-González
- Department of Genetics and Genomic Sciences, Icahn School of Medicine, Mount Sinai, New York City, NY 10029, USA
| | - Saul Garcia-Gonzalez
- Department of Genetics and Genomic Sciences, Icahn School of Medicine, Mount Sinai, New York City, NY 10029, USA
- Center for Excellence in Youth Education, Icahn School of Medicine, Mount Sinai, New York City, NY 10029, USA
| | - Lathan Liou
- Department of Genetics and Genomic Sciences, Icahn School of Medicine, Mount Sinai, New York City, NY 10029, USA
| | - Paul F O'Reilly
- Department of Genetics and Genomic Sciences, Icahn School of Medicine, Mount Sinai, New York City, NY 10029, USA
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16
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Strober BJ, Zhang MJ, Amariuta T, Rossen J, Price AL. Fine-mapping causal tissues and genes at disease-associated loci. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.11.01.23297909. [PMID: 37961337 PMCID: PMC10635248 DOI: 10.1101/2023.11.01.23297909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Heritable diseases often manifest in a highly tissue-specific manner, with different disease loci mediated by genes in distinct tissues or cell types. We propose Tissue-Gene Fine-Mapping (TGFM), a fine-mapping method that infers the posterior probability (PIP) for each gene-tissue pair to mediate a disease locus by analyzing GWAS summary statistics (and in-sample LD) and leveraging eQTL data from diverse tissues to build cis-predicted expression models; TGFM also assigns PIPs to causal variants that are not mediated by gene expression in assayed genes and tissues. TGFM accounts for both co-regulation across genes and tissues and LD between SNPs (generalizing existing fine-mapping methods), and incorporates genome-wide estimates of each tissue's contribution to disease as tissue-level priors. TGFM was well-calibrated and moderately well-powered in simulations; unlike previous methods, TGFM was able to attain correct calibration by modeling uncertainty in cis-predicted expression models. We applied TGFM to 45 UK Biobank diseases/traits (average N = 316K) using eQTL data from 38 GTEx tissues. TGFM identified an average of 147 PIP > 0.5 causal genetic elements per disease/trait, of which 11% were gene-tissue pairs. Implicated gene-tissue pairs were concentrated in known disease-critical tissues, and causal genes were strongly enriched in disease-relevant gene sets. Causal gene-tissue pairs identified by TGFM recapitulated known biology (e.g., TPO-thyroid for Hypothyroidism), but also included biologically plausible novel findings (e.g., SLC20A2-artery aorta for Diastolic blood pressure). Further application of TGFM to single-cell eQTL data from 9 cell types in peripheral blood mononuclear cells (PBMC), analyzed jointly with GTEx tissues, identified 30 additional causal gene-PBMC cell type pairs at PIP > 0.5-primarily for autoimmune disease and blood cell traits, including the biologically plausible example of CD52 in classical monocyte cells for Monocyte count. In conclusion, TGFM is a robust and powerful method for fine-mapping causal tissues and genes at disease-associated loci.
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Affiliation(s)
- Benjamin J. Strober
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Martin Jinye Zhang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Tiffany Amariuta
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Jordan Rossen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alkes L. Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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17
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Sun W, Xiong D, Ouyang J, Xiao X, Jiang Y, Wang Y, Li S, Xie Z, Wang J, Tang Z, Zhang Q. Altered chromatin topologies caused by balanced chromosomal translocation lead to central iris hypoplasia. Nat Commun 2024; 15:5048. [PMID: 38871723 DOI: 10.1038/s41467-024-49376-w] [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: 06/06/2023] [Accepted: 06/04/2024] [Indexed: 06/15/2024] Open
Abstract
Despite the advent of genomic sequencing, molecular diagnosis remains unsolved in approximately half of patients with Mendelian disorders, largely due to unclarified functions of noncoding regions and the difficulty in identifying complex structural variations. In this study, we map a unique form of central iris hypoplasia in a large family to 6q15-q23.3 and 18p11.31-q12.1 using a genome-wide linkage scan. Long-read sequencing reveals a balanced translocation t(6;18)(q22.31;p11.22) with intergenic breakpoints. By performing Hi-C on induced pluripotent stem cells from a patient, we identify two chromatin topologically associating domains spanning across the breakpoints. These alterations lead the ectopic chromatin interactions between APCDD1 on chromosome 18 and enhancers on chromosome 6, resulting in upregulation of APCDD1. Notably, APCDD1 is specifically localized in the iris of human eyes. Our findings demonstrate that noncoding structural variations can lead to Mendelian diseases by disrupting the 3D genome structure and resulting in altered gene expression.
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Affiliation(s)
- Wenmin Sun
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Dan Xiong
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Jiamin Ouyang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Xueshan Xiao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Yi Jiang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Yingwei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Shiqiang Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Ziying Xie
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Junwen Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Zhonghui Tang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Qingjiong Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China.
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18
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Hemerich D, Svenstrup V, Obrero VD, Preuss M, Moscati A, Hirschhorn JN, Loos RJF. An integrative framework to prioritize genes in more than 500 loci associated with body mass index. Am J Hum Genet 2024; 111:1035-1046. [PMID: 38754426 PMCID: PMC11179420 DOI: 10.1016/j.ajhg.2024.04.016] [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: 01/25/2024] [Revised: 04/20/2024] [Accepted: 04/22/2024] [Indexed: 05/18/2024] Open
Abstract
Obesity is a major risk factor for a myriad of diseases, affecting >600 million people worldwide. Genome-wide association studies (GWASs) have identified hundreds of genetic variants that influence body mass index (BMI), a commonly used metric to assess obesity risk. Most variants are non-coding and likely act through regulating genes nearby. Here, we apply multiple computational methods to prioritize the likely causal gene(s) within each of the 536 previously reported GWAS-identified BMI-associated loci. We performed summary-data-based Mendelian randomization (SMR), FINEMAP, DEPICT, MAGMA, transcriptome-wide association studies (TWASs), mutation significance cutoff (MSC), polygenic priority score (PoPS), and the nearest gene strategy. Results of each method were weighted based on their success in identifying genes known to be implicated in obesity, ranking all prioritized genes according to a confidence score (minimum: 0; max: 28). We identified 292 high-scoring genes (≥11) in 264 loci, including genes known to play a role in body weight regulation (e.g., DGKI, ANKRD26, MC4R, LEPR, BDNF, GIPR, AKT3, KAT8, MTOR) and genes related to comorbidities (e.g., FGFR1, ISL1, TFAP2B, PARK2, TCF7L2, GSK3B). For most of the high-scoring genes, however, we found limited or no evidence for a role in obesity, including the top-scoring gene BPTF. Many of the top-scoring genes seem to act through a neuronal regulation of body weight, whereas others affect peripheral pathways, including circadian rhythm, insulin secretion, and glucose and carbohydrate homeostasis. The characterization of these likely causal genes can increase our understanding of the underlying biology and offer avenues to develop therapeutics for weight loss.
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Affiliation(s)
- Daiane Hemerich
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Bristol Myers Squibb, Summit, NJ, USA
| | - Victor Svenstrup
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark; Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Virginia Diez Obrero
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark; Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Michael Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Arden Moscati
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Regeneron Genetics Center, Tarrytown, NY, USA
| | - Joel N Hirschhorn
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA 02115, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark; Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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19
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Tsuchiya H, Ota M, Takahashi H, Hatano H, Ogawa M, Nakajima S, Yoshihara R, Okamura T, Sumitomo S, Fujio K. Epigenetic targets of Janus kinase inhibitors are linked to genetic risks of rheumatoid arthritis. Inflamm Regen 2024; 44:29. [PMID: 38831367 PMCID: PMC11149281 DOI: 10.1186/s41232-024-00337-2] [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: 04/11/2024] [Accepted: 05/15/2024] [Indexed: 06/05/2024] Open
Abstract
BACKGROUND Current strategies that target cytokines (e.g., tumor necrosis factor (TNF)-α), or signaling molecules (e.g., Janus kinase (JAK)) have advanced the management for allergies and autoimmune diseases. Nevertheless, the molecular mechanism that underpins its clinical efficacy have largely remained elusive, especially in the local tissue environment. Here, we aimed to identify the genetic, epigenetic, and immunological targets of JAK inhibitors (JAKis), focusing on their effects on synovial fibroblasts (SFs), the major local effectors associated with destructive joint inflammation in rheumatoid arthritis (RA). METHODS SFs were activated by cytokines related to inflammation in RA, and were treated with three types of JAKis or a TNF-α inhibitor (TNFi). Dynamic changes in transcriptome and chromatin accessibility were profiled across samples to identify drug targets. Furthermore, the putative targets were validated using luciferase assays and clustered regularly interspaced short palindromic repeat (CRISPR)-based genome editing. RESULTS We found that both JAKis and the TNFi targeted the inflammatory module including IL6. Conversely, specific gene signatures that were preferentially inhibited by either of the drug classes were identified. Strikingly, RA risk enhancers for CD40 and TRAF1 were distinctively regulated by JAKis and the TNFi. We performed luciferase assays and CRISPR-based genome editing, and successfully fine-mapped the single causal variants in these loci, rs6074022-CD40 and rs7021049-TRAF1. CONCLUSIONS JAKis and the TNFi had a direct impact on different RA risk enhancers, and we identified nucleotide-resolution targets for both drugs. Distinctive targets of clinically effective drugs could be useful for tailoring the application of these drugs and future design of more efficient treatment strategies.
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Affiliation(s)
- Haruka Tsuchiya
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan
| | - Mineto Ota
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan
- Department of Functional Genomics and Immunological Diseases, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan
| | - Haruka Takahashi
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan
| | - Hiroaki Hatano
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan
- Department of Functional Genomics and Immunological Diseases, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan
| | - Megumi Ogawa
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan
| | - Sotaro Nakajima
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan
| | - Risa Yoshihara
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan
| | - Tomohisa Okamura
- Department of Functional Genomics and Immunological Diseases, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan
| | - Shuji Sumitomo
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan
| | - Keishi Fujio
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan.
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20
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Dorans E, Jagadeesh K, Dey K, Price AL. Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.24.24307813. [PMID: 38826240 PMCID: PMC11142273 DOI: 10.1101/2024.05.24.24307813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Methods that analyze single-cell paired RNA-seq and ATAC-seq multiome data have shown great promise in linking regulatory elements to genes. However, existing methods differ in their modeling assumptions and approaches to account for biological and technical noise-leading to low concordance in their linking scores-and do not capture the effects of genomic distance. We propose pgBoost, an integrative modeling framework that trains a non-linear combination of existing linking strategies (including genomic distance) on fine-mapped eQTL data to assign a probabilistic score to each candidate SNP-gene link. We applied pgBoost to single-cell multiome data from 85k cells representing 6 major immune/blood cell types. pgBoost attained higher enrichment for fine-mapped eSNP-eGene pairs (e.g. 21x at distance >10kb) than existing methods (1.2-10x; p-value for difference = 5e-13 vs. distance-based method and < 4e-35 for each other method), with larger improvements at larger distances (e.g. 35x vs. 0.89-6.6x at distance >100kb; p-value for difference < 0.002 vs. each other method). pgBoost also outperformed existing methods in enrichment for CRISPR-validated links (e.g. 4.8x vs. 1.6-4.1x at distance >10kb; p-value for difference = 0.25 vs. distance-based method and < 2e-5 for each other method), with larger improvements at larger distances (e.g. 15x vs. 1.6-2.5x at distance >100kb; p-value for difference < 0.009 for each other method). Similar improvements in enrichment were observed for links derived from Activity-By-Contact (ABC) scores and GWAS data. We further determined that restricting pgBoost to features from a focal cell type improved the identification of SNP-gene links relevant to that cell type. We highlight several examples where pgBoost linked fine-mapped GWAS variants to experimentally validated or biologically plausible target genes that were not implicated by other methods. In conclusion, a non-linear combination of linking strategies, including genomic distance, improves power to identify target genes underlying GWAS associations.
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21
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Zeng B, Bendl J, Deng C, Lee D, Misir R, Reach SM, Kleopoulos SP, Auluck P, Marenco S, Lewis DA, Haroutunian V, Ahituv N, Fullard JF, Hoffman GE, Roussos P. Genetic regulation of cell type-specific chromatin accessibility shapes brain disease etiology. Science 2024; 384:eadh4265. [PMID: 38781378 DOI: 10.1126/science.adh4265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 12/20/2023] [Indexed: 05/25/2024]
Abstract
Nucleotide variants in cell type-specific gene regulatory elements in the human brain are risk factors for human disease. We measured chromatin accessibility in 1932 aliquots of sorted neurons and non-neurons from 616 human postmortem brains and identified 34,539 open chromatin regions with chromatin accessibility quantitative trait loci (caQTLs). Only 10.4% of caQTLs are shared between neurons and non-neurons, which supports cell type-specific genetic regulation of the brain regulome. Incorporating allele-specific chromatin accessibility improves statistical fine-mapping and refines molecular mechanisms that underlie disease risk. Using massively parallel reporter assays in induced excitatory neurons, we screened 19,893 brain QTLs and identified the functional impact of 476 regulatory variants. Combined, this comprehensive resource captures variation in the human brain regulome and provides insights into disease etiology.
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Affiliation(s)
- Biao Zeng
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Chengyu Deng
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ruth Misir
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sarah M Reach
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Steven P Kleopoulos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Pavan Auluck
- Human Brain Collection Core, National Institute of Mental Health-Intramural Research Program, Bethesda, MD 20892, USA
| | - Stefano Marenco
- Human Brain Collection Core, National Institute of Mental Health-Intramural Research Program, Bethesda, MD 20892, USA
| | - David A Lewis
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Vahram Haroutunian
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, NY 10468, USA
| | - Nadav Ahituv
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94158, USA
| | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, NY 10468, USA
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Kim A, Zhang Z, Legros C, Lu Z, de Smith A, Moore JE, Mancuso N, Gazal S. Inferring causal cell types of human diseases and risk variants from candidate regulatory elements. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.17.24307556. [PMID: 38798383 PMCID: PMC11118635 DOI: 10.1101/2024.05.17.24307556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
The heritability of human diseases is extremely enriched in candidate regulatory elements (cRE) from disease-relevant cell types. Critical next steps are to infer which and how many cell types are truly causal for a disease (after accounting for co-regulation across cell types), and to understand how individual variants impact disease risk through single or multiple causal cell types. Here, we propose CT-FM and CT-FM-SNP, two methods that leverage cell-type-specific cREs to fine-map causal cell types for a trait and for its candidate causal variants, respectively. We applied CT-FM to 63 GWAS summary statistics (average N = 417K) using nearly one thousand cRE annotations, primarily coming from ENCODE4. CT-FM inferred 81 causal cell types with corresponding SNP-annotations explaining a high fraction of trait SNP-heritability (~2/3 of the SNP-heritability explained by existing cREs), identified 16 traits with multiple causal cell types, highlighted cell-disease relationships consistent with known biology, and uncovered previously unexplored cellular mechanisms in psychiatric and immune-related diseases. Finally, we applied CT-FM-SNP to 39 UK Biobank traits and predicted high confidence causal cell types for 2,798 candidate causal non-coding SNPs. Our results suggest that most SNPs impact a phenotype through a single cell type, and that pleiotropic SNPs target different cell types depending on the phenotype context. Altogether, CT-FM and CT-FM-SNP shed light on how genetic variants act collectively and individually at the cellular level to impact disease risk.
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Affiliation(s)
- Artem Kim
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Zixuan Zhang
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Come Legros
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Zeyun Lu
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Adam de Smith
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jill E Moore
- Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Nicholas Mancuso
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, 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
| | - Steven Gazal
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, 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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23
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Cai W, Song W, Yu S, Zhao M, Lin GN. Human lineage mutations regulate RNA-protein binding of conserved genes NTRK2 and ITPR1 involved in human evolution. Gen Psychiatr 2024; 37:e101425. [PMID: 38770356 PMCID: PMC11103204 DOI: 10.1136/gpsych-2023-101425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 04/14/2024] [Indexed: 05/22/2024] Open
Abstract
Background The role of human lineage mutations (HLMs) in human evolution through post-transcriptional modification is unclear. Aims To investigate the contribution of HLMs to human evolution through post-transcriptional modification. Methods We applied a deep learning model Seqweaver to predict how HLMs impact RNA-binding protein affinity. Results We found that only 0.27% of HLMs had significant impacts on RNA-binding proteins at the threshold of the top 1% of human common variations. These HLMs enriched in a set of conserved genes highly expressed in adult excitatory neurons and prenatal Purkinje neurons, and were involved in synapse organisation and the GTPase pathway. These genes also carried excess damaging coding mutations that caused neurodevelopmental disorders, ataxia and schizophrenia. Among these genes, NTRK2 and ITPR1 had the most aggregated evidence of functional importance, suggesting their essential roles in cognition and bipedalism. Conclusions Our findings suggest that a small subset of human-specific mutations have contributed to human speciation through impacts on post-transcriptional modification of critical brain-related genes.
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Affiliation(s)
- Wenxiang Cai
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - Weichen Song
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - Shunying Yu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - Min Zhao
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - Guan Ning Lin
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
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24
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Arthur TD, Joshua IN, Nguyen JP, D'Antonio-Chronowska A, Frazer KA, D'Antonio M. IFN-γ activates an immune-like regulatory network in the cardiac vascular endothelium. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.03.592380. [PMID: 38746472 PMCID: PMC11092750 DOI: 10.1101/2024.05.03.592380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The regulatory mechanisms underlying the response to pro-inflammatory cytokines during myocarditis are poorly understood. Here, we use iPSC-derived cardiovascular progenitor cells (CVPCs) to model the response to interferon gamma (IFN-γ) during myocarditis. We generate RNA-seq and ATAC-seq for four CVPCs that were treated with IFN-γ and compare them with paired untreated controls. Transcriptional differences after treatment show that IFN-γ initiates an innate immune cell-like response in the vascular cardiac endothelium. IFN-γ treatment also shifts the CVPC transcriptome towards the adult coronary artery and aorta profiles and expands the relative endothelial cell population in all four CVPC lines. Analysis of the accessible chromatin shows that IFN-γ is a potent chromatin remodeler and establishes an IRF-STAT immune-cell like regulatory network. Our findings reveal insights into the endothelial-specific protective mechanisms during myocarditis.
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25
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Saldana-Guerrero IM, Montano-Gutierrez LF, Boswell K, Hafemeister C, Poon E, Shaw LE, Stavish D, Lea RA, Wernig-Zorc S, Bozsaky E, Fetahu IS, Zoescher P, Pötschger U, Bernkopf M, Wenninger-Weinzierl A, Sturtzel C, Souilhol C, Tarelli S, Shoeb MR, Bozatzi P, Rados M, Guarini M, Buri MC, Weninger W, Putz EM, Huang M, Ladenstein R, Andrews PW, Barbaric I, Cresswell GD, Bryant HE, Distel M, Chesler L, Taschner-Mandl S, Farlik M, Tsakiridis A, Halbritter F. A human neural crest model reveals the developmental impact of neuroblastoma-associated chromosomal aberrations. Nat Commun 2024; 15:3745. [PMID: 38702304 PMCID: PMC11068915 DOI: 10.1038/s41467-024-47945-7] [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: 01/06/2023] [Accepted: 04/15/2024] [Indexed: 05/06/2024] Open
Abstract
Early childhood tumours arise from transformed embryonic cells, which often carry large copy number alterations (CNA). However, it remains unclear how CNAs contribute to embryonic tumourigenesis due to a lack of suitable models. Here we employ female human embryonic stem cell (hESC) differentiation and single-cell transcriptome and epigenome analysis to assess the effects of chromosome 17q/1q gains, which are prevalent in the embryonal tumour neuroblastoma (NB). We show that CNAs impair the specification of trunk neural crest (NC) cells and their sympathoadrenal derivatives, the putative cells-of-origin of NB. This effect is exacerbated upon overexpression of MYCN, whose amplification co-occurs with CNAs in NB. Moreover, CNAs potentiate the pro-tumourigenic effects of MYCN and mutant NC cells resemble NB cells in tumours. These changes correlate with a stepwise aberration of developmental transcription factor networks. Together, our results sketch a mechanistic framework for the CNA-driven initiation of embryonal tumours.
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Affiliation(s)
- Ingrid M Saldana-Guerrero
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK
- Neuroscience Institute, The University of Sheffield, Sheffield, UK
- Sheffield Institute for Nucleic Acids (SInFoNiA), School of Medicine and Population Health, The University of Sheffield, Sheffield, UK
| | | | - Katy Boswell
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK
- Neuroscience Institute, The University of Sheffield, Sheffield, UK
| | | | - Evon Poon
- Division of Clinical Studies, The Institute of Cancer Research (ICR) & Royal Marsden NHS Trust, London, UK
| | - Lisa E Shaw
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Dylan Stavish
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK
- Neuroscience Institute, The University of Sheffield, Sheffield, UK
| | - Rebecca A Lea
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK
- Neuroscience Institute, The University of Sheffield, Sheffield, UK
| | - Sara Wernig-Zorc
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Eva Bozsaky
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Irfete S Fetahu
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
- Medical University of Vienna, Department of Neurology, Division of Neuropathology and Neurochemistry, Vienna, Austria
| | - Peter Zoescher
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Ulrike Pötschger
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Marie Bernkopf
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
- Labdia Labordiagnostik GmbH, Vienna, Austria
| | | | - Caterina Sturtzel
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Celine Souilhol
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK
- Neuroscience Institute, The University of Sheffield, Sheffield, UK
- Biomolecular Sciences Research Centre, Department of Biosciences and Chemistry, Sheffield Hallam University, Sheffield, UK
| | - Sophia Tarelli
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK
- Neuroscience Institute, The University of Sheffield, Sheffield, UK
| | - Mohamed R Shoeb
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Polyxeni Bozatzi
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Magdalena Rados
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Maria Guarini
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Michelle C Buri
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Wolfgang Weninger
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Eva M Putz
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Miller Huang
- Children's Hospital Los Angeles, Cancer and Blood Disease Institutes, and The Saban Research Institute, Los Angeles, CA, USA
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ruth Ladenstein
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Peter W Andrews
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK
| | - Ivana Barbaric
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK
- Neuroscience Institute, The University of Sheffield, Sheffield, UK
| | | | - Helen E Bryant
- Sheffield Institute for Nucleic Acids (SInFoNiA), School of Medicine and Population Health, The University of Sheffield, Sheffield, UK
| | - Martin Distel
- St. Anna Children's Cancer Research Institute (CCRI), Vienna, Austria
| | - Louis Chesler
- Division of Clinical Studies, The Institute of Cancer Research (ICR) & Royal Marsden NHS Trust, London, UK
| | | | - Matthias Farlik
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Anestis Tsakiridis
- Centre for Stem Cell Biology, School of Biosciences, The University of Sheffield, Sheffield, UK.
- Neuroscience Institute, The University of Sheffield, Sheffield, UK.
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26
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Zheng Z, Liu S, Sidorenko J, Wang Y, Lin T, Yengo L, Turley P, Ani A, Wang R, Nolte IM, Snieder H, Yang J, Wray NR, Goddard ME, Visscher PM, Zeng J. Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries. Nat Genet 2024; 56:767-777. [PMID: 38689000 PMCID: PMC11096109 DOI: 10.1038/s41588-024-01704-y] [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: 10/01/2022] [Accepted: 03/05/2024] [Indexed: 05/02/2024]
Abstract
We develop a method, SBayesRC, that integrates genome-wide association study (GWAS) summary statistics with functional genomic annotations to improve polygenic prediction of complex traits. Our method is scalable to whole-genome variant analysis and refines signals from functional annotations by allowing them to affect both causal variant probability and causal effect distribution. We analyze 50 complex traits and diseases using ∼7 million common single-nucleotide polymorphisms (SNPs) and 96 annotations. SBayesRC improves prediction accuracy by 14% in European ancestry and up to 34% in cross-ancestry prediction compared to the baseline method SBayesR, which does not use annotations, and outperforms other methods, including LDpred2, LDpred-funct, MegaPRS, PolyPred-S and PRS-CSx. Investigation of factors affecting prediction accuracy identifies a significant interaction between SNP density and annotation information, suggesting whole-genome sequence variants with annotations may further improve prediction. Functional partitioning analysis highlights a major contribution of evolutionary constrained regions to prediction accuracy and the largest per-SNP contribution from nonsynonymous SNPs.
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Affiliation(s)
- Zhili Zheng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
| | - Shouye Liu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Julia Sidorenko
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Ying Wang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Tian Lin
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Patrick Turley
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
- Department of Economics, University of Southern California, Los Angeles, CA, USA
| | - Alireza Ani
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Rujia Wang
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Michael E Goddard
- Faculty of Veterinary and Agricultural Science, University of Melbourne, Parkville, Victoria, Australia
- Biosciences Research Division, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jian Zeng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.
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27
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Hu M, Kim I, Morán I, Peng W, Sun O, Bonnefond A, Khamis A, Bonàs-Guarch S, Froguel P, Rutter GA. Multiple genetic variants at the SLC30A8 locus affect local super-enhancer activity and influence pancreatic β-cell survival and function. FASEB J 2024; 38:e23610. [PMID: 38661000 PMCID: PMC11108099 DOI: 10.1096/fj.202301700rr] [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: 08/23/2023] [Revised: 03/22/2024] [Accepted: 04/01/2024] [Indexed: 04/26/2024]
Abstract
Variants at the SLC30A8 locus are associated with type 2 diabetes (T2D) risk. The lead variant, rs13266634, encodes an amino acid change, Arg325Trp (R325W), at the C-terminus of the secretory granule-enriched zinc transporter, ZnT8. Although this protein-coding variant was previously thought to be the sole driver of T2D risk at this locus, recent studies have provided evidence for lowered expression of SLC30A8 mRNA in protective allele carriers. In the present study, we examined multiple variants that influence SLC30A8 allele-specific expression. Epigenomic mapping has previously identified an islet-selective enhancer cluster at the SLC30A8 locus, hosting multiple T2D risk and cASE associations, which is spatially associated with the SLC30A8 promoter and additional neighboring genes. Here, we show that deletion of variant-bearing enhancer regions using CRISPR-Cas9 in human-derived EndoC-βH3 cells lowers the expression of SLC30A8 and several neighboring genes and improves glucose-stimulated insulin secretion. While downregulation of SLC30A8 had no effect on beta cell survival, loss of UTP23, RAD21, or MED30 markedly reduced cell viability. Although eQTL or cASE analyses in human islets did not support the association between these additional genes and diabetes risk, the transcriptional regulator JQ1 lowered the expression of multiple genes at the SLC30A8 locus and enhanced stimulated insulin secretion.
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Affiliation(s)
- Ming Hu
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Innah Kim
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Ignasi Morán
- Life Sciences Department, Barcelona Supercomputing Center (BSC-CNS), 08034 Barcelona, Spain
| | - Weicong Peng
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Orien Sun
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Amélie Bonnefond
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0NN, UK
- Inserm U1283, CNRS UMR 8199, EGID, Institut Pasteur de Lille, F-59000, France
- University of Lille, Lille University Hospital, Lille, F-59000, France.France
| | - Amna Khamis
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0NN, UK
- Inserm U1283, CNRS UMR 8199, EGID, Institut Pasteur de Lille, F-59000, France
- University of Lille, Lille University Hospital, Lille, F-59000, France.France
| | - Sílvia Bonàs-Guarch
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0NN, UK
- Center for Genomic Regulation (CRG), C/ Dr. Aiguader, 88, PRBB Building, 08003 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Spain
| | - Philippe Froguel
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0NN, UK
- Inserm U1283, CNRS UMR 8199, EGID, Institut Pasteur de Lille, F-59000, France
- University of Lille, Lille University Hospital, Lille, F-59000, France.France
| | - Guy A. Rutter
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London W12 0NN, UK
- Centre de Recherche du CHUM, Faculté de Médicine, Université de Montréal, Montréal, QC, Canada
- Lee Kong Chian Imperial Medical School, Nanyang Technological University, Singapore
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28
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Zhang T, Ambrodji A, Huang H, Bouchonville KJ, Etheridge AS, Schmidt RE, Bembenek BM, Temesgen ZB, Wang Z, Innocenti F, Stroka D, Diasio RB, Largiadèr CR, Offer SM. Germline cis variant determines epigenetic regulation of the anti-cancer drug metabolism gene dihydropyrimidine dehydrogenase ( DPYD). eLife 2024; 13:RP94075. [PMID: 38686795 PMCID: PMC11060711 DOI: 10.7554/elife.94075] [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] [Indexed: 05/02/2024] Open
Abstract
Enhancers are critical for regulating tissue-specific gene expression, and genetic variants within enhancer regions have been suggested to contribute to various cancer-related processes, including therapeutic resistance. However, the precise mechanisms remain elusive. Using a well-defined drug-gene pair, we identified an enhancer region for dihydropyrimidine dehydrogenase (DPD, DPYD gene) expression that is relevant to the metabolism of the anti-cancer drug 5-fluorouracil (5-FU). Using reporter systems, CRISPR genome-edited cell models, and human liver specimens, we demonstrated in vitro and vivo that genotype status for the common germline variant (rs4294451; 27% global minor allele frequency) located within this novel enhancer controls DPYD transcription and alters resistance to 5-FU. The variant genotype increases recruitment of the transcription factor CEBPB to the enhancer and alters the level of direct interactions between the enhancer and DPYD promoter. Our data provide insight into the regulatory mechanisms controlling sensitivity and resistance to 5-FU.
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Affiliation(s)
- Ting Zhang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo ClinicRochesterUnited States
| | - Alisa Ambrodji
- Department of Clinical Chemistry, Inselspital, Bern University Hospital, University of BernBernSwitzerland
- Graduate School for Cellular and Biomedical Sciences, University of BernBernSwitzerland
| | - Huixing Huang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo ClinicRochesterUnited States
| | - Kelly J Bouchonville
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo ClinicRochesterUnited States
| | - Amy S Etheridge
- Eshelman School of Pharmacy, Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina at Chapel HillChapel HillUnited States
| | - Remington E Schmidt
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo ClinicRochesterUnited States
| | - Brianna M Bembenek
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo ClinicRochesterUnited States
| | - Zoey B Temesgen
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo ClinicRochesterUnited States
| | - Zhiquan Wang
- Division of Hematology, Department of Medicine, Mayo ClinicRochesterUnited States
| | - Federico Innocenti
- Eshelman School of Pharmacy, Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina at Chapel HillChapel HillUnited States
| | - Deborah Stroka
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of BernBernSwitzerland
| | - Robert B Diasio
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo ClinicRochesterUnited States
| | - Carlo R Largiadèr
- Department of Clinical Chemistry, Inselspital, Bern University Hospital, University of BernBernSwitzerland
| | - Steven M Offer
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo ClinicRochesterUnited States
- Department of Pathology, University of Iowa Carver College of Medicine, University of IowaIowa CityUnited States
- Holden Comprehensive Cancer Center, University of Iowa Carver College of Medicine, University of IowaIowa CityUnited States
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29
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Foroozandeh Shahraki M, Farahbod M, Libbrecht MW. Robust chromatin state annotation. Genome Res 2024; 34:469-483. [PMID: 38514204 PMCID: PMC11067878 DOI: 10.1101/gr.278343.123] [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: 07/28/2023] [Accepted: 03/19/2024] [Indexed: 03/23/2024]
Abstract
With the goal of mapping genomic activity, international projects have recently measured epigenetic activity in hundreds of cell and tissue types. Chromatin state annotations produced by segmentation and genome annotation (SAGA) methods have emerged as the predominant way to summarize these epigenomic data sets in order to annotate the genome. These chromatin state annotations are essential for many genomic tasks, including identifying active regulatory elements and interpreting disease-associated genetic variation. However, despite the widespread applications of SAGA methods, no principled approach exists to evaluate the statistical significance of chromatin state assignments. Here, we propose the first method for assigning calibrated confidence scores to chromatin state annotations. Toward this goal, we performed a comprehensive evaluation of the reproducibility of the two most widely used existing SAGA methods, ChromHMM and Segway. We found that their predictions are frequently irreproducible. For example, when applying the same SAGA method on two sets of experimental replicates, 27%-69% of predicted enhancers fail to replicate. This suggests that a substantial fraction of predicted elements in existing chromatin state annotations cannot be relied upon. To remedy this problem, we introduce SAGAconf, a method for assigning a measure of confidence (r-value) to chromatin state annotations. SAGAconf works with any SAGA method and assigns an r-value to each genomic bin of a chromatin state annotation that represents the probability that the label of this bin will be reproduced in a replicated experiment. Thus, SAGAconf allows a researcher to select only the reliable predictions from a chromatin annotation for use in downstream analyses.
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Affiliation(s)
| | - Marjan Farahbod
- School of Computing Science, Simon Fraser University, Burnaby, British Columbia V51 1S6, Canada
| | - Maxwell W Libbrecht
- School of Computing Science, Simon Fraser University, Burnaby, British Columbia V51 1S6, Canada
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30
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Song W, Shi Y, Lin GN. Haplotype function score improves biological interpretation and cross-ancestry polygenic prediction of human complex traits. eLife 2024; 12:RP92574. [PMID: 38639992 PMCID: PMC11031082 DOI: 10.7554/elife.92574] [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] [Indexed: 04/20/2024] Open
Abstract
We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS-trait associations with a significance of p < 5 × 10-8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway-trait associations and 153 tissue-trait associations with strong biological interpretability, including 'circadian pathway-chronotype' and 'arachidonic acid-intelligence'. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1-39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.
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Affiliation(s)
- Weichen Song
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Bioengineering, Shanghai Jiao Tong UniversityShanghaiChina
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong UniversityShanghaiChina
| | - Yongyong Shi
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong UniversityShanghaiChina
- Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X12 Institutes), Qingdao UniversityQingdaoChina
| | - Guan Ning Lin
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Bioengineering, Shanghai Jiao Tong UniversityShanghaiChina
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31
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Kinyamu HK, Bennett BD, Ward JM, Archer TK. Proteasome Inhibition Reprograms Chromatin Landscape in Breast Cancer. CANCER RESEARCH COMMUNICATIONS 2024; 4:1082-1099. [PMID: 38625038 PMCID: PMC11019832 DOI: 10.1158/2767-9764.crc-23-0476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 02/26/2024] [Accepted: 03/22/2024] [Indexed: 04/17/2024]
Abstract
The 26S proteasome is the major protein degradation machinery in cells. Cancer cells use the proteasome to modulate gene expression networks that promote tumor growth. Proteasome inhibitors have emerged as effective cancer therapeutics, but how they work mechanistically remains unclear. Here, using integrative genomic analysis, we discovered unexpected reprogramming of the chromatin landscape and RNA polymerase II (RNAPII) transcription initiation in breast cancer cells treated with the proteasome inhibitor MG132. The cells acquired dynamic changes in chromatin accessibility at specific genomic loci termed differentially open chromatin regions (DOCR). DOCRs with decreased accessibility were promoter proximal and exhibited unique chromatin architecture associated with divergent RNAPII transcription. Conversely, DOCRs with increased accessibility were primarily distal to transcription start sites and enriched in oncogenic superenhancers predominantly accessible in non-basal breast tumor subtypes. These findings describe the mechanisms by which the proteasome modulates the expression of gene networks intrinsic to breast cancer biology. SIGNIFICANCE Our study provides a strong basis for understanding the mechanisms by which proteasome inhibitors exert anticancer effects. We find open chromatin regions that change during proteasome inhibition, are typically accessible in non-basal breast cancers.
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Affiliation(s)
- H. Karimi Kinyamu
- Chromatin and Gene Expression Section, National Institute of Environmental Health Sciences, Durham, North Carolina
- Epigenetics and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, Durham, North Carolina
- National Institute of Environmental Health Sciences, Durham, North Carolina
| | - Brian D. Bennett
- National Institute of Environmental Health Sciences, Durham, North Carolina
- Integrative Bioinformatics Group, National Institute of Environmental Health Sciences, Durham, North Carolina
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, North Carolina
| | - James M. Ward
- National Institute of Environmental Health Sciences, Durham, North Carolina
- Integrative Bioinformatics Group, National Institute of Environmental Health Sciences, Durham, North Carolina
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, North Carolina
| | - Trevor K. Archer
- Chromatin and Gene Expression Section, National Institute of Environmental Health Sciences, Durham, North Carolina
- Epigenetics and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, Durham, North Carolina
- National Institute of Environmental Health Sciences, Durham, North Carolina
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32
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Bell CG. Epigenomic insights into common human disease pathology. Cell Mol Life Sci 2024; 81:178. [PMID: 38602535 PMCID: PMC11008083 DOI: 10.1007/s00018-024-05206-2] [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: 01/19/2024] [Revised: 03/11/2024] [Accepted: 03/13/2024] [Indexed: 04/12/2024]
Abstract
The epigenome-the chemical modifications and chromatin-related packaging of the genome-enables the same genetic template to be activated or repressed in different cellular settings. This multi-layered mechanism facilitates cell-type specific function by setting the local sequence and 3D interactive activity level. Gene transcription is further modulated through the interplay with transcription factors and co-regulators. The human body requires this epigenomic apparatus to be precisely installed throughout development and then adequately maintained during the lifespan. The causal role of the epigenome in human pathology, beyond imprinting disorders and specific tumour suppressor genes, was further brought into the spotlight by large-scale sequencing projects identifying that mutations in epigenomic machinery genes could be critical drivers in both cancer and developmental disorders. Abrogation of this cellular mechanism is providing new molecular insights into pathogenesis. However, deciphering the full breadth and implications of these epigenomic changes remains challenging. Knowledge is accruing regarding disease mechanisms and clinical biomarkers, through pathogenically relevant and surrogate tissue analyses, respectively. Advances include consortia generated cell-type specific reference epigenomes, high-throughput DNA methylome association studies, as well as insights into ageing-related diseases from biological 'clocks' constructed by machine learning algorithms. Also, 3rd-generation sequencing is beginning to disentangle the complexity of genetic and DNA modification haplotypes. Cell-free DNA methylation as a cancer biomarker has clear clinical utility and further potential to assess organ damage across many disorders. Finally, molecular understanding of disease aetiology brings with it the opportunity for exact therapeutic alteration of the epigenome through CRISPR-activation or inhibition.
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Affiliation(s)
- Christopher G Bell
- William Harvey Research Institute, Barts & The London Faculty of Medicine, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.
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Roberts JB, Boldvig OLG, Aubourg G, Kanchenapally ST, Deehan DJ, Rice SJ, Loughlin J. Specific isoforms of the ubiquitin ligase gene WWP2 are targets of osteoarthritis genetic risk via a differentially methylated DNA sequence. Arthritis Res Ther 2024; 26:78. [PMID: 38570801 PMCID: PMC10988806 DOI: 10.1186/s13075-024-03315-8] [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: 11/30/2023] [Accepted: 03/21/2024] [Indexed: 04/05/2024] Open
Abstract
BACKGROUND Transitioning from a genetic association signal to an effector gene and a targetable molecular mechanism requires the application of functional fine-mapping tools such as reporter assays and genome editing. In this report, we undertook such studies on the osteoarthritis (OA) risk that is marked by single nucleotide polymorphism (SNP) rs34195470 (A > G). The OA risk-conferring G allele of this SNP associates with increased DNA methylation (DNAm) at two CpG dinucleotides within WWP2. This gene encodes a ubiquitin ligase and is the host gene of microRNA-140 (miR-140). WWP2 and miR-140 are both regulators of TGFβ signaling. METHODS Nucleic acids were extracted from adult OA (arthroplasty) and foetal cartilage. Samples were genotyped and DNAm quantified by pyrosequencing at the two CpGs plus 14 flanking CpGs. CpGs were tested for transcriptional regulatory effects using a chondrocyte cell line and reporter gene assay. DNAm was altered using epigenetic editing, with the impact on gene expression determined using RT-qPCR. In silico analysis complemented laboratory experiments. RESULTS rs34195470 genotype associates with differential methylation at 14 of the 16 CpGs in OA cartilage, forming a methylation quantitative trait locus (mQTL). The mQTL is less pronounced in foetal cartilage (5/16 CpGs). The reporter assay revealed that the CpGs reside within a transcriptional regulator. Epigenetic editing to increase their DNAm resulted in altered expression of the full-length and N-terminal transcript isoforms of WWP2. No changes in expression were observed for the C-terminal isoform of WWP2 or for miR-140. CONCLUSIONS As far as we are aware, this is the first experimental demonstration of an OA association signal targeting specific transcript isoforms of a gene. The WWP2 isoforms encode proteins with varying substrate specificities for the components of the TGFβ signaling pathway. Future analysis should focus on the substrates regulated by the two WWP2 isoforms that are the targets of this genetic risk.
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Affiliation(s)
- Jack B Roberts
- Biosciences Institute, Newcastle University, International Centre for Life, Newcastle upon Tyne, NE1 3BZ, UK.
| | - Olivia L G Boldvig
- Biosciences Institute, Newcastle University, International Centre for Life, Newcastle upon Tyne, NE1 3BZ, UK
| | - Guillaume Aubourg
- Biosciences Institute, Newcastle University, International Centre for Life, Newcastle upon Tyne, NE1 3BZ, UK
| | - S Tanishq Kanchenapally
- Biosciences Institute, Newcastle University, International Centre for Life, Newcastle upon Tyne, NE1 3BZ, UK
| | - David J Deehan
- Freeman Hospital, Newcastle University Teaching Hospitals NHS Trust, Newcastle upon Tyne, UK
| | - Sarah J Rice
- Biosciences Institute, Newcastle University, International Centre for Life, Newcastle upon Tyne, NE1 3BZ, UK
| | - John Loughlin
- Biosciences Institute, Newcastle University, International Centre for Life, Newcastle upon Tyne, NE1 3BZ, UK.
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34
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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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35
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Zhang T, Ambrodji A, Huang H, Bouchonville KJ, Etheridge AS, Schmidt RE, Bembenek BM, Temesgen ZB, Wang Z, Innocenti F, Stroka D, Diasio RB, Largiadèr CR, Offer SM. Germline cis variant determines epigenetic regulation of the anti-cancer drug metabolism gene dihydropyrimidine dehydrogenase ( DPYD). BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.01.565230. [PMID: 37961517 PMCID: PMC10635067 DOI: 10.1101/2023.11.01.565230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Enhancers are critical for regulating tissue-specific gene expression, and genetic variants within enhancer regions have been suggested to contribute to various cancer-related processes, including therapeutic resistance. However, the precise mechanisms remain elusive. Using a well-defined drug-gene pair, we identified an enhancer region for dihydropyrimidine dehydrogenase (DPD, DPYD gene) expression that is relevant to the metabolism of the anti-cancer drug 5-fluorouracil (5-FU). Using reporter systems, CRISPR genome edited cell models, and human liver specimens, we demonstrated in vitro and vivo that genotype status for the common germline variant (rs4294451; 27% global minor allele frequency) located within this novel enhancer controls DPYD transcription and alters resistance to 5-FU. The variant genotype increases recruitment of the transcription factor CEBPB to the enhancer and alters the level of direct interactions between the enhancer and DPYD promoter. Our data provide insight into the regulatory mechanisms controlling sensitivity and resistance to 5-FU.
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Affiliation(s)
- Ting Zhang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Alisa Ambrodji
- Department of Clinical Chemistry, Inselspital, Bern University Hospital, University of Bern, CH-3010 Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, Freiestrasse 1, CH-3010 Bern, Switzerland
| | - Huixing Huang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Kelly J. Bouchonville
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Amy S. Etheridge
- Eshelman School of Pharmacy, Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Remington E. Schmidt
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Brianna M. Bembenek
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Zoey B. Temesgen
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Zhiquan Wang
- Division of Hematology, Department of Medicine, Mayo Clinic, Rochester, MN 55905 USA
| | - Federico Innocenti
- Eshelman School of Pharmacy, Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Deborah Stroka
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Robert B. Diasio
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Carlo R. Largiadèr
- Department of Clinical Chemistry, Inselspital, Bern University Hospital, University of Bern, CH-3010 Bern, Switzerland
| | - Steven M. Offer
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
- Department of Pathology, University of Iowa Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
- Holden Comprehensive Cancer Center, University of Iowa Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
- Lead contact
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36
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Liu S, Luo H, Zhang P, Li Y, Hao D, Zhang S, Song T, Xu T, He S. Adaptive Selection of Cis-regulatory Elements in the Han Chinese. Mol Biol Evol 2024; 41:msae034. [PMID: 38377343 PMCID: PMC10917166 DOI: 10.1093/molbev/msae034] [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: 10/02/2023] [Revised: 01/18/2024] [Accepted: 02/05/2024] [Indexed: 02/22/2024] Open
Abstract
Cis-regulatory elements have an important role in human adaptation to the living environment. However, the lag in population genomic cohort studies and epigenomic studies, hinders the research in the adaptive analysis of cis-regulatory elements in human populations. In this study, we collected 4,013 unrelated individuals and performed a comprehensive analysis of adaptive selection of genome-wide cis-regulatory elements in the Han Chinese. In total, 12.34% of genomic regions are under the influence of adaptive selection, where 1.00% of enhancers and 2.06% of promoters are under positive selection, and 0.06% of enhancers and 0.02% of promoters are under balancing selection. Gene ontology enrichment analysis of these cis-regulatory elements under adaptive selection reveals that many positive selections in the Han Chinese occur in pathways involved in cell-cell adhesion processes, and many balancing selections are related to immune processes. Two classes of adaptive cis-regulatory elements related to cell adhesion were in-depth analyzed, one is the adaptive enhancers derived from neanderthal introgression, leads to lower hyaluronidase level in skin, and brings better performance on UV-radiation resistance to the Han Chinese. Another one is the cis-regulatory elements regulating wound healing, and the results suggest the positive selection inhibits coagulation and promotes angiogenesis and wound healing in the Han Chinese. Finally, we found that many pathogenic alleles, such as risky alleles of type 2 diabetes or schizophrenia, remain in the population due to the hitchhiking effect of positive selections. Our findings will help deepen our understanding of the adaptive evolution of genome regulation in the Han Chinese.
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Affiliation(s)
- Shuai Liu
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Huaxia Luo
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Peng Zhang
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yanyan Li
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Di Hao
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Sijia Zhang
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tingrui Song
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Tao Xu
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan 250117, Shandong, China
| | - Shunmin He
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
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37
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Saha D, Dang HX, Zhang M, Quigley DA, Feng FY, Maher CA. Single cell-transcriptomic analysis informs the lncRNA landscape in metastatic castration resistant prostate cancer. NPJ Genom Med 2024; 9:14. [PMID: 38396008 PMCID: PMC10891057 DOI: 10.1038/s41525-024-00401-3] [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: 08/03/2023] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
Metastatic castration-resistant prostate cancer (mCRPC) is a lethal form of prostate cancer. Although long-noncoding RNAs (lncRNAs) have been implicated in mCRPC, past studies have relied on bulk sequencing methods with low depth and lack of single-cell resolution. Hence, we performed a lncRNA-focused analysis of single-cell RNA-sequencing data (n = 14) from mCRPC biopsies followed by integration with bulk multi-omic datasets. This yielded 389 cell-enriched lncRNAs in prostate cancer cells and the tumor microenvironment (TME). These lncRNAs demonstrated enrichment with regulatory elements and exhibited alterations during prostate cancer progression. Prostate-lncRNAs were correlated with AR mutational status and response to treatment with enzalutamide, while TME-lncRNAs were associated with RB1 deletions and poor prognosis. Finally, lncRNAs identified between prostate adenocarcinomas and neuroendocrine tumors exhibited distinct expression and methylation profiles. Our findings demonstrate the ability of single-cell analysis to refine our understanding of lncRNAs in mCRPC and serve as a resource for future mechanistic studies.
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Affiliation(s)
- Debanjan Saha
- Medical Scientist Training Program, Washington University in St. Louis, St. Louis, MO, USA
- Department of Internal Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Ha X Dang
- Department of Internal Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Meng Zhang
- Department of Radiation Oncology, University of California at San Francisco, San Francisco, CA, USA
| | - David A Quigley
- Helen Diller Family Comprehensive Cancer Center, University of California at San Francisco, San Francisco, CA, USA
- Department of Urology, University of California at San Francisco, San Francisco, CA, USA
- Department of Epidemiology & Biostatistics, University of California at San Francisco, San Francisco, CA, USA
| | - Felix Y Feng
- Department of Radiation Oncology, University of California at San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California at San Francisco, San Francisco, CA, USA
- Department of Urology, University of California at San Francisco, San Francisco, CA, USA
- Division of Hematology and Oncology, Department of Medicine, University of California at San Francisco, San Francisco, CA, USA
| | - Christopher A Maher
- Department of Internal Medicine, Washington University in St. Louis, St. Louis, MO, USA.
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38
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Jiang F, Hu SY, Tian W, Wang NN, Yang N, Dong SS, Song HM, Zhang DJ, Gao HW, Wang C, Wu H, He CY, Zhu DL, Chen XF, Guo Y, Yang Z, Yang TL. A landscape of gene expression regulation for synovium in arthritis. Nat Commun 2024; 15:1409. [PMID: 38360850 PMCID: PMC10869817 DOI: 10.1038/s41467-024-45652-x] [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: 05/09/2023] [Accepted: 01/29/2024] [Indexed: 02/17/2024] Open
Abstract
The synovium is an important component of any synovial joint and is the major target tissue of inflammatory arthritis. However, the multi-omics landscape of synovium required for functional inference is absent from large-scale resources. Here we integrate genomics with transcriptomics and chromatin accessibility features of human synovium in up to 245 arthritic patients, to characterize the landscape of genetic regulation on gene expression and the regulatory mechanisms mediating arthritic diseases predisposition. We identify 4765 independent primary and 616 secondary cis-expression quantitative trait loci (cis-eQTLs) in the synovium and find that the eQTLs with multiple independent signals have stronger effects and heritability than single independent eQTLs. Integration of genome-wide association studies (GWASs) and eQTLs identifies 84 arthritis related genes, revealing 38 novel genes which have not been reported by previous studies using eQTL data from the GTEx project or immune cells. We further develop a method called eQTac to identify variants that could affect gene expression by affecting chromatin accessibility and identify 1517 regions with potential regulatory function of chromatin accessibility. Altogether, our study provides a comprehensive synovium multi-omics resource for arthritic diseases and gains new insights into the regulation of gene expression.
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Affiliation(s)
- Feng Jiang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Shou-Ye Hu
- Department of Joint Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, 710054, P.R. China
| | - Wen Tian
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Nai-Ning Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Ning Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Hui-Miao Song
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Da-Jin Zhang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Hui-Wu Gao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Chen Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Hao Wu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Chang-Yi He
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Dong-Li Zhu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Xiao-Feng Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Yan Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Zhi Yang
- Department of Joint Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, 710054, P.R. China.
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China.
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Alda-Catalinas C, Ibarra-Soria X, Flouri C, Gordillo JE, Cousminer D, Hutchinson A, Sun B, Pembroke W, Ullrich S, Krejci A, Cortes A, Acevedo A, Malla S, Fishwick C, Drewes G, Rapiteanu R. Mapping the functional impact of non-coding regulatory elements in primary T cells through single-cell CRISPR screens. Genome Biol 2024; 25:42. [PMID: 38308274 PMCID: PMC10835965 DOI: 10.1186/s13059-024-03176-z] [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: 06/18/2023] [Accepted: 01/18/2024] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND Drug targets with genetic evidence are expected to increase clinical success by at least twofold. Yet, translating disease-associated genetic variants into functional knowledge remains a fundamental challenge of drug discovery. A key issue is that the vast majority of complex disease associations cannot be cleanly mapped to a gene. Immune disease-associated variants are enriched within regulatory elements found in T-cell-specific open chromatin regions. RESULTS To identify genes and molecular programs modulated by these regulatory elements, we develop a CRISPRi-based single-cell functional screening approach in primary human T cells. Our pipeline enables the interrogation of transcriptomic changes induced by the perturbation of regulatory elements at scale. We first optimize an efficient CRISPRi protocol in primary CD4+ T cells via CROPseq vectors. Subsequently, we perform a screen targeting 45 non-coding regulatory elements and 35 transcription start sites and profile approximately 250,000 T -cell single-cell transcriptomes. We develop a bespoke analytical pipeline for element-to-gene (E2G) mapping and demonstrate that our method can identify both previously annotated and novel E2G links. Lastly, we integrate genetic association data for immune-related traits and demonstrate how our platform can aid in the identification of effector genes for GWAS loci. CONCLUSIONS We describe "primary T cell crisprQTL" - a scalable, single-cell functional genomics approach for mapping regulatory elements to genes in primary human T cells. We show how this framework can facilitate the interrogation of immune disease GWAS hits and propose that the combination of experimental and QTL-based techniques is likely to address the variant-to-function problem.
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Affiliation(s)
| | | | | | | | | | | | - Bin Sun
- Genomic Sciences, GSK, Stevenage, UK
| | | | | | | | | | | | | | | | - Gerard Drewes
- Genomic Sciences, GSK, Stevenage, UK
- Genomic Sciences, GSK, Collegeville, PA, USA
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40
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Raghavan A, Pirruccello JP, Ellinor PT, Lindsay ME. Using Genomics to Identify Novel Therapeutic Targets for Aortic Disease. Arterioscler Thromb Vasc Biol 2024; 44:334-351. [PMID: 38095107 PMCID: PMC10843699 DOI: 10.1161/atvbaha.123.318771] [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: 06/26/2023] [Accepted: 11/21/2023] [Indexed: 01/04/2024]
Abstract
Aortic disease, including dissection, aneurysm, and rupture, carries significant morbidity and mortality and is a notable cause of sudden cardiac death. Much of our knowledge regarding the genetic basis of aortic disease has relied on the study of individuals with Mendelian aortopathies and, until recently, the genetic determinants of population-level variance in aortic phenotypes remained unclear. However, the application of machine learning methodologies to large imaging datasets has enabled researchers to rapidly define aortic traits and mine dozens of novel genetic associations for phenotypes such as aortic diameter and distensibility. In this review, we highlight the emerging potential of genomics for identifying causal genes and candidate drug targets for aortic disease. We describe how deep learning technologies have accelerated the pace of genetic discovery in this field. We then provide a blueprint for translating genetic associations to biological insights, reviewing techniques for locus and cell type prioritization, high-throughput functional screening, and disease modeling using cellular and animal models of aortic disease.
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Affiliation(s)
- Avanthi Raghavan
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - James P. Pirruccello
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Patrick T. Ellinor
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Mark E. Lindsay
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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Thorolfsdottir RB, Jonsdottir AB, Sveinbjornsson G, Aegisdottir HM, Oddsson A, Stefansson OA, Halldorsson GH, Saevarsdottir S, Thorleifsson G, Stefansdottir L, Pedersen OB, Sørensen E, Ghouse J, Raja AA, Zheng C, Silajdzija E, Rand SA, Erikstrup C, Ullum H, Mikkelsen C, Banasik K, Brunak S, Ivarsdottir EV, Sigurdsson A, Beyter D, Sturluson A, Einarsson H, Tragante V, Helgason H, Lund SH, Halldorsson BV, Sigurpalsdottir BD, Olafsson I, Arnar DO, Thorgeirsson G, Knowlton KU, Nadauld LD, Gretarsdottir S, Helgadottir A, Ostrowski SR, Gudbjartssson DF, Jonsdottir I, Bundgaard H, Holm H, Sulem P, Stefansson K. Variants at the Interleukin 1 Gene Locus and Pericarditis. JAMA Cardiol 2024; 9:165-172. [PMID: 38150231 PMCID: PMC10753444 DOI: 10.1001/jamacardio.2023.4820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 10/14/2023] [Indexed: 12/28/2023]
Abstract
Importance Recurrent pericarditis is a treatment challenge and often a debilitating condition. Drugs inhibiting interleukin 1 cytokines are a promising new treatment option, but their use is based on scarce biological evidence and clinical trials of modest sizes, and the contributions of innate and adaptive immune processes to the pathophysiology are incompletely understood. Objective To use human genomics, transcriptomics, and proteomics to shed light on the pathogenesis of pericarditis. Design, Setting, and Participants This was a meta-analysis of genome-wide association studies of pericarditis from 5 countries. Associations were examined between the pericarditis-associated variants and pericarditis subtypes (including recurrent pericarditis) and secondary phenotypes. To explore mechanisms, associations with messenger RNA expression (cis-eQTL), plasma protein levels (pQTL), and CpG methylation of DNA (ASM-QTL) were assessed. Data from Iceland (deCODE genetics, 1983-2020), Denmark (Copenhagen Hospital Biobank/Danish Blood Donor Study, 1977-2022), the UK (UK Biobank, 1953-2021), the US (Intermountain, 1996-2022), and Finland (FinnGen, 1970-2022) were included. Data were analyzed from September 2022 to August 2023. Exposure Genotype. Main Outcomes and Measures Pericarditis. Results In this genome-wide association study of 4894 individuals with pericarditis (mean [SD] age at diagnosis, 51.4 [17.9] years, 2734 [67.6%] male, excluding the FinnGen cohort), associations were identified with 2 independent common intergenic variants at the interleukin 1 locus on chromosome 2q14. The lead variant was rs12992780 (T) (effect allele frequency [EAF], 31%-40%; odds ratio [OR], 0.83; 95% CI, 0.79-0.87; P = 6.67 × 10-16), downstream of IL1B and the secondary variant rs7575402 (A or T) (EAF, 45%-55%; adjusted OR, 0.89; 95% CI, 0.85-0.93; adjusted P = 9.6 × 10-8). The lead variant rs12992780 had a smaller odds ratio for recurrent pericarditis (0.76) than the acute form (0.86) (P for heterogeneity = .03) and rs7575402 was associated with CpG methylation overlapping binding sites of 4 transcription factors known to regulate interleukin 1 production: PU.1 (encoded by SPI1), STAT1, STAT3, and CCAAT/enhancer-binding protein β (encoded by CEBPB). Conclusions and Relevance This study found an association between pericarditis and 2 independent sequence variants at the interleukin 1 gene locus. This finding has the potential to contribute to development of more targeted and personalized therapy of pericarditis with interleukin 1-blocking drugs.
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Affiliation(s)
| | | | | | | | | | | | - Gisli H. Halldorsson
- deCODE genetics, Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Saedis Saevarsdottir
- deCODE genetics, Amgen, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- Department of Medicine, Landspitali, The National University Hospital of Iceland, Reykjavik, Iceland
| | | | | | - Ole B. Pedersen
- Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Erik Sørensen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Jonas Ghouse
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Laboratory for Molecular Cardiology, Department of Cardiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Anna Axelsson Raja
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Chaoqun Zheng
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Elvira Silajdzija
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Søren Albertsen Rand
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Laboratory for Molecular Cardiology, Department of Cardiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | | | - Christina Mikkelsen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Karina Banasik
- Department of Obstetrics and Gynaecology, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | | | | | | | - Hafsteinn Einarsson
- deCODE genetics, Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Hannes Helgason
- deCODE genetics, Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Bjarni V. Halldorsson
- deCODE genetics, Amgen, Reykjavik, Iceland
- School of Technology, Reykjavik University, Reykjavik, Iceland
| | - Brynja D. Sigurpalsdottir
- deCODE genetics, Amgen, Reykjavik, Iceland
- School of Technology, Reykjavik University, Reykjavik, Iceland
| | - Isleifur Olafsson
- Department of Clinical Biochemistry, Landspitali, National University Hospital of Iceland, Reykjavik, Iceland
| | - David O. Arnar
- deCODE genetics, Amgen, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- Department of Medicine, Landspitali, The National University Hospital of Iceland, Reykjavik, Iceland
| | | | - Kirk U. Knowlton
- Intermountain Medical Center, Intermountain Heart Institute, Salt Lake City, Utah
- School of Medicine, University of Utah, Salt Lake City
| | - Lincoln D. Nadauld
- Precision Genomics, Intermountain Healthcare, Saint George, Utah
- School of Medicine, Stanford University, Stanford, California
| | | | | | - Sisse R. Ostrowski
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Daniel F. Gudbjartssson
- deCODE genetics, Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Ingileif Jonsdottir
- deCODE genetics, Amgen, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- Department of Immunology, Landspitali, The National University Hospital of Iceland, Reykjavik, Iceland
| | - Henning Bundgaard
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Hilma Holm
- deCODE genetics, Amgen, Reykjavik, Iceland
| | | | - Kari Stefansson
- deCODE genetics, Amgen, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
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Zhu X, Huang Q, Huang L, Luo J, Li Q, Kong D, Deng B, Gu Y, Wang X, Li C, Kong S, Zhang Y. MAE-seq refines regulatory elements across the genome. Nucleic Acids Res 2024; 52:e9. [PMID: 38038259 PMCID: PMC10810209 DOI: 10.1093/nar/gkad1129] [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: 12/28/2022] [Revised: 10/23/2023] [Accepted: 11/10/2023] [Indexed: 12/02/2023] Open
Abstract
Proper cell fate determination relies on precise spatial and temporal genome-wide cooperation between regulatory elements (REs) and their targeted genes. However, the lengths of REs defined using different methods vary, which indicates that there is sequence redundancy and that the context of the genome may be unintelligible. We developed a method called MAE-seq (Massive Active Enhancers by Sequencing) to experimentally identify functional REs at a 25-bp scale. In this study, MAE-seq was used to identify 626879, 541617 and 554826 25-bp enhancers in mouse embryonic stem cells (mESCs), C2C12 and HEK 293T, respectively. Using ∼1.6 trillion 25 bp DNA fragments and screening 12 billion cells, we identified 626879 as active enhancers in mESCs as an example. Comparative analysis revealed that most of the histone modification datasets were annotated by MAE-Seq loci. Furthermore, 33.85% (212195) of the identified enhancers were identified as de novo ones with no epigenetic modification. Intriguingly, distinct chromatin states dictate the requirement for dissimilar cofactors in governing novel and known enhancers. Validation results show that these 25-bp sequences could act as a functional unit, which shows identical or similar expression patterns as the previously defined larger elements, Enhanced resolution facilitated the identification of numerous cell-specific enhancers and their accurate annotation as super enhancers. Moreover, we characterized novel elements capable of augmenting gene activity. By integrating with high-resolution Hi-C data, over 55.64% of novel elements may have a distal association with different targeted genes. For example, we found that the Cdh1 gene interacts with one novel and two known REs in mESCs. The biological effects of these interactions were investigated using CRISPR-Cas9, revealing their role in coordinating Cdh1 gene expression and mESC proliferation. Our study presents an experimental approach to refine the REs at 25-bp resolution, advancing the precision of genome annotation and unveiling the underlying genome context. This novel approach not only advances our understanding of gene regulation but also opens avenues for comprehensive exploration of the genomic landscape.
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Affiliation(s)
- Xiusheng Zhu
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
| | - Qitong Huang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
- Department of animal sciences, Wageningen University & Research, Wageningen, 6708PB, Netherlands
| | - Lei Huang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
| | - Jing Luo
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
| | - Qing Li
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
| | - Dashuai Kong
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
| | - Biao Deng
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
| | - Yi Gu
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
| | - Xueyan Wang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
| | - Chenying Li
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
| | - Siyuan Kong
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
| | - Yubo Zhang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
- Kunpeng Institute of Modern Agriculture at Foshan, Foshan, 528225, China
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Wang C, Wang YJ, Ying L, Wong RJ, Quaintance CC, Hong X, Neff N, Wang X, Biggio JR, Mesiano S, Quake SR, Alvira CM, Cornfield DN, Stevenson DK, Shaw GM, Li J. Integrative analysis of noncoding mutations identifies the druggable genome in preterm birth. SCIENCE ADVANCES 2024; 10:eadk1057. [PMID: 38241369 PMCID: PMC10798565 DOI: 10.1126/sciadv.adk1057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 12/21/2023] [Indexed: 01/21/2024]
Abstract
Preterm birth affects ~10% of pregnancies in the US. Despite familial associations, identifying at-risk genetic loci has been challenging. We built deep learning and graphical models to score mutational effects at base resolution via integrating the pregnant myometrial epigenome and large-scale patient genomes with spontaneous preterm birth (sPTB) from European and African American cohorts. We uncovered previously unidentified sPTB genes that are involved in myometrial muscle relaxation and inflammatory responses and that are regulated by the progesterone receptor near labor onset. We studied genomic variants in these genes in our recruited pregnant women administered progestin prophylaxis. We observed that mutation burden in these genes was predictive of responses to progestin treatment for preterm birth. To advance therapeutic development, we screened ~4000 compounds, identified candidate molecules that affect our identified genes, and experimentally validated their therapeutic effects on regulating labor. Together, our integrative approach revealed the druggable genome in preterm birth and provided a generalizable framework for studying complex diseases.
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Affiliation(s)
- Cheng Wang
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, Bakar Computational Health Sciences Institute, Parker Institute for Cancer Immunotherapy, and Department of Neurology, School of Medicine, University of California, San Francisco, CA, USA
| | - Yuejun Jessie Wang
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, Bakar Computational Health Sciences Institute, Parker Institute for Cancer Immunotherapy, and Department of Neurology, School of Medicine, University of California, San Francisco, CA, USA
| | - Lihua Ying
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Ronald J. Wong
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Cecele C. Quaintance
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Xiumei Hong
- Center on the Early Life Origins of Disease, Department of Population Family and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Norma Neff
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Xiaobin Wang
- Center on the Early Life Origins of Disease, Department of Population Family and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Joseph R. Biggio
- Department of Obstetrics and Gynecology, Division of Maternal Fetal Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Obstetrics and Gynecology, Ochsner Health, New Orleans, LA, USA
| | - Sam Mesiano
- Department of Reproductive Biology, Case Western Reserve University and Department of Obstetrics and Gynecology, University Hospitals of Cleveland, Cleveland, OH, USA
| | - Stephen R. Quake
- Chan Zuckerberg Biohub, San Francisco, CA, USA
- Department of Bioengineering, Stanford University School of Medicine, Stanford, CA, USA
| | - Cristina M. Alvira
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - David N. Cornfield
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - David K. Stevenson
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Gary M. Shaw
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Jingjing Li
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, Bakar Computational Health Sciences Institute, Parker Institute for Cancer Immunotherapy, and Department of Neurology, School of Medicine, University of California, San Francisco, CA, USA
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44
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Verma SS, Gudiseva HV, Chavali VRM, Salowe RJ, Bradford Y, Guare L, Lucas A, Collins DW, Vrathasha V, Nair RM, Rathi S, Zhao B, He J, Lee R, Zenebe-Gete S, Bowman AS, McHugh CP, Zody MC, Pistilli M, Khachatryan N, Daniel E, Murphy W, Henderer J, Kinzy TG, Iyengar SK, Peachey NS, Taylor KD, Guo X, Chen YDI, Zangwill L, Girkin C, Ayyagari R, Liebmann J, Chuka-Okosa CM, Williams SE, Akafo S, Budenz DL, Olawoye OO, Ramsay M, Ashaye A, Akpa OM, Aung T, Wiggs JL, Ross AG, Cui QN, Addis V, Lehman A, Miller-Ellis E, Sankar PS, Williams SM, Ying GS, Cooke Bailey J, Rotter JI, Weinreb R, Khor CC, Hauser MA, Ritchie MD, O'Brien JM. A multi-cohort genome-wide association study in African ancestry individuals reveals risk loci for primary open-angle glaucoma. Cell 2024; 187:464-480.e10. [PMID: 38242088 DOI: 10.1016/j.cell.2023.12.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 07/24/2023] [Accepted: 12/04/2023] [Indexed: 01/21/2024]
Abstract
Primary open-angle glaucoma (POAG), the leading cause of irreversible blindness worldwide, disproportionately affects individuals of African ancestry. We conducted a genome-wide association study (GWAS) for POAG in 11,275 individuals of African ancestry (6,003 cases; 5,272 controls). We detected 46 risk loci associated with POAG at genome-wide significance. Replication and post-GWAS analyses, including functionally informed fine-mapping, multiple trait co-localization, and in silico validation, implicated two previously undescribed variants (rs1666698 mapping to DBF4P2; rs34957764 mapping to ROCK1P1) and one previously associated variant (rs11824032 mapping to ARHGEF12) as likely causal. For individuals of African ancestry, a polygenic risk score (PRS) for POAG from our mega-analysis (African ancestry individuals) outperformed a PRS from summary statistics of a much larger GWAS derived from European ancestry individuals. This study quantifies the genetic architecture similarities and differences between African and non-African ancestry populations for this blinding disease.
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Affiliation(s)
- Shefali S Verma
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Harini V Gudiseva
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Venkata R M Chavali
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rebecca J Salowe
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuki Bradford
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lindsay Guare
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anastasia Lucas
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David W Collins
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Vrathasha Vrathasha
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rohini M Nair
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sonika Rathi
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Jie He
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Roy Lee
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Selam Zenebe-Gete
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anita S Bowman
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Maxwell Pistilli
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Naira Khachatryan
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ebenezer Daniel
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Jeffrey Henderer
- Department of Ophthalmology, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA
| | - Tyler G Kinzy
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
| | - Sudha K Iyengar
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
| | - Neal S Peachey
- Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA; Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Kent D Taylor
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Xiuqing Guo
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yii-Der Ida Chen
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Linda Zangwill
- Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA
| | - Christopher Girkin
- Department of Ophthalmology and Visual Sciences, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Radha Ayyagari
- Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA
| | - Jeffrey Liebmann
- Department of Ophthalmology, Columbia University Medical Center, Columbia University, New York, NY, USA
| | | | - Susan E Williams
- Division of Ophthalmology, Department of Neurosciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Stephen Akafo
- Unit of Ophthalmology, Department of Surgery, University of Ghana Medical School, Accra, Ghana
| | - Donald L Budenz
- Department of Ophthalmology, University of North Carolina, Chapel Hill, NC, USA
| | | | - Michele Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Adeyinka Ashaye
- Department of Ophthalmology, University of Ibadan, Ibadan, Nigeria
| | - Onoja M Akpa
- Department of Epidemiology and Medical Statistics, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Tin Aung
- Singapore Eye Research Institute, Singapore, Singapore
| | - Janey L Wiggs
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Ahmara G Ross
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Qi N Cui
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Victoria Addis
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amanda Lehman
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Eydie Miller-Ellis
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Prithvi S Sankar
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Scott M Williams
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Gui-Shuang Ying
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jessica Cooke Bailey
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA; Department of Pharmacology and Toxicology, Center for Health Disparities, Brody School of Medicine. East Carolina University, Greenville, NC, 27834, USA
| | - Jerome I Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Robert Weinreb
- Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA
| | | | | | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Joan M O'Brien
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. joan.o'
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45
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Kim SS, Truong B, Jagadeesh K, Dey KK, Shen AZ, Raychaudhuri S, Kellis M, Price AL. Leveraging single-cell ATAC-seq and RNA-seq to identify disease-critical fetal and adult brain cell types. Nat Commun 2024; 15:563. [PMID: 38233398 PMCID: PMC10794712 DOI: 10.1038/s41467-024-44742-0] [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: 04/30/2022] [Accepted: 01/02/2024] [Indexed: 01/19/2024] Open
Abstract
Prioritizing disease-critical cell types by integrating genome-wide association studies (GWAS) with functional data is a fundamental goal. Single-cell chromatin accessibility (scATAC-seq) and gene expression (scRNA-seq) have characterized cell types at high resolution, and studies integrating GWAS with scRNA-seq have shown promise, but studies integrating GWAS with scATAC-seq have been limited. Here, we identify disease-critical fetal and adult brain cell types by integrating GWAS summary statistics from 28 brain-related diseases/traits (average N = 298 K) with 3.2 million scATAC-seq and scRNA-seq profiles from 83 cell types. We identified disease-critical fetal (respectively adult) brain cell types for 22 (respectively 23) of 28 traits using scATAC-seq, and for 8 (respectively 17) of 28 traits using scRNA-seq. Significant scATAC-seq enrichments included fetal photoreceptor cells for major depressive disorder, fetal ganglion cells for BMI, fetal astrocytes for ADHD, and adult VGLUT2 excitatory neurons for schizophrenia. Our findings improve our understanding of brain-related diseases/traits and inform future analyses.
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Affiliation(s)
- Samuel S Kim
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, UK.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, UK.
| | - Buu Truong
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, UK.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, UK.
| | - Karthik Jagadeesh
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, UK
| | - Kushal K Dey
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, UK
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Amber Z Shen
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Soumya Raychaudhuri
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Manolis Kellis
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, UK
| | - Alkes L Price
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, UK.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, UK.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, UK.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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46
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Sarsani V, Brotman SM, Xianyong Y, Fernandes Silva L, Laakso M, Spracklen CN. A cross-ancestry genome-wide meta-analysis, fine-mapping, and gene prioritization approach to characterize the genetic architecture of adiponectin. HGG ADVANCES 2024; 5:100252. [PMID: 37859345 PMCID: PMC10652123 DOI: 10.1016/j.xhgg.2023.100252] [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: 06/29/2023] [Revised: 10/16/2023] [Accepted: 10/16/2023] [Indexed: 10/21/2023] Open
Abstract
Previous genome-wide association studies (GWASs) for adiponectin, a complex trait linked to type 2 diabetes and obesity, identified >20 associated loci. However, most loci were identified in populations of European ancestry, and many of the target genes underlying the associations remain unknown. We conducted a cross-ancestry adiponectin GWAS meta-analysis in ≤46,434 individuals from the Metabolic Syndrome in Men (METSIM) cohort and the ADIPOGen and AGEN consortiums. We combined study-specific association summary statistics using a fixed-effects, inverse variance-weighted approach. We identified 22 loci associated with adiponectin (p < 5×10-8), including 15 known and seven previously unreported loci. Among individuals of European ancestry, Genome-wide Complex Traits Analysis joint conditional analysis (GCTA-COJO) identified 14 additional distinct signals at the ADIPOQ, CDH13, HCAR1, and ZNF664 loci. Leveraging the cross-ancestry data, FINEMAP + SuSiE identified 45 causal variants (PP > 0.9), which also exhibited potential pleiotropy for cardiometabolic traits. To prioritize target genes at associated loci, we propose a combinatorial likelihood scoring formalism (Gene Priority Score [GPScore]) based on measures derived from 11 gene prioritization strategies and the physical distance to the transcription start site. With GPScore, we prioritize the 30 most probable target genes underlying the adiponectin-associated variants in the cross-ancestry analysis, including well-known causal genes (e.g., ADIPOQ, CDH13) and additional genes (e.g., CSF1, RGS17). Functional association networks revealed complex interactions of prioritized genes, their functionally connected genes, and their underlying pathways centered around insulin and adiponectin signaling, indicating an essential role in regulating energy balance in the body, inflammation, coagulation, fibrinolysis, insulin resistance, and diabetes. Overall, our analyses identify and characterize adiponectin association signals and inform experimental interrogation of target genes for adiponectin.
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Affiliation(s)
- Vishal Sarsani
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA, USA
| | - Sarah M Brotman
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yin Xianyong
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Lillian Fernandes Silva
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Cassandra N Spracklen
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA.
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47
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Stricker M, Zhang W, Cheng WY, Gazal S, Dendrou C, Nahkuri S, Palamara PF. Genome-wide classification of epigenetic activity reveals regions of enriched heritability in immune-related traits. CELL GENOMICS 2024; 4:100469. [PMID: 38190103 PMCID: PMC10794845 DOI: 10.1016/j.xgen.2023.100469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 07/04/2023] [Accepted: 11/29/2023] [Indexed: 01/09/2024]
Abstract
Epigenetics underpins the regulation of genes known to play a key role in the adaptive and innate immune system (AIIS). We developed a method, EpiNN, that leverages epigenetic data to detect AIIS-relevant genomic regions and used it to detect 2,765 putative AIIS loci. Experimental validation of one of these loci, DNMT1, provided evidence for a novel AIIS-specific transcription start site. We built a genome-wide AIIS annotation and used linkage disequilibrium (LD) score regression to test whether it predicts regional heritability using association statistics for 176 traits. We detected significant heritability effects (average |τ∗|=1.65) for 20 out of 26 immune-relevant traits. In a meta-analysis, immune-relevant traits and diseases were 4.45× more enriched for heritability than other traits. The EpiNN annotation was also depleted of trans-ancestry genetic correlation, indicating ancestry-specific effects. These results underscore the effectiveness of leveraging supervised learning algorithms and epigenetic data to detect loci implicated in specific classes of traits and diseases.
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Affiliation(s)
| | - Weijiao Zhang
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Wei-Yi Cheng
- Data & Analytics, Roche Pharma Research & Early Development, Roche Innovation Center New York, Little Falls, NJ, USA
| | - Steven Gazal
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Calliope Dendrou
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Satu Nahkuri
- Data & Analytics, Roche Pharma Research & Early Development, Roche Innovation Center Zürich, Zürich, Switzerland.
| | - Pier Francesco Palamara
- Department of Statistics, University of Oxford, Oxford, UK; Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
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48
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Wang Z, Zhao G, Zhu Z, Wang Y, Xiang X, Zhang S, Luo T, Zhou Q, Qiu J, Tang B, Xia K, Li B, Li J. VarCards2: an integrated genetic and clinical database for ACMG-AMP variant-interpretation guidelines in the human whole genome. Nucleic Acids Res 2024; 52:D1478-D1489. [PMID: 37956311 PMCID: PMC10767961 DOI: 10.1093/nar/gkad1061] [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: 09/15/2023] [Revised: 10/21/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
VarCards, an online database, combines comprehensive variant- and gene-level annotation data to streamline genetic counselling for coding variants. Recognising the increasing clinical relevance of non-coding variations, there has been an accelerated development of bioinformatics tools dedicated to interpreting non-coding variations, including single-nucleotide variants and copy number variations. Regrettably, most tools remain as either locally installed databases or command-line tools dispersed across diverse online platforms. Such a landscape poses inconveniences and challenges for genetic counsellors seeking to utilise these resources without advanced bioinformatics expertise. Consequently, we developed VarCards2, which incorporates nearly nine billion artificially generated single-nucleotide variants (including those from mitochondrial DNA) and compiles vital annotation information for genetic counselling based on ACMG-AMP variant-interpretation guidelines. These annotations include (I) functional effects; (II) minor allele frequencies; (III) comprehensive function and pathogenicity predictions covering all potential variants, such as non-synonymous substitutions, non-canonical splicing variants, and non-coding variations and (IV) gene-level information. Furthermore, VarCards2 incorporates 368 820 266 documented short insertions and deletions and 2 773 555 documented copy number variations, complemented by their corresponding annotation and prediction tools. In conclusion, VarCards2, by integrating over 150 variant- and gene-level annotation sources, significantly enhances the efficiency of genetic counselling and can be freely accessed at http://www.genemed.tech/varcards2/.
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Affiliation(s)
- Zheng Wang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Hunan Key Laboratory of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Guihu Zhao
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Zhaopo Zhu
- Center for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - Yijing Wang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Xudong Xiang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Shiyu Zhang
- Xiangya School of Medicine, Central South University, Changsha, Hunan 410013, China
| | - Tengfei Luo
- Center for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - Qiao Zhou
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Jian Qiu
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Hunan Key Laboratory of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Beisha Tang
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, & Multi-Omics Research Center for Brain Disorders, The First Affiliated Hospital, University of South China, Hengyang, Hunan, China
| | - Kun Xia
- Center for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - Bin Li
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Jinchen Li
- National Clinical Research Center for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Center for Medical Genetics & Hunan Key Laboratory, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
- Bioinformatics Center, Furong Laboratory & Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
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49
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Zhu X, Ma S, Wong WH. Genetic effects of sequence-conserved enhancer-like elements on human complex traits. Genome Biol 2024; 25:1. [PMID: 38167462 PMCID: PMC10759394 DOI: 10.1186/s13059-023-03142-1] [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: 09/02/2022] [Accepted: 12/08/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND The vast majority of findings from human genome-wide association studies (GWAS) map to non-coding sequences, complicating their mechanistic interpretations and clinical translations. Non-coding sequences that are evolutionarily conserved and biochemically active could offer clues to the mechanisms underpinning GWAS discoveries. However, genetic effects of such sequences have not been systematically examined across a wide range of human tissues and traits, hampering progress to fully understand regulatory causes of human complex traits. RESULTS Here we develop a simple yet effective strategy to identify functional elements exhibiting high levels of human-mouse sequence conservation and enhancer-like biochemical activity, which scales well to 313 epigenomic datasets across 106 human tissues and cell types. Combined with 468 GWAS of European (EUR) and East Asian (EAS) ancestries, these elements show tissue-specific enrichments of heritability and causal variants for many traits, which are significantly stronger than enrichments based on enhancers without sequence conservation. These elements also help prioritize candidate genes that are functionally relevant to body mass index (BMI) and schizophrenia but were not reported in previous GWAS with large sample sizes. CONCLUSIONS Our findings provide a comprehensive assessment of how sequence-conserved enhancer-like elements affect complex traits in diverse tissues and demonstrate a generalizable strategy of integrating evolutionary and biochemical data to elucidate human disease genetics.
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Affiliation(s)
- Xiang Zhu
- Department of Statistics, The Pennsylvania State University, 326 Thomas Building, University Park, 16802, PA, USA.
- Huck Institutes of the Life Sciences, The Pennsylvania State University, 201 Huck Life Sciences Building, University Park, 16802, PA, USA.
- Department of Statistics, Stanford University, 390 Jane Stanford Way, Stanford, 94305, CA, USA.
| | - Shining Ma
- Department of Statistics, Stanford University, 390 Jane Stanford Way, Stanford, 94305, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, 1265 Welch Road MC5464, Stanford, 94305, CA, USA
| | - Wing Hung Wong
- Department of Statistics, Stanford University, 390 Jane Stanford Way, Stanford, 94305, CA, USA.
- Department of Biomedical Data Science, Stanford University School of Medicine, 1265 Welch Road MC5464, Stanford, 94305, CA, USA.
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50
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Feng Y, Zhang Z, Hong Y, Ding Y, Liu L, Gao S, Fang H, Shi J. A DNA methylation haplotype block landscape in human tissues and preimplantation embryos reveals regulatory elements defined by comethylation patterns. Genome Res 2023; 33:2041-2052. [PMID: 37940553 PMCID: PMC10760529 DOI: 10.1101/gr.278146.123] [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: 06/01/2023] [Accepted: 11/03/2023] [Indexed: 11/10/2023]
Abstract
DNA methylation and associated regulatory elements play a crucial role in gene expression regulation. Previous studies have focused primarily on the distribution of mean methylation levels. Advances in whole-genome bisulfite sequencing (WGBS) have enabled the characterization of DNA methylation haplotypes (MHAPs), representing CpG sites from the same read fragment on a single chromosome, and the subsequent identification of methylation haplotype blocks (MHBs), in which adjacent CpGs on the same fragment are comethylated. Using our expert-curated WGBS data sets, we report comprehensive landscapes of MHBs in 17 representative normal somatic human tissues and during early human embryonic development. Integrative analysis reveals MHBs as a distinctive type of regulatory element characterized by comethylation patterns rather than mean methylation levels. We show the enrichment of MHBs in open chromatin regions, tissue-specific histone marks, and enhancers, including super-enhancers. Moreover, we find that MHBs tend to localize near tissue-specific genes and show an association with differential gene expression that is independent of mean methylation. Similar findings are observed in the context of human embryonic development, highlighting the dynamic nature of MHBs during early development. Collectively, our comprehensive MHB landscapes provide valuable insights into the tissue specificity and developmental dynamics of DNA methylation.
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Affiliation(s)
- Yan Feng
- 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
| | - 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 200030, China
| | - Yuyang Hong
- 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
| | - Yi Ding
- 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
| | - Leiqin Liu
- 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
| | - Siqi Gao
- 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
| | - 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
| | - 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;
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