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Tambets R, Kolde A, Kolberg P, Love MI, Alasoo K. Extensive co-regulation of neighboring genes complicates the use of eQTLs in target gene prioritization. HGG ADVANCES 2024; 5:100348. [PMID: 39210598 PMCID: PMC11416642 DOI: 10.1016/j.xhgg.2024.100348] [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: 02/02/2024] [Revised: 08/27/2024] [Accepted: 08/27/2024] [Indexed: 09/04/2024] Open
Abstract
Identifying causal genes underlying genome-wide association studies (GWASs) is a fundamental problem in human genetics. Although colocalization with gene expression quantitative trait loci (eQTLs) is often used to prioritize GWAS target genes, systematic benchmarking has been limited due to unavailability of large ground truth datasets. Here, we re-analyzed plasma protein QTL data from 3,301 individuals of the INTERVAL cohort together with 131 eQTL Catalog datasets. Focusing on variants located within or close to the affected protein identified 793 proteins with at least one cis-pQTL where we could assume that the most likely causal gene was the gene coding for the protein. We then benchmarked the ability of cis-eQTLs to recover these causal genes by comparing three Bayesian colocalization methods (coloc.susie, coloc.abf, and CLPP) and five Mendelian randomization (MR) approaches (three varieties of inverse-variance weighted MR, MR-RAPS, and MRLocus). We found that assigning fine-mapped pQTLs to their closest protein coding genes outperformed all colocalization methods regarding both precision (71.9%) and recall (76.9%). Furthermore, the colocalization method with the highest recall (coloc.susie - 46.3%) also had the lowest precision (45.1%). Combining evidence from multiple conditionally distinct colocalizing QTLs with MR increased precision to 81%, but this was accompanied by a large reduction in recall to 7.1%. Furthermore, the choice of the MR method greatly affected performance, with the standard inverse-variance-weighted MR often producing many false positives. Our results highlight that linking GWAS variants to target genes remains challenging with eQTL evidence alone, and prioritizing novel targets requires triangulation of evidence from multiple sources.
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Affiliation(s)
- Ralf Tambets
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Anastassia Kolde
- Institute of Genomics, University of Tartu, Tartu, Estonia; Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
| | - Peep Kolberg
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Michael I Love
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kaur Alasoo
- Institute of Computer Science, University of Tartu, Tartu, Estonia.
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2
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Su C, Lee D, Jin P, Zhang J. Cell-type-specific mapping of enhancers and target genes from single-cell multimodal data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.24.614814. [PMID: 39386519 PMCID: PMC11463474 DOI: 10.1101/2024.09.24.614814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/12/2024]
Abstract
Mapping enhancers and target genes in disease-related cell types has provided critical insights into the functional mechanisms of genetic variants identified by genomewide association studies (GWAS). However, most existing analyses rely on bulk data or cultured cell lines, which may fail to identify cell-type-specific enhancers and target genes. Recently, single-cell multimodal data measuring both gene expression and chromatin accessibility within the same cells have enabled the inference of enhancer-gene pairs in a cell-type-specific and context-specific manner. However, this task is challenged by the data's high sparsity, sequencing depth variation, and the computational burden of analyzing a large number of enhancer-gene pairs. To address these challenges, we propose scMultiMap, a statistical method that infers enhancer-gene association from sparse multimodal counts using a joint latent-variable model. It adjusts for technical confounding, permits fast moment-based estimation and provides analytically derived p -values. In systematic analyses of blood and brain data, scMultiMap shows appropriate type I error control, high statistical power with greater reproducibility across independent datasets and stronger consistency with orthogonal data modalities. Meanwhile, its computational cost is less than 1% of existing methods. When applied to single-cell multimodal data from postmortem brain samples from Alzheimer's disease (AD) patients and controls, scMultiMap gave the highest heritability enrichment in microglia and revealed new insights into the regulatory mechanisms of AD GWAS variants in microglia.
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3
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Wenz BM, He Y, Chen NC, Pickrell JK, Li JH, Dudek MF, Li T, Keener R, Voight BF, Brown CD, Battle A. Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.04.610850. [PMID: 39282458 PMCID: PMC11398312 DOI: 10.1101/2024.09.04.610850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/21/2024]
Abstract
Background Understanding the genetic causes for variability in chromatin accessibility can shed light on the molecular mechanisms through which genetic variants may affect complex traits. Thousands of ATAC-seq samples have been collected that hold information about chromatin accessibility across diverse cell types and contexts, but most of these are not paired with genetic information and come from diverse distinct projects and laboratories. Results We report here joint genotyping, chromatin accessibility peak calling, and discovery of quantitative trait loci which influence chromatin accessibility (caQTLs), demonstrating the capability of performing caQTL analysis on a large scale in a diverse sample set without pre-existing genotype information. Using 10,293 profiling samples representing 1,454 unique donor individuals across 653 studies from public databases, we catalog 23,381 caQTLs in total. After joint discovery analysis, we cluster samples based on accessible chromatin profiles to identify context-specific caQTLs. We find that caQTLs are strongly enriched for annotations of gene regulatory elements across diverse cell types and tissues and are often strongly linked with genetic variation associated with changes in expression (eQTLs), indicating that caQTLs can mediate genetic effects on gene expression. We demonstrate sharing of causal variants for chromatin accessibility and diverse complex human traits, enabling a more complete picture of the genetic mechanisms underlying complex human phenotypes. Conclusions Our work provides a proof of principle for caQTL calling from previously ungenotyped samples, and represents one of the largest, most diverse caQTL resources currently available, informing mechanisms of genetic regulation of gene expression and contribution to disease.
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Affiliation(s)
- Brandon M Wenz
- Genetics and Epigenetics Program, Cell and Molecular Biology Graduate Group, Biomedical Graduate Studies, University of Pennsylvania - Perelman School of Medicine, Philadelphia PA 19104
| | - Yuan He
- Department of Biomedical Engineering, Johns Hopkins University; Baltimore, MD, 21218
| | - Nae-Chyun Chen
- Department of Computer Science, Johns Hopkins University; Baltimore, MD, 21218
| | | | | | - Max F Dudek
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA 19104
| | - Taibo Li
- Department of Biomedical Engineering, Johns Hopkins University; Baltimore, MD, 21218
| | - Rebecca Keener
- Department of Biomedical Engineering, Johns Hopkins University; Baltimore, MD, 21218
| | - Benjamin F Voight
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia PA, 19104
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104
| | - Christopher D Brown
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University; Baltimore, MD, 21218
- Department of Computer Science, Johns Hopkins University; Baltimore, MD, 21218
- Department of Genetic Medicine, Johns Hopkins University; Baltimore, MD, 21218
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, 21218
- Data Science and AI Institute, Johns Hopkins University, Baltimore, MD, 21218
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4
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Weinstock JS, Chaudhry SA, Ioannou M, Viskadourou M, Reventun P, Jakubek YA, Liggett LA, Laurie C, Broome JG, Khan A, Taylor KD, Guo X, Peyser PA, Boerwinkle E, Chami N, Kenny EE, Loos RJ, Psaty BM, Russell TP, Brody JA, Yun JH, Cho MH, Vasan RS, Kardia SL, Smith JA, Raffield LM, Bidulescu A, O'Brien E, de Andrade M, Rotter JI, Rich SS, Tracy RP, Chen YDI, Gu CC, Hsiung CA, Kooperberg C, Haring B, Nassir R, Mathias R, Reiner A, Sankaran V, Lowenstein CJ, Blackwell TW, Abecasis GR, Smith AV, Kang HM, Natarajan P, Jaiswal S, Bick A, Post WS, Scheet P, Auer P, Karantanos T, Battle A, Arvanitis M. The Genetic Determinants and Genomic Consequences of Non-Leukemogenic Somatic Point Mutations. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.22.24312319. [PMID: 39228737 PMCID: PMC11370504 DOI: 10.1101/2024.08.22.24312319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Clonal hematopoiesis (CH) is defined by the expansion of a lineage of genetically identical cells in blood. Genetic lesions that confer a fitness advantage, such as point mutations or mosaic chromosomal alterations (mCAs) in genes associated with hematologic malignancy, are frequent mediators of CH. However, recent analyses of both single cell-derived colonies of hematopoietic cells and population sequencing cohorts have revealed CH frequently occurs in the absence of known driver genetic lesions. To characterize CH without known driver genetic lesions, we used 51,399 deeply sequenced whole genomes from the NHLBI TOPMed sequencing initiative to perform simultaneous germline and somatic mutation analyses among individuals without leukemogenic point mutations (LPM), which we term CH-LPMneg. We quantified CH by estimating the total mutation burden. Because estimating somatic mutation burden without a paired-tissue sample is challenging, we developed a novel statistical method, the Genomic and Epigenomic informed Mutation (GEM) rate, that uses external genomic and epigenomic data sources to distinguish artifactual signals from true somatic mutations. We performed a genome-wide association study of GEM to discover the germline determinants of CH-LPMneg. After fine-mapping and variant-to-gene analyses, we identified seven genes associated with CH-LPMneg (TCL1A, TERT, SMC4, NRIP1, PRDM16, MSRA, SCARB1), and one locus associated with a sex-associated mutation pathway (SRGAP2C). We performed a secondary analysis excluding individuals with mCAs, finding that the genetic architecture was largely unaffected by their inclusion. Functional analyses of SMC4 and NRIP1 implicated altered HSC self-renewal and proliferation as the primary mediator of mutation burden in blood. We then performed comprehensive multi-tissue transcriptomic analyses, finding that the expression levels of 404 genes are associated with GEM. Finally, we performed phenotypic association meta-analyses across four cohorts, finding that GEM is associated with increased white blood cell count and increased risk for incident peripheral artery disease, but is not significantly associated with incident stroke or coronary disease events. Overall, we develop GEM for quantifying mutation burden from WGS without a paired-tissue sample and use GEM to discover the genetic, genomic, and phenotypic correlates of CH-LPMneg.
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Affiliation(s)
- Joshua S Weinstock
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA, USA
| | - Sharjeel A Chaudhry
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD
- Department of Surgery, Division of Vascular and Endovascular Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Maria Ioannou
- Division of Hematological Malignancies, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine
| | - Maria Viskadourou
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD
| | - Paula Reventun
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD
| | | | - L Alexander Liggett
- Division of Hematology/Oncology, Boston Childrens Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Cecelia Laurie
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Jai G Broome
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Alyna Khan
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, Boston University, Boxton, MA USA
| | - Eric Boerwinkle
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Nathalie Chami
- The Charles Bronfman Institute of Personalized Medicine
- The Mindich Child Health and Developlement Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Ruth J Loos
- The Charles Bronfman Institute of Personalized Medicine
- The Mindich Child Health and Developlement Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Tracy P Russell
- Department of Pathology & Laboratory Medicine and Biochemistry, Larner College of Medicine at the University of Vermont, Colchester, VT, USA
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Jeong H Yun
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA USA
| | - Michael H Cho
- Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA USA
| | - Ramachandran S Vasan
- National Heart Lung and Blood Institute's, Boston University's Framingham Heart Study, Framingham, MA, USA
| | - Sharon L Kardia
- Department of Epidemiology, University of Michigan, Ann Arbor, MI
| | - Jennifer A Smith
- Department of Epidemiology, University of Michigan, Ann Arbor, MI
- Survey Research Center, Institute for Social Research, University of Michgian, Ann Arbor, MI
| | - Laura M Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27514
| | - Aurelian Bidulescu
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health Bloomington, Bloomington, IN, USA
| | | | - Mariza de Andrade
- Mayo Clinic, Department of Health Sciences Research, Rochester, MN, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA USA
| | - Stephen S Rich
- Department of Public Health Sciences, Center for Public Health Genomics, University of Virginia, Charlottesville, VA USA
| | - Russell P Tracy
- Department of Pathology & Laboratory Medicine and Biochemistry, Larner College of Medicine at the University of Vermont, Colchester, VT, USA
| | - Yii Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA USA
| | - C Charles Gu
- Center for Biostatistics and Data Sciences, Washington University, St. Louis, MO USA
| | - Chao A Hsiung
- Department of Medicine, Taipei Veterans General Hospital, Taipei Taiwan - 201 Shi-Pai Rd. Sec. 2, Taipei Taiwan
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Bernhard Haring
- Department of Medicine III, Saarland University Hospital, Homburg, Saarland, Germany - Department of Medicine I, University of Wrzburg, Wrzburg, Bavaria, Germany
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA. Electronic address
| | - Rami Nassir
- University of California Davis, Davis, CA, USA
| | - Rasika Mathias
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alex Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Vijay Sankaran
- Division of Hematology/Oncology, Boston Childrens Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
| | | | - Thomas W Blackwell
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Goncalo R Abecasis
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | - Albert V Smith
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Hyun M Kang
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Pradeep Natarajan
- Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Program in Medical and Population Genetics, Broad Institute of Harvard & MIT, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | | | - Alexander Bick
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University, Nashville, TN, USA
| | - Wendy S Post
- Department of Medicine, Cardiology Division, Johns Hopkins University
| | - Paul Scheet
- Department of Epidemiology, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Paul Auer
- Department of Biostatistics, Medical College of WisconsinDivision of Biostatistics, Institute for Health and Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Theodoros Karantanos
- Division of Hematological Malignancies, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD
- Department of Computer Science, Johns Hopkins University, Baltimore, MD
| | - Marios Arvanitis
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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5
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Leask MP, Crișan TO, Ji A, Matsuo H, Köttgen A, Merriman TR. The pathogenesis of gout: molecular insights from genetic, epigenomic and transcriptomic studies. Nat Rev Rheumatol 2024; 20:510-523. [PMID: 38992217 DOI: 10.1038/s41584-024-01137-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/11/2024] [Indexed: 07/13/2024]
Abstract
The pathogenesis of gout involves a series of steps beginning with hyperuricaemia, followed by the deposition of monosodium urate crystal in articular structures and culminating in an innate immune response, mediated by the NLRP3 inflammasome, to the deposited crystals. Large genome-wide association studies (GWAS) of serum urate levels initially identified the genetic variants with the strongest effects, mapping mainly to genes that encode urate transporters in the kidney and gut. Other GWAS highlighted the importance of uncommon genetic variants. More recently, genetic and epigenetic genome-wide studies have revealed new pathways in the inflammatory process of gout, including genetic associations with epigenomic modifiers. Epigenome-wide association studies are also implicating epigenomic remodelling in gout, which perhaps regulates the responsiveness of the innate immune system to monosodium urate crystals. Notably, genes implicated in gout GWAS do not include those encoding components of the NLRP3 inflammasome itself, but instead include genes encoding molecules involved in its regulation. Knowledge of the molecular mechanisms underlying gout has advanced through the translation of genetic associations into specific molecular mechanisms. Notable examples include ABCG2, HNF4A, PDZK1, MAF and IL37. Current genetic studies are dominated by participants of European ancestry; however, studies focusing on other population groups are discovering informative population-specific variants associated with gout.
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Affiliation(s)
- Megan P Leask
- Department of Physiology, University of Otago, Dunedin, Aotearoa, New Zealand
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Tania O Crișan
- Department of Medical Genetics, "Iuliu Haţieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Aichang Ji
- Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, China
| | - Hirotaka Matsuo
- Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Saitama, Japan
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Tony R Merriman
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA.
- Department of Microbiology and Immunology, University of Otago, Dunedin, Aotearoa, New Zealand.
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6
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Yao D, Binan L, Bezney J, Simonton B, Freedman J, Frangieh CJ, Dey K, Geiger-Schuller K, Eraslan B, Gusev A, Regev A, Cleary B. Scalable genetic screening for regulatory circuits using compressed Perturb-seq. Nat Biotechnol 2024; 42:1282-1295. [PMID: 37872410 PMCID: PMC11035494 DOI: 10.1038/s41587-023-01964-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 08/22/2023] [Indexed: 10/25/2023]
Abstract
Pooled CRISPR screens with single-cell RNA sequencing readout (Perturb-seq) have emerged as a key technique in functional genomics, but they are limited in scale by cost and combinatorial complexity. In this study, we modified the design of Perturb-seq by incorporating algorithms applied to random, low-dimensional observations. Compressed Perturb-seq measures multiple random perturbations per cell or multiple cells per droplet and computationally decompresses these measurements by leveraging the sparse structure of regulatory circuits. Applied to 598 genes in the immune response to bacterial lipopolysaccharide, compressed Perturb-seq achieves the same accuracy as conventional Perturb-seq with an order of magnitude cost reduction and greater power to learn genetic interactions. We identified known and novel regulators of immune responses and uncovered evolutionarily constrained genes with downstream targets enriched for immune disease heritability, including many missed by existing genome-wide association studies. Our framework enables new scales of interrogation for a foundational method in functional genomics.
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Affiliation(s)
- Douglas Yao
- Program in Systems, Synthetic, and Quantitative Biology, Harvard University, Cambridge, MA, USA
| | - Loic Binan
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jon Bezney
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Brooke Simonton
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jahanara Freedman
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Chris J Frangieh
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kushal Dey
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | | | - Alexander Gusev
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital, Boston, MA, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Genentech, South San Francisco, CA, USA
| | - Brian Cleary
- Faculty of Computing and Data Sciences, Boston University, Boston, MA, USA.
- Department of Biology, Boston University, Boston, MA, USA.
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
- Program in Bioinformatics, Boston University, Boston, MA, USA.
- Biological Design Center, Boston University, Boston, MA, USA.
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7
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Xiong G, Li J, Yao F, Yang F, Xiang Y. New insight into the CNC-bZIP member, NFE2L3, in human diseases. Front Cell Dev Biol 2024; 12:1430486. [PMID: 39149514 PMCID: PMC11325725 DOI: 10.3389/fcell.2024.1430486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 07/08/2024] [Indexed: 08/17/2024] Open
Abstract
Nuclear factor erythroid 2 (NF-E2)-related factor 3 (NFE2L3), a member of the CNC-bZIP subfamily and widely found in a variety of tissues, is an endoplasmic reticulum (ER) membrane-anchored transcription factor that can be released from the ER and moved into the nucleus to bind the promoter region to regulate a series of target genes involved in antioxidant, inflammatory responses, and cell cycle regulation in response to extracellular or intracellular stress. Recent research, particularly in the past 5 years, has shed light on NFE2L3's participation in diverse biological processes, including cell differentiation, inflammatory responses, lipid homeostasis, immune responses, and tumor growth. Notably, NFE2L3 has been identified as a key player in the development and prognosis of multiple cancers including colorectal cancer, thyroid cancer, breast cancer, hepatocellular carcinoma, gastric cancer, renal cancer, bladder cancer, esophageal squamous cell carcinoma, T cell lymphoblastic lymphoma, pancreatic cancer, and squamous cell carcinoma. Furthermore, research has linked NFE2L3 to other cancers such as lung adenocarcinoma, malignant pleural mesothelioma, ovarian cancer, glioblastoma multiforme, and laryngeal carcinoma, indicating its potential as a target for innovative cancer treatment approaches. Therefore, to gain a better understanding of the role of NFE2L3 in disease, this review offers insights into the discovery, structure, function, and recent advancements in the study of NFE2L3 to lay the groundwork for the development of NFE2L3-targeted cancer therapies.
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Affiliation(s)
- Guanghui Xiong
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southwest Medical University, Luzhou, Sichuan, China
- Department of Children Rehabilitation, Maternal and Child Health Hospital of Jintang County, Chendu, Sichuan, China
| | - Jie Li
- Department of Anaesthesia, The Affiliated Hospital, Southwest Medical University, Luzhou, Sichuan, China
| | - Fuli Yao
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southwest Medical University, Luzhou, Sichuan, China
| | - Fang Yang
- Department of Physiology, School of Basic Medical Sciences, Southwest Medical University, Luzhou, Sichuan, China
- Department of Pathophysiology, College of High Altitude Military Medicine, Third Military Medical University (Army Medical University), Chongqing, China
| | - Yuancai Xiang
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southwest Medical University, Luzhou, Sichuan, China
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8
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Xi X, Ruffieux H. A modeling framework for detecting and leveraging node-level information in Bayesian network inference. Biostatistics 2024:kxae021. [PMID: 38916966 DOI: 10.1093/biostatistics/kxae021] [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: 09/06/2023] [Revised: 03/11/2024] [Accepted: 06/02/2024] [Indexed: 06/27/2024] Open
Abstract
Bayesian graphical models are powerful tools to infer complex relationships in high dimension, yet are often fraught with computational and statistical challenges. If exploited in a principled way, the increasing information collected alongside the data of primary interest constitutes an opportunity to mitigate these difficulties by guiding the detection of dependence structures. For instance, gene network inference may be informed by the use of publicly available summary statistics on the regulation of genes by genetic variants. Here we present a novel Gaussian graphical modeling framework to identify and leverage information on the centrality of nodes in conditional independence graphs. Specifically, we consider a fully joint hierarchical model to simultaneously infer (i) sparse precision matrices and (ii) the relevance of node-level information for uncovering the sought-after network structure. We encode such information as candidate auxiliary variables using a spike-and-slab submodel on the propensity of nodes to be hubs, which allows hypothesis-free selection and interpretation of a sparse subset of relevant variables. As efficient exploration of large posterior spaces is needed for real-world applications, we develop a variational expectation conditional maximization algorithm that scales inference to hundreds of samples, nodes and auxiliary variables. We illustrate and exploit the advantages of our approach in simulations and in a gene network study which identifies hub genes involved in biological pathways relevant to immune-mediated diseases.
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Affiliation(s)
- Xiaoyue Xi
- MRC Biostatistics Unit, University of Cambridge, East Forvie Building, Forvie Site, Robinson Way, Cambridge CB2 0SR, United Kingdom
| | - Hélène Ruffieux
- MRC Biostatistics Unit, University of Cambridge, East Forvie Building, Forvie Site, Robinson Way, Cambridge CB2 0SR, United Kingdom
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9
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Pottier C, Küçükali F, Baker M, Batzler A, Jenkins GD, van Blitterswijk M, Vicente CT, De Coster W, Wynants S, Van de Walle P, Ross OA, Murray ME, Faura J, Haggarty SJ, van Rooij JG, Mol MO, Hsiung GYR, Graff C, Öijerstedt L, Neumann M, Asmann Y, McDonnell SK, Baheti S, Josephs KA, Whitwell JL, Bieniek KF, Forsberg L, Heuer H, Lago AL, Geier EG, Yokoyama JS, Oddi AP, Flanagan M, Mao Q, Hodges JR, Kwok JB, Domoto-Reilly K, Synofzik M, Wilke C, Onyike C, Dickerson BC, Evers BM, Dugger BN, Munoz DG, Keith J, Zinman L, Rogaeva E, Suh E, Gefen T, Geula C, Weintraub S, Diehl-Schmid J, Farlow MR, Edbauer D, Woodruff BK, Caselli RJ, Donker Kaat LL, Huey ED, Reiman EM, Mead S, King A, Roeber S, Nana AL, Ertekin-Taner N, Knopman DS, Petersen RC, Petrucelli L, Uitti RJ, Wszolek ZK, Ramos EM, Grinberg LT, Gorno Tempini ML, Rosen HJ, Spina S, Piguet O, Grossman M, Trojanowski JQ, Keene DC, Lee-Way J, Prudlo J, Geschwind DH, Rissman RA, Cruchaga C, Ghetti B, Halliday GM, Beach TG, Serrano GE, Arzberger T, Herms J, Boxer AL, Honig LS, Vonsattel JP, Lopez OL, Kofler J, White CL, Gearing M, Glass J, Rohrer JD, Irwin DJ, Lee EB, Van Deerlin V, Castellani R, Mesulam MM, Tartaglia MC, Finger EC, Troakes C, Al-Sarraj S, Miller BL, Seelaar H, Graff-Radford NR, Boeve BF, Mackenzie IR, van Swieten JC, Seeley WW, Sleegers K, Dickson DW, Biernacka JM, Rademakers R. Deciphering Distinct Genetic Risk Factors for FTLD-TDP Pathological Subtypes via Whole-Genome Sequencing. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.24.24309088. [PMID: 38978643 PMCID: PMC11230325 DOI: 10.1101/2024.06.24.24309088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Frontotemporal lobar degeneration with neuronal inclusions of the TAR DNA-binding protein 43 (FTLD-TDP) is a fatal neurodegenerative disorder with only a limited number of risk loci identified. We report our comprehensive genome-wide association study as part of the International FTLD-TDP Whole-Genome Sequencing Consortium, including 985 cases and 3,153 controls, and meta-analysis with the Dementia-seq cohort, compiled from 26 institutions/brain banks in the United States, Europe and Australia. We confirm UNC13A as the strongest overall FTLD-TDP risk factor and identify TNIP1 as a novel FTLD-TDP risk factor. In subgroup analyses, we further identify for the first time genome-wide significant loci specific to each of the three main FTLD-TDP pathological subtypes (A, B and C), as well as enrichment of risk loci in distinct tissues, brain regions, and neuronal subtypes, suggesting distinct disease aetiologies in each of the subtypes. Rare variant analysis confirmed TBK1 and identified VIPR1 , RBPJL , and L3MBTL1 as novel subtype specific FTLD-TDP risk genes, further highlighting the role of innate and adaptive immunity and notch signalling pathway in FTLD-TDP, with potential diagnostic and novel therapeutic implications.
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10
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Kailankangas V, Katayama S, Gröndahl-Yli-Hannuksela K, Vilhonen J, Tervaniemi MH, Rantakokko-Jalava K, Seiskari T, Lönnqvist E, Kere J, Oksi J, Syrjänen J, Vuopio J. Low expression of the CCL5 gene and low serum concentrations of CCL5 in severe invasive group a streptococcal disease. Infection 2024:10.1007/s15010-024-02318-6. [PMID: 38865072 DOI: 10.1007/s15010-024-02318-6] [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: 02/05/2024] [Accepted: 06/05/2024] [Indexed: 06/13/2024]
Abstract
PURPOSE Our objective was to elucidate host dependent factors of disease severity in invasive group A Streptococcal disease (iGAS) using transcriptome profiling of iGAS cases of varying degrees of severity at different timepoints. To our knowledge there are no previous transcriptome studies in iGAS patients. METHODS We recruited iGAS cases from June 2018 to July 2020. Whole blood samples for transcriptome analysis and serum for biomarker analysis were collected at three timepoints representing the acute (A), the convalescent (B) and the post-infection phase (C). Gene expression was compared against clinical traits and disease course. Serum chemokine ligand 5 (CCL5, an inflammatory cytokine) concentration was also measured. RESULTS Forty-five patients were enrolled. After disqualifying degraded or impure RNAs we had 34, 31 and 21 subjects at timepoints A, B, and C, respectively. Low expression of the CCL5 gene correlated strongly with severity (death or need for intensive care) at timepoint A (AUC = 0.92), supported by low concentrations of CCL5 in sera. CONCLUSIONS Low gene expression levels and low serum concentration of CCL5 in the early stages of an iGAS infection were associated with a more severe disease course. CCL5 might have potential as a predictor of disease severity. Low expression of genes of cytotoxic immunity, especially CCL5, and corresponding low serum concentrations of CCL5 associated with a severe disease course, i.e. death, or need for intensive care, in early phase of invasive group A Streptococcal disease.
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Affiliation(s)
- V Kailankangas
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
- Department of Internal Medicine, Tampere University Hospital, Tampere, Finland.
| | - S Katayama
- Folkhälsan Research Center, Helsinki, Finland
- Stem Cells and Metabolism Research Program, University of Helsinki, Helsinki, Finland
- Department of Biosciences and Nutrition, Karolinska Institutet, Solna, Sweden
| | | | - J Vilhonen
- Department of Infectious Diseases, Turku University Hospital, Turku, Finland
| | - M H Tervaniemi
- Folkhälsan Research Center, Helsinki, Finland
- Stem Cells and Metabolism Research Program, University of Helsinki, Helsinki, Finland
| | - K Rantakokko-Jalava
- Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Clinical Microbiology, Laboratory Division, Turku University Hospital, Turku, Finland
| | - T Seiskari
- Department of Clinical Microbiology, Fimlab Laboratories, Tampere, Finland
| | - E Lönnqvist
- Department of Clinical Microbiology, Fimlab Laboratories, Tampere, Finland
| | - J Kere
- Folkhälsan Research Center, Helsinki, Finland
- Stem Cells and Metabolism Research Program, University of Helsinki, Helsinki, Finland
- Department of Biosciences and Nutrition, Karolinska Institutet, Solna, Sweden
| | - J Oksi
- Department of Infectious Diseases, Turku University Hospital, Turku, Finland
- Faculty of Medicine, University of Turku, Turku, Finland
| | - J Syrjänen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Department of Internal Medicine, Tampere University Hospital, Tampere, Finland
| | - J Vuopio
- Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Clinical Microbiology, Laboratory Division, Turku University Hospital, Turku, Finland
- Finnish Institute for Health and Welfare (THL), Helsinki, Finland
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11
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Stankey CT, Bourges C, Haag LM, Turner-Stokes T, Piedade AP, Palmer-Jones C, Papa I, Silva Dos Santos M, Zhang Q, Cameron AJ, Legrini A, Zhang T, Wood CS, New FN, Randzavola LO, Speidel L, Brown AC, Hall A, Saffioti F, Parkes EC, Edwards W, Direskeneli H, Grayson PC, Jiang L, Merkel PA, Saruhan-Direskeneli G, Sawalha AH, Tombetti E, Quaglia A, Thorburn D, Knight JC, Rochford AP, Murray CD, Divakar P, Green M, Nye E, MacRae JI, Jamieson NB, Skoglund P, Cader MZ, Wallace C, Thomas DC, Lee JC. A disease-associated gene desert directs macrophage inflammation through ETS2. Nature 2024; 630:447-456. [PMID: 38839969 PMCID: PMC11168933 DOI: 10.1038/s41586-024-07501-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 05/01/2024] [Indexed: 06/07/2024]
Abstract
Increasing rates of autoimmune and inflammatory disease present a burgeoning threat to human health1. This is compounded by the limited efficacy of available treatments1 and high failure rates during drug development2, highlighting an urgent need to better understand disease mechanisms. Here we show how functional genomics could address this challenge. By investigating an intergenic haplotype on chr21q22-which has been independently linked to inflammatory bowel disease, ankylosing spondylitis, primary sclerosing cholangitis and Takayasu's arteritis3-6-we identify that the causal gene, ETS2, is a central regulator of human inflammatory macrophages and delineate the shared disease mechanism that amplifies ETS2 expression. Genes regulated by ETS2 were prominently expressed in diseased tissues and more enriched for inflammatory bowel disease GWAS hits than most previously described pathways. Overexpressing ETS2 in resting macrophages reproduced the inflammatory state observed in chr21q22-associated diseases, with upregulation of multiple drug targets, including TNF and IL-23. Using a database of cellular signatures7, we identified drugs that might modulate this pathway and validated the potent anti-inflammatory activity of one class of small molecules in vitro and ex vivo. Together, this illustrates the power of functional genomics, applied directly in primary human cells, to identify immune-mediated disease mechanisms and potential therapeutic opportunities.
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Affiliation(s)
- C T Stankey
- Genetic Mechanisms of Disease Laboratory, The Francis Crick Institute, London, UK
- Department of Immunology and Inflammation, Imperial College London, London, UK
- Washington University School of Medicine, St Louis, MO, USA
| | - C Bourges
- Genetic Mechanisms of Disease Laboratory, The Francis Crick Institute, London, UK
| | - L M Haag
- Division of Gastroenterology, Infectious Diseases and Rheumatology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - T Turner-Stokes
- Genetic Mechanisms of Disease Laboratory, The Francis Crick Institute, London, UK
- Department of Immunology and Inflammation, Imperial College London, London, UK
| | - A P Piedade
- Genetic Mechanisms of Disease Laboratory, The Francis Crick Institute, London, UK
| | - C Palmer-Jones
- Department of Gastroenterology, Royal Free Hospital, London, UK
- Institute for Liver and Digestive Health, Division of Medicine, University College London, London, UK
| | - I Papa
- Genetic Mechanisms of Disease Laboratory, The Francis Crick Institute, London, UK
| | | | - Q Zhang
- Genomics of Inflammation and Immunity Group, Human Genetics Programme, Wellcome Sanger Institute, Hinxton, UK
| | - A J Cameron
- Wolfson Wohl Cancer Centre, School of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - A Legrini
- Wolfson Wohl Cancer Centre, School of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - T Zhang
- Wolfson Wohl Cancer Centre, School of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - C S Wood
- Wolfson Wohl Cancer Centre, School of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - F N New
- NanoString Technologies, Seattle, WA, USA
| | - L O Randzavola
- Department of Immunology and Inflammation, Imperial College London, London, UK
| | - L Speidel
- Ancient Genomics Laboratory, The Francis Crick Institute, London, UK
- Genetics Institute, University College London, London, UK
| | - A C Brown
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - A Hall
- The Sheila Sherlock Liver Centre, Royal Free Hospital, London, UK
- Department of Cellular Pathology, Royal Free Hospital, London, UK
| | - F Saffioti
- Institute for Liver and Digestive Health, Division of Medicine, University College London, London, UK
- The Sheila Sherlock Liver Centre, Royal Free Hospital, London, UK
| | - E C Parkes
- Genetic Mechanisms of Disease Laboratory, The Francis Crick Institute, London, UK
| | - W Edwards
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK
| | - H Direskeneli
- Department of Internal Medicine, Division of Rheumatology, Marmara University, Istanbul, Turkey
| | - P C Grayson
- Systemic Autoimmunity Branch, NIAMS, National Institutes of Health, Bethesda, MD, USA
| | - L Jiang
- Department of Rheumatology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - P A Merkel
- Division of Rheumatology, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Division of Epidemiology, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - G Saruhan-Direskeneli
- Department of Physiology, Istanbul University, Istanbul Faculty of Medicine, Istanbul, Turkey
| | - A H Sawalha
- Division of Rheumatology, Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA
- Division of Rheumatology and Clinical Immunology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Lupus Center of Excellence, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Immunology, University of Pittsburgh, Pittsburgh, PA, USA
| | - E Tombetti
- Department of Biomedical and Clinical Sciences, Milan University, Milan, Italy
- Internal Medicine and Rheumatology, ASST FBF-Sacco, Milan, Italy
| | - A Quaglia
- Department of Cellular Pathology, Royal Free Hospital, London, UK
- UCL Cancer Institute, London, UK
| | - D Thorburn
- Institute for Liver and Digestive Health, Division of Medicine, University College London, London, UK
- The Sheila Sherlock Liver Centre, Royal Free Hospital, London, UK
| | - J C Knight
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Chinese Academy of Medical Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- NIHR Comprehensive Biomedical Research Centre, Oxford, UK
| | - A P Rochford
- Department of Gastroenterology, Royal Free Hospital, London, UK
- Institute for Liver and Digestive Health, Division of Medicine, University College London, London, UK
| | - C D Murray
- Department of Gastroenterology, Royal Free Hospital, London, UK
- Institute for Liver and Digestive Health, Division of Medicine, University College London, London, UK
| | - P Divakar
- NanoString Technologies, Seattle, WA, USA
| | - M Green
- Experimental Histopathology STP, The Francis Crick Institute, London, UK
| | - E Nye
- Experimental Histopathology STP, The Francis Crick Institute, London, UK
| | - J I MacRae
- Metabolomics STP, The Francis Crick Institute, London, UK
| | - N B Jamieson
- Wolfson Wohl Cancer Centre, School of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - P Skoglund
- Ancient Genomics Laboratory, The Francis Crick Institute, London, UK
| | - M Z Cader
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - C Wallace
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK
- MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK
| | - D C Thomas
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge, UK
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - J C Lee
- Genetic Mechanisms of Disease Laboratory, The Francis Crick Institute, London, UK.
- Department of Gastroenterology, Royal Free Hospital, London, UK.
- Institute for Liver and Digestive Health, Division of Medicine, University College London, London, UK.
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12
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Nieto-Caballero VE, Reijneveld JF, Ruvalcaba A, Innocenzi G, Abeydeera N, Asgari S, Lopez K, Iwany SK, Luo Y, Nathan A, Fernandez-Salinas D, Chiñas M, Huang CC, Zhang Z, León SR, Calderon RI, Lecca L, Budzik JM, Murray M, Van Rhijn I, Raychaudhuri S, Moody DB, Suliman S, Gutierrez-Arcelus M. History of tuberculosis disease is associated with genetic regulatory variation in Peruvians. PLoS Genet 2024; 20:e1011313. [PMID: 38870230 PMCID: PMC11208071 DOI: 10.1371/journal.pgen.1011313] [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: 09/13/2023] [Revised: 06/26/2024] [Accepted: 05/21/2024] [Indexed: 06/15/2024] Open
Abstract
A quarter of humanity is estimated to have been exposed to Mycobacterium tuberculosis (Mtb) with a 5-10% risk of developing tuberculosis (TB) disease. Variability in responses to Mtb infection could be due to host or pathogen heterogeneity. Here, we focused on host genetic variation in a Peruvian population and its associations with gene regulation in monocyte-derived macrophages and dendritic cells (DCs). We recruited former household contacts of TB patients who previously progressed to TB (cases, n = 63) or did not progress to TB (controls, n = 63). Transcriptomic profiling of monocyte-derived DCs and macrophages measured the impact of genetic variants on gene expression by identifying expression quantitative trait loci (eQTL). We identified 330 and 257 eQTL genes in DCs and macrophages (False Discovery Rate (FDR) < 0.05), respectively. Four genes in DCs showed interaction between eQTL variants and TB progression status. The top eQTL interaction for a protein-coding gene was with FAH, the gene encoding fumarylacetoacetate hydrolase, which mediates the last step in mammalian tyrosine catabolism. FAH expression was associated with genetic regulatory variation in cases but not controls. Using public transcriptomic and epigenomic data of Mtb-infected monocyte-derived dendritic cells, we found that Mtb infection results in FAH downregulation and DNA methylation changes in the locus. Overall, this study demonstrates effects of genetic variation on gene expression levels that are dependent on history of infectious disease and highlights a candidate pathogenic mechanism through pathogen-response genes. Furthermore, our results point to tyrosine metabolism and related candidate TB progression pathways for further investigation.
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Affiliation(s)
- Victor E. Nieto-Caballero
- Division of Immunology, Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Undergraduate Program in Genomic Sciences, Center for Genomic Sciences, Universidad Nacional Autónoma de México (UNAM), Morelos, Mexico
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Josephine F. Reijneveld
- Zuckerberg San Francisco General Hospital, Division of Experimental Medicine, University of California San Francisco, San Francisco, California, United States of America
| | - Angel Ruvalcaba
- Zuckerberg San Francisco General Hospital, Division of Experimental Medicine, University of California San Francisco, San Francisco, California, United States of America
| | - Gabriel Innocenzi
- Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, University of California San Francisco, San Francisco, California, United States of America
| | - Nalin Abeydeera
- Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, University of California San Francisco, San Francisco, California, United States of America
| | - Samira Asgari
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
- Center for Data Sciences, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Kattya Lopez
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Socios En Salud Sucursal Peru, Lima, Peru
| | - Sarah K. Iwany
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Yang Luo
- Division of Immunology, Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
- Center for Data Sciences, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Kennedy Institute of Rheumatology, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom
| | - Aparna Nathan
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
- Center for Data Sciences, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Daniela Fernandez-Salinas
- Division of Immunology, Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Marcos Chiñas
- Division of Immunology, Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Chuan-Chin Huang
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Zibiao Zhang
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Segundo R. León
- Socios En Salud Sucursal Peru, Lima, Peru
- Medical Technology School and Global Health Research Institute, San Juan Bautista Private University, Lima, Perú
| | | | | | - Jonathan M. Budzik
- Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, University of California San Francisco, San Francisco, California, United States of America
| | - Megan Murray
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Ildiko Van Rhijn
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Infectious Diseases and Immunology, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
| | - Soumya Raychaudhuri
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
- Center for Data Sciences, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - D. Branch Moody
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Sara Suliman
- Zuckerberg San Francisco General Hospital, Division of Experimental Medicine, University of California San Francisco, San Francisco, California, United States of America
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Gladstone-UCSF Institute of Genomic Immunology, University of California San Francisco, San Francisco, California, United States of America
- Chan Zuckerberg Initiative Biohub, San Francisco, California, United States of America
| | - Maria Gutierrez-Arcelus
- Division of Immunology, Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
- Center for Data Sciences, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
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13
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Boye C, Nirmalan S, Ranjbaran A, Luca F. Genotype × environment interactions in gene regulation and complex traits. Nat Genet 2024; 56:1057-1068. [PMID: 38858456 DOI: 10.1038/s41588-024-01776-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: 06/13/2023] [Accepted: 04/25/2024] [Indexed: 06/12/2024]
Abstract
Genotype × environment interactions (GxE) have long been recognized as a key mechanism underlying human phenotypic variation. Technological developments over the past 15 years have dramatically expanded our appreciation of the role of GxE in both gene regulation and complex traits. The richness and complexity of these datasets also required parallel efforts to develop robust and sensitive statistical and computational approaches. Although our understanding of the genetic architecture of molecular and complex traits has been maturing, a large proportion of complex trait heritability remains unexplained. Furthermore, there are increasing efforts to characterize the effect of environmental exposure on human health. We therefore review GxE in human gene regulation and complex traits, advocating for a comprehensive approach that jointly considers genetic and environmental factors in human health and disease.
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Affiliation(s)
- Carly Boye
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, US
| | - Shreya Nirmalan
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, US
| | - Ali Ranjbaran
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, US
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, US.
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, US.
- Department of Biology, University of Rome "Tor Vergata", Rome, Italy.
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14
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Aschenbrenner D, Nassiri I, Venkateswaran S, Pandey S, Page M, Drowley L, Armstrong M, Kugathasan S, Fairfax B, Uhlig HH. An isoform quantitative trait locus in SBNO2 links genetic susceptibility to Crohn's disease with defective antimicrobial activity. Nat Commun 2024; 15:4529. [PMID: 38806456 PMCID: PMC11133462 DOI: 10.1038/s41467-024-47218-3] [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: 05/05/2023] [Accepted: 03/25/2024] [Indexed: 05/30/2024] Open
Abstract
Despite major advances in linking single genetic variants to single causal genes, the significance of genetic variation on transcript-level regulation of expression, transcript-specific functions, and relevance to human disease has been poorly investigated. Strawberry notch homolog 2 (SBNO2) is a candidate gene in a susceptibility locus with different variants associated with Crohn's disease and bone mineral density. The SBNO2 locus is also differentially methylated in Crohn's disease but the functional mechanisms are unknown. Here we show that the isoforms of SBNO2 are differentially regulated by lipopolysaccharide and IL-10. We identify Crohn's disease associated isoform quantitative trait loci that negatively regulate the expression of the noncanonical isoform 2 corresponding with the methylation signals at the isoform 2 promoter in IBD and CD. The two isoforms of SBNO2 drive differential gene networks with isoform 2 dominantly impacting antimicrobial activity in macrophages. Our data highlight the role of isoform quantitative trait loci to understand disease susceptibility and resolve underlying mechanisms of disease.
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Affiliation(s)
- Dominik Aschenbrenner
- Translational Gastroenterology Unit, University of Oxford, Oxford, UK.
- Immunology Disease Area, Novartis Biomedical Research, Basel, CH, Switzerland.
| | - Isar Nassiri
- Oxford-GSK Institute of Molecular and Computational Medicine (IMCM), Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
| | | | - Sumeet Pandey
- Translational Gastroenterology Unit, University of Oxford, Oxford, UK
- GSK Immunology Network, GSK Medicines Research Center, Stevenage, UK
| | - Matthew Page
- Translational Bioinformatics, UCB Pharma, Slough, UK
| | | | | | | | - Benjamin Fairfax
- MRC-Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- Department of Oncology, University of Oxford & Oxford Cancer Centre, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Holm H Uhlig
- Translational Gastroenterology Unit, University of Oxford, Oxford, UK.
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
- Department of Paediatrics, University of Oxford, Oxford, UK.
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15
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Gilchrist JJ, Fang H, Danielli S, Tomkova M, Nassiri I, Ng E, Tong O, Taylor C, Muldoon D, Cohen LRZ, Al-Mossawi H, Lau E, Neville M, Schuster-Boeckler B, Knight JC, Fairfax BP. Characterization of the genetic determinants of context-specific DNA methylation in primary monocytes. CELL GENOMICS 2024; 4:100541. [PMID: 38663408 PMCID: PMC11099345 DOI: 10.1016/j.xgen.2024.100541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 11/24/2023] [Accepted: 03/27/2024] [Indexed: 05/12/2024]
Abstract
To better understand inter-individual variation in sensitivity of DNA methylation (DNAm) to immune activity, we characterized effects of inflammatory stimuli on primary monocyte DNAm (n = 190). We find that monocyte DNAm is site-dependently sensitive to lipopolysaccharide (LPS), with LPS-induced demethylation occurring following hydroxymethylation. We identify 7,359 high-confidence immune-modulated CpGs (imCpGs) that differ in genomic localization and transcription factor usage according to whether they represent a gain or loss in DNAm. Demethylated imCpGs are profoundly enriched for enhancers and colocalize to genes enriched for disease associations, especially cancer. DNAm is age associated, and we find that 24-h LPS exposure triggers approximately 6 months of gain in epigenetic age, directly linking epigenetic aging with innate immune activity. By integrating LPS-induced changes in DNAm with genetic variation, we identify 234 imCpGs under local genetic control. Exploring shared causal loci between LPS-induced DNAm responses and human disease traits highlights examples of disease-associated loci that modulate imCpG formation.
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Affiliation(s)
- James J Gilchrist
- Department of Paediatrics, University of Oxford, Oxford OX3 9DU, UK; MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Hai Fang
- Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Sara Danielli
- Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Marketa Tomkova
- Ludwig Cancer Research Oxford, University of Oxford, Oxford OX3 7DQ, UK
| | - Isar Nassiri
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK; Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Esther Ng
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Orion Tong
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Chelsea Taylor
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Dylan Muldoon
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Lea R Z Cohen
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
| | - Hussein Al-Mossawi
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Evelyn Lau
- Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Matt Neville
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LE, UK
| | | | - Julian C Knight
- Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Benjamin P Fairfax
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK; Department of Oncology, University of Oxford, Oxford OX3 9DS, UK.
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16
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Shellard EM, Rane SS, Eyre S, Warren RB. Functional Genomics and Insights into the Pathogenesis and Treatment of Psoriasis. Biomolecules 2024; 14:548. [PMID: 38785955 PMCID: PMC11117854 DOI: 10.3390/biom14050548] [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: 03/11/2024] [Revised: 04/17/2024] [Accepted: 04/24/2024] [Indexed: 05/25/2024] Open
Abstract
Psoriasis is a lifelong, systemic, immune mediated inflammatory skin condition, affecting 1-3% of the world's population, with an impact on quality of life similar to diseases like cancer or diabetes. Genetics are the single largest risk factor in psoriasis, with Genome-Wide Association (GWAS) studies showing that many psoriasis risk genes lie along the IL-23/Th17 axis. Potential psoriasis risk genes determined through GWAS can be annotated and characterised using functional genomics, allowing the identification of novel drug targets and the repurposing of existing drugs. This review is focused on the IL-23/Th17 axis, providing an insight into key cell types, cytokines, and intracellular signaling pathways involved. This includes examination of currently available biological treatments, time to relapse post drug withdrawal, and rates of primary/secondary drug failure, showing the need for greater understanding of the underlying genetic mechanisms of psoriasis and how they can impact treatment. This could allow for patient stratification towards the treatment most likely to reduce the burden of disease for the longest period possible.
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Affiliation(s)
- Elan May Shellard
- Faculty of Biology, Medicine and Health, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, The University of Manchester, Manchester M13 9PT, UK
| | - Shraddha S. Rane
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, NIHR Manchester Biomedical Research Centre, The University of Manchester, Manchester M13 9PT, UK; (S.S.R.); (S.E.)
| | - Stephen Eyre
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, NIHR Manchester Biomedical Research Centre, The University of Manchester, Manchester M13 9PT, UK; (S.S.R.); (S.E.)
| | - Richard B. Warren
- Dermatology Centre, Northern Care Alliance NHS Foundation Trust, Manchester M6 8HD, UK;
- NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M23 9LT, UK
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17
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Yuan Q, Duren Z. Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data. Nat Biotechnol 2024:10.1038/s41587-024-02182-7. [PMID: 38609714 DOI: 10.1038/s41587-024-02182-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 02/26/2024] [Indexed: 04/14/2024]
Abstract
Existing methods for gene regulatory network (GRN) inference rely on gene expression data alone or on lower resolution bulk data. Despite the recent integration of chromatin accessibility and RNA sequencing data, learning complex mechanisms from limited independent data points still presents a daunting challenge. Here we present LINGER (Lifelong neural network for gene regulation), a machine-learning method to infer GRNs from single-cell paired gene expression and chromatin accessibility data. LINGER incorporates atlas-scale external bulk data across diverse cellular contexts and prior knowledge of transcription factor motifs as a manifold regularization. LINGER achieves a fourfold to sevenfold relative increase in accuracy over existing methods and reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Following the GRN inference from reference single-cell multiome data, LINGER enables the estimation of transcription factor activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies.
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Affiliation(s)
- Qiuyue Yuan
- Center for Human Genetics, Department of Genetics and Biochemistry, Clemson University, Greenwood, SC, USA
| | - Zhana Duren
- Center for Human Genetics, Department of Genetics and Biochemistry, Clemson University, Greenwood, SC, USA.
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18
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Aracena KA, Lin YL, Luo K, Pacis A, Gona S, Mu Z, Yotova V, Sindeaux R, Pramatarova A, Simon MM, Chen X, Groza C, Lougheed D, Gregoire R, Brownlee D, Boye C, Pique-Regi R, Li Y, He X, Bujold D, Pastinen T, Bourque G, Barreiro LB. Epigenetic variation impacts individual differences in the transcriptional response to influenza infection. Nat Genet 2024; 56:408-419. [PMID: 38424460 DOI: 10.1038/s41588-024-01668-z] [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: 05/27/2023] [Accepted: 01/16/2024] [Indexed: 03/02/2024]
Abstract
Humans display remarkable interindividual variation in their immune response to identical challenges. Yet, our understanding of the genetic and epigenetic factors contributing to such variation remains limited. Here we performed in-depth genetic, epigenetic and transcriptional profiling on primary macrophages derived from individuals of European and African ancestry before and after infection with influenza A virus. We show that baseline epigenetic profiles are strongly predictive of the transcriptional response to influenza A virus across individuals. Quantitative trait locus (QTL) mapping revealed highly coordinated genetic effects on gene regulation, with many cis-acting genetic variants impacting concomitantly gene expression and multiple epigenetic marks. These data reveal that ancestry-associated differences in the epigenetic landscape can be genetically controlled, even more than gene expression. Lastly, among QTL variants that colocalized with immune-disease loci, only 7% were gene expression QTL, while the remaining genetic variants impact epigenetic marks, stressing the importance of considering molecular phenotypes beyond gene expression in disease-focused studies.
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Affiliation(s)
| | - Yen-Lung Lin
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Kaixuan Luo
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Alain Pacis
- Canadian Centre for Computational Genomics, McGill University, Montreal, Quebec, Canada
| | - Saideep Gona
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Zepeng Mu
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Vania Yotova
- Department of Genetics, CHU Sainte-Justine Research Center, Montreal, Quebec, Canada
| | - Renata Sindeaux
- Department of Genetics, CHU Sainte-Justine Research Center, Montreal, Quebec, Canada
| | | | | | - Xun Chen
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
| | - Cristian Groza
- Quantitative Life Sciences, McGill University, Montreal, Quebec, Canada
| | - David Lougheed
- Canadian Centre for Computational Genomics, McGill University, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Romain Gregoire
- Canadian Centre for Computational Genomics, McGill University, Montreal, Quebec, Canada
| | - David Brownlee
- Canadian Centre for Computational Genomics, McGill University, Montreal, Quebec, Canada
| | - Carly Boye
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, USA
| | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, USA
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, USA
| | - Yang Li
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Xin He
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - David Bujold
- Canadian Centre for Computational Genomics, McGill University, Montreal, Quebec, Canada
- McGill Genome Centre, Montreal, Quebec, Canada
| | - Tomi Pastinen
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Genomic Medicine Center, Children's Mercy, Kansas City, MO, USA
| | - Guillaume Bourque
- Canadian Centre for Computational Genomics, McGill University, Montreal, Quebec, Canada.
- McGill Genome Centre, Montreal, Quebec, Canada.
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan.
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.
| | - Luis B Barreiro
- Department of Human Genetics, University of Chicago, Chicago, IL, USA.
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA.
- Committee on Immunology, University of Chicago, Chicago, IL, USA.
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19
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Fadahunsi N, Petersen J, Metz S, Jakobsen A, Vad Mathiesen C, Silke Buch-Rasmussen A, Kurgan N, Kjærgaard Larsen J, Andersen RC, Topilko T, Svendsen C, Apuschkin M, Skovbjerg G, Hendrik Schmidt J, Houser G, Elgaard Jager S, Bach A, Deshmukh AS, Kilpeläinen TO, Strømgaard K, Madsen KL, Clemmensen C. Targeting postsynaptic glutamate receptor scaffolding proteins PSD-95 and PICK1 for obesity treatment. SCIENCE ADVANCES 2024; 10:eadg2636. [PMID: 38427737 PMCID: PMC10906926 DOI: 10.1126/sciadv.adg2636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 01/29/2024] [Indexed: 03/03/2024]
Abstract
Human genome-wide association studies (GWAS) suggest a functional role for central glutamate receptor signaling and plasticity in body weight regulation. Here, we use UK Biobank GWAS summary statistics of body mass index (BMI) and body fat percentage (BF%) to identify genes encoding proteins known to interact with postsynaptic α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) and N-methyl-d-aspartate (NMDA) receptors. Loci in/near discs large homolog 4 (DLG4) and protein interacting with C kinase 1 (PICK1) reached genome-wide significance (P < 5 × 10-8) for BF% and/or BMI. To further evaluate the functional role of postsynaptic density protein-95 (PSD-95; gene name: DLG4) and PICK1 in energy homeostasis, we used dimeric PSD-95/disc large/ZO-1 (PDZ) domain-targeting peptides of PSD-95 and PICK1 to demonstrate that pharmacological inhibition of PSD-95 and PICK1 induces prolonged weight-lowering effects in obese mice. Collectively, these data demonstrate that the glutamate receptor scaffolding proteins, PICK1 and PSD-95, are genetically linked to obesity and that pharmacological targeting of their PDZ domains represents a promising therapeutic avenue for sustained weight loss.
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Affiliation(s)
- Nicole Fadahunsi
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Jonas Petersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Sophia Metz
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Alexander Jakobsen
- Molecular Neuropharmacology and Genetics Laboratory, Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Cecilie Vad Mathiesen
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Alberte Silke Buch-Rasmussen
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
- Global Drug Discovery, Novo Nordisk A/S, Måløv, Denmark
| | - Nigel Kurgan
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Jeppe Kjærgaard Larsen
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Rita C. Andersen
- Molecular Neuropharmacology and Genetics Laboratory, Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Charlotte Svendsen
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Mia Apuschkin
- Molecular Neuropharmacology and Genetics Laboratory, Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Grethe Skovbjerg
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
- Gubra, Hørsholm, Denmark
| | - Jan Hendrik Schmidt
- Molecular Neuropharmacology and Genetics Laboratory, Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Grace Houser
- Molecular Neuropharmacology and Genetics Laboratory, Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sara Elgaard Jager
- Molecular Neuropharmacology and Genetics Laboratory, Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Anders Bach
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Atul S. Deshmukh
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Tuomas O. Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Kristian Strømgaard
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Kenneth L. Madsen
- Molecular Neuropharmacology and Genetics Laboratory, Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Christoffer Clemmensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
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20
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O'Brien CL, Summers KM, Martin NM, Carter-Cusack D, Yang Y, Barua R, Dixit OVA, Hume DA, Pavli P. The relationship between extreme inter-individual variation in macrophage gene expression and genetic susceptibility to inflammatory bowel disease. Hum Genet 2024; 143:233-261. [PMID: 38421405 PMCID: PMC11043138 DOI: 10.1007/s00439-024-02642-9] [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: 08/25/2023] [Accepted: 01/14/2024] [Indexed: 03/02/2024]
Abstract
The differentiation of resident intestinal macrophages from blood monocytes depends upon signals from the macrophage colony-stimulating factor receptor (CSF1R). Analysis of genome-wide association studies (GWAS) indicates that dysregulation of macrophage differentiation and response to microorganisms contributes to susceptibility to chronic inflammatory bowel disease (IBD). Here, we analyzed transcriptomic variation in monocyte-derived macrophages (MDM) from affected and unaffected sib pairs/trios from 22 IBD families and 6 healthy controls. Transcriptional network analysis of the data revealed no overall or inter-sib distinction between affected and unaffected individuals in basal gene expression or the temporal response to lipopolysaccharide (LPS). However, the basal or LPS-inducible expression of individual genes varied independently by as much as 100-fold between subjects. Extreme independent variation in the expression of pairs of HLA-associated transcripts (HLA-B/C, HLA-A/F and HLA-DRB1/DRB5) in macrophages was associated with HLA genotype. Correlation analysis indicated the downstream impacts of variation in the immediate early response to LPS. For example, variation in early expression of IL1B was significantly associated with local SNV genotype and with subsequent peak expression of target genes including IL23A, CXCL1, CXCL3, CXCL8 and NLRP3. Similarly, variation in early IFNB1 expression was correlated with subsequent expression of IFN target genes. Our results support the view that gene-specific dysregulation in macrophage adaptation to the intestinal milieu is associated with genetic susceptibility to IBD.
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Affiliation(s)
- Claire L O'Brien
- Centre for Research in Therapeutics Solutions, Faculty of Science and Technology, University of Canberra, Canberra, ACT, Australia
- Inflammatory Bowel Disease Research Group, Canberra Hospital, Canberra, ACT, Australia
| | - Kim M Summers
- Mater Research Institute-University of Queensland, Translational Research Institute, Brisbane, QLD, Australia
| | - Natalia M Martin
- Inflammatory Bowel Disease Research Group, Canberra Hospital, Canberra, ACT, Australia
| | - Dylan Carter-Cusack
- Mater Research Institute-University of Queensland, Translational Research Institute, Brisbane, QLD, Australia
| | - Yuanhao Yang
- Mater Research Institute-University of Queensland, Translational Research Institute, Brisbane, QLD, Australia
| | - Rasel Barua
- Inflammatory Bowel Disease Research Group, Canberra Hospital, Canberra, ACT, Australia
| | - Ojas V A Dixit
- Centre for Research in Therapeutics Solutions, Faculty of Science and Technology, University of Canberra, Canberra, ACT, Australia
| | - David A Hume
- Mater Research Institute-University of Queensland, Translational Research Institute, Brisbane, QLD, Australia.
| | - Paul Pavli
- Inflammatory Bowel Disease Research Group, Canberra Hospital, Canberra, ACT, Australia.
- School of Medicine and Psychology, College of Health and Medicine, Australian National University, Canberra, ACT, Australia.
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21
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Zhang Z, Wang S, Jiang L, Wei J, Lu C, Li S, Diao Y, Fang Z, He S, Tan T, Yang Y, Zou K, Shi J, Lin J, Chen L, Bao C, Fei J, Fang H. Priority index for critical Covid-19 identifies clinically actionable targets and drugs. Commun Biol 2024; 7:189. [PMID: 38366110 PMCID: PMC10873402 DOI: 10.1038/s42003-024-05897-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 02/07/2024] [Indexed: 02/18/2024] Open
Abstract
While genome-wide studies have identified genomic loci in hosts associated with life-threatening Covid-19 (critical Covid-19), the challenge of resolving these loci hinders further identification of clinically actionable targets and drugs. Building upon our previous success, we here present a priority index solution designed to address this challenge, generating the target and drug resource that consists of two indexes: the target index and the drug index. The primary purpose of the target index is to identify clinically actionable targets by prioritising genes associated with Covid-19. We illustrate the validity of the target index by demonstrating its ability to identify pre-existing Covid-19 phase-III drug targets, with the majority of these targets being found at the leading prioritisation (leading targets). These leading targets have their evolutionary origins in Amniota ('four-leg vertebrates') and are predominantly involved in cytokine-cytokine receptor interactions and JAK-STAT signaling. The drug index highlights opportunities for repurposing clinically approved JAK-STAT inhibitors, either individually or in combination. This proposed strategic focus on the JAK-STAT pathway is supported by the active pursuit of therapeutic agents targeting this pathway in ongoing phase-II/III clinical trials for Covid-19.
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Affiliation(s)
- Zhiqiang Zhang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Shan Wang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Lulu Jiang
- Translational Health Sciences, University of Bristol, Bristol, BS1 3NY, UK
| | - Jianwen Wei
- Network and Information Center, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Chang Lu
- MRC London Institute of Medical Sciences, Imperial College London, London, W12 0HS, UK
| | - Shengli Li
- Precision Research Center for Refractory Diseases, Institute for Clinical Research, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201620, China
| | - Yizhu Diao
- College of Finance and Statistics, Hunan University, Changsha, 410079, Hunan, China
| | - Zhongcheng Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Shuo He
- College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Tingting Tan
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yisheng Yang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Kexin Zou
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Jiantao Shi
- Key Laboratory of RNA Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, 200031, China
| | - James Lin
- Network and Information Center, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Liye Chen
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
| | - Chaohui Bao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- Department of General Surgery, Ruijin Hospital Luwan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, 200020, China.
| | - Jian Fei
- Department of General Surgery, Ruijin Hospital Luwan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, 200020, China.
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Hai Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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22
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Boye C, Kalita CA, Findley AS, Alazizi A, Wei J, Wen X, Pique-Regi R, Luca F. Characterization of caffeine response regulatory variants in vascular endothelial cells. eLife 2024; 13:e85235. [PMID: 38334359 PMCID: PMC10901511 DOI: 10.7554/elife.85235] [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: 11/30/2022] [Accepted: 02/08/2024] [Indexed: 02/10/2024] Open
Abstract
Genetic variants in gene regulatory sequences can modify gene expression and mediate the molecular response to environmental stimuli. In addition, genotype-environment interactions (GxE) contribute to complex traits such as cardiovascular disease. Caffeine is the most widely consumed stimulant and is known to produce a vascular response. To investigate GxE for caffeine, we treated vascular endothelial cells with caffeine and used a massively parallel reporter assay to measure allelic effects on gene regulation for over 43,000 genetic variants. We identified 665 variants with allelic effects on gene regulation and 6 variants that regulate the gene expression response to caffeine (GxE, false discovery rate [FDR] < 5%). When overlapping our GxE results with expression quantitative trait loci colocalized with coronary artery disease and hypertension, we dissected their regulatory mechanisms and showed a modulatory role for caffeine. Our results demonstrate that massively parallel reporter assay is a powerful approach to identify and molecularly characterize GxE in the specific context of caffeine consumption.
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Affiliation(s)
- Carly Boye
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Cynthia A Kalita
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Anthony S Findley
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Adnan Alazizi
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Julong Wei
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Xiaoquan Wen
- Department of Biostatistics, University of MichiganAnn ArborUnited States
| | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
- Department of Obstetrics and Gynecology, Wayne State UniversityDetroitUnited States
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
- Department of Obstetrics and Gynecology, Wayne State UniversityDetroitUnited States
- Department of Biology, University of Rome Tor VergataRomeItaly
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23
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Müller S, Kröger C, Schultze JL, Aschenbrenner AC. Whole blood stimulation as a tool for studying the human immune system. Eur J Immunol 2024; 54:e2350519. [PMID: 38103010 DOI: 10.1002/eji.202350519] [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: 06/01/2023] [Revised: 12/05/2023] [Accepted: 12/07/2023] [Indexed: 12/17/2023]
Abstract
The human immune system is best accessible via tissues and organs not requiring major surgical intervention, such as blood. In many circumstances, circulating immune cells correlate with an individual's health state and give insight into physiological and pathophysiological processes. Stimulating whole blood ex vivo is a powerful tool to investigate immune responses. In the context of clinical research, the applications of whole blood stimulation include host immunity, disease characterization, diagnosis, treatment, and drug development. Here, we summarize different setups and readouts of whole blood assays and discuss applications for preclinical research and clinical practice. Finally, we propose combining whole blood stimulation with high-throughput technologies, such as single-cell RNA-sequencing, to comprehensively analyze the human immune system for the identification of biomarkers, therapeutic interventions as well as companion diagnostics.
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Affiliation(s)
- Sophie Müller
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V., Bonn, Germany
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
- Genomics & Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Charlotte Kröger
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V., Bonn, Germany
- Genomics & Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Joachim L Schultze
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V., Bonn, Germany
- Genomics & Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
- PRECISE Platform for Single Cell Genomics and Epigenomics, DZNE and University of Bonn, Bonn, Germany
| | - Anna C Aschenbrenner
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V., Bonn, Germany
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24
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Chong AY, Brenner N, Jimenez-Kaufmann A, Cortes A, Hill M, Littlejohns TJ, Gilchrist JJ, Fairfax BP, Knight JC, Hodel F, Fellay J, McVean G, Moreno-Estrada A, Waterboer T, Hill AVS, Mentzer AJ. A common NFKB1 variant detected through antibody analysis in UK Biobank predicts risk of infection and allergy. Am J Hum Genet 2024; 111:295-308. [PMID: 38232728 PMCID: PMC10870136 DOI: 10.1016/j.ajhg.2023.12.013] [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] [Revised: 12/07/2023] [Accepted: 12/10/2023] [Indexed: 01/19/2024] Open
Abstract
Infectious agents contribute significantly to the global burden of diseases through both acute infection and their chronic sequelae. We leveraged the UK Biobank to identify genetic loci that influence humoral immune response to multiple infections. From 45 genome-wide association studies in 9,611 participants from UK Biobank, we identified NFKB1 as a locus associated with quantitative antibody responses to multiple pathogens, including those from the herpes, retro-, and polyoma-virus families. An insertion-deletion variant thought to affect NFKB1 expression (rs28362491), was mapped as the likely causal variant and could play a key role in regulation of the immune response. Using 121 infection- and inflammation-related traits in 487,297 UK Biobank participants, we show that the deletion allele was associated with an increased risk of infection from diverse pathogens but had a protective effect against allergic disease. We propose that altered expression of NFKB1, as a result of the deletion, modulates hematopoietic pathways and likely impacts cell survival, antibody production, and inflammation. Taken together, we show that disruptions to the tightly regulated immune processes may tip the balance between exacerbated immune responses and allergy, or increased risk of infection and impaired resolution of inflammation.
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Affiliation(s)
- Amanda Y Chong
- The Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
| | - Nicole Brenner
- Division of Infections and Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Andres Jimenez-Kaufmann
- Advanced Genomics Unit, National Laboratory of Genomics for Biodiversity (LANGEBIO), CINVESTAV, Irapuato, Mexico
| | - Adrian Cortes
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Michael Hill
- MRC-Population Health Research Unit, University of Oxford, Oxford, UK
| | | | - James J Gilchrist
- The Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK; Department of Paediatrics, University of Oxford, Oxford, UK
| | | | - Julian C Knight
- The Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Flavia Hodel
- Global Health Institute, School of Life Sciences, EPFL, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Jacques Fellay
- Global Health Institute, School of Life Sciences, EPFL, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland; Precision Medicine Unit, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Gil McVean
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Andres Moreno-Estrada
- Advanced Genomics Unit, National Laboratory of Genomics for Biodiversity (LANGEBIO), CINVESTAV, Irapuato, Mexico
| | - Tim Waterboer
- Division of Infections and Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Adrian V S Hill
- The Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK; The Jenner Institute, University of Oxford, Oxford, UK
| | - Alexander J Mentzer
- The Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
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25
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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: 7] [Impact Index Per Article: 7.0] [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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26
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Vochteloo M, Deelen P, Vink B, Tsai EA, Runz H, Andreu-Sánchez S, Fu J, Zhernakova A, Westra HJ, Franke L. PICALO: principal interaction component analysis for the identification of discrete technical, cell-type, and environmental factors that mediate eQTLs. Genome Biol 2024; 25:29. [PMID: 38254182 PMCID: PMC10802033 DOI: 10.1186/s13059-023-03151-0] [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: 12/22/2022] [Accepted: 12/20/2023] [Indexed: 01/24/2024] Open
Abstract
Expression quantitative trait loci (eQTL) offer insights into the regulatory mechanisms of trait-associated variants, but their effects often rely on contexts that are unknown or unmeasured. We introduce PICALO, a method for hidden variable inference of eQTL contexts. PICALO identifies and disentangles technical from biological context in heterogeneous blood and brain bulk eQTL datasets. These contexts are biologically informative and reproducible, outperforming cell counts or expression-based principal components. Furthermore, we show that RNA quality and cell type proportions interact with thousands of eQTLs. Knowledge of hidden eQTL contexts may aid in the inference of functional mechanisms underlying disease variants.
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Affiliation(s)
- Martijn Vochteloo
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Patrick Deelen
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Britt Vink
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Institute for Life Science & Technology, Hanze University of Applied Sciences, Groningen, The Netherlands
| | - Ellen A Tsai
- Translational Sciences, Research and Development, Biogen, Cambridge, MA, USA
| | - Heiko Runz
- Translational Sciences, Research and Development, Biogen, Cambridge, MA, USA
| | - Sergio Andreu-Sánchez
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Pediatrics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Jingyuan Fu
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Pediatrics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Alexandra Zhernakova
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Harm-Jan Westra
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
- Oncode Institute, Utrecht, The Netherlands.
| | - Lude Franke
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
- Oncode Institute, Utrecht, The Netherlands.
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27
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Ma Y, Bendl J, Hartley BJ, Fullard JF, Abdelaal R, Ho SM, Kosoy R, Gochman P, Rapoport J, Hoffman GE, Brennand KJ, Roussos P. Activity-Dependent Transcriptional Program in NGN2+ Neurons Enriched for Genetic Risk for Brain-Related Disorders. Biol Psychiatry 2024; 95:187-198. [PMID: 37454787 PMCID: PMC10787819 DOI: 10.1016/j.biopsych.2023.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 06/07/2023] [Accepted: 07/09/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND Converging evidence from large-scale genetic and postmortem studies highlights the role of aberrant neurotransmission and genetic regulation in brain-related disorders. However, identifying neuronal activity-regulated transcriptional programs in the human brain and understanding how changes contribute to disease remain challenging. METHODS To better understand how the activity-dependent regulome contributes to risk for brain-related disorders, we profiled the transcriptomic and epigenomic changes following neuronal depolarization in human induced pluripotent stem cell-derived glutamatergic neurons (NGN2) from 6 patients with schizophrenia and 5 control participants. RESULTS Multiomic data integration associated global patterns of chromatin accessibility with gene expression and identified enhancer-promoter interactions in glutamatergic neurons. Within 1 hour of potassium chloride-induced depolarization, independent of diagnosis, glutamatergic neurons displayed substantial activity-dependent changes in the expression of genes regulating synaptic function. Depolarization-induced changes in the regulome revealed significant heritability enrichment for schizophrenia and Parkinson's disease, adding to mounting evidence that sequence variation within activation-dependent regulatory elements contributes to the genetic risk for brain-related disorders. Gene coexpression network analysis elucidated interactions among activity-dependent and disease-associated genes and pointed to a key driver (NAV3) that interacted with multiple genes involved in axon guidance. CONCLUSIONS Overall, we demonstrated that deciphering the activity-dependent regulome in glutamatergic neurons reveals novel targets for advanced diagnosis and therapy.
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Affiliation(s)
- Yixuan Ma
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, New York; Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, New York; Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, New York; Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Brigham J Hartley
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York; Black Family Stem Cell Institute, New York, New York
| | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, New York; Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Rawan Abdelaal
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York; Black Family Stem Cell Institute, New York, New York
| | - Seok-Man Ho
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York; Black Family Stem Cell Institute, New York, New York
| | - Roman Kosoy
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, New York; Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Peter Gochman
- Childhood Psychiatry Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Judith Rapoport
- Childhood Psychiatry Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Kristen J Brennand
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, New York; Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York; Black Family Stem Cell Institute, New York, New York.
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, New York; Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, New York; Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York; Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, New York.
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28
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Johnson AD. Cell perturbation and lasers illuminate the genetics of latent blood cell traits. Nat Genet 2024; 56:16-18. [PMID: 38135721 DOI: 10.1038/s41588-023-01623-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2023]
Affiliation(s)
- Andrew D Johnson
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham, MA, USA.
- The Framingham Heart Study, Framingham, MA, USA.
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29
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Frkatović-Hodžić A, Mijakovac A, Miškec K, Nostaeva A, Sharapov SZ, Landini A, Haller T, van den Akker E, Sharma S, Cuadrat RRC, Mangino M, Li Y, Keser T, Rudman N, Štambuk T, Pučić-Baković M, Trbojević-Akmačić I, Gudelj I, Štambuk J, Pribić T, Radovani B, Tominac P, Fischer K, Beekman M, Wuhrer M, Gieger C, Schulze MB, Wittenbecher C, Polasek O, Hayward C, Wilson JF, Spector TD, Köttgen A, Vučković F, Aulchenko YS, Vojta A, Krištić J, Klarić L, Zoldoš V, Lauc G. Mapping of the gene network that regulates glycan clock of ageing. Aging (Albany NY) 2023; 15:14509-14552. [PMID: 38149987 PMCID: PMC10781487 DOI: 10.18632/aging.205106] [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: 05/12/2023] [Accepted: 09/06/2023] [Indexed: 12/28/2023]
Abstract
Glycans are an essential structural component of immunoglobulin G (IgG) that modulate its structure and function. However, regulatory mechanisms behind this complex posttranslational modification are not well known. Previous genome-wide association studies (GWAS) identified 29 genomic regions involved in regulation of IgG glycosylation, but only a few were functionally validated. One of the key functional features of IgG glycosylation is the addition of galactose (galactosylation), a trait which was shown to be associated with ageing. We performed GWAS of IgG galactosylation (N=13,705) and identified 16 significantly associated loci, indicating that IgG galactosylation is regulated by a complex network of genes that extends beyond the galactosyltransferase enzyme that adds galactose to IgG glycans. Gene prioritization identified 37 candidate genes. Using a recently developed CRISPR/dCas9 system we manipulated gene expression of candidate genes in the in vitro IgG expression system. Upregulation of three genes, EEF1A1, MANBA and TNFRSF13B, changed the IgG glycome composition, which confirmed that these three genes are involved in IgG galactosylation in this in vitro expression system.
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Affiliation(s)
| | - Anika Mijakovac
- Department of Biology, Division of Molecular Biology, Faculty of Science, University of Zagreb, Zagreb, Croatia
| | - Karlo Miškec
- Department of Biology, Division of Molecular Biology, Faculty of Science, University of Zagreb, Zagreb, Croatia
| | - Arina Nostaeva
- Laboratory of Theoretical and Applied Functional Genomics, Novosibirsk State University, Novosibirsk, Russia
| | - Sodbo Z. Sharapov
- MSU Institute for Artificial Intelligence, Lomonosov Moscow State University, Moscow, Russia
| | - Arianna Landini
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Toomas Haller
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Erik van den Akker
- Department of Biomedical Data Sciences, Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, The Netherlands
| | - Sapna Sharma
- Research Unit Molecular Endocrinology and Metabolism, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Rafael R. C. Cuadrat
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München –Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Munich, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
- NIHR Biomedical Research Centre at Guy’s and St Thomas’ Foundation Trust, London, UK
| | - Yong Li
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Toma Keser
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
| | - Najda Rudman
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
| | | | | | | | - Ivan Gudelj
- Genos Glycoscience Research Laboratory, Zagreb, Croatia
- Department of Biotechnology, University of Rijeka, Rijeka, Croatia
| | - Jerko Štambuk
- Genos Glycoscience Research Laboratory, Zagreb, Croatia
| | - Tea Pribić
- Genos Glycoscience Research Laboratory, Zagreb, Croatia
| | - Barbara Radovani
- Genos Glycoscience Research Laboratory, Zagreb, Croatia
- Department of Biotechnology, University of Rijeka, Rijeka, Croatia
| | - Petra Tominac
- Genos Glycoscience Research Laboratory, Zagreb, Croatia
| | - Krista Fischer
- Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
| | - Marian Beekman
- Department of Biomedical Data Sciences, Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Manfred Wuhrer
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München –Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Munich, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Matthias B. Schulze
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany
| | - Clemens Wittenbecher
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- SciLifeLab, Division of Food and Nutrition Science, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Ozren Polasek
- University of Split School of Medicine, Split, Croatia
- Algebra University College, Zagreb, Croatia
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - James F. Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Tim D. Spector
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | | | - Yurii S. Aulchenko
- MSU Institute for Artificial Intelligence, Lomonosov Moscow State University, Moscow, Russia
- Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
| | - Aleksandar Vojta
- Department of Biology, Division of Molecular Biology, Faculty of Science, University of Zagreb, Zagreb, Croatia
| | | | - Lucija Klarić
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Vlatka Zoldoš
- Department of Biology, Division of Molecular Biology, Faculty of Science, University of Zagreb, Zagreb, Croatia
| | - Gordan Lauc
- Genos Glycoscience Research Laboratory, Zagreb, Croatia
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
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30
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Li L, Ma X, Cui Y, Rotival M, Chen W, Zou X, Ding R, Qin Y, Wang Q, Quintana-Murci L, Li W. Immune-response 3'UTR alternative polyadenylation quantitative trait loci contribute to variation in human complex traits and diseases. Nat Commun 2023; 14:8347. [PMID: 38102153 PMCID: PMC10724249 DOI: 10.1038/s41467-023-44191-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 12/04/2023] [Indexed: 12/17/2023] Open
Abstract
Genome-wide association studies (GWASs) have identified thousands of non-coding variants that are associated with human complex traits and diseases. The analysis of such GWAS variants in different contexts and physiological states is essential for deciphering the regulatory mechanisms underlying human disease. Alternative polyadenylation (APA) is a key post-transcriptional modification for most human genes that substantially impacts upon cell behavior. Here, we mapped 9,493 3'-untranslated region APA quantitative trait loci in 18 human immune baseline cell types and 8 stimulation conditions (immune 3'aQTLs). Through the comparison between baseline and stimulation data, we observed the high responsiveness of 3'aQTLs to immune stimulation (response 3'aQTLs). Co-localization and mendelian randomization analyses of immune 3'aQTLs identified 678 genes where 3'aQTL are associated with variation in complex traits, 27.3% of which were derived from response 3'aQTLs. Overall, these analyses reveal the role of immune 3'aQTLs in the determination of complex traits, providing new insights into the regulatory mechanisms underlying disease etiologies.
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Affiliation(s)
- Lei Li
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China.
| | - Xuelian Ma
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Ya Cui
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, CA, 92697, USA
| | - Maxime Rotival
- Institut Pasteur, Université de Paris, CNRS UMR2000, Human Evolutionary Genetics Unit, F-75015, Paris, France
| | - Wenyan Chen
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Xudong Zou
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Ruofan Ding
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Yangmei Qin
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Qixuan Wang
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Lluis Quintana-Murci
- Institut Pasteur, Université de Paris, CNRS UMR2000, Human Evolutionary Genetics Unit, F-75015, Paris, France
- Human Genomics and Evolution, Collège de France, F-75005, Paris, France
| | - Wei Li
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, CA, 92697, USA.
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31
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Hernangomez-Laderas A, Cilleros-Portet A, Martínez Velasco S, Marí S, Legarda M, González-García BP, Tutau C, García-Santisteban I, Irastorza I, Fernandez-Jimenez N, Bilbao JR. Sex bias in celiac disease: XWAS and monocyte eQTLs in women identify TMEM187 as a functional candidate gene. Biol Sex Differ 2023; 14:86. [PMID: 38072919 PMCID: PMC10712119 DOI: 10.1186/s13293-023-00572-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Celiac disease (CeD) is an immune-mediated disorder that develops in genetically predisposed individuals upon gluten consumption. HLA risk alleles explain 40% of the genetic component of CeD, so there have been continuing efforts to uncover non-HLA loci that can explain the remaining heritability. As in most autoimmune disorders, the prevalence of CeD is significantly higher in women. Here, we investigated the possible involvement of the X chromosome on the sex bias of CeD. METHODS We performed a X chromosome-wide association study (XWAS) and a gene-based association study in women from the CeD Immunochip (7062 cases, 5446 controls). We also constructed a database of X chromosome cis-expression quantitative trait loci (eQTLs) in monocytes from unstimulated (n = 226) and lipopolysaccharide (LPS)-stimulated (n = 130) female donors and performed a Summary-data-based MR (SMR) analysis to integrate XWAS and eQTL information. We interrogated the expression of the potentially causal gene (TMEM187) in peripheral blood mononuclear cells (PBMCs) from celiac patients at onset, on a gluten-free diet, potential celiac patients and non-celiac controls. RESULTS The XWAS and gene-based analyses identified 13 SNPs and 25 genes, respectively, 22 of which had not been previously associated with CeD. The X chromosome cis-eQTL analysis found 18 genes with at least one cis-eQTL in naïve female monocytes and 8 genes in LPS-stimulated female monocytes, 2 of which were common to both situations and 6 were unique to LPS stimulation. SMR identified a potentially causal association of TMEM187 expression in naïve monocytes with CeD in women, regulated by CeD-associated, eQTL-SNPs rs7350355 and rs5945386. The CeD-risk alleles were correlated with lower TMEM187 expression. These results were replicated using eQTLs from LPS-stimulated monocytes. We observed higher levels of TMEM187 expression in PBMCs from female CeD patients at onset compared to female non-celiac controls, but not in male CeD individuals. CONCLUSION Using X chromosome genotypes and gene expression data from female monocytes, SMR has identified TMEM187 as a potentially causal candidate in CeD. Further studies are needed to understand the implication of the X chromosome in the higher prevalence of CeD in women.
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Affiliation(s)
- Alba Hernangomez-Laderas
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain
- Biobizkaia Health Research Institute, Barakaldo, Basque Country, Spain
| | - Ariadna Cilleros-Portet
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain
- Biobizkaia Health Research Institute, Barakaldo, Basque Country, Spain
| | - Silvia Martínez Velasco
- Biobizkaia Health Research Institute, Barakaldo, Basque Country, Spain
- Pediatric Gastroenterology Unit, Cruces University Hospital, Barakaldo, Basque Country, Spain
| | - Sergi Marí
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain
- Biobizkaia Health Research Institute, Barakaldo, Basque Country, Spain
| | - María Legarda
- Biobizkaia Health Research Institute, Barakaldo, Basque Country, Spain
- Pediatric Gastroenterology Unit, Cruces University Hospital, Barakaldo, Basque Country, Spain
| | - Bárbara Paola González-García
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain
- Biobizkaia Health Research Institute, Barakaldo, Basque Country, Spain
| | - Carlos Tutau
- Biobizkaia Health Research Institute, Barakaldo, Basque Country, Spain
- Pediatric Gastroenterology Unit, Cruces University Hospital, Barakaldo, Basque Country, Spain
| | - Iraia García-Santisteban
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain
- Biobizkaia Health Research Institute, Barakaldo, Basque Country, Spain
| | - Iñaki Irastorza
- Biobizkaia Health Research Institute, Barakaldo, Basque Country, Spain
- Pediatric Gastroenterology Unit, Cruces University Hospital, Barakaldo, Basque Country, Spain
| | - Nora Fernandez-Jimenez
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain.
- Biobizkaia Health Research Institute, Barakaldo, Basque Country, Spain.
| | - Jose Ramon Bilbao
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain.
- Biobizkaia Health Research Institute, Barakaldo, Basque Country, Spain.
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain.
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32
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Wang Q, Martínez-Bonet M, Kim T, Sparks JA, Ishigaki K, Chen X, Sudman M, Aguiar V, Sim S, Hernandez MC, Chiu DJ, Wactor A, Wauford B, Marion MC, Gutierrez-Arcelus M, Bowes J, Eyre S, Nordal E, Prahalad S, Rygg M, Videm V, Raychaudhuri S, Weirauch MT, Langefeld CD, Thompson SD, Nigrovic PA. Identification of a regulatory pathway governing TRAF1 via an arthritis-associated non-coding variant. CELL GENOMICS 2023; 3:100420. [PMID: 38020975 PMCID: PMC10667332 DOI: 10.1016/j.xgen.2023.100420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 06/16/2023] [Accepted: 09/11/2023] [Indexed: 12/01/2023]
Abstract
TRAF1/C5 was among the first loci shown to confer risk for inflammatory arthritis in the absence of an associated coding variant, but its genetic mechanism remains undefined. Using Immunochip data from 3,939 patients with juvenile idiopathic arthritis (JIA) and 14,412 control individuals, we identified 132 plausible common non-coding variants, reduced serially by single-nucleotide polymorphism sequencing (SNP-seq), electrophoretic mobility shift, and luciferase studies to the single variant rs7034653 in the third intron of TRAF1. Genetically manipulated experimental cells and primary monocytes from genotyped donors establish that the risk G allele reduces binding of Fos-related antigen 2 (FRA2), encoded by FOSL2, resulting in reduced TRAF1 expression and enhanced tumor necrosis factor (TNF) production. Conditioning on this JIA variant eliminated attributable risk for rheumatoid arthritis, implicating a mechanism shared across the arthritis spectrum. These findings reveal that rs7034653, FRA2, and TRAF1 mediate a pathway through which a non-coding functional variant drives risk of inflammatory arthritis in children and adults.
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Affiliation(s)
- Qiang Wang
- Division of Immunology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Marta Martínez-Bonet
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Laboratory of Immune-regulation, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - Taehyeung Kim
- Division of Immunology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Jeffrey A. Sparks
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Kazuyoshi Ishigaki
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Xiaoting Chen
- Center of Autoimmune Genomics and Etiology, Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - Marc Sudman
- Center of Autoimmune Genomics and Etiology, Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - Vitor Aguiar
- Division of Immunology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Sangwan Sim
- Division of Immunology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Darren J. Chiu
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexandra Wactor
- Division of Immunology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Brian Wauford
- Division of Immunology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Miranda C. Marion
- Department of Biostatistics and Data Science, and Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Maria Gutierrez-Arcelus
- Division of Immunology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
- Center of Autoimmune Genomics and Etiology, Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - John Bowes
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Oxford Road, Manchester, UK
- NIHR Manchester Musculoskeletal Biomedical Research Unit, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Stephen Eyre
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Oxford Road, Manchester, UK
- NIHR Manchester Musculoskeletal Biomedical Research Unit, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Ellen Nordal
- University Hospital of North Norway and UIT The Arctic University of Norway, Tromsø, Norway
| | - Sampath Prahalad
- Emory University Department of Pediatrics and Children’s Healthcare of Atlanta, Atlanta, GA, USA
| | - Marite Rygg
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of Pediatrics, St. Olav’s University Hospital, Trondheim, Norway
| | - Vibeke Videm
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Soumya Raychaudhuri
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Oxford Road, Manchester, UK
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Center for Data Science, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Matthew T. Weirauch
- Center of Autoimmune Genomics and Etiology, Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Divisions of Human Genetics, Biomedical Informatics, and Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Carl D. Langefeld
- Department of Biostatistics and Data Science, and Center for Precision Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Susan D. Thompson
- Center of Autoimmune Genomics and Etiology, Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Peter A. Nigrovic
- Division of Immunology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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33
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Mostafavi H, Spence JP, Naqvi S, Pritchard JK. Systematic differences in discovery of genetic effects on gene expression and complex traits. Nat Genet 2023; 55:1866-1875. [PMID: 37857933 DOI: 10.1038/s41588-023-01529-1] [Citation(s) in RCA: 58] [Impact Index Per Article: 58.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 09/14/2023] [Indexed: 10/21/2023]
Abstract
Most signals in genome-wide association studies (GWAS) of complex traits implicate noncoding genetic variants with putative gene regulatory effects. However, currently identified regulatory variants, notably expression quantitative trait loci (eQTLs), explain only a small fraction of GWAS signals. Here, we show that GWAS and cis-eQTL hits are systematically different: eQTLs cluster strongly near transcription start sites, whereas GWAS hits do not. Genes near GWAS hits are enriched in key functional annotations, are under strong selective constraint and have complex regulatory landscapes across different tissue/cell types, whereas genes near eQTLs are depleted of most functional annotations, show relaxed constraint, and have simpler regulatory landscapes. We describe a model to understand these observations, including how natural selection on complex traits hinders discovery of functionally relevant eQTLs. Our results imply that GWAS and eQTL studies are systematically biased toward different types of variant, and support the use of complementary functional approaches alongside the next generation of eQTL studies.
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Affiliation(s)
| | | | - Sahin Naqvi
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA
| | - Jonathan K Pritchard
- Department of Genetics, Stanford University, Stanford, CA, USA.
- Department of Biology, Stanford University, Stanford, CA, USA.
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34
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Snijders GJLJ, de Paiva Lopes K, Sneeboer MAM, Muller BZ, Gigase FAJ, Vialle RA, Missall R, Kubler R, Raj T, Humphrey J, de Witte LD. The human microglia responsome: a resource to better understand microglia states in health and disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.12.562067. [PMID: 37873223 PMCID: PMC10592813 DOI: 10.1101/2023.10.12.562067] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Microglia, the immune cells of the brain, are increasingly implicated in neurodegenerative disorders through genetic studies. However, how genetic risk factors for these diseases are related to microglial gene expression, microglial function, and ultimately disease, is still largely unknown. Microglia change rapidly in response to alterations in their cellular environment, which is regulated through changes in transcriptional programs, which are as yet poorly understood. Here, we compared the effects of a set of inflammatory and restorative stimuli (lipopolysaccharide, interferon-gamma, resiquimod, tumor necrosis factor-alpha, adenosine triphosphate, dexamethasone, and interleukin-4) on human microglial cells from 67 different donors (N = 398 samples) at the gene and transcript level. We show that microglia from different anatomical brain regions show distinct responses to inflammatory stimuli. We observed a greater overlap between human stimulated microglia and human monocytes than with mouse microglia. We define specific microglial signatures across conditions which are highly relevant for a wide range of biological functions and complex human diseases. Finally, we used our stimulation signatures to interpret associations from Alzheimer's disease (AD) genetic studies and microglia by integrating our inflammatory gene expression profiles with common genetic variants to map cis -expression QTLs (eQTLs). Together, we provide the most comprehensive transcriptomic database of the human microglia responsome. Highlights RNA-sequencing of 398 human microglial samples exposed to six different triggers.Microglia from different anatomical regions show distinct stimulation responses.Responses in human microglia show a greater overlap with human monocytes than murine microglia.Mapping of response Quantitative Trait Loci identifies interactions between genotype and effect of stimulation on gene expression.Our atlas provides a reference map for interpreting microglia signatures in health and disease.
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35
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Amarasinghe HE, Zhang P, Whalley JP, Allcock A, Migliorini G, Brown AC, Scozzafava G, Knight JC. Mapping the epigenomic landscape of human monocytes following innate immune activation reveals context-specific mechanisms driving endotoxin tolerance. BMC Genomics 2023; 24:595. [PMID: 37805492 PMCID: PMC10559536 DOI: 10.1186/s12864-023-09663-0] [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: 02/17/2023] [Accepted: 09/08/2023] [Indexed: 10/09/2023] Open
Abstract
BACKGROUND Monocytes are key mediators of innate immunity to infection, undergoing profound and dynamic changes in epigenetic state and immune function which are broadly protective but may be dysregulated in disease. Here, we aimed to advance understanding of epigenetic regulation following innate immune activation, acutely and in endotoxin tolerant states. METHODS We exposed human primary monocytes from healthy donors (n = 6) to interferon-γ or differing combinations of endotoxin (lipopolysaccharide), including acute response (2 h) and two models of endotoxin tolerance: repeated stimulations (6 + 6 h) and prolonged exposure to endotoxin (24 h). Another subset of monocytes was left untreated (naïve). We identified context-specific regulatory elements based on epigenetic signatures for chromatin accessibility (ATAC-seq) and regulatory non-coding RNAs from total RNA sequencing. RESULTS We present an atlas of differential gene expression for endotoxin and interferon response, identifying widespread context specific changes. Across assayed states, only 24-29% of genes showing differential exon usage are also differential at the gene level. Overall, 19.9% (6,884 of 34,616) of repeatedly observed ATAC peaks were differential in at least one condition, the majority upregulated on stimulation and located in distal regions (64.1% vs 45.9% of non-differential peaks) within which sequences were less conserved than non-differential peaks. We identified enhancer-derived RNA signatures specific to different monocyte states that correlated with chromatin accessibility changes. The endotoxin tolerance models showed distinct chromatin accessibility and transcriptomic signatures, with integrated analysis identifying genes and pathways involved in the inflammatory response, detoxification, metabolism and wound healing. We leveraged eQTL mapping for the same monocyte activation states to link potential enhancers with specific genes, identifying 1,946 unique differential ATAC peaks with 1,340 expression associated genes. We further use this to inform understanding of reported GWAS, for example involving FCHO1 and coronary artery disease. CONCLUSION This study reports context-specific regulatory elements based on transcriptomic profiling and epigenetic signatures for enhancer-derived RNAs and chromatin accessibility in immune tolerant monocyte states, and demonstrates the informativeness of linking such elements and eQTL to inform future mechanistic studies aimed at defining therapeutic targets of immunosuppression and diseases.
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Affiliation(s)
- Harindra E Amarasinghe
- Wellcome Centre for Human Genetics, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, OX3 7BN, UK.
| | - Ping Zhang
- Wellcome Centre for Human Genetics, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, OX3 7BN, UK
- Chinese Academy of Medical Science Oxford Institute, University of Oxford, Oxford, OX3 7BN, UK
| | - Justin P Whalley
- Wellcome Centre for Human Genetics, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, OX3 7BN, UK
| | - Alice Allcock
- Wellcome Centre for Human Genetics, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, OX3 7BN, UK
| | - Gabriele Migliorini
- Wellcome Centre for Human Genetics, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, OX3 7BN, UK
| | - Andrew C Brown
- Wellcome Centre for Human Genetics, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, OX3 7BN, UK
| | - Giuseppe Scozzafava
- Wellcome Centre for Human Genetics, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, OX3 7BN, UK
| | - Julian C Knight
- Wellcome Centre for Human Genetics, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, OX3 7BN, UK.
- Chinese Academy of Medical Science Oxford Institute, University of Oxford, Oxford, OX3 7BN, UK.
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36
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Xiao Q, Mears J, Nathan A, Ishigaki K, Baglaenko Y, Lim N, Cooney LA, Harris KM, Anderson MS, Fox DA, Smilek DE, Krueger JG, Raychaudhuri S. Immunosuppression causes dynamic changes in expression QTLs in psoriatic skin. Nat Commun 2023; 14:6268. [PMID: 37805522 PMCID: PMC10560299 DOI: 10.1038/s41467-023-41984-2] [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: 10/13/2022] [Accepted: 09/25/2023] [Indexed: 10/09/2023] Open
Abstract
Psoriasis is a chronic, systemic inflammatory condition primarily affecting skin. While the role of the immune compartment (e.g., T cells) is well established, the changes in the skin compartment are more poorly understood. Using longitudinal skin biopsies (n = 375) from the "Psoriasis Treatment with Abatacept and Ustekinumab: A Study of Efficacy"(PAUSE) clinical trial (n = 101), we report 953 expression quantitative trait loci (eQTLs). Of those, 116 eQTLs have effect sizes that were modulated by local skin inflammation (eQTL interactions). By examining these eQTL genes (eGenes), we find that most are expressed in the skin tissue compartment, and a subset overlap with the NRF2 pathway. Indeed, the strongest eQTL interaction signal - rs1491377616-LCE3C - links a psoriasis risk locus with a gene specifically expressed in the epidermis. This eQTL study highlights the potential to use biospecimens from clinical trials to discover in vivo eQTL interactions with therapeutically relevant environmental variables.
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Affiliation(s)
- Qian Xiao
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joseph Mears
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kazuyoshi Ishigaki
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Laboratory for Human Immunogenetics, RIKEN Center for Integrative Medical Sciences, Yokohama City, Kanagawa, Japan
| | - Yuriy Baglaenko
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Noha Lim
- Immune Tolerance Network, Diabetes Center, University of California, San Francisco, San Francisco, CA, USA
| | - Laura A Cooney
- Immune Tolerance Network, Diabetes Center, University of California, San Francisco, San Francisco, CA, USA
- Division of Rheumatology, Department of Internal Medicine and Clinical Autoimmunity Center of Excellence, University of Michigan, Ann Arbor, MI, USA
| | - Kristina M Harris
- Immune Tolerance Network, Diabetes Center, University of California, San Francisco, San Francisco, CA, USA
| | - Mark S Anderson
- Immune Tolerance Network, Diabetes Center, University of California, San Francisco, San Francisco, CA, USA
| | - David A Fox
- Division of Rheumatology, Department of Internal Medicine and Clinical Autoimmunity Center of Excellence, University of Michigan, Ann Arbor, MI, USA
| | - Dawn E Smilek
- Immune Tolerance Network, Diabetes Center, University of California, San Francisco, San Francisco, CA, USA
| | - James G Krueger
- Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA.
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK.
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37
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Budu-Aggrey A, Kilanowski A, Sobczyk MK, Shringarpure SS, Mitchell R, Reis K, Reigo A, Mägi R, Nelis M, Tanaka N, Brumpton BM, Thomas LF, Sole-Navais P, Flatley C, Espuela-Ortiz A, Herrera-Luis E, Lominchar JVT, Bork-Jensen J, Marenholz I, Arnau-Soler A, Jeong A, Fawcett KA, Baurecht H, Rodriguez E, Alves AC, Kumar A, Sleiman PM, Chang X, Medina-Gomez C, Hu C, Xu CJ, Qi C, El-Heis S, Titcombe P, Antoun E, Fadista J, Wang CA, Thiering E, Wu B, Kress S, Kothalawala DM, Kadalayil L, Duan J, Zhang H, Hadebe S, Hoffmann T, Jorgenson E, Choquet H, Risch N, Njølstad P, Andreassen OA, Johansson S, Almqvist C, Gong T, Ullemar V, Karlsson R, Magnusson PKE, Szwajda A, Burchard EG, Thyssen JP, Hansen T, Kårhus LL, Dantoft TM, Jeanrenaud ACSN, Ghauri A, Arnold A, Homuth G, Lau S, Nöthen MM, Hübner N, Imboden M, Visconti A, Falchi M, Bataille V, Hysi P, Ballardini N, Boomsma DI, Hottenga JJ, Müller-Nurasyid M, Ahluwalia TS, Stokholm J, Chawes B, Schoos AMM, Esplugues A, Bustamante M, Raby B, Arshad S, German C, Esko T, Milani LA, Metspalu A, Terao C, Abuabara K, Løset M, Hveem K, Jacobsson B, Pino-Yanes M, Strachan DP, Grarup N, Linneberg A, Lee YA, Probst-Hensch N, Weidinger S, Jarvelin MR, Melén E, Hakonarson H, Irvine AD, Jarvis D, Nijsten T, Duijts L, Vonk JM, Koppelmann GH, Godfrey KM, Barton SJ, Feenstra B, Pennell CE, Sly PD, Holt PG, Williams LK, Bisgaard H, Bønnelykke K, Curtin J, Simpson A, Murray C, Schikowski T, Bunyavanich S, Weiss ST, Holloway JW, Min JL, Brown SJ, Standl M, Paternoster L. European and multi-ancestry genome-wide association meta-analysis of atopic dermatitis highlights importance of systemic immune regulation. Nat Commun 2023; 14:6172. [PMID: 37794016 PMCID: PMC10550990 DOI: 10.1038/s41467-023-41180-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 08/24/2023] [Indexed: 10/06/2023] Open
Abstract
Atopic dermatitis (AD) is a common inflammatory skin condition and prior genome-wide association studies (GWAS) have identified 71 associated loci. In the current study we conducted the largest AD GWAS to date (discovery N = 1,086,394, replication N = 3,604,027), combining previously reported cohorts with additional available data. We identified 81 loci (29 novel) in the European-only analysis (which all replicated in a separate European analysis) and 10 additional loci in the multi-ancestry analysis (3 novel). Eight variants from the multi-ancestry analysis replicated in at least one of the populations tested (European, Latino or African), while two may be specific to individuals of Japanese ancestry. AD loci showed enrichment for DNAse I hypersensitivity and eQTL associations in blood. At each locus we prioritised candidate genes by integrating multi-omic data. The implicated genes are predominantly in immune pathways of relevance to atopic inflammation and some offer drug repurposing opportunities.
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Affiliation(s)
- Ashley Budu-Aggrey
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, England
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, England
| | - Anna Kilanowski
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children's Hospital, University of Munich Medical Center, Munich, Germany
- Pettenkofer School of Public Health, Ludwig-Maximilians University Munich, Munich, Germany
| | - Maria K Sobczyk
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, England
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, England
| | | | - Ruth Mitchell
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, England
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, England
| | - Kadri Reis
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Anu Reigo
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Mari Nelis
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Core Facility of Genomics, University of Tartu, Tartu, Estonia
| | - Nao Tanaka
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Rheumatology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Ben M Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, 7030, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, 7600, Norway
- Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, 7030, Norway
| | - Laurent F Thomas
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, 7030, Norway
- Department of Clinical and Molecular Medicine, NTNU Norwegian University of Science and Technology, Trondheim, Norway
- BioCore - Bioinformatics Core Facility, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Laboratory Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Pol Sole-Navais
- Department of Obstetrics and Gynecology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Christopher Flatley
- Department of Obstetrics and Gynecology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Antonio Espuela-Ortiz
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna, La Laguna, Tenerife, Spain
| | - Esther Herrera-Luis
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna, La Laguna, Tenerife, Spain
| | - Jesus V T Lominchar
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, København, Denmark
| | - Jette Bork-Jensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, København, Denmark
| | - Ingo Marenholz
- Max-Delbrück-Center for Molecular Medicine, Berlin, Germany
- Clinic for Pediatric Allergy, Experimental and Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Aleix Arnau-Soler
- Max-Delbrück-Center for Molecular Medicine, Berlin, Germany
- Clinic for Pediatric Allergy, Experimental and Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Ayoung Jeong
- Swiss Tropical and Public Health Institute, CH-4123, Basel, Switzerland
- University of Basel, CH-4001, Basel, Switzerland
| | - Katherine A Fawcett
- Department of Health Sciences, University of Leicester, Leicester, LE1 7RH, UK
| | - Hansjorg Baurecht
- Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Elke Rodriguez
- Department of Dermatology and Allergy, University Hospital Schleswig-Holstein, Kiel, Germany
| | | | - Ashish Kumar
- Department of Clinical Science and Education Södersjukhuset, Karolinska Institutet, Solna, Sweden
| | - Patrick M Sleiman
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
- Rhythm Pharmaceuticals, 222 Berkley Street, Boston, 02116, USA
| | - Xiao Chang
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Carolina Medina-Gomez
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Chen Hu
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Dermatology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Cheng-Jian Xu
- University of Groningen, University Medical Center Groningen, Department of Pediatric Pulmonology and Pediatric Allergy, Beatrix Children's Hospital, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, GRIAC Research Institute, Groningen, The Netherlands
- Centre for Individualized Infection Medicine, CiiM, a joint venture between Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- TWINCORE, Centre for Experimental and Clinical Infection Research, a joint venture between the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
| | - Cancan Qi
- University of Groningen, University Medical Center Groningen, Department of Pediatric Pulmonology and Pediatric Allergy, Beatrix Children's Hospital, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, GRIAC Research Institute, Groningen, The Netherlands
| | - Sarah El-Heis
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK
| | - Philip Titcombe
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK
| | - Elie Antoun
- Faculty of Medicine, University of Southampton, Southampton, UK
- Institute of Developmental Sciences, University of Southampton, Southampton, UK
| | - João Fadista
- Department of Bioinformatics & Data Mining, Måløv, Denmark
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
- Department of Clinical Sciences, Lund University Diabetes Centre, Malmö, Sweden
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Carol A Wang
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
- Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Elisabeth Thiering
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children's Hospital, University of Munich Medical Center, Munich, Germany
| | - Baojun Wu
- Center for Individualized and Genomic Medicine Research (CIGMA), Department of Medicine, Henry Ford Health, Detroit, MI, 48104, USA
| | - Sara Kress
- Environmental Epidemiology of Lung, Brain and Skin Aging, IUF - Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany
| | - Dilini M Kothalawala
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, UK
| | - Latha Kadalayil
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Jiasong Duan
- Division of Epidemiology, Biostatistics, and Environmental Health, School of Public Health, University of Memphis, Memphis, TN, USA
| | - Hongmei Zhang
- Division of Epidemiology, Biostatistics, and Environmental Health, School of Public Health, University of Memphis, Memphis, TN, USA
| | - Sabelo Hadebe
- Division of Immunology, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Thomas Hoffmann
- Institute for Human Genetics, UCSF, San Francisco, CA, 94143, USA
- Department of Epidemiology and Biostatistics, UCSF, San Francisco, CA, 94158, USA
| | | | - Hélène Choquet
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Neil Risch
- Institute for Human Genetics, UCSF, San Francisco, CA, 94143, USA
- Department of Epidemiology and Biostatistics, UCSF, San Francisco, CA, 94158, USA
| | - Pål Njølstad
- Center for Diabetes Research, Department of Clinical Science, University of Bergen, NO-5020, Bergen, Norway
- Children and Youth Clinic, Haukeland University Hospital, NO-5021, Bergen, Norway
| | - Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, 0450, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, 0450, Oslo, Norway
| | - Stefan Johansson
- Center for Diabetes Research, Department of Clinical Science, University of Bergen, NO-5020, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, NO-5021, Bergen, Norway
| | - Catarina Almqvist
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Pediatric Lung and Allergy Unit, Astrid Lindgren Children's Hospital, Karolinska University Hospital, Stockholm, Sweden
| | - Tong Gong
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Vilhelmina Ullemar
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Robert Karlsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Agnieszka Szwajda
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Esteban G Burchard
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Jacob P Thyssen
- Department of Dermatology, Bispebjerg Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, København, Denmark
| | - Line L Kårhus
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
| | - Thomas M Dantoft
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
| | - Alexander C S N Jeanrenaud
- Max-Delbrück-Center for Molecular Medicine, Berlin, Germany
- Clinic for Pediatric Allergy, Experimental and Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Ahla Ghauri
- Max-Delbrück-Center for Molecular Medicine, Berlin, Germany
- Clinic for Pediatric Allergy, Experimental and Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas Arnold
- Clinic and Polyclinic of Dermatology, University Medicine Greifswald, Greifswald, Germany
| | - Georg Homuth
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Susanne Lau
- Department of Pediatric Respiratory Medicine, Immunology, and Critical Care Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Norbert Hübner
- Max-Delbrück-Center for Molecular Medicine, Berlin, Germany
- Charite-Universitätsmedizin Berlin, Berlin, Germany
| | - Medea Imboden
- Swiss Tropical and Public Health Institute, CH-4123, Basel, Switzerland
- University of Basel, CH-4001, Basel, Switzerland
| | - Alessia Visconti
- Department of Twin Research & Genetics Epidemiology, Kings College London, London, UK
| | - Mario Falchi
- Department of Twin Research & Genetics Epidemiology, Kings College London, London, UK
| | - Veronique Bataille
- Department of Twin Research & Genetics Epidemiology, Kings College London, London, UK
- Dermatology Department, West Herts NHS Trust, Watford, UK
| | - Pirro Hysi
- Department of Twin Research & Genetics Epidemiology, Kings College London, London, UK
| | - Natalia Ballardini
- Department of Clinical Science and Education Södersjukhuset, Karolinska Institutet, Solna, Sweden
| | - Dorret I Boomsma
- Dept Biological Psychology, Netherlands Twin Register, VU University, Amsterdam, the Netherlands
- Institute for Health and Care Research (EMGO), VU University, Amsterdam, the Netherlands
| | - Jouke J Hottenga
- Dept Biological Psychology, Netherlands Twin Register, VU University, Amsterdam, the Netherlands
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- IBE, Faculty of Medicine, LMU Munich, Munich, Germany
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Tarunveer S Ahluwalia
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
- Steno Diabetes Center Copenhagen, Herlev, Denmark
- Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Jakob Stokholm
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
- Department of Pediatrics, Slagelse Hospital, Slagelse, Denmark
| | - Bo Chawes
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Ann-Marie M Schoos
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
- Department of Pediatrics, Slagelse Hospital, Slagelse, Denmark
| | - Ana Esplugues
- Nursing School, University of Valencia, FISABIO-University Jaume I-University of Valencia, Valencia, Spain
- Joint Research Unit of Epidemiology and Environmental Health, CIBERESP, Valencia, Spain
| | - Mariona Bustamante
- ISGlobal, Institute for Global Health, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Benjamin Raby
- Channing Division of Network Medicine, Brigham & Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Syed Arshad
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
- David Hide Asthma and Allergy Research Centre, Isle of Wight, UK
| | | | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Lili A Milani
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - 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
| | - Katrina Abuabara
- Department of Dermatology, University of California San Francisco, San Francisco, CA, USA
| | - Mari Løset
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, 7030, Norway
- Department of Dermatology, Clinic of Orthopaedy, Rheumatology and Dermatology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, 7030, Norway
- HUNT Research Centre, Department of Public Health and General Practice, Norwegian University of Science and Technology, Levanger, Norway
| | - Bo Jacobsson
- Department of Obstetrics and Gynecology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway
| | - Maria Pino-Yanes
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna, La Laguna, Tenerife, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna, San Cristóbal de La Laguna, Santa Cruz de Tenerife, Spain
| | - David P Strachan
- Population Health Research Institute, St George's, University of London, Cranmer Terrace, London, SW17 0RE, UK
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, København, Denmark
| | - Allan Linneberg
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Young-Ae Lee
- Max-Delbrück-Center for Molecular Medicine, Berlin, Germany
- Clinic for Pediatric Allergy, Experimental and Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Nicole Probst-Hensch
- Swiss Tropical and Public Health Institute, CH-4123, Basel, Switzerland
- University of Basel, CH-4001, Basel, Switzerland
| | - Stephan Weidinger
- Department of Dermatology, Allergology and Venereology, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Marjo-Riitta Jarvelin
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment & Health, School of Public Health,Imperial College London, London, UK
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Erik Melén
- Department of Clinical Science and Education Södersjukhuset, Karolinska Institutet, Solna, Sweden
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Pediatrics, Divisions of Human Genetics and Pulmonary Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Faculty of Medicine, University of Iceland, 101, Reykjavík, Iceland
| | - Alan D Irvine
- Department of Clinical Medicine, Trinity College, Dublin, Ireland
| | - Deborah Jarvis
- Respiratory Epidemiology, Occupational Medicine and Public Health, National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Medical Research Council and Public Health England Centre for Environment and Health, London, United Kingdom
| | - Tamar Nijsten
- Department of Dermatology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Liesbeth Duijts
- Department of Pediatrics, division of Respiratory Medicine and Allergology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, division of Neonatology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Judith M Vonk
- University of Groningen, University Medical Center Groningen, GRIAC Research Institute, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands
| | - Gerard H Koppelmann
- University of Groningen, University Medical Center Groningen, Department of Pediatric Pulmonology and Pediatric Allergy, Beatrix Children's Hospital, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, GRIAC Research Institute, Groningen, The Netherlands
| | - Keith M Godfrey
- MRC Lifecourse Epidemiology Centre and NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Sheila J Barton
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK
| | - Bjarke Feenstra
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Craig E Pennell
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
- Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Peter D Sly
- Children's Health and Environment Program, Child Health Research Centre, The University of Queensland, South Brisbane, 4101, Queensland, Australia
- Australian Infectious Diseases Research Centre, The University of Queensland, St Lucia, 4072, QLD, Australia
| | - Patrick G Holt
- Telethon Kids Institute, University of Western Australia, Perth, WA, Australia
| | - L Keoki Williams
- Center for Individualized and Genomic Medicine Research (CIGMA), Department of Medicine, Henry Ford Health, Detroit, MI, 48104, USA
| | - Hans Bisgaard
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Klaus Bønnelykke
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - John Curtin
- Division of Immunology, Immunity to Infection and Respiratory Medicine, School of Biological Sciences, The University of Manchester, Manchester Academic Health Science Centre, and Manchester University NHS Foundation Trust, Manchester, England
| | - Angela Simpson
- Division of Immunology, Immunity to Infection and Respiratory Medicine, School of Biological Sciences, The University of Manchester, Manchester Academic Health Science Centre, and Manchester University NHS Foundation Trust, Manchester, England
| | - Clare Murray
- Division of Immunology, Immunity to Infection and Respiratory Medicine, School of Biological Sciences, The University of Manchester, Manchester Academic Health Science Centre, and Manchester University NHS Foundation Trust, Manchester, England
| | - Tamara Schikowski
- Environmental Epidemiology of Lung, Brain and Skin Aging, Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany
| | - Supinda Bunyavanich
- Division of Allergy and Immunology, Department of Pediatrics, and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Brigham & Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - John W Holloway
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Josine L Min
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, England
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, England
| | - Sara J Brown
- Centre for Genomics and Experimental Medicine, Institute for Genetics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, UK EH4 2XU, Scotland
| | - Marie Standl
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Lung Research (DZL), Munich, Germany
| | - Lavinia Paternoster
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, England.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, England.
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Moser L, Laskari K, Ospelt C, Houtman M. Allele-specific protein binding within the CD40 locus in human synovial fibroblasts and immune cells. RMD Open 2023; 9:e003168. [PMID: 37880178 PMCID: PMC10603347 DOI: 10.1136/rmdopen-2023-003168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/17/2023] [Indexed: 10/27/2023] Open
Affiliation(s)
- Larissa Moser
- Center of Experimental Rheumatology, Department of Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Katerina Laskari
- Center of Experimental Rheumatology, Department of Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Caroline Ospelt
- Center of Experimental Rheumatology, Department of Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Miranda Houtman
- Center of Experimental Rheumatology, Department of Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Division of Rheumatology, Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
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39
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Antontseva EV, Degtyareva AO, Korbolina EE, Damarov IS, Merkulova TI. Human-genome single nucleotide polymorphisms affecting transcription factor binding and their role in pathogenesis. Vavilovskii Zhurnal Genet Selektsii 2023; 27:662-675. [PMID: 37965371 PMCID: PMC10641029 DOI: 10.18699/vjgb-23-77] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/24/2023] [Accepted: 03/30/2023] [Indexed: 11/16/2023] Open
Abstract
Single nucleotide polymorphisms (SNPs) are the most common type of variation in the human genome. The vast majority of SNPs identified in the human genome do not have any effect on the phenotype; however, some can lead to changes in the function of a gene or the level of its expression. Most SNPs associated with certain traits or pathologies are mapped to regulatory regions of the genome and affect gene expression by changing transcription factor binding sites. In recent decades, substantial effort has been invested in searching for such regulatory SNPs (rSNPs) and understanding the mechanisms by which they lead to phenotypic differences, primarily to individual differences in susceptibility to diseases and in sensitivity to drugs. The development of the NGS (next-generation sequencing) technology has contributed not only to the identification of a huge number of SNPs and to the search for their association (genome-wide association studies, GWASs) with certain diseases or phenotypic manifestations, but also to the development of more productive approaches to their functional annotation. It should be noted that the presence of an association does not allow one to identify a functional, truly disease-associated DNA sequence variant among multiple marker SNPs that are detected due to linkage disequilibrium. Moreover, determination of associations of genetic variants with a disease does not provide information about the functionality of these variants, which is necessary to elucidate the molecular mechanisms of the development of pathology and to design effective methods for its treatment and prevention. In this regard, the functional analysis of SNPs annotated in the GWAS catalog, both at the genome-wide level and at the level of individual SNPs, became especially relevant in recent years. A genome-wide search for potential rSNPs is possible without any prior knowledge of their association with a trait. Thus, mapping expression quantitative trait loci (eQTLs) makes it possible to identify an SNP for which - among transcriptomes of homozygotes and heterozygotes for its various alleles - there are differences in the expression level of certain genes, which can be located at various distances from the SNP. To predict rSNPs, approaches based on searches for allele-specific events in RNA-seq, ChIP-seq, DNase-seq, ATAC-seq, MPRA, and other data are also used. Nonetheless, for a more complete functional annotation of such rSNPs, it is necessary to establish their association with a trait, in particular, with a predisposition to a certain pathology or sensitivity to drugs. Thus, approaches to finding SNPs important for the development of a trait can be categorized into two groups: (1) starting from data on an association of SNPs with a certain trait, (2) starting from the determination of allele-specific changes at the molecular level (in a transcriptome or regulome). Only comprehensive use of strategically different approaches can considerably enrich our knowledge about the role of genetic determinants in the molecular mechanisms of trait formation, including predisposition to multifactorial diseases.
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Affiliation(s)
- E V Antontseva
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - A O Degtyareva
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - E E Korbolina
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - I S Damarov
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - T I Merkulova
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
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Kerimov N, Tambets R, Hayhurst JD, Rahu I, Kolberg P, Raudvere U, Kuzmin I, Chowdhary A, Vija A, Teras HJ, Kanai M, Ulirsch J, Ryten M, Hardy J, Guelfi S, Trabzuni D, Kim-Hellmuth S, Rayner W, Finucane H, Peterson H, Mosaku A, Parkinson H, Alasoo K. eQTL Catalogue 2023: New datasets, X chromosome QTLs, and improved detection and visualisation of transcript-level QTLs. PLoS Genet 2023; 19:e1010932. [PMID: 37721944 PMCID: PMC10538656 DOI: 10.1371/journal.pgen.1010932] [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: 04/17/2023] [Revised: 09/28/2023] [Accepted: 08/22/2023] [Indexed: 09/20/2023] Open
Abstract
The eQTL Catalogue is an open database of uniformly processed human molecular quantitative trait loci (QTLs). We are continuously updating the resource to further increase its utility for interpreting genetic associations with complex traits. Over the past two years, we have increased the number of uniformly processed studies from 21 to 31 and added X chromosome QTLs for 19 compatible studies. We have also implemented Leafcutter to directly identify splice-junction usage QTLs in all RNA sequencing datasets. Finally, to improve the interpretability of transcript-level QTLs, we have developed static QTL coverage plots that visualise the association between the genotype and average RNA sequencing read coverage in the region for all 1.7 million fine mapped associations. To illustrate the utility of these updates to the eQTL Catalogue, we performed colocalisation analysis between vitamin D levels in the UK Biobank and all molecular QTLs in the eQTL Catalogue. Although most GWAS loci colocalised both with eQTLs and transcript-level QTLs, we found that visual inspection could sometimes be used to distinguish primary splicing QTLs from those that appear to be secondary consequences of large-effect gene expression QTLs. While these visually confirmed primary splicing QTLs explain just 6/53 of the colocalising signals, they are significantly less pleiotropic than eQTLs and identify a prioritised causal gene in 4/6 cases.
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Affiliation(s)
- Nurlan Kerimov
- Institute of Computer Science, University of Tartu, Tartu, Estonia
- Open Targets, South Building, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Ralf Tambets
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - James D. Hayhurst
- Open Targets, South Building, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Ida Rahu
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Peep Kolberg
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Uku Raudvere
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Ivan Kuzmin
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Anshika Chowdhary
- Institute of Translational Genomics, Helmholtz Munich, Neuherberg, Germany
| | - Andreas Vija
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Hans J. Teras
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Jacob Ulirsch
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Mina Ryten
- Department of Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
| | - John Hardy
- Department of Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
| | - Sebastian Guelfi
- Department of Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
| | - Daniah Trabzuni
- Department of Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
| | - Sarah Kim-Hellmuth
- Institute of Translational Genomics, Helmholtz Munich, Neuherberg, Germany
- Department of Pediatrics, Dr. von Hauner Children’s Hospital, University Hospital LMU Munich, Munich, Germany
| | - William Rayner
- Institute of Translational Genomics, Helmholtz Munich, Neuherberg, Germany
| | - Hilary Finucane
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Hedi Peterson
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Abayomi Mosaku
- Open Targets, South Building, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Helen Parkinson
- Open Targets, South Building, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Kaur Alasoo
- Institute of Computer Science, University of Tartu, Tartu, Estonia
- Open Targets, South Building, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
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Lea AJ, Clark AG, Dahl AW, Devinsky O, Garcia AR, Golden CD, Kamau J, Kraft TS, Lim YAL, Martins DJ, Mogoi D, Pajukanta P, Perry GH, Pontzer H, Trumble BC, Urlacher SS, Venkataraman VV, Wallace IJ, Gurven M, Lieberman DE, Ayroles JF. Applying an evolutionary mismatch framework to understand disease susceptibility. PLoS Biol 2023; 21:e3002311. [PMID: 37695771 PMCID: PMC10513379 DOI: 10.1371/journal.pbio.3002311] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 09/21/2023] [Indexed: 09/13/2023] Open
Abstract
Noncommunicable diseases (NCDs) are on the rise worldwide. Obesity, cardiovascular disease, and type 2 diabetes are among a long list of "lifestyle" diseases that were rare throughout human history but are now common. The evolutionary mismatch hypothesis posits that humans evolved in environments that radically differ from those we currently experience; consequently, traits that were once advantageous may now be "mismatched" and disease causing. At the genetic level, this hypothesis predicts that loci with a history of selection will exhibit "genotype by environment" (GxE) interactions, with different health effects in "ancestral" versus "modern" environments. To identify such loci, we advocate for combining genomic tools in partnership with subsistence-level groups experiencing rapid lifestyle change. In these populations, comparisons of individuals falling on opposite extremes of the "matched" to "mismatched" spectrum are uniquely possible. More broadly, the work we propose will inform our understanding of environmental and genetic risk factors for NCDs across diverse ancestries and cultures.
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Affiliation(s)
- Amanda J. Lea
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Andrew G. Clark
- Department of Molecular Biology & Genetics, Cornell University, Ithaca, New York, United States of America
| | - Andrew W. Dahl
- Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Orrin Devinsky
- Department of Neurology, NYU Langone Comprehensive Epilepsy Center, NYU Grossman School of Medicine, New York, New York, United States of America
| | - Angela R. Garcia
- Department of Anthropology, Stanford University, Stanford, California, United States of America
| | - Christopher D. Golden
- Department of Nutrition, Harvard T H Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Joseph Kamau
- One Health Centre, Institute of Primate Research, Karen, Nairobi, Kenya
| | - Thomas S. Kraft
- Department of Anthropology, University of Utah, Salt Lake City, Utah, United States of America
| | - Yvonne A. L. Lim
- Department of Parasitology, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Dino J. Martins
- Turkana Basin Institute, Stony Brook University, Stony Brook, New York, United States of America
| | - Donald Mogoi
- Department of Medical Services and Public Health, Ministry of Health Laikipia County, Nanyuki, Kenya
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, United States of America
| | - George H. Perry
- Departments of Anthropology and Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Herman Pontzer
- Department of Evolutionary Anthropology, Duke University, Durham, North Carolina, United States of America
- Duke Global Health Institute, Duke University, Durham, North Carolina, United States of America
| | - Benjamin C. Trumble
- School of Human Evolution and Social Change, Arizona State University, Tempe, Arizona, United States of America
- Center for Evolution and Medicine, Arizona State University, Tempe, Arizona, United States of America
| | - Samuel S. Urlacher
- Department of Anthropology, Baylor University, Waco, Texas, United States of America
| | - Vivek V. Venkataraman
- Department of Anthropology and Archaeology, University of Calgary, Calgary, Alberta, Canada
| | - Ian J. Wallace
- Department of Anthropology, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Michael Gurven
- Department of Anthropology, University of California Santa Barbara, Santa Barbara, California, United States of America
| | - Daniel E. Lieberman
- Department of Human Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Julien F. Ayroles
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America
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42
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Zhao JH, Stacey D, Eriksson N, Macdonald-Dunlop E, Hedman ÅK, Kalnapenkis A, Enroth S, Cozzetto D, Digby-Bell J, Marten J, Folkersen L, Herder C, Jonsson L, Bergen SE, Gieger C, Needham EJ, Surendran P, Paul DS, Polasek O, Thorand B, Grallert H, Roden M, Võsa U, Esko T, Hayward C, Johansson Å, Gyllensten U, Powell N, Hansson O, Mattsson-Carlgren N, Joshi PK, Danesh J, Padyukov L, Klareskog L, Landén M, Wilson JF, Siegbahn A, Wallentin L, Mälarstig A, Butterworth AS, Peters JE. Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets. Nat Immunol 2023; 24:1540-1551. [PMID: 37563310 PMCID: PMC10457199 DOI: 10.1038/s41590-023-01588-w] [Citation(s) in RCA: 100] [Impact Index Per Article: 100.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 07/13/2023] [Indexed: 08/12/2023]
Abstract
Circulating proteins have important functions in inflammation and a broad range of diseases. To identify genetic influences on inflammation-related proteins, we conducted a genome-wide protein quantitative trait locus (pQTL) study of 91 plasma proteins measured using the Olink Target platform in 14,824 participants. We identified 180 pQTLs (59 cis, 121 trans). Integration of pQTL data with eQTL and disease genome-wide association studies provided insight into pathogenesis, implicating lymphotoxin-α in multiple sclerosis. Using Mendelian randomization (MR) to assess causality in disease etiology, we identified both shared and distinct effects of specific proteins across immune-mediated diseases, including directionally discordant effects of CD40 on risk of rheumatoid arthritis versus multiple sclerosis and inflammatory bowel disease. MR implicated CXCL5 in the etiology of ulcerative colitis (UC) and we show elevated gut CXCL5 transcript expression in patients with UC. These results identify targets of existing drugs and provide a powerful resource to facilitate future drug target prioritization.
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Affiliation(s)
- Jing Hua Zhao
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - David Stacey
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Australian Centre for Precision Health, Unit of Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| | - Niclas Eriksson
- Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | - Erin Macdonald-Dunlop
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Åsa K Hedman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Development and Medical, Pfizer Worldwide Research, Stockholm, Sweden
| | - Anette Kalnapenkis
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Stefan Enroth
- Department of Immunology, Genetics, and Pathology, Biomedical Center, SciLifeLab Uppsala, Uppsala University, Uppsala, Sweden
| | - Domenico Cozzetto
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Jonathan Digby-Bell
- School of Immunology and Microbial Sciences, King's College London, London, UK
| | - Jonathan Marten
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | | | - Christian Herder
- Institute for Clinical Diabetology, German Diabetes Center, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research, Munich-Neuherberg, Germany
| | - Lina Jonsson
- Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden
| | - Sarah E Bergen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Elise J Needham
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Praveen Surendran
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
| | - Dirk S Paul
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | | | - Barbara Thorand
- German Center for Diabetes Research, Munich-Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Harald Grallert
- German Center for Diabetes Research, Munich-Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Michael Roden
- Institute for Clinical Diabetology, German Diabetes Center, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research, Munich-Neuherberg, Germany
| | - Urmo Võsa
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tonu Esko
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Åsa Johansson
- Department of Immunology, Genetics, and Pathology, Biomedical Center, SciLifeLab Uppsala, Uppsala University, Uppsala, Sweden
| | - Ulf Gyllensten
- Department of Immunology, Genetics, and Pathology, Biomedical Center, SciLifeLab Uppsala, Uppsala University, Uppsala, Sweden
| | - Nick Powell
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Skåne University Hospital, Malmö, Sweden
| | - Niklas Mattsson-Carlgren
- Wallenberg Centre for Molecular Medicine, Lund University, Lund, Sweden
- Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund University, Lund, Sweden
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK, Wellcome Genome Campus and University of Cambridge, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Leonid Padyukov
- Division of Rheumatology, Department of Medicine (Solna), Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Lars Klareskog
- Division of Rheumatology, Department of Medicine (Solna), Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Mikael Landén
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden
| | - James F Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Agneta Siegbahn
- Department of Medical Sciences and Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | - Lars Wallentin
- Department of Medical Sciences and Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | - Anders Mälarstig
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Development and Medical, Pfizer Worldwide Research, Stockholm, Sweden
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK.
- Health Data Research UK, Wellcome Genome Campus and University of Cambridge, Hinxton, UK.
- NIHR Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK.
| | - James E Peters
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Health Data Research UK, Wellcome Genome Campus and University of Cambridge, Hinxton, UK.
- Department of Immunology and Inflammation, Imperial College London, London, UK.
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43
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Kang JB, Raveane A, Nathan A, Soranzo N, Raychaudhuri S. Methods and Insights from Single-Cell Expression Quantitative Trait Loci. Annu Rev Genomics Hum Genet 2023; 24:277-303. [PMID: 37196361 PMCID: PMC10784788 DOI: 10.1146/annurev-genom-101422-100437] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Recent advancements in single-cell technologies have enabled expression quantitative trait locus (eQTL) analysis across many individuals at single-cell resolution. Compared with bulk RNA sequencing, which averages gene expression across cell types and cell states, single-cell assays capture the transcriptional states of individual cells, including fine-grained, transient, and difficult-to-isolate populations at unprecedented scale and resolution. Single-cell eQTL (sc-eQTL) mapping can identify context-dependent eQTLs that vary with cell states, including some that colocalize with disease variants identified in genome-wide association studies. By uncovering the precise contexts in which these eQTLs act, single-cell approaches can unveil previously hidden regulatory effects and pinpoint important cell states underlying molecular mechanisms of disease. Here, we present an overview of recently deployed experimental designs in sc-eQTL studies. In the process, we consider the influence of study design choices such as cohort, cell states, and ex vivo perturbations. We then discuss current methodologies, modeling approaches, and technical challenges as well as future opportunities and applications.
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Affiliation(s)
- Joyce B Kang
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
| | | | - Aparna Nathan
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
| | - Nicole Soranzo
- Human Technopole, Milan, Italy; ,
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, United Kingdom
- British Heart Foundation Centre of Research Excellence and Department of Haematology, University of Cambridge, Cambridge, United Kingdom
| | - Soumya Raychaudhuri
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
- Centre for Genetics and Genomics Versus Arthritis, University of Manchester, Manchester, United Kingdom
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44
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Yuan Q, Duren Z. Continuous lifelong learning for modeling of gene regulation from single cell multiome data by leveraging atlas-scale external data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.01.551575. [PMID: 37577525 PMCID: PMC10418251 DOI: 10.1101/2023.08.01.551575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Accurate context-specific Gene Regulatory Networks (GRNs) inference from genomics data is a crucial task in computational biology. However, existing methods face limitations, such as reliance on gene expression data alone, lower resolution from bulk data, and data scarcity for specific cellular systems. Despite recent technological advancements, including single-cell sequencing and the integration of ATAC-seq and RNA-seq data, learning such complex mechanisms from limited independent data points still presents a daunting challenge, impeding GRN inference accuracy. To overcome this challenge, we present LINGER (LIfelong neural Network for GEne Regulation), a novel deep learning-based method to infer GRNs from single-cell multiome data with paired gene expression and chromatin accessibility data from the same cell. LINGER incorporates both 1) atlas-scale external bulk data across diverse cellular contexts and 2) the knowledge of transcription factor (TF) motif matching to cis-regulatory elements as a manifold regularization to address the challenge of limited data and extensive parameter space in GRN inference. Our results demonstrate that LINGER achieves 2-3 fold higher accuracy over existing methods. LINGER reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Additionally, following the GRN inference from a reference sc-multiome data, LINGER allows for the estimation of TF activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies. Overall, LINGER provides a comprehensive tool for robust gene regulation inference from genomics data, empowering deeper insights into cellular mechanisms.
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Affiliation(s)
- Qiuyue Yuan
- Center for Human Genetics, Department of Genetics and Biochemistry, Clemson University, Greenwood, SC 29646, USA
| | - Zhana Duren
- Center for Human Genetics, Department of Genetics and Biochemistry, Clemson University, Greenwood, SC 29646, USA
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45
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Cuomo ASE, Nathan A, Raychaudhuri S, MacArthur DG, Powell JE. Single-cell genomics meets human genetics. Nat Rev Genet 2023; 24:535-549. [PMID: 37085594 PMCID: PMC10784789 DOI: 10.1038/s41576-023-00599-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/29/2023] [Indexed: 04/23/2023]
Abstract
Single-cell genomic technologies are revealing the cellular composition, identities and states in tissues at unprecedented resolution. They have now scaled to the point that it is possible to query samples at the population level, across thousands of individuals. Combining single-cell information with genotype data at this scale provides opportunities to link genetic variation to the cellular processes underpinning key aspects of human biology and disease. This strategy has potential implications for disease diagnosis, risk prediction and development of therapeutic solutions. But, effectively integrating large-scale single-cell genomic data, genetic variation and additional phenotypic data will require advances in data generation and analysis methods. As single-cell genetics begins to emerge as a field in its own right, we review its current state and the challenges and opportunities ahead.
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Affiliation(s)
- Anna S E Cuomo
- Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia.
- Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Divisions of Rheumatology and Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Divisions of Rheumatology and Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniel G MacArthur
- Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Joseph E Powell
- Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia.
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia.
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46
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Scholtz D, Jooste T, Möller M, van Coller A, Kinnear C, Glanzmann B. Challenges of Diagnosing Mendelian Susceptibility to Mycobacterial Diseases in South Africa. Int J Mol Sci 2023; 24:12119. [PMID: 37569495 PMCID: PMC10418440 DOI: 10.3390/ijms241512119] [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: 06/29/2023] [Revised: 07/25/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
Inborn errors of immunity (IEI) are genetic disorders with extensive clinical presentations. They can range from increased susceptibility to infections to significant immune dysregulation that results in immune impairment. While IEI cases are individually rare, they collectively represent a significant burden of disease, especially in developing countries such as South Africa, where infectious diseases like tuberculosis (TB) are endemic. This is particularly alarming considering that certain high penetrance mutations that cause IEI, such as Mendelian Susceptibility to Mycobacterial Disease (MSMD), put individuals at higher risk for developing TB and other mycobacterial diseases. MSMD patients in South Africa often present with different clinical phenotypes than those from the developed world, therefore complicating the identification of disease-associated variants in this setting with a high burden of infectious diseases. The lack of available data, limited resources, as well as variability in clinical phenotype are the reasons many MSMD cases remain undetected or misdiagnosed. This article highlights the challenges in diagnosing MSMD in South Africa and proposes the use of transcriptomic analysis as a means of potentially identifying dysregulated pathways in affected African populations.
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Affiliation(s)
- Denise Scholtz
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa; (D.S.); (T.J.); (M.M.); (C.K.)
| | - Tracey Jooste
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa; (D.S.); (T.J.); (M.M.); (C.K.)
| | - Marlo Möller
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa; (D.S.); (T.J.); (M.M.); (C.K.)
- Centre for Bioinformatics and Computational Biology, Stellenbosch University, Stellenbosch 7600, South Africa
| | - Ansia van Coller
- South African Medical Research Council (SAMRC) Genomics Platform, Cape Town 7505, South Africa;
| | - Craig Kinnear
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa; (D.S.); (T.J.); (M.M.); (C.K.)
- South African Medical Research Council (SAMRC) Genomics Platform, Cape Town 7505, South Africa;
| | - Brigitte Glanzmann
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa; (D.S.); (T.J.); (M.M.); (C.K.)
- South African Medical Research Council (SAMRC) Genomics Platform, Cape Town 7505, South Africa;
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47
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Suliman S, Nieto-Caballero VE, Asgari S, Lopez K, Iwany SK, Luo Y, Nathan A, Fernandez-Salinas D, Chiñas M, Huang CC, Zhang Z, León SR, Calderon RI, Lecca L, Murray M, Van Rhijn I, Raychaudhuri S, Moody DB, Gutierrez-Arcelus M. History of tuberculosis disease is associated with genetic regulatory variation in Peruvians. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.20.23291558. [PMID: 37425785 PMCID: PMC10327177 DOI: 10.1101/2023.06.20.23291558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
A quarter of humanity is estimated to be latently infected with Mycobacterium tuberculosis (Mtb) with a 5-10% risk of developing tuberculosis (TB) disease. Variability in responses to Mtb infection could be due to host or pathogen heterogeneity. Here, we focused on host genetic variation in a Peruvian population and its associations with gene regulation in monocyte-derived macrophages and dendritic cells (DCs). We recruited former household contacts of TB patients who previously progressed to TB (cases, n=63) or did not progress to TB (controls, n=63). Transcriptomic profiling of monocyte-derived dendritic cells (DCs) and macrophages measured the impact of genetic variants on gene expression by identifying expression quantitative trait loci (eQTL). We identified 330 and 257 eQTL genes in DCs and macrophages (False Discovery Rate (FDR) < 0.05), respectively. Five genes in DCs showed interaction between eQTL variants and TB progression status. The top eQTL interaction for a protein-coding gene was with FAH, the gene encoding fumarylacetoacetate hydrolase, which mediates the last step in mammalian tyrosine catabolism. FAH expression was associated with genetic regulatory variation in cases but not controls. Using public transcriptomic and epigenomic data of Mtb-infected monocyte-derived dendritic cells, we found that Mtb infection results in FAH downregulation and DNA methylation changes in the locus. Overall, this study demonstrates effects of genetic variation on gene expression levels that are dependent on history of infectious disease and highlights a candidate pathogenic mechanism through pathogen-response genes. Furthermore, our results point to tyrosine metabolism and related candidate TB progression pathways for further investigation.
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Affiliation(s)
- Sara Suliman
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Zuckerberg San Francisco General Hospital, Division of Experimental Medicine, University of California San Francisco, San Francisco, CA, USA
- Gladstone-UCSF Institute of Genomic Immunology, University of California San Francisco, San Francisco, CA, USA
- Chan Zuckerberg Initiative Biohub, San Francisco, CA, USA
| | - Victor E. Nieto-Caballero
- Division of Immunology, Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
- Undergraduate Program in Genomic Sciences, Center for Genomic Sciences, Universidad Nacional Autónoma de México (UNAM), Morelos 62210, Mexico
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Samira Asgari
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kattya Lopez
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Socios En Salud Sucursal Peru, Lima, Peru
| | - Sarah K. Iwany
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Yang Luo
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Kennedy Institute of Rheumatology, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Aparna Nathan
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Daniela Fernandez-Salinas
- Division of Immunology, Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Marcos Chiñas
- Division of Immunology, Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Chuan-Chin Huang
- Department of Global Health and Social Medicine, and Division of Global Health Equity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Zibiao Zhang
- Department of Global Health and Social Medicine, and Division of Global Health Equity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Segundo R León
- Socios En Salud Sucursal Peru, Lima, Peru
- Medical Technology School and Global Health Research Institute, San Juan Bautista Private University, Lima, Perú
| | | | | | - Megan Murray
- Department of Global Health and Social Medicine, and Division of Global Health Equity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Ildiko Van Rhijn
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Infectious Diseases and Immunology, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
| | - Soumya Raychaudhuri
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - D. Branch Moody
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Maria Gutierrez-Arcelus
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Division of Immunology, Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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48
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Brown AC, Cohen CJ, Mielczarek O, Migliorini G, Costantino F, Allcock A, Davidson C, Elliott KS, Fang H, Lledó Lara A, Martin AC, Osgood JA, Sanniti A, Scozzafava G, Vecellio M, Zhang P, Black MH, Li S, Truong D, Molineros J, Howe T, Wordsworth BP, Bowness P, Knight JC. Comprehensive epigenomic profiling reveals the extent of disease-specific chromatin states and informs target discovery in ankylosing spondylitis. CELL GENOMICS 2023; 3:100306. [PMID: 37388915 PMCID: PMC10300554 DOI: 10.1016/j.xgen.2023.100306] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 01/30/2023] [Accepted: 03/27/2023] [Indexed: 07/01/2023]
Abstract
Ankylosing spondylitis (AS) is a common, highly heritable inflammatory arthritis characterized by enthesitis of the spine and sacroiliac joints. Genome-wide association studies (GWASs) have revealed more than 100 genetic associations whose functional effects remain largely unresolved. Here, we present a comprehensive transcriptomic and epigenomic map of disease-relevant blood immune cell subsets from AS patients and healthy controls. We find that, while CD14+ monocytes and CD4+ and CD8+ T cells show disease-specific differences at the RNA level, epigenomic differences are only apparent upon multi-omics integration. The latter reveals enrichment at disease-associated loci in monocytes. We link putative functional SNPs to genes using high-resolution Capture-C at 10 loci, including PTGER4 and ETS1, and show how disease-specific functional genomic data can be integrated with GWASs to enhance therapeutic target discovery. This study combines epigenetic and transcriptional analysis with GWASs to identify disease-relevant cell types and gene regulation of likely pathogenic relevance and prioritize drug targets.
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Affiliation(s)
- Andrew C. Brown
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Carla J. Cohen
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DS, UK
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Olga Mielczarek
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- Horizon Discovery (PerkinElmer) Cambridge Research Park, 8100 Beach Dr., Waterbeach, Cambridge CB25 9TL, UK
| | - Gabriele Migliorini
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, UK
| | - Félicie Costantino
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- UVSQ, INSERM UMR1173, Infection et Inflammation, Laboratory of Excellence INFLAMEX, Université Paris-Saclay, Paris, France
- Rheumatology Department, AP-HP, Ambroise Paré Hospital, Paris, France
| | - Alice Allcock
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Connor Davidson
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | | | - Hai Fang
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Centre for Translational Medicine at Shanghai, Ruijin Hospital affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Alicia Lledó Lara
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Alice C. Martin
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Julie A. Osgood
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Anna Sanniti
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Giuseppe Scozzafava
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Matteo Vecellio
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
- Centro Ricerche Fondazione Italiana Ricerca sull’Artrite (FIRA), Fondazione Pisana per la Scienza ONLUS, Via Ferruccio Giovannini 13, 56017 San Giuliano Terme (Pisa), Italy
| | - Ping Zhang
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | - Mary Helen Black
- Data Science, Population Analytics, Janssen R&D, Spring House, PA 19002, USA
| | - Shuwei Li
- Data Science, Population Analytics, Janssen R&D, Spring House, PA 19002, USA
| | - Dongnhu Truong
- Data Science, Population Analytics, Janssen R&D, Spring House, PA 19002, USA
| | - Julio Molineros
- Data Science, Population Analytics, Janssen R&D, Spring House, PA 19002, USA
| | - Trevor Howe
- Data Science, External Innovation, Janssen R&D, London W1G 0BG, UK
| | - B. Paul Wordsworth
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
- National Institute for Health Research, Comprehensive Biomedical Research Centre, Oxford OX4 2PG, UK
| | - Paul Bowness
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
- National Institute for Health Research, Comprehensive Biomedical Research Centre, Oxford OX4 2PG, UK
| | - Julian C. Knight
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- Chinese Academy of Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
- National Institute for Health Research, Comprehensive Biomedical Research Centre, Oxford OX4 2PG, UK
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49
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Neavin DR, Steinmann AM, Farbehi N, Chiu HS, Daniszewski MS, Arora H, Bermudez Y, Moutinho C, Chan CL, Bax M, Tyebally M, Gnanasambandapillai V, Lam CE, Nguyen U, Hernández D, Lidgerwood GE, Graham RM, Hewitt AW, Pébay A, Palpant NJ, Powell JE. A village in a dish model system for population-scale hiPSC studies. Nat Commun 2023; 14:3240. [PMID: 37296104 PMCID: PMC10256711 DOI: 10.1038/s41467-023-38704-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 04/26/2023] [Indexed: 06/12/2023] Open
Abstract
The mechanisms by which DNA alleles contribute to disease risk, drug response, and other human phenotypes are highly context-specific, varying across cell types and different conditions. Human induced pluripotent stem cells are uniquely suited to study these context-dependent effects but cell lines from hundreds or thousands of individuals are required. Village cultures, where multiple induced pluripotent stem lines are cultured and differentiated in a single dish, provide an elegant solution for scaling induced pluripotent stem experiments to the necessary sample sizes required for population-scale studies. Here, we show the utility of village models, demonstrating how cells can be assigned to an induced pluripotent stem line using single-cell sequencing and illustrating that the genetic, epigenetic or induced pluripotent stem line-specific effects explain a large percentage of gene expression variation for many genes. We demonstrate that village methods can effectively detect induced pluripotent stem line-specific effects, including sensitive dynamics of cell states.
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Affiliation(s)
- Drew R Neavin
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, 2010, Sydney, Australia
| | - Angela M Steinmann
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, 2010, Sydney, Australia
| | - Nona Farbehi
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, 2010, Sydney, Australia
- Graduate School of Biomedical Engineering, University of New South Wales, Kensington, 2033, Sydney, Australia
| | - Han Sheng Chiu
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Maciej S Daniszewski
- Department of Anatomy and Physiology, the University of Melbourne, Melbourne, Australia
| | - Himanshi Arora
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, 2010, Sydney, Australia
| | - Yasmin Bermudez
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, 2010, Sydney, Australia
| | - Cátia Moutinho
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, 2010, Sydney, Australia
| | - Chia-Ling Chan
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, 2010, Sydney, Australia
| | - Monique Bax
- Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia
- UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
| | - Mubarika Tyebally
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, 2010, Sydney, Australia
| | | | - Chuan E Lam
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, 2010, Sydney, Australia
| | - Uyen Nguyen
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, 2010, Sydney, Australia
| | - Damián Hernández
- Department of Anatomy and Physiology, the University of Melbourne, Melbourne, Australia
| | - Grace E Lidgerwood
- Department of Anatomy and Physiology, the University of Melbourne, Melbourne, Australia
| | - Robert M Graham
- Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia
- UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
- St Vincent's Hospital, Darlinghurst, 2010, NSW, Australia
| | - Alex W Hewitt
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, Australia
- School of Medicine, Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia
| | - Alice Pébay
- Department of Anatomy and Physiology, the University of Melbourne, Melbourne, Australia
- Department of Surgery, Royal Melbourne Hospital, Anatomy and Neuroscience, the University of Melbourne, Melbourne, Australia
| | - Nathan J Palpant
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Joseph E Powell
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, 2010, Sydney, Australia.
- UNSW Cellular Genomics Futures Institute, School of Medical Sciences, University of New South Wales, 2052, Sydney, Australia.
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50
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Häder A, Schäuble S, Gehlen J, Thielemann N, Buerfent BC, Schüller V, Hess T, Wolf T, Schröder J, Weber M, Hünniger K, Löffler J, Vylkova S, Panagiotou G, Schumacher J, Kurzai O. Pathogen-specific innate immune response patterns are distinctly affected by genetic diversity. Nat Commun 2023; 14:3239. [PMID: 37277347 DOI: 10.1038/s41467-023-38994-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 05/25/2023] [Indexed: 06/07/2023] Open
Abstract
Innate immune responses vary by pathogen and host genetics. We analyze quantitative trait loci (eQTLs) and transcriptomes of monocytes from 215 individuals stimulated by fungal, Gram-negative or Gram-positive bacterial pathogens. We identify conserved monocyte responses to bacterial pathogens and a distinct antifungal response. These include 745 response eQTLs (reQTLs) and corresponding genes with pathogen-specific effects, which we find first in samples of male donors and subsequently confirm for selected reQTLs in females. reQTLs affect predominantly upregulated genes that regulate immune response via e.g., NOD-like, C-type lectin, Toll-like and complement receptor-signaling pathways. Hence, reQTLs provide a functional explanation for individual differences in innate response patterns. Our identified reQTLs are also associated with cancer, autoimmunity, inflammatory and infectious diseases as shown by external genome-wide association studies. Thus, reQTLs help to explain interindividual variation in immune response to infection and provide candidate genes for variants associated with a range of diseases.
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Affiliation(s)
- Antje Häder
- Research Group Fungal Septomics, Leibniz Institute for Natural Product Research and Infection Biology-Hans Knoell Institute, 07745, Jena, Germany
| | - Sascha Schäuble
- Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology-Hans Knoell Institute, 07745, Jena, Germany
| | - Jan Gehlen
- Institute of Human Genetics, Philipps University of Marburg, 35033, Marburg, Germany
| | - Nadja Thielemann
- Institute for Hygiene and Microbiology, Julius Maximilians University of Wuerzburg, 97080, Wuerzburg, Germany
| | - Benedikt C Buerfent
- Institute of Human Genetics, Philipps University of Marburg, 35033, Marburg, Germany
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, 53127, Bonn, Germany
| | - Vitalia Schüller
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, 53127, Bonn, Germany
| | - Timo Hess
- Institute of Human Genetics, Philipps University of Marburg, 35033, Marburg, Germany
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, 53127, Bonn, Germany
| | - Thomas Wolf
- Systems Biology and Bioinformatics Unit, Leibniz Institute for Natural Product Research and Infection Biology-Hans Knoell Institute, 07745, Jena, Germany
| | - Julia Schröder
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, 53127, Bonn, Germany
| | - Michael Weber
- Research Group Fungal Septomics, Leibniz Institute for Natural Product Research and Infection Biology-Hans Knoell Institute, 07745, Jena, Germany
- Systems Biology and Bioinformatics Unit, Leibniz Institute for Natural Product Research and Infection Biology-Hans Knoell Institute, 07745, Jena, Germany
- Institute of Molecular Pathogenesis, Friedrich-Loeffler-Institute, 07743, Jena, Germany
| | - Kerstin Hünniger
- Research Group Fungal Septomics, Leibniz Institute for Natural Product Research and Infection Biology-Hans Knoell Institute, 07745, Jena, Germany
- Institute for Hygiene and Microbiology, Julius Maximilians University of Wuerzburg, 97080, Wuerzburg, Germany
| | - Jürgen Löffler
- Department of Internal Medicine II, University Hospital Wuerzburg, Josef-Schneider-Strasse 2 /C11, 97080, Wuerzburg, Germany
| | - Slavena Vylkova
- Research Group Host Fungal Interfaces, Septomics Research Center and Leibniz Institute for Natural Product Research and Infection Biology-Hans Knoell Institute, 07745, Jena, Germany
| | - Gianni Panagiotou
- Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology-Hans Knoell Institute, 07745, Jena, Germany
- Faculty of Biological Sciences, Friedrich Schiller University, 07743, Jena, Germany
- Department of Medicine and State Key Laboratory of Pharmaceutical Biotechnology, University of Hong Kong, Hong Kong SAR, China
| | - Johannes Schumacher
- Institute of Human Genetics, Philipps University of Marburg, 35033, Marburg, Germany.
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, 53127, Bonn, Germany.
| | - Oliver Kurzai
- Research Group Fungal Septomics, Leibniz Institute for Natural Product Research and Infection Biology-Hans Knoell Institute, 07745, Jena, Germany.
- Institute for Hygiene and Microbiology, Julius Maximilians University of Wuerzburg, 97080, Wuerzburg, Germany.
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