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Chiñas M, Fernandez-Salinas D, Aguiar VRC, Nieto-Caballero VE, Lefton M, Nigrovic PA, Ermann J, Gutierrez-Arcelus M. Functional genomics implicates natural killer cells in the pathogenesis of ankylosing spondylitis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.09.21.23295912. [PMID: 37808698 PMCID: PMC10557806 DOI: 10.1101/2023.09.21.23295912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Objective Multiple lines of evidence indicate that ankylosing spondylitis (AS) is a lymphocyte-driven disease. However, which lymphocyte populations are critical in AS pathogenesis is not known. In this study, we aimed to identify the key cell types mediating the genetic risk in AS using an unbiased functional genomics approach. Methods We integrated genome-wide association study (GWAS) data with epigenomic and transcriptomic datasets of human immune cells. To quantify enrichment of cell type-specific open chromatin or gene expression in AS risk loci, we used three published methods that have successfully identified relevant cell types in other diseases. We performed co-localization analyses between GWAS risk loci and genetic variants associated with gene expression (eQTL) to find putative target genes. Results Natural killer (NK) cell-specific open chromatin regions are significantly enriched in heritability for AS, compared to other immune cell types such as T cells, B cells, and monocytes. This finding was consistent between two AS GWAS. Using RNA-seq data, we validated that genes in AS risk loci are enriched in NK cell-specific gene expression. Using the human Space-Time Gut Cell Atlas, we also found significant upregulation of AS-associated genes predominantly in NK cells. Co-localization analysis revealed four AS risk loci affecting regulation of candidate target genes in NK cells: two known loci, ERAP1 and TNFRSF1A, and two under-studied loci, ENTR1 (aka SDCCAG3) and B3GNT2. Conclusion Our findings suggest that NK cells may play a crucial role in AS development and highlight four putative target genes for functional follow-up in NK cells.
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Affiliation(s)
- Marcos Chiñas
- Division of Immunology, Boston Children’s Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Daniela Fernandez-Salinas
- Division of Immunology, Boston Children’s Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Licenciatura en Ciencias Genomicas, Centro de Ciencias Genomicas, Universidad Nacional Autónoma de México (UNAM), Morelos 62210, Mexico
| | - Vitor R. C. Aguiar
- Division of Immunology, Boston Children’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Victor E. Nieto-Caballero
- Division of Immunology, Boston Children’s Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Licenciatura en Ciencias Genomicas, Centro de Ciencias Genomicas, Universidad Nacional Autónoma de México (UNAM), Morelos 62210, Mexico
| | - Micah Lefton
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Peter A. Nigrovic
- Division of Immunology, Boston Children’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Joerg Ermann
- Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Maria Gutierrez-Arcelus
- Division of Immunology, Boston Children’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
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2
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Kim T, Martínez-Bonet M, Wang Q, Hackert N, Sparks JA, Baglaenko Y, Koh B, Darbousset R, Laza-Briviesca R, Chen X, Aguiar VRC, Chiu DJ, Westra HJ, Gutierrez-Arcelus M, Weirauch MT, Raychaudhuri S, Rao DA, Nigrovic PA. Non-coding autoimmune risk variant defines role for ICOS in T peripheral helper cell development. Nat Commun 2024; 15:2150. [PMID: 38459032 PMCID: PMC10923805 DOI: 10.1038/s41467-024-46457-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 02/26/2024] [Indexed: 03/10/2024] Open
Abstract
Fine-mapping and functional studies implicate rs117701653, a non-coding single nucleotide polymorphism in the CD28/CTLA4/ICOS locus, as a risk variant for rheumatoid arthritis and type 1 diabetes. Here, using DNA pulldown, mass spectrometry, genome editing and eQTL analysis, we establish that the disease-associated risk allele is functional, reducing affinity for the inhibitory chromosomal regulator SMCHD1 to enhance expression of inducible T-cell costimulator (ICOS) in memory CD4+ T cells from healthy donors. Higher ICOS expression is paralleled by an increase in circulating T peripheral helper (Tph) cells and, in rheumatoid arthritis patients, of blood and joint fluid Tph cells as well as circulating plasmablasts. Correspondingly, ICOS ligation and carriage of the rs117701653 risk allele accelerate T cell differentiation into CXCR5-PD-1high Tph cells producing IL-21 and CXCL13. Thus, mechanistic dissection of a functional non-coding variant in human autoimmunity discloses a previously undefined pathway through which ICOS regulates Tph development and abundance.
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Affiliation(s)
- Taehyeung Kim
- 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
| | - Qiang Wang
- Division of Immunology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Nicolaj Hackert
- Division of Immunology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Rheumatology, Department of Medicine V, Heidelberg University Hospital, Heidelberg, Germany
- Institute for Immunology, Heidelberg University Hospital, Heidelberg, Germany
| | - Jeffrey A Sparks
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yuriy Baglaenko
- 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
| | - Byunghee Koh
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Roxane Darbousset
- Division of Immunology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Raquel Laza-Briviesca
- Division of Immunology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Xiaoting Chen
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Medical Center, Cincinnati, OH, USA
| | - Vitor R C Aguiar
- Division of Immunology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Darren J Chiu
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Harm-Jan Westra
- 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
- Department of Genetics, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Groningen, The Netherlands
| | - Maria Gutierrez-Arcelus
- Division of Immunology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matthew T Weirauch
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's 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
| | - 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
| | - Deepak A Rao
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 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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3
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Lagattuta KA, Park HL, Rumker L, Ishigaki K, Nathan A, Raychaudhuri S. The genetic basis of autoimmunity seen through the lens of T cell functional traits. Nat Commun 2024; 15:1204. [PMID: 38331990 PMCID: PMC10853555 DOI: 10.1038/s41467-024-45170-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 01/15/2024] [Indexed: 02/10/2024] Open
Abstract
Autoimmune disease heritability is enriched in T cell-specific regulatory regions of the genome. Modern-day T cell datasets now enable association studies between single nucleotide polymorphisms (SNPs) and a myriad of molecular phenotypes, including chromatin accessibility, gene expression, transcriptional programs, T cell antigen receptor (TCR) amino acid usage, and cell state abundances. Such studies have identified hundreds of quantitative trait loci (QTLs) in T cells that colocalize with genetic risk for autoimmune disease. The key challenge facing immunologists today lies in synthesizing these results toward a unified understanding of the autoimmune T cell: which genes, cell states, and antigens drive tissue destruction?
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Affiliation(s)
- Kaitlyn A Lagattuta
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Hannah L Park
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Laurie Rumker
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kazuyoshi Ishigaki
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Laboratory for Human Immunogenetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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4
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el Bouhaddani S, Höllerhage M, Uh HW, Moebius C, Bickle M, Höglinger G, Houwing-Duistermaat J. Statistical integration of multi-omics and drug screening data from cell lines. PLoS Comput Biol 2024; 20:e1011809. [PMID: 38295113 PMCID: PMC10878536 DOI: 10.1371/journal.pcbi.1011809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 02/20/2024] [Accepted: 01/08/2024] [Indexed: 02/02/2024] Open
Abstract
Data integration methods are used to obtain a unified summary of multiple datasets. For multi-modal data, we propose a computational workflow to jointly analyze datasets from cell lines. The workflow comprises a novel probabilistic data integration method, named POPLS-DA, for multi-omics data. The workflow is motivated by a study on synucleinopathies where transcriptomics, proteomics, and drug screening data are measured in affected LUHMES cell lines and controls. The aim is to highlight potentially druggable pathways and genes involved in synucleinopathies. First, POPLS-DA is used to prioritize genes and proteins that best distinguish cases and controls. For these genes, an integrated interaction network is constructed where the drug screen data is incorporated to highlight druggable genes and pathways in the network. Finally, functional enrichment analyses are performed to identify clusters of synaptic and lysosome-related genes and proteins targeted by the protective drugs. POPLS-DA is compared to other single- and multi-omics approaches. We found that HSPA5, a member of the heat shock protein 70 family, was one of the most targeted genes by the validated drugs, in particular by AT1-blockers. HSPA5 and AT1-blockers have been previously linked to α-synuclein pathology and Parkinson's disease, showing the relevance of our findings. Our computational workflow identified new directions for therapeutic targets for synucleinopathies. POPLS-DA provided a larger interpretable gene set than other single- and multi-omic approaches. An implementation based on R and markdown is freely available online.
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Affiliation(s)
| | | | - Hae-Won Uh
- Dept. Data science & Biostatistics, UMC Utrecht, Utrecht, Netherlands
| | - Claudia Moebius
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Marc Bickle
- Roche Institute for Translational Bioengineering, Basel, Switzerland
| | - Günter Höglinger
- Department of Neurology, Hannover Medical School, Hannover, Germany
- Department of Neurology, Ludwig-Maximilians-Universität, Munich, Germany
- German Center for Neurodegenerative Diseases, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Jeanine Houwing-Duistermaat
- Dept. Data science & Biostatistics, UMC Utrecht, Utrecht, Netherlands
- Dept. of Mathematics, Radboud University, Nijmegen, Netherlands
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5
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Gupta A, Weinand K, Nathan A, Sakaue S, Zhang MJ, Donlin L, Wei K, Price AL, Amariuta T, Raychaudhuri S. Dynamic regulatory elements in single-cell multimodal data implicate key immune cell states enriched for autoimmune disease heritability. Nat Genet 2023; 55:2200-2210. [PMID: 38036783 PMCID: PMC10787644 DOI: 10.1038/s41588-023-01577-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 10/18/2023] [Indexed: 12/02/2023]
Abstract
In autoimmune diseases such as rheumatoid arthritis, the immune system attacks the body's own cells. Developing a precise understanding of the cell states where noncoding autoimmune risk variants impart causal mechanisms is critical to developing curative therapies. Here, to identify noncoding regions with accessible chromatin that associate with cell-state-defining gene expression patterns, we leveraged multimodal single-nucleus RNA and assay for transposase-accessible chromatin (ATAC) sequencing data across 28,674 cells from the inflamed synovial tissue of 12 donors. Specifically, we used a multivariate Poisson model to predict peak accessibility from single-nucleus RNA sequencing principal components. For 14 autoimmune diseases, we discovered that cell-state-dependent ('dynamic') chromatin accessibility peaks in immune cell types were enriched for heritability, compared with cell-state-invariant ('cs-invariant') peaks. These dynamic peaks marked regulatory elements associated with T peripheral helper, regulatory T, dendritic and STAT1+CXCL10+ myeloid cell states. We argue that dynamic regulatory elements can help identify precise cell states enriched for disease-critical genetic variation.
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Affiliation(s)
- Anika Gupta
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, 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
| | - Kathryn Weinand
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, 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
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, 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
| | - Saori Sakaue
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, 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
| | - Martin Jinye Zhang
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Laura Donlin
- Weill Cornell Medicine, New York, NY, USA
- Hospital for Special Surgery, New York, NY, USA
| | - Kevin Wei
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alkes L Price
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Tiffany Amariuta
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA.
- Department of Medicine, University of California San Diego, La Jolla, CA, USA.
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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6
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Chepelev I, Harley IT, Harley JB. Modeling of horizontal pleiotropy identifies possible causal gene expression in systemic lupus erythematosus. FRONTIERS IN LUPUS 2023; 1:1234578. [PMID: 37799268 PMCID: PMC10554754 DOI: 10.3389/flupu.2023.1234578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/07/2023]
Abstract
Background Systemic lupus erythematosus (SLE) is a chronic autoimmune condition with complex causes involving genetic and environmental factors. While genome-wide association studies (GWASs) have identified genetic loci associated with SLE, the functional genomic elements responsible for disease development remain largely unknown. Mendelian Randomization (MR) is an instrumental variable approach to causal inference based on data from observational studies, where genetic variants are employed as instrumental variables (IVs). Methods This study utilized a two-step strategy to identify causal genes for SLE. In the first step, the classical MR method was employed, assuming the absence of horizontal pleiotropy, to estimate the causal effect of gene expression on SLE. In the second step, advanced probabilistic MR methods (PMR-Egger, MRAID, and MR-MtRobin) were applied to the genes identified in the first step, considering horizontal pleiotropy, to filter out false positives. PMR-Egger and MRAID analyses utilized whole blood expression quantitative trait loci (eQTL) and SLE GWAS summary data, while MR-MtRobin analysis used an independent eQTL dataset from multiple immune cell types along with the same SLE GWAS data. Results The initial MR analysis identified 142 genes, including 43 outside of chromosome 6. Subsequently, applying the advanced MR methods reduced the number of genes with significant causal effects on SLE to 66. PMR-Egger, MRAID, and MR-MtRobin, respectively, identified 13, 7, and 16 non-chromosome 6 genes with significant causal effects. All methods identified expression of PHRF1 gene as causal for SLE. A comprehensive literature review was conducted to enhance understanding of the functional roles and mechanisms of the identified genes in SLE development. Conclusions The findings from the three MR methods exhibited overlapping genes with causal effects on SLE, demonstrating consistent results. However, each method also uncovered unique genes due to different modelling assumptions and technical factors, highlighting the complementary nature of the approaches. Importantly, MRAID demonstrated a reduced percentage of causal genes from the Major Histocompatibility complex (MHC) region on chromosome 6, indicating its potential in minimizing false positive findings. This study contributes to unraveling the mechanisms underlying SLE by employing advanced probabilistic MR methods to identify causal genes, thereby enhancing our understanding of SLE pathogenesis.
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Affiliation(s)
- Iouri Chepelev
- Research Service, US Department of Veterans Affairs Medical Center, Cincinnati, OH, United States
- Cincinnati Education and Research for Veterans Foundation, Cincinnati, OH, United States
| | - Isaac T.W. Harley
- US Department of Veterans Affairs Medical Center, Aurora, CO, United States
- Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, CO, United States
- Division of Rheumatology, Department of Medicine, University of Colorado School of Medicine, Aurora, CO, United States
| | - John B. Harley
- Research Service, US Department of Veterans Affairs Medical Center, Cincinnati, OH, United States
- Cincinnati Education and Research for Veterans Foundation, Cincinnati, OH, United States
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7
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Ma Y, Deng C, Zhou Y, Zhang Y, Qiu F, Jiang D, Zheng G, Li J, Shuai J, Zhang Y, Yang J, Su J. Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data. CELL GENOMICS 2023; 3:100383. [PMID: 37719150 PMCID: PMC10504677 DOI: 10.1016/j.xgen.2023.100383] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 05/26/2023] [Accepted: 07/25/2023] [Indexed: 09/19/2023]
Abstract
Advances in single-cell RNA sequencing (scRNA-seq) techniques have accelerated functional interpretation of disease-associated variants discovered from genome-wide association studies (GWASs). However, identification of trait-relevant cell populations is often impeded by inherent technical noise and high sparsity in scRNA-seq data. Here, we developed scPagwas, a computational approach that uncovers trait-relevant cellular context by integrating pathway activation transformation of scRNA-seq data and GWAS summary statistics. scPagwas effectively prioritizes trait-relevant genes, which facilitates identification of trait-relevant cell types/populations with high accuracy in extensive simulated and real datasets. Cellular-level association results identified a novel subpopulation of naive CD8+ T cells related to COVID-19 severity and oligodendrocyte progenitor cell and microglia subsets with critical pathways by which genetic variants influence Alzheimer's disease. Overall, our approach provides new insights for the discovery of trait-relevant cell types and improves the mechanistic understanding of disease variants from a pathway perspective.
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Affiliation(s)
- Yunlong Ma
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
| | - Chunyu Deng
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150080, China
| | - Yijun Zhou
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
| | - Yaru Zhang
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
| | - Fei Qiu
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
| | - Dingping Jiang
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
| | - Gongwei Zheng
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
| | - Jingjing Li
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
| | - Jianwei Shuai
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
| | - Yan Zhang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150080, China
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310012, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Jianzhong Su
- School of Biomedical Engineering, School of OphthalmoFlogy & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
- Oujiang Laboratory, Zhejiang Lab for Regenerative Medicine, Vision and Brain Health, Wenzhou, Zhejiang 325101, China
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8
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Vinuesa CG, Shen N, Ware T. Genetics of SLE: mechanistic insights from monogenic disease and disease-associated variants. Nat Rev Nephrol 2023; 19:558-572. [PMID: 37438615 DOI: 10.1038/s41581-023-00732-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/25/2023] [Indexed: 07/14/2023]
Abstract
The past few years have provided important insights into the genetic architecture of systemic autoimmunity through aggregation of findings from genome-wide association studies (GWAS) and whole-exome or whole-genome sequencing studies. In the prototypic systemic autoimmune disease systemic lupus erythematosus (SLE), monogenic disease accounts for a small fraction of cases but has been instrumental in the elucidation of disease mechanisms. Defects in the clearance or digestion of extracellular or intracellular DNA or RNA lead to increased sensing of nucleic acids, which can break B cell tolerance and induce the production of type I interferons leading to tissue damage. Current data suggest that multiple GWAS SLE risk alleles act in concert with rare functional variants to promote SLE development. Moreover, introduction of orthologous variant alleles into mice has revealed that pathogenic X-linked dominant and recessive SLE can be caused by novel variants in TLR7 and SAT1, respectively. Such bespoke models of disease help to unravel pathogenic pathways and can be used to test targeted therapies. Cell type-specific expression data revealed that most GWAS SLE risk genes are highly expressed in age-associated B cells (ABCs), which supports the view that ABCs produce lupus autoantibodies and contribute to end-organ damage by persisting in inflamed tissues, including the kidneys. ABCs have thus emerged as key targets of promising precision therapeutics.
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Affiliation(s)
- Carola G Vinuesa
- The Francis Crick Institute, London, UK.
- University College London, London, UK.
- China Australia Centre for Personalized Immunology (CACPI), Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, China.
| | - Nan Shen
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, China
- Center for Autoimmune Genomics and Aetiology, Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Paediatrics, University of Cincinnati, Cincinnati, OH, USA
| | - Thuvaraka Ware
- The Francis Crick Institute, London, UK
- University College London, London, UK
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9
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Gerussi A, Soskic B, Asselta R, Invernizzi P, Gershwin ME. GWAS and autoimmunity: What have we learned and what next. J Autoimmun 2022; 133:102922. [PMID: 36209690 DOI: 10.1016/j.jaut.2022.102922] [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: 07/22/2022] [Revised: 09/22/2022] [Accepted: 09/23/2022] [Indexed: 12/07/2022]
Abstract
Autoimmune diseases are common conditions characterized by loss of tolerance, female predominance and a remarkable heterogeneity among different populations. Most often they are polygenic and several genetic loci have been linked with the risk of developing autoimmune diseases. However, causal inference is difficult. When the genomic revolution began there were high hopes of translating fast genetic analyses to the bedside but this has proven to be challenging. Nonetheless, over the last decade, fine-mapping strategies have greatly improved; one of the most significant research lines focuses on the in vivo and ex vivo definition of the effect of genetic variants within the target tissues and within specific subpopulations of immune cells that are involved in the disease pathogenesis. This strategy also includes the longitudinal tracking of a large number of immunophenotypes in many individuals to build a large reference atlas for variant characterization. In this review, we discuss the results obtained by GWAS in autoimmune diseases and review recent advances in fine mapping strategies. More importantly, we discuss gaps and future directions.
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Affiliation(s)
- Alessio Gerussi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy; European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy.
| | - Blagoje Soskic
- Human Technopole, Viale Rita Levi-Montalcini 1, 20157 Milan, Italy
| | - Rosanna Asselta
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Pietro Invernizzi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy; European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
| | - Merrill E Gershwin
- Division of Rheumatology, Allergy and Clinical Immunology, University of California, Davis, Davis, CA, United States
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10
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Zhang MJ, Hou K, Dey KK, Sakaue S, Jagadeesh KA, Weinand K, Taychameekiatchai A, Rao P, Pisco AO, Zou J, Wang B, Gandal M, Raychaudhuri S, Pasaniuc B, Price AL. Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data. Nat Genet 2022; 54:1572-1580. [PMID: 36050550 PMCID: PMC9891382 DOI: 10.1038/s41588-022-01167-z] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 07/19/2022] [Indexed: 02/03/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) provides unique insights into the pathology and cellular origin of disease. We introduce single-cell disease relevance score (scDRS), an approach that links scRNA-seq with polygenic disease risk at single-cell resolution, independent of annotated cell types. scDRS identifies cells exhibiting excess expression across disease-associated genes implicated by genome-wide association studies (GWASs). We applied scDRS to 74 diseases/traits and 1.3 million single-cell gene-expression profiles across 31 tissues/organs. Cell-type-level results broadly recapitulated known cell-type-disease associations. Individual-cell-level results identified subpopulations of disease-associated cells not captured by existing cell-type labels, including T cell subpopulations associated with inflammatory bowel disease, partially characterized by their effector-like states; neuron subpopulations associated with schizophrenia, partially characterized by their spatial locations; and hepatocyte subpopulations associated with triglyceride levels, partially characterized by their higher ploidy levels. Genes whose expression was correlated with the scDRS score across cells (reflecting coexpression with GWAS disease-associated genes) were strongly enriched for gold-standard drug target and Mendelian disease genes.
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Affiliation(s)
- Martin Jinye Zhang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Kushal K Dey
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Saori Sakaue
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Karthik A Jagadeesh
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kathryn Weinand
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Aris Taychameekiatchai
- Department of Medicine and Liver Center, University of California, San Francisco, San Francisco, CA, USA
- Developmental and Stem Cell Biology Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Poorvi Rao
- Department of Medicine and Liver Center, University of California, San Francisco, San Francisco, CA, USA
| | | | - James Zou
- Chan Zuckerberg Biohub, San Francisco, CA, USA
- Department of Electrical Engineering, Stanford University, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA
| | - Bruce Wang
- Department of Medicine and Liver Center, University of California, San Francisco, San Francisco, CA, USA
| | - Michael Gandal
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Soumya Raychaudhuri
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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11
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Li K, Wang M, Zhao L, Liu Y, Zhang X. ACPA-negative rheumatoid arthritis: From immune mechanisms to clinical translation. EBioMedicine 2022; 83:104233. [PMID: 36027873 PMCID: PMC9404277 DOI: 10.1016/j.ebiom.2022.104233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/13/2022] [Accepted: 08/05/2022] [Indexed: 11/28/2022] Open
Abstract
The presence of anti-citrullinated protein autoantibodies (ACPA) is a hallmark feature of rheumatoid arthritis (RA), which causes chronic joint destruction and systemic inflammation. Based on ACPA status, RA patients can be sub-grouped into two major subsets: ACPA-positive RA (ACPA+ RA) and ACPA-negative RA (ACPA– RA). Accumulating evidence have suggested that ACPA+ RA and ACPA– RA are two distinct disease entities with different underlying pathophysiology. In contrast to the well-characterized pathogenic mechanisms of ACPA+ RA, the etiology of ACPA– RA remains largely unknown. In this review, we summarized current knowledge about the primary drivers of ACPA– RA, particularly focusing on the serological, cellular, and molecular aspects of immune mechanisms. A better understanding of the immunopathogenesis in ACPA– RA will help in designing more precisely targeting strategies, and paving the road to personalized treatment. In addition, identification of novel biomarkers in ACPA– RA will substantially promote early treatment and improve the outcomes.
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Affiliation(s)
- Ketian Li
- Department of Rheumatology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Clinical Immunology Center, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, PR China
| | - Min Wang
- Department of Rheumatology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Clinical Immunology Center, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, PR China
| | - Lidan Zhao
- Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, PR China
| | - Yudong Liu
- Department of Rheumatology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Clinical Immunology Center, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, PR China; The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, PR China.
| | - Xuan Zhang
- Department of Rheumatology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Clinical Immunology Center, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, PR China.
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12
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Musters A, Balzaretti G, van Schaik BDC, Jongejan A, van der Weele L, Tas SW, van Kampen AHC, de Vries N. In rheumatoid arthritis inflamed joints share dominant patient-specific B-cell clones. Front Immunol 2022; 13:915687. [PMID: 35967291 PMCID: PMC9363889 DOI: 10.3389/fimmu.2022.915687] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 06/30/2022] [Indexed: 11/15/2022] Open
Abstract
Background In patients with rheumatoid arthritis (RA) different joints were shown to share the same dominant T-cell clones, suggesting shared characteristics of the inflammatory process and indicating that strategies to selectively target the antigen receptor might be feasible. Since T- and B-lymphocytes closely interact in adaptive responses, we analysed to what extent different joints also share dominant B-cell clones. Methods In 11 RA patients, quantitative B-cell receptor (BCR) repertoire analysis was performed in simultaneously obtained samples from inflamed synovial tissue (ST) from distinct locations within one joint, from multiple joints, from synovial fluid (SF) and peripheral blood (PB). Results ST biopsies from different locations in the same joint showed clear overlap in the top-25 dominant BCR clones (16.7%, SD 12.5), in the same range as the overlap between ST and SF in the same joint (8.0%, SD 8.8) and the overlap between ST-ST between different joints (9.1%, SD 8.2), but clearly higher than the overlap between ST and PB (1.7%, SD 2.4; p<0.05) and SF and PB (2.7%, SD 4.1; p<0.05). Interestingly, these figures were substantially lower than the overlap observed in previous T-cell clonality studies. Conclusions We conclude that in RA BCR clonal responses may be more localized than TCR clonal responses, pointing to antigen-selective influx, proliferation and/or maturation of B-cells. B lineage cells in the SF may adequately represent the dominant BCR clones of the ST, which is in contrast to T-cells. Collectively, the presence of shared B- and especially T-cells in different joints from the same patient suggests that approaches might be feasible that aim to develop antigen-receptor specific targeting of lymphocyte clones in RA as an alternative to more generalized immunosuppressive strategies.
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Affiliation(s)
- Anne Musters
- Department of Clinical Immunology & Rheumatology, Amsterdam University Medical Center (Location AMC)/University of Amsterdam, Amsterdam, Netherlands
| | - Giulia Balzaretti
- Department of Clinical Immunology & Rheumatology, Amsterdam University Medical Center (Location AMC)/University of Amsterdam, Amsterdam, Netherlands
- Laboratory of Experimental Immunology, Amsterdam University Medical Center (Location AMC)/University of Amsterdam, Amsterdam, Netherlands
| | - Barbera D. C. van Schaik
- Bioinformatics Laboratory, Department of Epidemiology and Data Science, Amsterdam University Medical Center (Location AMC)/University of Amsterdam, Amsterdam, Netherlands
| | - Aldo Jongejan
- Bioinformatics Laboratory, Department of Epidemiology and Data Science, Amsterdam University Medical Center (Location AMC)/University of Amsterdam, Amsterdam, Netherlands
| | - Linda van der Weele
- Department of Clinical Immunology & Rheumatology, Amsterdam University Medical Center (Location AMC)/University of Amsterdam, Amsterdam, Netherlands
- Laboratory of Experimental Immunology, Amsterdam University Medical Center (Location AMC)/University of Amsterdam, Amsterdam, Netherlands
| | - Sander W. Tas
- Department of Clinical Immunology & Rheumatology, Amsterdam University Medical Center (Location AMC)/University of Amsterdam, Amsterdam, Netherlands
- Laboratory of Experimental Immunology, Amsterdam University Medical Center (Location AMC)/University of Amsterdam, Amsterdam, Netherlands
| | - Antoine H. C. van Kampen
- Bioinformatics Laboratory, Department of Epidemiology and Data Science, Amsterdam University Medical Center (Location AMC)/University of Amsterdam, Amsterdam, Netherlands
| | - Niek de Vries
- Department of Clinical Immunology & Rheumatology, Amsterdam University Medical Center (Location AMC)/University of Amsterdam, Amsterdam, Netherlands
- Laboratory of Experimental Immunology, Amsterdam University Medical Center (Location AMC)/University of Amsterdam, Amsterdam, Netherlands
- *Correspondence: Niek de Vries,
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13
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Perez RK, Gordon MG, Subramaniam M, Kim MC, Hartoularos GC, Targ S, Sun Y, Ogorodnikov A, Bueno R, Lu A, Thompson M, Rappoport N, Dahl A, Lanata CM, Matloubian M, Maliskova L, Kwek SS, Li T, Slyper M, Waldman J, Dionne D, Rozenblatt-Rosen O, Fong L, Dall’Era M, Balliu B, Regev A, Yazdany J, Criswell LA, Zaitlen N, Ye CJ. Single-cell RNA-seq reveals cell type-specific molecular and genetic associations to lupus. Science 2022; 376:eabf1970. [PMID: 35389781 PMCID: PMC9297655 DOI: 10.1126/science.abf1970] [Citation(s) in RCA: 140] [Impact Index Per Article: 70.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease. Knowledge of circulating immune cell types and states associated with SLE remains incomplete. We profiled more than 1.2 million peripheral blood mononuclear cells (162 cases, 99 controls) with multiplexed single-cell RNA sequencing (mux-seq). Cases exhibited elevated expression of type 1 interferon-stimulated genes (ISGs) in monocytes, reduction of naïve CD4+ T cells that correlated with monocyte ISG expression, and expansion of repertoire-restricted cytotoxic GZMH+ CD8+ T cells. Cell type-specific expression features predicted case-control status and stratified patients into two molecular subtypes. We integrated dense genotyping data to map cell type-specific cis-expression quantitative trait loci and to link SLE-associated variants to cell type-specific expression. These results demonstrate mux-seq as a systematic approach to characterize cellular composition, identify transcriptional signatures, and annotate genetic variants associated with SLE.
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Affiliation(s)
- Richard K. Perez
- School of Medicine, University of California, San Francisco, CA, USA
| | - M. Grace Gordon
- Biological and Medical Informatics Graduate Program, University of California, San Francisco, CA, USA
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Meena Subramaniam
- Biological and Medical Informatics Graduate Program, University of California, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, CA, USA
| | - Min Cheol Kim
- School of Medicine, University of California, San Francisco, CA, USA
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, CA, USA
- Medical Scientist Training Program, University of California, San Francisco, CA, USA
- UC Berkeley–UCSF Graduate Program in Bioengineering, San Francisco, CA, USA
| | - George C. Hartoularos
- Biological and Medical Informatics Graduate Program, University of California, San Francisco, CA, USA
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, CA, USA
| | - Sasha Targ
- School of Medicine, University of California, San Francisco, CA, USA
- Biological and Medical Informatics Graduate Program, University of California, San Francisco, CA, USA
- Medical Scientist Training Program, University of California, San Francisco, CA, USA
| | - Yang Sun
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, CA, USA
| | - Anton Ogorodnikov
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, CA, USA
| | - Raymund Bueno
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, CA, USA
| | - Andrew Lu
- UCLA-Caltech Medical Scientist Training Program, Los Angeles, CA, USA
| | - Mike Thompson
- Department of Computer Science, University of California, Los Angeles, CA, USA
| | - Nadav Rappoport
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Be’er Sheva, Israel
| | - Andrew Dahl
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Cristina M. Lanata
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, CA, USA
- Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center, University of California, San Francisco, CA, USA
| | - Mehrdad Matloubian
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, CA, USA
- Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center, University of California, San Francisco, CA, USA
| | - Lenka Maliskova
- Institute for Human Genetics, University of California, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Serena S. Kwek
- Division of Hematology/Oncology, Department of Medicine, University of California, San Francisco, CA, USA
| | - Tony Li
- Division of Hematology/Oncology, Department of Medicine, University of California, San Francisco, CA, USA
| | - Michal Slyper
- Klarman Cell Observatory, Broad Institute, Cambridge, MA, USA
| | - Julia Waldman
- Klarman Cell Observatory, Broad Institute, Cambridge, MA, USA
| | - Danielle Dionne
- Klarman Cell Observatory, Broad Institute, Cambridge, MA, USA
| | | | - Lawrence Fong
- Division of Hematology/Oncology, Department of Medicine, University of California, San Francisco, CA, USA
| | - Maria Dall’Era
- School of Medicine, University of California, San Francisco, CA, USA
| | - Brunilda Balliu
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Howard Hughes Medical Institute, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jinoos Yazdany
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, CA, USA
| | - Lindsey A. Criswell
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, CA, USA
- Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center, University of California, San Francisco, CA, USA
| | - Noah Zaitlen
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Chun Jimmie Ye
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, CA, USA
- Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center, University of California, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
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14
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Mena J, Alloza I, Tulloch Navarro R, Aldekoa A, Díez García J, Villanueva Etxebarria A, Lindskog C, Antigüedad A, Boyero S, Mendibe-Bilbao MDM, Álvarez de Arcaya A, Sánchez Menoyo JL, Midaglia L, Villarrubia N, Malhotra S, Montalban X, Villar LM, Comabella M, Vandenbroeck K. Genomic Multiple Sclerosis Risk Variants Modulate the Expression of the ANKRD55- IL6ST Gene Region in Immature Dendritic Cells. Front Immunol 2022; 12:816930. [PMID: 35111166 PMCID: PMC8801523 DOI: 10.3389/fimmu.2021.816930] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 12/22/2021] [Indexed: 12/25/2022] Open
Abstract
Intronic single-nucleotide polymorphisms (SNPs) in the ANKRD55 gene are associated with the risk for multiple sclerosis (MS) and rheumatoid arthritis by genome-wide association studies (GWAS). The risk alleles have been linked to higher expression levels of ANKRD55 and the neighboring IL6ST (gp130) gene in CD4+ T lymphocytes of healthy controls. The biological function of ANKRD55, its role in the immune system, and cellular sources of expression other than lymphocytes remain uncharacterized. Here, we show that monocytes gain capacity to express ANKRD55 during differentiation in immature monocyte-derived dendritic cells (moDCs) in the presence of interleukin (IL)-4/granulocyte-macrophage colony-stimulating factor (GM-CSF). ANKRD55 expression levels are further enhanced by retinoic acid agonist AM580 but downregulated following maturation with interferon (IFN)-γ and lipopolysaccharide (LPS). ANKRD55 was detected in the nucleus of moDC in nuclear speckles. We also analyzed the adjacent IL6ST, IL31RA, and SLC38A9 genes. Of note, in healthy controls, MS risk SNP genotype influenced ANKRD55 and IL6ST expression in immature moDC in opposite directions to that in CD4+ T cells. This effect was stronger for a partially correlated SNP, rs13186299, that is located, similar to the main MS risk SNPs, in an ANKRD55 intron. Upon analysis in MS patients, the main GWAS MS risk SNP rs7731626 was associated with ANKRD55 expression levels in CD4+ T cells. MoDC-specific ANKRD55 and IL6ST mRNA levels showed significant differences according to the clinical form of the disease, but, in contrast to healthy controls, were not influenced by genotype. We also measured serum sgp130 levels, which were found to be higher in homozygotes of the protective allele of rs7731626. Our study characterizes ANKRD55 expression in moDC and indicates monocyte-to-dendritic cell (Mo-DC) differentiation as a process potentially influenced by MS risk SNPs.
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Affiliation(s)
- Jorge Mena
- Inflammation & Biomarkers Group, Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain
| | - Iraide Alloza
- Inflammation & Biomarkers Group, Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain
| | - Raquel Tulloch Navarro
- Inflammation & Biomarkers Group, Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain
| | - Ane Aldekoa
- Inflammation & Biomarkers Group, Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain
| | - Javier Díez García
- Microscopy Facility, Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain
| | - Ane Villanueva Etxebarria
- Kronikgune Institute for Health Services Research, Barakaldo, Spain
- Health Service Research Network on Chronic Diseases Red de Investigación en Servicios de Salud en Enfermedades Crónicas (REDISSEC), Bizkaia, Spain
- Osakidetza-Basque Health Service, Research Unit, Galdakao University Hospital, Galdakao, Spain
| | - Cecilia Lindskog
- Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Alfredo Antigüedad
- Department of Neurology, Cruces University Hospital, Osakidetza-Basque Health Service, Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain
| | - Sabas Boyero
- Department of Neurology, Cruces University Hospital, Osakidetza-Basque Health Service, Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain
| | - María del Mar Mendibe-Bilbao
- Department of Neurology, Cruces University Hospital, Osakidetza-Basque Health Service, Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain
| | - Amaya Álvarez de Arcaya
- Department of Neurology, Txagorritxu University Hospital, Osakidetza-Basque Health Service, Bioaraba Health Research Institute, Vitoria-Gasteiz, Spain
| | - José Luis Sánchez Menoyo
- Department of Neurology, Galdakao-Usansolo University Hospital, Osakidetza-Basque Health Service, Biocruces-Bizkaia Health Research Institute, Galdakao, Spain
| | - Luciana Midaglia
- Servei de Neurologia-Neuroimmunologia, Centre d’Esclerosi Múltiple de Catalunya (Cemcat), Institut de Recerca Vall d’Hebron (VHIR), Hospital Universitari Vall d’Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Noelia Villarrubia
- Department of Immunology, Hospital Universitario Ramón y Cajal, Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Red Española de Esclerosis Múltiple (REEM), Madrid, Spain
| | - Sunny Malhotra
- Servei de Neurologia-Neuroimmunologia, Centre d’Esclerosi Múltiple de Catalunya (Cemcat), Institut de Recerca Vall d’Hebron (VHIR), Hospital Universitari Vall d’Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Xavier Montalban
- Servei de Neurologia-Neuroimmunologia, Centre d’Esclerosi Múltiple de Catalunya (Cemcat), Institut de Recerca Vall d’Hebron (VHIR), Hospital Universitari Vall d’Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Luisa María Villar
- Department of Immunology, Hospital Universitario Ramón y Cajal, Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Red Española de Esclerosis Múltiple (REEM), Madrid, Spain
| | - Manuel Comabella
- Servei de Neurologia-Neuroimmunologia, Centre d’Esclerosi Múltiple de Catalunya (Cemcat), Institut de Recerca Vall d’Hebron (VHIR), Hospital Universitari Vall d’Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Koen Vandenbroeck
- Inflammation & Biomarkers Group, Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco (UPV/EHU), Barrio Sarriena, Leioa, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
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Functional Implications of Intergenic GWAS SNPs in Immune-Related LncRNAs. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1363:147-160. [DOI: 10.1007/978-3-030-92034-0_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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16
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Rajabi F, Jabalameli N, Rezaei N. The Concept of Immunogenetics. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1367:1-17. [DOI: 10.1007/978-3-030-92616-8_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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17
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Afrasiabi A, Keane JT, Ong LTC, Alinejad-Rokny H, Fewings NL, Booth DR, Parnell GP, Swaminathan S. Genetic and transcriptomic analyses support a switch to lytic phase in Epstein Barr virus infection as an important driver in developing Systemic Lupus Erythematosus. J Autoimmun 2021; 127:102781. [PMID: 34952359 DOI: 10.1016/j.jaut.2021.102781] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 12/04/2021] [Accepted: 12/10/2021] [Indexed: 12/20/2022]
Abstract
To investigate the molecular mechanisms through which Epstein-Barr virus (EBV) may contribute to Systemic Lupus Erythematosus (SLE) pathogenesis, we interrogated SLE genetic risk loci for signatures of EBV infection. We first compared the gene expression profile of SLE risk genes across 459 different cell/tissue types. EBV-infected B cells (LCLs) had the strongest representation of highly expressed SLE risk genes. By determining an SLE risk allele effect on gene expression (expression quantitative trait loci, eQTL) in LCLs and 16 other immune cell types, we identified 79 SLE risk locus:gene pairs putatively interacting with EBV infection. A total of 10 SLE risk genes from this list (CD40, LYST, JAZF1, IRF5, BLK, IKZF2, IL12RB2, FAM167A, PTPRC and SLC15A) were targeted by the EBV transcription factor, EBNA2, differentially expressed between LCLs and B cells, and the majority were also associated with EBV DNA copy number, and expression level of EBV encoded genes. Our final gene network model based on these genes is suggestive of a nexus involving SLE risk loci and EBV latency III and B cell proliferation signalling pathways. Collectively, our findings provide further evidence to support the interaction between SLE risk loci and EBV infection that is in part mediated by EBNA2. This interplay may increase the tendency towards EBV lytic switching dependent on the presence of SLE risk alleles. These results support further investigation into targeting EBV as a therapeutic strategy for SLE.
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Affiliation(s)
- Ali Afrasiabi
- EBV Molecular Lab, Centre for Immunology and Allergy Research, Westmead Institute for Medical Research, University of Sydney, Sydney, NSW, Australia; BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, Australia
| | - Jeremy Thomas Keane
- EBV Molecular Lab, Centre for Immunology and Allergy Research, Westmead Institute for Medical Research, University of Sydney, Sydney, NSW, Australia
| | - Lawrence T C Ong
- EBV Molecular Lab, Centre for Immunology and Allergy Research, Westmead Institute for Medical Research, University of Sydney, Sydney, NSW, Australia
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, Australia; Health Data Analytics Program Leader, AI-enabled Processes (AIP) Research Centre, Macquarie University, Sydney, 2109, Australia; Core Member of UNSW Data Science Hub, The University of New South Wales (UNSW Sydney), Sydney, NSW, 2052, Australia
| | - Nicole Louise Fewings
- EBV Molecular Lab, Centre for Immunology and Allergy Research, Westmead Institute for Medical Research, University of Sydney, Sydney, NSW, Australia; Westmead Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - David Richmond Booth
- EBV Molecular Lab, Centre for Immunology and Allergy Research, Westmead Institute for Medical Research, University of Sydney, Sydney, NSW, Australia
| | - Grant Peter Parnell
- EBV Molecular Lab, Centre for Immunology and Allergy Research, Westmead Institute for Medical Research, University of Sydney, Sydney, NSW, Australia; Biomedical Informatics and Digital Health, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.
| | - Sanjay Swaminathan
- EBV Molecular Lab, Centre for Immunology and Allergy Research, Westmead Institute for Medical Research, University of Sydney, Sydney, NSW, Australia; Department of Medicine, Western Sydney University, Sydney, NSW, Australia.
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18
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Fava A, Rao DA. Cellular and molecular heterogeneity in systemic lupus erythematosus. Semin Immunol 2021; 58:101653. [PMID: 36184357 DOI: 10.1016/j.smim.2022.101653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 11/15/2021] [Accepted: 11/15/2021] [Indexed: 12/14/2022]
Affiliation(s)
- Andrea Fava
- Division of Rheumatology, Johns Hopkins University, Baltimore, MD, USA.
| | - Deepak A Rao
- Division of Rheumatology, Inflammation, Immunity, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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Inamo J, Suzuki K, Takeshita M, Kondo Y, Okuzono Y, Koga K, Kassai Y, Takiguchi M, Kurisu R, Yoshimura A, Takeuchi T. Molecular remission at T cell level in patients with rheumatoid arthritis. Sci Rep 2021; 11:16691. [PMID: 34404865 PMCID: PMC8371080 DOI: 10.1038/s41598-021-96300-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Accepted: 07/31/2021] [Indexed: 12/29/2022] Open
Abstract
While numerous disease-modifying anti-rheumatic drugs (DMARDs) have brought about a dramatic paradigm shift in the management of rheumatoid arthritis (RA), unmet needs remain, such as the small proportion of patients who achieve drug-free status. The aim of this study was to explore key molecules for remission at the T cell level, which are known to be deeply involved in RA pathogenesis, and investigate the disease course of patients who achieved molecular remission (MR). We enrolled a total of 46 patients with RA and 10 healthy controls (HCs). We performed gene expression profiling and selected remission signature genes in CD4+ T cells and CD8+ T cells from patients with RA using machine learning methods. In addition, we investigated the benefits of achieving MR on disease control. We identified 9 and 23 genes that were associated with clinical remission in CD4+ and CD8+ T cells, respectively. Principal component analysis (PCA) demonstrated that their expression profiling was similar to those in HCs. For the remission signature genes in CD4+ T cells, the PCA result was reproduced using a validation cohort, indicating the robustness of these genes. A trend toward better disease control was observed during 12 months of follow-up in patients treated with tocilizumab in deep MR compared with those in non-deep MR, although the difference was not significant. The current study will promote our understanding of the molecular mechanisms necessary to achieve deep remission during the management of RA.
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Affiliation(s)
- Jun Inamo
- Division of Rheumatology, Department of Internal Medicine, School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Katsuya Suzuki
- Division of Rheumatology, Department of Internal Medicine, School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Masaru Takeshita
- Division of Rheumatology, Department of Internal Medicine, School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Yasushi Kondo
- Division of Rheumatology, Department of Internal Medicine, School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Yuumi Okuzono
- Immunology Unit, Takeda Pharmaceutical Co Ltd, Fujisawa-Shi, ResearchKanagawa, Japan
| | - Keiko Koga
- Immunology Unit, Takeda Pharmaceutical Co Ltd, Fujisawa-Shi, ResearchKanagawa, Japan
| | - Yoshiaki Kassai
- Immunology Unit, Takeda Pharmaceutical Co Ltd, Fujisawa-Shi, ResearchKanagawa, Japan
| | - Maiko Takiguchi
- Immunology Unit, Takeda Pharmaceutical Co Ltd, Fujisawa-Shi, ResearchKanagawa, Japan
| | - Rina Kurisu
- Immunology Unit, Takeda Pharmaceutical Co Ltd, Fujisawa-Shi, ResearchKanagawa, Japan
| | - Akihiko Yoshimura
- Department of Microbiology and Immunology, School of Medicine, Keio University, Shinjuku-ku, Tokyo, Japan
| | - Tsutomu Takeuchi
- Division of Rheumatology, Department of Internal Medicine, School of Medicine, Keio University, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
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20
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Mu Z, Wei W, Fair B, Miao J, Zhu P, Li YI. The impact of cell type and context-dependent regulatory variants on human immune traits. Genome Biol 2021; 22:122. [PMID: 33926512 PMCID: PMC8082814 DOI: 10.1186/s13059-021-02334-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Accepted: 03/30/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND The vast majority of trait-associated variants identified using genome-wide association studies (GWAS) are noncoding, and therefore assumed to impact gene regulation. However, the majority of trait-associated loci are unexplained by regulatory quantitative trait loci (QTLs). RESULTS We perform a comprehensive characterization of the putative mechanisms by which GWAS loci impact human immune traits. By harmonizing four major immune QTL studies, we identify 26,271 expression QTLs (eQTLs) and 23,121 splicing QTLs (sQTLs) spanning 18 immune cell types. Our colocalization analyses between QTLs and trait-associated loci from 72 GWAS reveals that genetic effects on RNA expression and splicing in immune cells colocalize with 40.4% of GWAS loci for immune-related traits, in many cases increasing the fraction of colocalized loci by two fold compared to previous studies. Notably, we find that the largest contributors of this increase are splicing QTLs, which colocalize on average with 14% of all GWAS loci that do not colocalize with eQTLs. By contrast, we find that cell type-specific eQTLs, and eQTLs with small effect sizes contribute very few new colocalizations. To investigate the 60% of GWAS loci that remain unexplained, we collect H3K27ac CUT&Tag data from rheumatoid arthritis and healthy controls, and find large-scale differences between immune cells from the different disease contexts, including at regions overlapping unexplained GWAS loci. CONCLUSION Altogether, our work supports RNA splicing as an important mediator of genetic effects on immune traits, and suggests that we must expand our study of regulatory processes in disease contexts to improve functional interpretation of as yet unexplained GWAS loci.
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Affiliation(s)
- Zepeng Mu
- Committee on Genetics, Genomics & Systems Biology, University of Chicago, Chicago, IL USA
| | - Wei Wei
- Department of Clinical Immunology, Xijing Hospital, Xi’an, China
- National Translational Science Center for Molecular Medicine, Xi’an, China
| | - Benjamin Fair
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL USA
| | - Jinlin Miao
- Department of Clinical Immunology, Xijing Hospital, Xi’an, China
- National Translational Science Center for Molecular Medicine, Xi’an, China
| | - Ping Zhu
- Department of Clinical Immunology, Xijing Hospital, Xi’an, China
- National Translational Science Center for Molecular Medicine, Xi’an, China
| | - Yang I. Li
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL USA
- Department of Human Genetics, Department of Medicine, University of Chicago, Chicago, IL USA
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21
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Global discovery of lupus genetic risk variant allelic enhancer activity. Nat Commun 2021; 12:1611. [PMID: 33712590 PMCID: PMC7955039 DOI: 10.1038/s41467-021-21854-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2020] [Accepted: 02/16/2021] [Indexed: 12/17/2022] Open
Abstract
Genome-wide association studies of Systemic Lupus Erythematosus (SLE) nominate 3073 genetic variants at 91 risk loci. To systematically screen these variants for allelic transcriptional enhancer activity, we construct a massively parallel reporter assay (MPRA) library comprising 12,396 DNA oligonucleotides containing the genomic context around every allele of each SLE variant. Transfection into the Epstein-Barr virus-transformed B cell line GM12878 reveals 482 variants with enhancer activity, with 51 variants showing genotype-dependent (allelic) enhancer activity at 27 risk loci. Comparison of MPRA results in GM12878 and Jurkat T cell lines highlights shared and unique allelic transcriptional regulatory mechanisms at SLE risk loci. In-depth analysis of allelic transcription factor (TF) binding at and around allelic variants identifies one class of TFs whose DNA-binding motif tends to be directly altered by the risk variant and a second class of TFs that bind allelically without direct alteration of their motif by the variant. Collectively, our approach provides a blueprint for the discovery of allelic gene regulation at risk loci for any disease and offers insight into the transcriptional regulatory mechanisms underlying SLE. Thousands of genetic variants have been associated with lupus, but causal variants and mechanisms are unknown. Here, the authors combine a massively parallel reporter assay with genome-wide ChIP experiments to identify risk variants with allelic enhancer activity mediated through transcription factor binding.
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22
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Rao S, Yao Y, Bauer DE. Editing GWAS: experimental approaches to dissect and exploit disease-associated genetic variation. Genome Med 2021; 13:41. [PMID: 33691767 PMCID: PMC7948363 DOI: 10.1186/s13073-021-00857-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 02/12/2021] [Indexed: 12/17/2022] Open
Abstract
Genome-wide association studies (GWAS) have uncovered thousands of genetic variants that influence risk for human diseases and traits. Yet understanding the mechanisms by which these genetic variants, mainly noncoding, have an impact on associated diseases and traits remains a significant hurdle. In this review, we discuss emerging experimental approaches that are being applied for functional studies of causal variants and translational advances from GWAS findings to disease prevention and treatment. We highlight the use of genome editing technologies in GWAS functional studies to modify genomic sequences, with proof-of-principle examples. We discuss the challenges in interrogating causal variants, points for consideration in experimental design and interpretation of GWAS locus mechanisms, and the potential for novel therapeutic opportunities. With the accumulation of knowledge of functional genetics, therapeutic genome editing based on GWAS discoveries will become increasingly feasible.
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Affiliation(s)
- Shuquan Rao
- Division of Hematology/Oncology, Boston Children's Hospital; Department of Pediatric Oncology, Dana-Farber Cancer Institute; Harvard Stem Cell Institute; Broad Institute; Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
| | - Yao Yao
- Division of Hematology/Oncology, Boston Children's Hospital; Department of Pediatric Oncology, Dana-Farber Cancer Institute; Harvard Stem Cell Institute; Broad Institute; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Daniel E Bauer
- Division of Hematology/Oncology, Boston Children's Hospital; Department of Pediatric Oncology, Dana-Farber Cancer Institute; Harvard Stem Cell Institute; Broad Institute; Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
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23
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Zhu H, Shang L, Zhou X. A Review of Statistical Methods for Identifying Trait-Relevant Tissues and Cell Types. Front Genet 2021; 11:587887. [PMID: 33584792 PMCID: PMC7874162 DOI: 10.3389/fgene.2020.587887] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 12/30/2020] [Indexed: 11/17/2022] Open
Abstract
Genome-wide association studies (GWASs) have identified and replicated many genetic variants that are associated with diseases and disease-related complex traits. However, the biological mechanisms underlying these identified associations remain largely elusive. Exploring the biological mechanisms underlying these associations requires identifying trait-relevant tissues and cell types, as genetic variants likely influence complex traits in a tissue- and cell type-specific manner. Recently, several statistical methods have been developed to integrate genomic data with GWASs for identifying trait-relevant tissues and cell types. These methods often rely on different genomic information and use different statistical models for trait-tissue relevance inference. Here, we present a comprehensive technical review to summarize ten existing methods for trait-tissue relevance inference. These methods make use of different genomic information that include functional annotation information, expression quantitative trait loci information, genetically regulated gene expression information, as well as gene co-expression network information. These methods also use different statistical models that range from linear mixed models to covariance network models. We hope that this review can serve as a useful reference both for methodologists who develop methods and for applied analysts who apply these methods for identifying trait relevant tissues and cell types.
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Affiliation(s)
- Huanhuan Zhu
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States
| | - Lulu Shang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, United States
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24
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Ha E, Bang SY, Lim J, Yun JH, Kim JM, Bae JB, Lee HS, Kim BJ, Kim K, Bae SC. Genetic variants shape rheumatoid arthritis-specific transcriptomic features in CD4 + T cells through differential DNA methylation, explaining a substantial proportion of heritability. Ann Rheum Dis 2021; 80:876-883. [PMID: 33436383 DOI: 10.1136/annrheumdis-2020-219152] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 12/10/2020] [Accepted: 12/30/2020] [Indexed: 01/03/2023]
Abstract
OBJECTIVE CD4+ T cells have been suggested as the most disease-relevant cell type in rheumatoid arthritis (RA) in which RA-risk non-coding variants exhibit allele-specific effects on regulation of RA-driving genes. This study aimed to understand RA-specific signatures in CD4+ T cells using multi-omics data, interpreting inter-omics relationships in shaping the RA transcriptomic landscape. METHODS We profiled genome-wide variants, gene expression and DNA methylation in CD4+ T cells from 82 patients with RA and 40 healthy controls using high-throughput technologies. We investigated differentially expressed genes (DEGs) and differential methylated regions (DMRs) in RA and localised quantitative trait loci (QTLs) for expression and methylation. We then integrated these based on individual-level correlations to inspect DEG-regulating sources and investigated the potential regulatory roles of RA-risk variants by a partitioned-heritability enrichment analysis with RA genome-wide association summary statistics. RESULTS A large number of RA-specific DEGs were identified (n=2575), highlighting T cell differentiation and activation pathways. RA-specific DMRs, preferentially located in T cell regulatory regions, were correlated with the expression levels of 548 DEGs mostly in the same topologically associating domains. In addition, expressional variances in 771 and 83 DEGs were partially explained by expression QTLs for DEGs and methylation QTLs (meQTLs) for DEG-correlated DMRs, respectively. A large number of RA variants were moderately to strongly correlated with meQTLs. DEG-correlated DMRs, enriched with meQTLs, had strongly enriched heritability of RA. CONCLUSION Our findings revealed that the methylomic changes, driven by RA heritability-explaining variants, shape the differential expression of a substantial fraction of DEGs in CD4+ T cells in patients with RA, reinforcing the importance of a multidimensional approach in disease-relevant tissues.
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Affiliation(s)
- Eunji Ha
- Department of Biology and Department of Life and Nanopharmaceutical SciencesBiology, Kyung Hee University, Seoul, Republic of Korea
| | - So-Young Bang
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Republic of Korea.,Hanyang University Institute for Rheumatology Research, Seoul, Republic of Korea
| | - Jiwoo Lim
- Department of Biology and Department of Life and Nanopharmaceutical SciencesBiology, Kyung Hee University, Seoul, Republic of Korea
| | - Jun Ho Yun
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Osong Health Technology Administration Complex, Cheongju, Republic of Korea
| | - Jeong-Min Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Osong Health Technology Administration Complex, Cheongju, Republic of Korea
| | - Jae-Bum Bae
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Osong Health Technology Administration Complex, Cheongju, Republic of Korea
| | - Hye-Soon Lee
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Republic of Korea.,Hanyang University Institute for Rheumatology Research, Seoul, Republic of Korea
| | - Bong-Jo Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Osong Health Technology Administration Complex, Cheongju, Republic of Korea
| | - Kwangwoo Kim
- Department of Biology and Department of Life and Nanopharmaceutical SciencesBiology, Kyung Hee University, Seoul, Republic of Korea
| | - Sang-Cheol Bae
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Republic of Korea .,Hanyang University Institute for Rheumatology Research, Seoul, Republic of Korea
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25
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Nandakumar SK, Liao X, Sankaran VG. In The Blood: Connecting Variant to Function In Human Hematopoiesis. Trends Genet 2020; 36:563-576. [PMID: 32534791 PMCID: PMC7363574 DOI: 10.1016/j.tig.2020.05.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 05/12/2020] [Accepted: 05/13/2020] [Indexed: 02/07/2023]
Abstract
Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with a range of human diseases and traits. However, understanding the mechanisms by which these genetic variants have an impact on associated diseases and traits, often referred to as the variant-to-function (V2F) problem, remains a significant hurdle. Solving the V2F challenge requires us to identify causative genetic variants, relevant cell types/states, target genes, and mechanisms by which variants can cause diseases or alter phenotypic traits. We discuss emerging functional approaches that are being applied to tackle the V2F problem for blood cell traits, illuminating how human genetic variation can impact on key mechanisms in hematopoiesis, as well as highlighting future prospects for this nascent field.
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Affiliation(s)
- Satish K Nandakumar
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - Xiaotian Liao
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - Vijay G Sankaran
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, MA, USA; Harvard Stem Cell Institute, Cambridge, MA, USA.
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26
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Lian S, Liu Z, Zhou Y, Guo J, Gong K, Wang T. The differential expression patterns and co-expression networks of paralogs as an indicator of the TNM stages of lung adenocarcinoma and squamous cell carcinoma. Genomics 2020; 112:4115-4124. [PMID: 32659329 DOI: 10.1016/j.ygeno.2020.07.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 07/03/2020] [Accepted: 07/07/2020] [Indexed: 12/27/2022]
Abstract
Cancers constitute a severe threat to human health. Elucidating the association between the expression patterns of the paralogous genes and transcription factors (TF) and the progression of cancers by comprehensively investigating the expression patterns and co-expression networks will contribute to the in-depth understanding of the pathogenesis of cancers. Here, we identified the paralogous gene pairs and systematically analyzed the expression patterns of these paralogs and the known TFs to elucidate the associations with Tumor, Node, Metastasis (TNM) staging information across ten cancers. We found that the expression of ~60% paralogs was cancer-dependent, and more than 50% of the differentially expressed TFs pairs showed positive expression correlations. The down-regulation patterns of paralogs and TFs were closely associated with the M and N developmental stages of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Our results will help to understand the roles of paralogs and TFs in cancer progression and to screen prognostic biomarkers for early cancer diagnosis.
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Affiliation(s)
- Shuaibin Lian
- College of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, China.
| | - Zixiao Liu
- College of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, China
| | - Yongjie Zhou
- College of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, China
| | - Jiantao Guo
- College of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, China
| | - Ke Gong
- College of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, China
| | - Tianwen Wang
- College of Life Sciences, and Institute for Conservation and Utilization of Agro-bioresources in Dabie Mountains, Xinyang Normal, University, Xinyang 464000, Henan, China.
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27
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Cano-Gamez E, Trynka G. From GWAS to Function: Using Functional Genomics to Identify the Mechanisms Underlying Complex Diseases. Front Genet 2020; 11:424. [PMID: 32477401 PMCID: PMC7237642 DOI: 10.3389/fgene.2020.00424] [Citation(s) in RCA: 269] [Impact Index Per Article: 67.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 04/06/2020] [Indexed: 12/19/2022] Open
Abstract
Genome-wide association studies (GWAS) have successfully mapped thousands of loci associated with complex traits. These associations could reveal the molecular mechanisms altered in common complex diseases and result in the identification of novel drug targets. However, GWAS have also left a number of outstanding questions. In particular, the majority of disease-associated loci lie in non-coding regions of the genome and, even though they are thought to play a role in gene expression regulation, it is unclear which genes they regulate and in which cell types or physiological contexts this regulation occurs. This has hindered the translation of GWAS findings into clinical interventions. In this review we summarize how these challenges have been addressed over the last decade, with a particular focus on the integration of GWAS results with functional genomics datasets. Firstly, we investigate how the tissues and cell types involved in diseases can be identified using methods that test for enrichment of GWAS variants in genomic annotations. Secondly, we explore how to find the genes regulated by GWAS loci using methods that test for colocalization of GWAS signals with molecular phenotypes such as quantitative trait loci (QTLs). Finally, we highlight potential future research avenues such as integrating GWAS results with single-cell sequencing read-outs, designing functionally informed polygenic risk scores (PRS), and validating disease associated genes using genetic engineering. These tools will be crucial to identify new drug targets for common complex diseases.
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Affiliation(s)
- Eddie Cano-Gamez
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Gosia Trynka
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
- Open Targets, Wellcome Genome Campus, Cambridge, United Kingdom
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Klareskog L, Rönnelid J, Saevarsdottir S, Padyukov L, Alfredsson L. The importance of differences; On environment and its interactions with genes and immunity in the causation of rheumatoid arthritis. J Intern Med 2020; 287:514-533. [PMID: 32176395 DOI: 10.1111/joim.13058] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2019] [Revised: 02/03/2020] [Accepted: 02/25/2020] [Indexed: 12/19/2022]
Abstract
The current review uses rheumatoid arthritis (RA) as a prominent example for how studies on the interplay between environmental and genetic factors in defined subsets of a disease can be used to formulate aetiological hypotheses that subsequently can be tested for causality using molecular and functional studies. Major discussed findings are that exposures to airways from many different noxious agents including cigarette smoke, silica dust and more interact with major susceptibility genes, mainly HLA-DR genetic variants in triggering antigen-specific immune reactions specific for RA. We also discuss how several other environmental and lifestyle factors, including microbial, neural and metabolic factors, can influence risk for RA in ways that are different in different subsets of RA.The description of these processes in RA provides the best example so far in any immune-mediated disease of how triggering of immunity at one anatomical site in the context of known environmental and genetic factors subsequently can lead to symptoms that precede the classical inflammatory disease symptoms and later contribute also to the classical RA joint inflammation. The findings referred to in the review have led to a change of paradigms for very early therapy and prevention of RA and to efforts towards what we have named 'personalized prevention'. We believe that the progress described here for RA will be of relevance for research and practice also in other immune-mediated diseases.
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Affiliation(s)
- L Klareskog
- From the, Division of Rheumatology, Department of Medicine, Karolinska Institutet and Karolinska University Hospital (Solna), Stockholm, Sweden
| | - J Rönnelid
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - S Saevarsdottir
- Division of Clinical Epidemiology, Department of Medicine, Karolinska Institutet and Karolinska University Hospital (Solna), Stockholm, Sweden.,Faculty of Medicine, School of Health Sciences, University of Iceland, Karolinska Institutet, Stockholm, Sweden
| | - L Padyukov
- From the, Division of Rheumatology, Department of Medicine, Karolinska Institutet and Karolinska University Hospital (Solna), Stockholm, Sweden
| | - L Alfredsson
- Institute for Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
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29
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Single-cell transcriptomics identifies an effectorness gradient shaping the response of CD4 + T cells to cytokines. Nat Commun 2020; 11:1801. [PMID: 32286271 PMCID: PMC7156481 DOI: 10.1038/s41467-020-15543-y] [Citation(s) in RCA: 131] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 03/18/2020] [Indexed: 12/14/2022] Open
Abstract
Naïve CD4+ T cells coordinate the immune response by acquiring an effector phenotype in response to cytokines. However, the cytokine responses in memory T cells remain largely understudied. Here we use quantitative proteomics, bulk RNA-seq, and single-cell RNA-seq of over 40,000 human naïve and memory CD4+ T cells to show that responses to cytokines differ substantially between these cell types. Memory T cells are unable to differentiate into the Th2 phenotype, and acquire a Th17-like phenotype in response to iTreg polarization. Single-cell analyses show that T cells constitute a transcriptional continuum that progresses from naïve to central and effector memory T cells, forming an effectorness gradient accompanied by an increase in the expression of chemokines and cytokines. Finally, we show that T cell activation and cytokine responses are influenced by the effectorness gradient. Our results illustrate the heterogeneity of T cell responses, furthering our understanding of inflammation. Cytokines critically control the differentiation and functions of activated naïve and memory T cells. Here the authors show, using multi-omics and single-cell analyses, that naïve and memory T cells exhibit distinct cytokine responses, in which an ‘effectorness gradient’ is depicted by a transcriptional continuum, which shapes the downstream genetic programs.
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30
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Wagle MV, Marchingo JM, Howitt J, Tan SS, Goodnow CC, Parish IA. The Ubiquitin Ligase Adaptor NDFIP1 Selectively Enforces a CD8 + T Cell Tolerance Checkpoint to High-Dose Antigen. Cell Rep 2019; 24:577-584. [PMID: 30021156 DOI: 10.1016/j.celrep.2018.06.060] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2017] [Revised: 05/17/2018] [Accepted: 06/12/2018] [Indexed: 01/11/2023] Open
Abstract
Escape from peripheral tolerance checkpoints that control cytotoxic CD8+ T cells is important for cancer immunotherapy and autoimmunity, but pathways enforcing these checkpoints are mostly uncharted. We reveal that the HECT-type ubiquitin ligase activator, NDFIP1, enforces a cell-intrinsic CD8+ T cell checkpoint that desensitizes TCR signaling during in vivo exposure to high antigen levels. Ndfip1-deficient OT-I CD8+ T cells responding to high exogenous tolerogenic antigen doses that normally induce anergy aberrantly expanded and differentiated into effector cells that could precipitate autoimmune diabetes in RIP-OVAhi mice. In contrast, NDFIP1 was dispensable for peripheral deletion to low-dose exogenous or pancreatic islet-derived antigen and had little impact upon effector responses to Listeria or acute LCMV infection. These data provide evidence that NDFIP1 mediates a CD8+ T cell tolerance checkpoint, with a different mechanism to CD4+ T cells, and indicates that CD8+ T cell deletion and anergy are molecularly separable checkpoints.
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Affiliation(s)
- Mayura V Wagle
- John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia; Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Julia M Marchingo
- Division of Immunology, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia; Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia
| | - Jason Howitt
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia; Department of Health and Medical Sciences, Swinburne University, Melbourne, VIC, Australia
| | - Seong-Seng Tan
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Christopher C Goodnow
- John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia; Garvan Institute of Medical Research, Sydney, NSW, Australia; St Vincent's Clinical School, University of New South Wales, Sydney, NSW, Australia.
| | - Ian A Parish
- John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, Australia; Cancer Immunology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
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31
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Amariuta T, Luo Y, Knevel R, Okada Y, Raychaudhuri S. Advances in genetics toward identifying pathogenic cell states of rheumatoid arthritis. Immunol Rev 2019; 294:188-204. [PMID: 31782165 DOI: 10.1111/imr.12827] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 11/07/2019] [Indexed: 12/11/2022]
Abstract
Rheumatoid arthritis (RA) risk has a large genetic component (~60%) that is still not fully understood. This has hampered the design of effective treatments that could promise lifelong remission. RA is a polygenic disease with 106 known genome-wide significant associated loci and thousands of small effect causal variants. Our current understanding of RA risk has suggested cell-type-specific contexts for causal variants, implicating CD4 + effector memory T cells, as well as monocytes, B cells and stromal fibroblasts. While these cellular states and categories are still mechanistically broad, future studies may identify causal cell subpopulations. These efforts are propelled by advances in single cell profiling. Identification of causal cell subpopulations may accelerate therapeutic intervention to achieve lifelong remission.
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Affiliation(s)
- Tiffany Amariuta
- Center for Data Sciences, Harvard Medical School, Boston, MA, USA.,Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Graduate School of Arts and Sciences, Harvard University, Boston, MA, USA
| | - Yang Luo
- Center for Data Sciences, Harvard Medical School, Boston, MA, USA.,Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Rachel Knevel
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Rheumatology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Yukinori Okada
- Division of Medicine, Osaka University, Osaka, Japan.,Osaka University Graduate School of Medicine, Osaka, Japan
| | - Soumya Raychaudhuri
- Center for Data Sciences, Harvard Medical School, Boston, MA, USA.,Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
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32
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Anderson D, Baynam G, Blackwell JM, Lassmann T. Personalised analytics for rare disease diagnostics. Nat Commun 2019; 10:5274. [PMID: 31754101 PMCID: PMC6872807 DOI: 10.1038/s41467-019-13345-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 11/01/2019] [Indexed: 12/21/2022] Open
Abstract
Whole genome and exome sequencing is a standard tool for the diagnosis of patients suffering from rare and other genetic disorders. The interpretation of the tens of thousands of variants returned from such tests remains a major challenge. Here we focus on the problem of prioritising variants with respect to the observed disease phenotype. We hypothesise that linking patterns of gene expression across multiple tissues to the phenotypes will aid in discovering disease causing variants. To test this, we construct classifiers that learn associations between tissue-specific gene expression and disease phenotypes. We find that using Genotype-Tissue Expression project (GTEx) expression data in conjunction with disease agnostic variant prioritisation methods (CADD or MetaSVM) results in consistent improvements in classification accuracy. Our method represents a previously overlooked avenue of utilising existing expression data for clinical diagnostics, and also opens the door to use of other functional genomic data sets in the same manner.
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Affiliation(s)
- Denise Anderson
- Telethon Kids Institute, The University of Western Australia, PO Box 855, West Perth, WA, 6872, Australia.
| | - Gareth Baynam
- Office of Population Health Genomics, Department of Health, PO Box 8172, Perth Business Centre, Perth, WA, 6849, Australia
- Genetic Services of Western Australia, King Edward Memorial Hospital, PO Box 134, Subiaco, WA, 6904, Australia
- Western Australian Register of Developmental Anomalies (WARDA), King Edward Memorial Hospital, PO Box 134, Subiaco, WA, 6904, Australia
| | - Jenefer M Blackwell
- Telethon Kids Institute, The University of Western Australia, PO Box 855, West Perth, WA, 6872, Australia
| | - Timo Lassmann
- Telethon Kids Institute, The University of Western Australia, PO Box 855, West Perth, WA, 6872, Australia.
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33
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Rare variants in non-coding regulatory regions of the genome that affect gene expression in systemic lupus erythematosus. Sci Rep 2019; 9:15433. [PMID: 31659207 PMCID: PMC6817816 DOI: 10.1038/s41598-019-51864-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Accepted: 10/09/2019] [Indexed: 12/16/2022] Open
Abstract
Personalized medicine approaches are increasingly sought for diseases with a heritable component. Systemic lupus erythematosus (SLE) is the prototypic autoimmune disease resulting from loss of immunologic tolerance, but the genetic basis of SLE remains incompletely understood. Genome wide association studies (GWAS) identify regions associated with disease, based on common single nucleotide polymorphisms (SNPs) within them, but these SNPs may simply be markers in linkage disequilibrium with other, causative mutations. Here we use an hierarchical screening approach for prediction and testing of true functional variants within regions identified in GWAS; this involved bioinformatic identification of putative regulatory elements within close proximity to SLE SNPs, screening those regions for potentially causative mutations by high resolution melt analysis, and functional validation using reporter assays. Using this approach, we screened 15 SLE associated loci in 143 SLE patients, identifying 7 new variants including 5 SNPs and 2 insertions. Reporter assays revealed that the 5 SNPs were functional, altering enhancer activity. One novel variant was linked to the relatively well characterized rs9888739 SNP at the ITGAM locus, and may explain some of the SLE heritability at this site. Our study demonstrates that non-coding regulatory elements can contain private sequence variants affecting gene expression, which may explain part of the heritability of SLE.
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34
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Fonseka CY, Rao DA, Teslovich NC, Korsunsky I, Hannes SK, Slowikowski K, Gurish MF, Donlin LT, Lederer JA, Weinblatt ME, Massarotti EM, Coblyn JS, Helfgott SM, Todd DJ, Bykerk VP, Karlson EW, Ermann J, Lee YC, Brenner MB, Raychaudhuri S. Mixed-effects association of single cells identifies an expanded effector CD4 + T cell subset in rheumatoid arthritis. Sci Transl Med 2019; 10:10/463/eaaq0305. [PMID: 30333237 DOI: 10.1126/scitranslmed.aaq0305] [Citation(s) in RCA: 103] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Revised: 05/09/2018] [Accepted: 09/25/2018] [Indexed: 11/02/2022]
Abstract
High-dimensional single-cell analyses have improved the ability to resolve complex mixtures of cells from human disease samples; however, identifying disease-associated cell types or cell states in patient samples remains challenging because of technical and interindividual variation. Here, we present mixed-effects modeling of associations of single cells (MASC), a reverse single-cell association strategy for testing whether case-control status influences the membership of single cells in any of multiple cellular subsets while accounting for technical confounders and biological variation. Applying MASC to mass cytometry analyses of CD4+ T cells from the blood of rheumatoid arthritis (RA) patients and controls revealed a significantly expanded population of CD4+ T cells, identified as CD27- HLA-DR+ effector memory cells, in RA patients (odds ratio, 1.7; P = 1.1 × 10-3). The frequency of CD27- HLA-DR+ cells was similarly elevated in blood samples from a second RA patient cohort, and CD27- HLA-DR+ cell frequency decreased in RA patients who responded to immunosuppressive therapy. Mass cytometry and flow cytometry analyses indicated that CD27- HLA-DR+ cells were associated with RA (meta-analysis P = 2.3 × 10-4). Compared to peripheral blood, synovial fluid and synovial tissue samples from RA patients contained about fivefold higher frequencies of CD27- HLA-DR+ cells, which comprised ~10% of synovial CD4+ T cells. CD27- HLA-DR+ cells expressed a distinctive effector memory transcriptomic program with T helper 1 (TH1)- and cytotoxicity-associated features and produced abundant interferon-γ (IFN-γ) and granzyme A protein upon stimulation. We propose that MASC is a broadly applicable method to identify disease-associated cell populations in high-dimensional single-cell data.
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Affiliation(s)
- Chamith Y Fonseka
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.,Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.,Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.,Department of Biomedical Informatics, Harvard University, Cambridge, MA 02138, USA.,Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Deepak A Rao
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Nikola C Teslovich
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Ilya Korsunsky
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Susan K Hannes
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Kamil Slowikowski
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.,Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.,Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.,Department of Biomedical Informatics, Harvard University, Cambridge, MA 02138, USA.,Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Michael F Gurish
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Laura T Donlin
- Arthritis and Tissue Degeneration Program, Hospital for Special Surgery, New York, NY 10021, USA.,David Z. Rosensweig Genomics Research Center, Hospital for Special Surgery, New York, NY 10021, USA.,Department of Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10021, USA
| | - James A Lederer
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Michael E Weinblatt
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Elena M Massarotti
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Jonathan S Coblyn
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Simon M Helfgott
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Derrick J Todd
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Vivian P Bykerk
- Department of Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10021, USA.,Division of Rheumatology, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021, USA
| | - Elizabeth W Karlson
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Joerg Ermann
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Yvonne C Lee
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.,Division of Rheumatology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Michael B Brenner
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA. .,Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.,Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.,Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA.,Institute of Inflammation and Repair, University of Manchester, Manchester, UK
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35
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Soskic B, Cano-Gamez E, Smyth DJ, Rowan WC, Nakic N, Esparza-Gordillo J, Bossini-Castillo L, Tough DF, Larminie CGC, Bronson PG, Willé D, Trynka G. Chromatin activity at GWAS loci identifies T cell states driving complex immune diseases. Nat Genet 2019; 51:1486-1493. [PMID: 31548716 PMCID: PMC6872452 DOI: 10.1038/s41588-019-0493-9] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 07/30/2019] [Indexed: 12/15/2022]
Abstract
Immune-disease-associated variants are enriched in active chromatin regions of T cells and macrophages. However, whether these variants function in specific cell states is unknown. Here we stimulated T cells and macrophages in the presence of 13 cytokines and profiled active and open chromatin regions. T cell activation induced major chromatin remodeling, while the presence of cytokines fine-tuned the magnitude of changes. We developed a statistical method that accounts for subtle changes in the chromatin landscape to identify SNP enrichment across cell states. Our results point towards the role of immune-disease-associated variants in early rather than late activation of memory CD4+ T cells, with modest differences across cytokines. Furthermore, variants associated with inflammatory bowel disease are enriched in type 1 T helper (TH1) cells, whereas variants associated with Alzheimer's disease are enriched in different macrophage cell states. Our results represent an in-depth analysis of immune-disease-associated variants across a comprehensive panel of activation states of T cells and macrophages.
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Affiliation(s)
- Blagoje Soskic
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Open Targets, Wellcome Genome Campus, Cambridge, UK
| | - Eddie Cano-Gamez
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Open Targets, Wellcome Genome Campus, Cambridge, UK
| | - Deborah J Smyth
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Open Targets, Wellcome Genome Campus, Cambridge, UK
| | - Wendy C Rowan
- Novel Human Genetics, GSK Research, GSK Medicines Research Centre, Stevenage, UK
| | - Nikolina Nakic
- Functional Genomics, Molecular Science and Technology R&D, GSK Medicines Research Centre, Stevenage, UK
| | | | | | - David F Tough
- Epigenetics RU, Oncology R&D, GSK Medicines Research Centre, Stevenage, UK
| | | | - Paola G Bronson
- Human Target Validation Core, RED Translational Biology, Biogen, Cambridge, MA, USA
| | - David Willé
- Biostatistics, GSK Research, GSK Medicines Research Centre, Stevenage, UK
| | - Gosia Trynka
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.
- Open Targets, Wellcome Genome Campus, Cambridge, UK.
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36
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Landscape of stimulation-responsive chromatin across diverse human immune cells. Nat Genet 2019; 51:1494-1505. [PMID: 31570894 PMCID: PMC6858557 DOI: 10.1038/s41588-019-0505-9] [Citation(s) in RCA: 145] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 08/27/2019] [Indexed: 01/08/2023]
Abstract
A hallmark of the immune system is the interplay among specialized cell types transitioning between resting and stimulated states. The gene regulatory landscape of this dynamic system has not been fully characterized in human cells. Here, we collected ATAC-seq and RNA-seq data under resting and stimulated conditions for up to 32 immune cell populations. Stimulation caused widespread chromatin remodeling, including response elements shared between stimulated B and T cells. Furthermore, several autoimmune traits showed significant heritability in stimulation-responsive elements from distinct cell types, highlighting the importance of these cell states in autoimmunity. Use of allele-specific read-mapping identified variants that alter chromatin accessibility in particular conditions, allowing us to observe evidence of function for a candidate causal variant that is undetected by existing large-scale studies in resting cells. Our results provide a resource of chromatin dynamics and highlight the need for characterization of effects of genetic variation in stimulated cells. Analysis of gene expression and open chromatin regions in up to 32 immune cell populations under resting and stimulated conditions identifies widespread chromatin remodeling and shared response elements between stimulated B and T cells.
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37
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Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science 2019; 365:365/6460/eaav7188. [PMID: 31604244 PMCID: PMC7241648 DOI: 10.1126/science.aav7188] [Citation(s) in RCA: 588] [Impact Index Per Article: 117.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2018] [Accepted: 08/06/2019] [Indexed: 02/02/2023]
Abstract
We analyzed genetic data of 47,429 multiple sclerosis (MS) and 68,374 control subjects and established a reference map of the genetic architecture of MS that includes 200 autosomal susceptibility variants outside the major histocompatibility complex (MHC), one chromosome X variant, and 32 variants within the extended MHC. We used an ensemble of methods to prioritize 551 putative susceptibility genes that implicate multiple innate and adaptive pathways distributed across the cellular components of the immune system. Using expression profiles from purified human microglia, we observed enrichment for MS genes in these brain-resident immune cells, suggesting that these may have a role in targeting an autoimmune process to the central nervous system, although MS is most likely initially triggered by perturbation of peripheral immune responses.
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Affiliation(s)
- International Multiple Sclerosis Genetics Consortium
- Correspondence to: Philip L. De Jager, MD PhD, Center for Translational & Computational Neuroimmunology, Multiple Sclerosis Center, Department of Neurology, Columbia University Medical Center, 630 W 168th Street P&S Box 16, New York, NY 10032, T: 212.305.3609,
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38
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Galea R, Nel HJ, Talekar M, Liu X, Ooi JD, Huynh M, Hadjigol S, Robson KJ, Ting YT, Cole S, Cochlin K, Hitchcock S, Zeng B, Yekollu S, Boks M, Goh N, Roberts H, Rossjohn J, Reid HH, Boyd BJ, Malaviya R, Shealy DJ, Baker DG, Madakamutil L, Kitching AR, O’Sullivan BJ, Thomas R. PD-L1- and calcitriol-dependent liposomal antigen-specific regulation of systemic inflammatory autoimmune disease. JCI Insight 2019; 4:126025. [PMID: 31487265 PMCID: PMC6795297 DOI: 10.1172/jci.insight.126025] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Accepted: 08/21/2019] [Indexed: 12/29/2022] Open
Abstract
Autoimmune diseases resulting from MHC class II-restricted autoantigen-specific T cell immunity include the systemic inflammatory autoimmune conditions rheumatoid arthritis and vasculitis. While currently treated with broad-acting immunosuppressive drugs, a preferable strategy is to regulate antigen-specific effector T cells (Teffs) to restore tolerance by exploiting DC antigen presentation. We targeted draining lymph node (dLN) phagocytic DCs using liposomes encapsulating 1α,25-dihydroxyvitamin D3 (calcitriol) and antigenic peptide to elucidate mechanisms of tolerance used by DCs and responding T cells under resting and immunized conditions. PD-L1 expression was upregulated in dLNs of immunized relative to naive mice. Subcutaneous administration of liposomes encapsulating OVA323-339 and calcitriol targeted dLN PD-L1hi DCs of immunized mice and reduced their MHC class II expression. OVA323-339/calcitriol liposomes suppressed expansion, differentiation, and function of Teffs and induced Foxp3+ and IL-10+ peripheral Tregs in an antigen-specific manner, which was dependent on PD-L1. Peptide/calcitriol liposomes modulated CD40 expression by human DCs and promoted Treg induction in vitro. Liposomes encapsulating calcitriol and disease-associated peptides suppressed the severity of rheumatoid arthritis and Goodpasture's vasculitis models with suppression of antigen-specific memory T cell differentiation and function. Accordingly, peptide/calcitriol liposomes leverage DC PD-L1 for antigen-specific T cell regulation and induce antigen-specific tolerance in inflammatory autoimmune diseases.
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Affiliation(s)
- Ryan Galea
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Queensland, Australia
| | - Hendrik J. Nel
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Queensland, Australia
| | - Meghna Talekar
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Queensland, Australia
| | - Xiao Liu
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Queensland, Australia
| | - Joshua D. Ooi
- Centre for Inflammatory Diseases, Monash University Department of Medicine, Monash Medical Centre, Clayton, Victoria, Australia
| | - Megan Huynh
- Centre for Inflammatory Diseases, Monash University Department of Medicine, Monash Medical Centre, Clayton, Victoria, Australia
| | - Sara Hadjigol
- Centre for Inflammatory Diseases, Monash University Department of Medicine, Monash Medical Centre, Clayton, Victoria, Australia
| | - Kate J. Robson
- Centre for Inflammatory Diseases, Monash University Department of Medicine, Monash Medical Centre, Clayton, Victoria, Australia
| | - Yi Tian Ting
- Infection and Immunity Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - Suzanne Cole
- Discovery Immunology, Janssen Research & Development LLC, Spring House, Pennsylvania, USA
| | - Karyn Cochlin
- Discovery Immunology, Janssen Research & Development LLC, Spring House, Pennsylvania, USA
| | - Shannon Hitchcock
- Discovery Immunology, Janssen Research & Development LLC, Spring House, Pennsylvania, USA
| | - Bijun Zeng
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Queensland, Australia
| | - Suman Yekollu
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Queensland, Australia
| | - Martine Boks
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Queensland, Australia
| | - Natalie Goh
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Queensland, Australia
| | | | - Jamie Rossjohn
- Infection and Immunity Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
- Australian Research Council Centre of Excellence in Advanced Molecular Imaging, Monash University, Clayton, Victoria, Australia
- Institute of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Hugh H. Reid
- Infection and Immunity Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - Ben J. Boyd
- Drug Delivery, Disposition and Dynamics and Australian Research Council Centre of Excellence in Convergent Bio-Nano Science and Technology, Monash Institute of Pharmaceutical Sciences, Parkville, Victoria, Australia
| | - Ravi Malaviya
- Discovery Immunology, Janssen Research & Development LLC, Spring House, Pennsylvania, USA
| | - David J. Shealy
- Discovery Immunology, Janssen Research & Development LLC, Spring House, Pennsylvania, USA
| | - Daniel G. Baker
- Discovery Immunology, Janssen Research & Development LLC, Spring House, Pennsylvania, USA
| | - Loui Madakamutil
- Discovery Immunology, Janssen Research & Development LLC, Spring House, Pennsylvania, USA
| | - A. Richard Kitching
- Centre for Inflammatory Diseases, Monash University Department of Medicine, Monash Medical Centre, Clayton, Victoria, Australia
- Departments of Nephrology and Paediatric Nephrology, Monash Health, Clayton, Victoria, Australia
| | - Brendan J. O’Sullivan
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Queensland, Australia
| | - Ranjeny Thomas
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Queensland, Australia
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Watanabe K, Umićević Mirkov M, de Leeuw CA, van den Heuvel MP, Posthuma D. Genetic mapping of cell type specificity for complex traits. Nat Commun 2019; 10:3222. [PMID: 31324783 PMCID: PMC6642112 DOI: 10.1038/s41467-019-11181-1] [Citation(s) in RCA: 163] [Impact Index Per Article: 32.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 06/11/2019] [Indexed: 01/11/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) data allows to create cell type specific transcriptome profiles. Such profiles can be aligned with genome-wide association studies (GWASs) to implicate cell type specificity of the traits. Current methods typically rely only on a small subset of available scRNA-seq datasets, and integrating multiple datasets is hampered by complex batch effects. Here we collated 43 publicly available scRNA-seq datasets. We propose a 3-step workflow with conditional analyses within and between datasets, circumventing batch effects, to uncover associations of traits with cell types. Applying this method to 26 traits, we identify independent associations of multiple cell types. These results lead to starting points for follow-up functional studies aimed at gaining a mechanistic understanding of these traits. The proposed framework as well as the curated scRNA-seq datasets are made available via an online platform, FUMA, to facilitate rapid evaluation of cell type specificity by other researchers. Tissue- and cell type-specific information helps to interpret findings from genome-wide association studies. Here, the authors leverage multiple single cell expression datasets to infer cell type specificity of traits.
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Affiliation(s)
- Kyoko Watanabe
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University Amsterdam, Amsterdam, The Netherlands
| | - Maša Umićević Mirkov
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University Amsterdam, Amsterdam, The Netherlands
| | - Christiaan A de Leeuw
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University Amsterdam, Amsterdam, The Netherlands
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University Amsterdam, Amsterdam, The Netherlands.,Department of Clinical Genetics, section Complex Trait Genetics, Neuroscience Campus Amsterdam, VU Medical Center, Amsterdam, The Netherlands
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University Amsterdam, Amsterdam, The Netherlands. .,Department of Clinical Genetics, section Complex Trait Genetics, Neuroscience Campus Amsterdam, VU Medical Center, Amsterdam, The Netherlands.
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Takeshita M, Suzuki K, Kondo Y, Morita R, Okuzono Y, Koga K, Kassai Y, Gamo K, Takiguchi M, Kurisu R, Mototani H, Ebisuno Y, Yoshimura A, Takeuchi T. Multi-dimensional analysis identified rheumatoid arthritis-driving pathway in human T cell. Ann Rheum Dis 2019; 78:1346-1356. [PMID: 31167762 PMCID: PMC6788883 DOI: 10.1136/annrheumdis-2018-214885] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 05/19/2019] [Accepted: 05/21/2019] [Indexed: 12/25/2022]
Abstract
Objectives Rheumatoid arthritis (RA) is an autoimmune disease accompanied by lymphocyte infiltration into joint synovium. While T cells are considered to be important for its pathogenesis, the features that are the most relevant to disease and how they change after treatment remain unclear. The aim of this study was to clarify the characteristics of T cells in RA, comprehensively. Methods We enrolled a total of 311 patients with RA and 73 healthy participants, and carefully classified them by disease state, constructed multiple cohorts and analysed clinical samples from them in a stepwise manner. We performed immunophenotyping with multiple evaluation axes, and two independent transcriptome analyses complementary to each other. Results We identified that ‘effector memory-Tfh’ subset was specifically expanded in the peripheral blood (PB) of patients with RA in correlation with disease activity, and reverted after treatment. Besides, we revealed distinct features of T cells in synovial fluid (SF) that the expression of Tfh/Tph-related genes and pro-inflammatory cytokines and chemokines, including CXCL13, were significantly enriched, whereas these phenotype were Th1-like. Finally, we identified specific pathways, such as mTORC1, IL-2-stat5, E2F, cell cycle and interferon-related genes, that were significantly enriched in SF, in particular, as well as PB of untreated patients with RA, and notably, these features reverted after treatment. Conclusion Our multi-dimensional investigation identified disease relevant T-cell subsets and gene signatures deeply involved in pathogenesis of RA. These findings could aid in our understanding of essential roles of T cells in RA and will facilitate to development better diagnostic and therapeutic interventions.
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Affiliation(s)
- Masaru Takeshita
- Division of Rheumatology, Department of Internal Medicine, School of Medicine, Keio University, Shinjuku-ku, Tokyo, Japan
| | - Katsuya Suzuki
- Division of Rheumatology, Department of Internal Medicine, School of Medicine, Keio University, Shinjuku-ku, Tokyo, Japan
| | - Yasushi Kondo
- Division of Rheumatology, Department of Internal Medicine, School of Medicine, Keio University, Shinjuku-ku, Tokyo, Japan
| | - Rimpei Morita
- Department of Microbiology and Immunology, School of Medicine, Keio University, Shinjuku-ku, Tokyo, Japan
| | - Yuumi Okuzono
- Research, Immunology Unit, Takeda Pharmaceutical Co Ltd, Fujisawa-Shi, Kanagawa, Japan
| | - Keiko Koga
- Research, Immunology Unit, Takeda Pharmaceutical Co Ltd, Fujisawa-Shi, Kanagawa, Japan
| | - Yoshiaki Kassai
- Research, Immunology Unit, Takeda Pharmaceutical Co Ltd, Fujisawa-Shi, Kanagawa, Japan
| | - Kanae Gamo
- Research, Immunology Unit, Takeda Pharmaceutical Co Ltd, Fujisawa-Shi, Kanagawa, Japan
| | - Maiko Takiguchi
- Research, Immunology Unit, Takeda Pharmaceutical Co Ltd, Fujisawa-Shi, Kanagawa, Japan
| | - Rina Kurisu
- Research, Immunology Unit, Takeda Pharmaceutical Co Ltd, Fujisawa-Shi, Kanagawa, Japan
| | - Hideyuki Mototani
- Research, Immunology Unit, Takeda Pharmaceutical Co Ltd, Fujisawa-Shi, Kanagawa, Japan
| | - Yukihiko Ebisuno
- Research, Immunology Unit, Takeda Pharmaceutical Co Ltd, Fujisawa-Shi, Kanagawa, Japan
| | - Akihiko Yoshimura
- Department of Microbiology and Immunology, School of Medicine, Keio University, Shinjuku-ku, Tokyo, Japan
| | - Tsutomu Takeuchi
- Division of Rheumatology, Department of Internal Medicine, School of Medicine, Keio University, Shinjuku-ku, Tokyo, Japan
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Dozmorov MG. Disease classification: from phenotypic similarity to integrative genomics and beyond. Brief Bioinform 2019; 20:1769-1780. [DOI: 10.1093/bib/bby049] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 05/01/2018] [Indexed: 02/06/2023] Open
Abstract
Abstract
A fundamental challenge of modern biomedical research is understanding how diseases that are similar on the phenotypic level are similar on the molecular level. Integration of various genomic data sets with the traditionally used phenotypic disease similarity revealed novel genetic and molecular mechanisms and blurred the distinction between monogenic (Mendelian) and complex diseases. Network-based medicine has emerged as a complementary approach for identifying disease-causing genes, genetic mediators, disruptions in the underlying cellular functions and for drug repositioning. The recent development of machine and deep learning methods allow for leveraging real-life information about diseases to refine genetic and phenotypic disease relationships. This review describes the historical development and recent methodological advancements for studying disease classification (nosology).
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Affiliation(s)
- Mikhail G Dozmorov
- Department of Biostatistics, Virginia Commonwealth University, 830 East Main Street, Richmond, VA, USA
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42
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Amariuta T, Luo Y, Gazal S, Davenport EE, van de Geijn B, Ishigaki K, Westra HJ, Teslovich N, Okada Y, Yamamoto K, Price AL, Raychaudhuri S. IMPACT: Genomic Annotation of Cell-State-Specific Regulatory Elements Inferred from the Epigenome of Bound Transcription Factors. Am J Hum Genet 2019; 104:879-895. [PMID: 31006511 PMCID: PMC6506796 DOI: 10.1016/j.ajhg.2019.03.012] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Accepted: 03/14/2019] [Indexed: 12/18/2022] Open
Abstract
Despite significant progress in annotating the genome with experimental methods, much of the regulatory noncoding genome remains poorly defined. Here we assert that regulatory elements may be characterized by leveraging local epigenomic signatures where specific transcription factors (TFs) are bound. To link these two features, we introduce IMPACT, a genome annotation strategy that identifies regulatory elements defined by cell-state-specific TF binding profiles, learned from 515 chromatin and sequence annotations. We validate IMPACT using multiple compelling applications. First, IMPACT distinguishes between bound and unbound TF motif sites with high accuracy (average AUPRC 0.81, SE 0.07; across 8 tested TFs) and outperforms state-of-the-art TF binding prediction methods, MocapG, MocapS, and Virtual ChIP-seq. Second, in eight tested cell types, RNA polymerase II IMPACT annotations capture more cis-eQTL variation than sequence-based annotations, such as promoters and TSS windows (25% average increase in enrichment). Third, integration with rheumatoid arthritis (RA) summary statistics from European (N = 38,242) and East Asian (N = 22,515) populations revealed that the top 5% of CD4+ Treg IMPACT regulatory elements capture 85.7% of RA h2, the most comprehensive explanation for RA h2 to date. In comparison, the average RA h2 captured by compared CD4+ T histone marks is 42.3% and by CD4+ T specifically expressed gene sets is 36.4%. Lastly, we find that IMPACT may be used in many different cell types to identify complex trait associated regulatory elements.
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Affiliation(s)
- Tiffany Amariuta
- Center for Data Sciences, Harvard Medical School, Boston, MA 02115, USA; Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Graduate School of Arts and Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Yang Luo
- Center for Data Sciences, Harvard Medical School, Boston, MA 02115, USA; Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Steven Gazal
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Emma E Davenport
- Center for Data Sciences, Harvard Medical School, Boston, MA 02115, USA; Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Bryce van de Geijn
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Kazuyoshi Ishigaki
- Center for Data Sciences, Harvard Medical School, Boston, MA 02115, USA; Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Harm-Jan Westra
- Center for Data Sciences, Harvard Medical School, Boston, MA 02115, USA; Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Faculty of Medical Sciences, University of Groningen, Groningen, the Netherlands
| | - Nikola Teslovich
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Yukinori Okada
- Osaka University Graduate School of Medicine, Osaka, Japan; Immunology Frontier Research Center (WPI-IFReC), Osaka University, Osaka, Japan
| | - Kazuhiko Yamamoto
- RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
| | - Alkes L Price
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
| | - Soumya Raychaudhuri
- Center for Data Sciences, Harvard Medical School, Boston, MA 02115, USA; Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Graduate School of Arts and Sciences, Harvard University, Cambridge, MA 02138, USA; Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.
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Luque-Campos N, Contreras-López RA, Jose Paredes-Martínez M, Torres MJ, Bahraoui S, Wei M, Espinoza F, Djouad F, Elizondo-Vega RJ, Luz-Crawford P. Mesenchymal Stem Cells Improve Rheumatoid Arthritis Progression by Controlling Memory T Cell Response. Front Immunol 2019; 10:798. [PMID: 31040848 PMCID: PMC6477064 DOI: 10.3389/fimmu.2019.00798] [Citation(s) in RCA: 77] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 03/26/2019] [Indexed: 12/14/2022] Open
Abstract
In the last years, mesenchymal stem cell (MSC)-based therapies have become an interesting therapeutic opportunity for the treatment of rheumatoid arthritis (RA) due to their capacity to potently modulate the immune response. RA is a chronic autoimmune inflammatory disorder with an incompletely understood etiology. However, it has been well described that peripheral tolerance defects and the subsequent abnormal infiltration and activation of diverse immune cells into the synovial membrane, are critical for RA development and progression. Moreover, the imbalance between the immune response of pro-inflammatory and anti-inflammatory cells, in particular between memory Th17 and memory regulatory T cells (Treg), respectively, is well admitted to be associated to RA immunopathogenesis. In this context, MSCs, which are able to alter the frequency and function of memory lymphocytes including Th17, follicular helper T (Tfh) cells and gamma delta (γδ) T cells while promoting Treg cell generation, have been proposed as a candidate of choice for RA cell therapy. Indeed, given the plasticity of memory CD4+ T cells, it is reasonable to think that MSCs will restore the balance between pro-inflammatory and anti-inflammatory memory T cells populations deregulated in RA leading to prompt their therapeutic function. In the present review, we will discuss the role of memory T cells implicated in RA pathogenesis and the beneficial effects exerted by MSCs on the phenotype and functions of these immune cells abnormally regulated in RA and how this regulation could impact RA progression.
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Affiliation(s)
- Noymar Luque-Campos
- Laboratorio de Inmunología Celular y Molecular, Centro de Investigación Biomédica, Facultad de Medicina, Universidad de los Andes, Santiago, Chile
| | - Rafael A Contreras-López
- Laboratorio de Inmunología Celular y Molecular, Centro de Investigación Biomédica, Facultad de Medicina, Universidad de los Andes, Santiago, Chile
| | - María Jose Paredes-Martínez
- Laboratorio de Inmunología Celular y Molecular, Centro de Investigación Biomédica, Facultad de Medicina, Universidad de los Andes, Santiago, Chile
| | - Maria Jose Torres
- Escuela de Ingeniería Bioquímica, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
| | | | - Mingxing Wei
- Cellvax, SAS, Parc BIOCITECH, Romainville, France
| | | | | | - Roberto Javier Elizondo-Vega
- Laboratorio de Biología Celular, Departamento de Biología Celular, Facultad de Ciencias Biológicas, Universidad de Concepción, Concepción, Chile
| | - Patricia Luz-Crawford
- Laboratorio de Inmunología Celular y Molecular, Centro de Investigación Biomédica, Facultad de Medicina, Universidad de los Andes, Santiago, Chile
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Tsubata T. CD72 is a Negative Regulator of B Cell Responses to Nuclear Lupus Self-antigens and Development of Systemic Lupus Erythematosus. Immune Netw 2019; 19:e1. [PMID: 30838156 PMCID: PMC6399098 DOI: 10.4110/in.2019.19.e1] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 02/04/2019] [Accepted: 02/07/2019] [Indexed: 12/22/2022] Open
Abstract
Systemic lupus erythematosus (SLE) is the prototypic systemic autoimmune disease characterized by production of autoantibodies to various nuclear antigens and overexpression of genes regulated by IFN-I called IFN signature. Genetic studies on SLE patients and mutational analyses of mouse models demonstrate crucial roles of nucleic acid (NA) sensors in development of SLE. Although NA sensors are involved in induction of anti-microbial immune responses by recognizing microbial NAs, recognition of self NAs by NA sensors induces production of autoantibodies to NAs in B cells and production of IFN-I in plasmacytoid dendritic cells. Among various NA sensors, the endosomal RNA sensor TLR7 plays an essential role in development of SLE at least in mouse models. CD72 is an inhibitory B cell co-receptor containing an immunoreceptor tyrosine-based inhibition motif (ITIM) in the cytoplasmic region and a C-type lectin like-domain (CTLD) in the extracellular region. CD72 is known to regulate development of SLE because CD72 polymorphisms associate with SLE in both human and mice and CD72−/− mice develop relatively severe lupus-like disease. CD72 specifically recognizes the RNA-containing endogenous TLR7 ligand Sm/RNP by its extracellular CTLD, and inhibits B cell responses to Sm/RNP by ITIM-mediated signal inhibition. These findings indicate that CD72 inhibits development of SLE by suppressing TLR7-dependent B cell response to self NAs. CD72 is thus involved in discrimination of self-NAs from microbial NAs by specifically suppressing autoimmune responses to self-NAs.
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Affiliation(s)
- Takeshi Tsubata
- Department of Immunology, Medical Research Institute, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
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Lambert SA, Jolma A, Campitelli LF, Das PK, Yin Y, Albu M, Chen X, Taipale J, Hughes TR, Weirauch MT. The Human Transcription Factors. Cell 2019; 172:650-665. [PMID: 29425488 DOI: 10.1016/j.cell.2018.01.029] [Citation(s) in RCA: 1547] [Impact Index Per Article: 309.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Revised: 01/15/2018] [Accepted: 01/22/2018] [Indexed: 12/13/2022]
Abstract
Transcription factors (TFs) recognize specific DNA sequences to control chromatin and transcription, forming a complex system that guides expression of the genome. Despite keen interest in understanding how TFs control gene expression, it remains challenging to determine how the precise genomic binding sites of TFs are specified and how TF binding ultimately relates to regulation of transcription. This review considers how TFs are identified and functionally characterized, principally through the lens of a catalog of over 1,600 likely human TFs and binding motifs for two-thirds of them. Major classes of human TFs differ markedly in their evolutionary trajectories and expression patterns, underscoring distinct functions. TFs likewise underlie many different aspects of human physiology, disease, and variation, highlighting the importance of continued effort to understand TF-mediated gene regulation.
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Affiliation(s)
- Samuel A Lambert
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Arttu Jolma
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | - Laura F Campitelli
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Pratyush K Das
- Genome-Scale Biology Program, University of Helsinki, Helsinki, Finland
| | - Yimeng Yin
- Division of Functional Genomics and Systems Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Solna, Sweden
| | - Mihai Albu
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | - Xiaoting Chen
- Center for Autoimmune Genomics and Etiology (CAGE), Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Jussi Taipale
- Genome-Scale Biology Program, University of Helsinki, Helsinki, Finland; Division of Functional Genomics and Systems Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Solna, Sweden; Department of Biochemistry, Cambridge University, Cambridge CB2 1GA, United Kingdom.
| | - Timothy R Hughes
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada; Donnelly Centre, University of Toronto, Toronto, ON, Canada.
| | - Matthew T Weirauch
- Center for Autoimmune Genomics and Etiology (CAGE), Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA; Divisions of Biomedical Informatics and Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA.
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Kredel LI, Jödicke LJ, Scheffold A, Gröne J, Glauben R, Erben U, Kühl AA, Siegmund B. T-cell Composition in Ileal and Colonic Creeping Fat - Separating Ileal from Colonic Crohn's Disease. J Crohns Colitis 2019; 13:79-91. [PMID: 30272118 DOI: 10.1093/ecco-jcc/jjy146] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND AND AIMS Creeping fat [CF] is a hyperplasia of adipose tissue adjacent to inflamed intestine in Crohn's disease [CD]. Data from genome-wide association studies [GWAS] distinguished Crohn's colitis from ileal CD and ulcerative colitis [UC]. This study analysed the T-cell compartments of ileal and colonic mesenteric fat and corresponding mucosa to provide cellular proof for the suggested GWAS classification. METHODS Samples were obtained from 34 CD or UC patients. Cells were analysed by immunohistochemistry and flow cytometry, and tissue cytokine release was assessed by cytometric bead array. RESULTS Only ileal CF revealed the distinct adipocyte hyperplasia combined with dense T-cell infiltration and fibrosis; colonic fat from CD and UC patients lacked these findings. T-cell subpopulations differed between mesenteric fat in ileal CD, colonic CD and UC: ileal CF had nearly 10 times more T-cells than colonic fat. The proportions of regulatory and central memory T-cells were significantly higher in ileal CF compared with colonic fat in CD and UC. In all groups, the mucosal T-cell compartment was distinct from the mesenteric fat. Remarkably, correlation between disease activity and proportion of pro- and anti-inflammatory T-cell subpopulations was inverse, comparing ileal and colonic fat in CD. CONCLUSIONS This first in-depth analysis of the T-cell compartment in ileal and colonic mesenteric adipose tissue in CD and UC identifies a unique T-cell niche in the ileal mesenteric fat tissue in CD. From a clinical point of view, our findings underscore the novel concept of colonic and ileal CD as distinct IBD entities.
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Affiliation(s)
- Lea I Kredel
- Medical Department, Division of Gastroenterology, Infectiology and Rheumatology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Lisa J Jödicke
- Medical Department, Division of Gastroenterology, Infectiology and Rheumatology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Alexander Scheffold
- Medical Department [Rheumatology, Clinical Immunology], Department of Cellular Immunology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Jörn Gröne
- Department of Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Department of Surgery, Rotes Kreuz Krankenhaus Bremen, Bremen, Germany
| | - Rainer Glauben
- Medical Department, Division of Gastroenterology, Infectiology and Rheumatology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Ulrike Erben
- Medical Department, Division of Gastroenterology, Infectiology and Rheumatology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,iPATH, Core Unit/Research Center ImmunoSciences, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Anja A Kühl
- Medical Department, Division of Gastroenterology, Infectiology and Rheumatology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,iPATH, Core Unit/Research Center ImmunoSciences, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Britta Siegmund
- Medical Department, Division of Gastroenterology, Infectiology and Rheumatology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
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Fine-mapping and functional studies highlight potential causal variants for rheumatoid arthritis and type 1 diabetes. Nat Genet 2018; 50:1366-1374. [PMID: 30224649 DOI: 10.1038/s41588-018-0216-7] [Citation(s) in RCA: 103] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 07/30/2018] [Indexed: 12/19/2022]
Abstract
To define potentially causal variants for autoimmune disease, we fine-mapped1,2 76 rheumatoid arthritis (11,475 cases, 15,870 controls)3 and type 1 diabetes loci (9,334 cases, 11,111 controls)4. After sequencing 799 1-kilobase regulatory (H3K4me3) regions within these loci in 568 individuals, we observed accurate imputation for 89% of common variants. We defined credible sets of ≤5 causal variants at 5 rheumatoid arthritis and 10 type 1 diabetes loci. We identified potentially causal missense variants at DNASE1L3, PTPN22, SH2B3, and TYK2, and noncoding variants at MEG3, CD28-CTLA4, and IL2RA. We also identified potential candidate causal variants at SIRPG and TNFAIP3. Using functional assays, we confirmed allele-specific protein binding and differential enhancer activity for three variants: the CD28-CTLA4 rs117701653 SNP, MEG3 rs34552516 indel, and TNFAIP3 rs35926684 indel.
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Abstract
The contribution of genetic inheritance in multiple sclerosis was established early on. Although multiple sclerosis is not a Mendelian disease, its incidence and prevalence is higher in family members of affected individuals compared with the general population. Throughout the last decade, several small studies failed to identify any robust genetic associations besides the classic associations in the major histocompatibility complex region. During the past few years, genome-wide association studies (GWAS) have revolutionized the genetics of multiple sclerosis, uncovering more than 200 implicated genetic loci. Here, we describe these main findings and discuss the new avenues that these discoveries lay open.
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Affiliation(s)
- Nikolaos A Patsopoulos
- Department of Neurology, Brigham & Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142
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Patel ZH, Lu X, Miller D, Forney CR, Lee J, Lynch A, Schroeder C, Parks L, Magnusen AF, Chen X, Pujato M, Maddox A, Zoller EE, Namjou B, Brunner HI, Henrickson M, Huggins JL, Williams AH, Ziegler JT, Comeau ME, Marion MC, Glenn SB, Adler A, Shen N, Nath SK, Stevens AM, Freedman BI, Pons-Estel BA, Tsao BP, Jacob CO, Kamen DL, Brown EE, Gilkeson GS, Alarcón GS, Martin J, Reveille JD, Anaya JM, James JA, Sivils KL, Criswell LA, Vilá LM, Petri M, Scofield RH, Kimberly RP, Edberg JC, Ramsey-Goldman R, Bang SY, Lee HS, Bae SC, Boackle SA, Cunninghame Graham D, Vyse TJ, Merrill JT, Niewold TB, Ainsworth HC, Silverman ED, Weisman MH, Wallace DJ, Raj P, Guthridge JM, Gaffney PM, Kelly JA, Alarcón-Riquelme ME, Langefeld CD, Wakeland EK, Kaufman KM, Weirauch MT, Harley JB, Kottyan LC. A plausibly causal functional lupus-associated risk variant in the STAT1-STAT4 locus. Hum Mol Genet 2018; 27:2392-2404. [PMID: 29912393 PMCID: PMC6005081 DOI: 10.1093/hmg/ddy140] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Revised: 03/21/2018] [Accepted: 04/13/2018] [Indexed: 01/01/2023] Open
Abstract
Systemic lupus erythematosus (SLE or lupus) (OMIM: 152700) is a chronic autoimmune disease with debilitating inflammation that affects multiple organ systems. The STAT1-STAT4 locus is one of the first and most highly replicated genetic loci associated with lupus risk. We performed a fine-mapping study to identify plausible causal variants within the STAT1-STAT4 locus associated with increased lupus disease risk. Using complementary frequentist and Bayesian approaches in trans-ancestral Discovery and Replication cohorts, we found one variant whose association with lupus risk is supported across ancestries in both the Discovery and Replication cohorts: rs11889341. In B cell lines from patients with lupus and healthy controls, the lupus risk allele of rs11889341 was associated with increased STAT1 expression. We demonstrated that the transcription factor HMGA1, a member of the HMG transcription factor family with an AT-hook DNA-binding domain, has enriched binding to the risk allele compared with the non-risk allele of rs11889341. We identified a genotype-dependent repressive element in the DNA within the intron of STAT4 surrounding rs11889341. Consistent with expression quantitative trait locus (eQTL) analysis, the lupus risk allele of rs11889341 decreased the activity of this putative repressor. Altogether, we present a plausible molecular mechanism for increased lupus risk at the STAT1-STAT4 locus in which the risk allele of rs11889341, the most probable causal variant, leads to elevated STAT1 expression in B cells due to decreased repressor activity mediated by increased binding of HMGA1.
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Affiliation(s)
- Zubin H Patel
- Immunology Graduate Program, University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Medical Scientist Training Program, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - Xiaoming Lu
- Immunology Graduate Program, University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Daniel Miller
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Carmy R Forney
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Joshua Lee
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Arthur Lynch
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Connor Schroeder
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Lois Parks
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Albert F Magnusen
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Xiaoting Chen
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Mario Pujato
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Avery Maddox
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Erin E Zoller
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Bahram Namjou
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45220, USA
| | - Hermine I Brunner
- Division of Rheumatology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Michael Henrickson
- Division of Rheumatology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Jennifer L Huggins
- Division of Rheumatology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Adrienne H Williams
- Center for Public Health Genomics and the Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Julie T Ziegler
- Center for Public Health Genomics and the Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Mary E Comeau
- Center for Public Health Genomics and the Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Miranda C Marion
- Center for Public Health Genomics and the Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Stuart B Glenn
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK 73104, USA
| | - Adam Adler
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK 73104, USA
| | - Nan Shen
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Shanghai Institute of Rheumatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200001, P.R. China
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45220, USA
| | - Swapan K Nath
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK 73104, USA
| | - Anne M Stevens
- Center for Immunity and Immunotherapies, Seattle Children’s Research Institute, Seattle, WA 98101, USA
- Division of Rheumatology, Department of Pediatrics, University of Washington, Seattle, WA 98195, USA
| | - Barry I Freedman
- Section on Nephrology, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | | | - Betty P Tsao
- Division of Rheumatology and Immunology, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Chaim O Jacob
- Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Diane L Kamen
- Division of Rheumatology and Immunology, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Elizabeth E Brown
- Division of Molecular and Cellular Pathology, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, 35294, United States of America
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Gary S Gilkeson
- Division of Rheumatology and Immunology, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Graciela S Alarcón
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Javier Martin
- Instituto de Parasitologia y Biomedicina Lopez-Neyra, CSIC, Granada 18001-18016, Spain
| | - John D Reveille
- Rheumatology and Clinical Immunogenetics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Juan-Manuel Anaya
- Center for Autoimmune Diseases Research (CREA), Universidad del Rosario, Bogota 111711, Colombia
| | - Judith A James
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK 73104, USA
- Department of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, United States of America
- Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Kathy L Sivils
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK 73104, USA
| | - Lindsey A Criswell
- Department of Medicine, Rosalind Russell/Ephraim P Engleman Rheumatology Research Center, University of California San Francisco, San Francisco, CA 94143-0500, USA
| | - Luis M Vilá
- Division of Rheumatology, Department of Medicine, University of Puerto Rico Medical Sciences Campus, San Juan, PR 00936, USA
| | - Michelle Petri
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21224, USA
| | - R Hal Scofield
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK 73104, USA
- Department of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, United States of America
- United States Department of Veterans Affairs Medical Center, Oklahoma City, OK 73104, USA
| | - Robert P Kimberly
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Jeffrey C Edberg
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Rosalind Ramsey-Goldman
- Division of Rheumatology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - So-Young Bang
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul 04763, Korea
| | - Hye-Soon Lee
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul 04763, Korea
| | - Sang-Cheol Bae
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul 04763, Korea
| | - Susan A Boackle
- Division of Rheumatology, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Deborah Cunninghame Graham
- Divisions of Genetics/Molecular Medicine and Immunology, King’s College London, Guy’s Hospital, London SE1 9RT, UK
| | - Timothy J Vyse
- Divisions of Genetics/Molecular Medicine and Immunology, King’s College London, Guy’s Hospital, London SE1 9RT, UK
| | - Joan T Merrill
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK 73104, USA
- Department of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, United States of America
| | - Timothy B Niewold
- Division of Rheumatology, Department of Pathology, New York University, New York, NY 10016, USA
| | - Hannah C Ainsworth
- Center for Public Health Genomics and the Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Earl D Silverman
- Division of Rheumatology, The Hospital for Sick Children, Hospital for Sick Research Institute, University of Toronto, Toronto, ON M5G 1X8, Canada
| | - Michael H Weisman
- Division of Rheumatology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Daniel J Wallace
- Division of Rheumatology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Prithvi Raj
- Department of Immunology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Joel M Guthridge
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK 73104, USA
- Department of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, United States of America
- Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Patrick M Gaffney
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK 73104, USA
| | - Jennifer A Kelly
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK 73104, USA
| | - Marta E Alarcón-Riquelme
- Unit of Chronic Inflammatory Diseases, Institute of Environmental Medicine, Karolinska Institutet, Stockholm 17167, Sweden
- Center for Genomics and Oncological Research, Pfizer-University of Granada-Junta de Andalucia, Parque Tecnológica de la Salud, Granada 18016, Spain
| | - Carl D Langefeld
- Center for Public Health Genomics and the Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Edward K Wakeland
- Department of Immunology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Kenneth M Kaufman
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- United States Department of Veterans Affairs Medical Center, Cincinnati, OH 45220, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45220, USA
- Divisions of Biomedical Informatics and Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Matthew T Weirauch
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45220, USA
- Divisions of Biomedical Informatics and Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - John B Harley
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- United States Department of Veterans Affairs Medical Center, Cincinnati, OH 45220, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45220, USA
| | - Leah C Kottyan
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45220, USA
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Musters A, Klarenbeek PL, Doorenspleet ME, Balzaretti G, Esveldt REE, van Schaik BDC, Jongejan A, Tas SW, van Kampen AHC, Baas F, de Vries N. In Rheumatoid Arthritis, Synovitis at Different Inflammatory Sites Is Dominated by Shared but Patient-Specific T Cell Clones. THE JOURNAL OF IMMUNOLOGY 2018; 201:417-422. [DOI: 10.4049/jimmunol.1800421] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Accepted: 05/17/2018] [Indexed: 12/19/2022]
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