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Damerau A, Rosenow E, Alkhoury D, Buttgereit F, Gaber T. Fibrotic pathways and fibroblast-like synoviocyte phenotypes in osteoarthritis. Front Immunol 2024; 15:1385006. [PMID: 38895122 PMCID: PMC11183113 DOI: 10.3389/fimmu.2024.1385006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Accepted: 05/14/2024] [Indexed: 06/21/2024] Open
Abstract
Osteoarthritis (OA) is the most common form of arthritis, characterized by osteophyte formation, cartilage degradation, and structural and cellular alterations of the synovial membrane. Activated fibroblast-like synoviocytes (FLS) of the synovial membrane have been identified as key drivers, secreting humoral mediators that maintain inflammatory processes, proteases that cause cartilage and bone destruction, and factors that drive fibrotic processes. In normal tissue repair, fibrotic processes are terminated after the damage has been repaired. In fibrosis, tissue remodeling and wound healing are exaggerated and prolonged. Various stressors, including aging, joint instability, and inflammation, lead to structural damage of the joint and micro lesions within the synovial tissue. One result is the reduced production of synovial fluid (lubricants), which reduces the lubricity of the cartilage areas, leading to cartilage damage. In the synovial tissue, a wound-healing cascade is initiated by activating macrophages, Th2 cells, and FLS. The latter can be divided into two major populations. The destructive thymocyte differentiation antigen (THY)1─ phenotype is restricted to the synovial lining layer. In contrast, the THY1+ phenotype of the sublining layer is classified as an invasive one with immune effector function driving synovitis. The exact mechanisms involved in the transition of fibroblasts into a myofibroblast-like phenotype that drives fibrosis remain unclear. The review provides an overview of the phenotypes and spatial distribution of FLS in the synovial membrane of OA, describes the mechanisms of fibroblast into myofibroblast activation, and the metabolic alterations of myofibroblast-like cells.
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Affiliation(s)
- Alexandra Damerau
- Department of Rheumatology and Clinical Immunology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- German Rheumatism Research Center Berlin, a Leibniz Institute, Glucocorticoids - Bioenergetics - 3R Research Lab, Berlin, Germany
| | - Emely Rosenow
- Department of Rheumatology and Clinical Immunology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Dana Alkhoury
- Department of Rheumatology and Clinical Immunology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
| | - Frank Buttgereit
- Department of Rheumatology and Clinical Immunology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- German Rheumatism Research Center Berlin, a Leibniz Institute, Glucocorticoids - Bioenergetics - 3R Research Lab, Berlin, Germany
| | - Timo Gaber
- Department of Rheumatology and Clinical Immunology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- German Rheumatism Research Center Berlin, a Leibniz Institute, Glucocorticoids - Bioenergetics - 3R Research Lab, Berlin, Germany
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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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Mishra B, Ivashkiv LB. Interferons and epigenetic mechanisms in training, priming and tolerance of monocytes and hematopoietic progenitors. Immunol Rev 2024; 323:257-275. [PMID: 38567833 PMCID: PMC11102283 DOI: 10.1111/imr.13330] [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: 01/27/2024] [Accepted: 03/11/2024] [Indexed: 05/18/2024]
Abstract
Training and priming of innate immune cells involve preconditioning by PAMPs, DAMPs, and/or cytokines that elicits stronger induction of inflammatory genes upon secondary challenge. Previous models distinguish training and priming based upon whether immune activation returns to baseline prior to secondary challenge. Tolerance is a protective mechanism whereby potent stimuli induce refractoriness to secondary challenge. Training and priming are important for innate memory responses that protect against infection, efficacy of vaccines, and maintaining innate immune cells in a state of readiness; tolerance prevents toxicity from excessive immune activation. Dysregulation of these processes can contribute to pathogenesis of autoimmune/inflammatory conditions, post-COVID-19 hyperinflammatory states, or sepsis-associated immunoparalysis. Training, priming, and tolerance regulate similar "signature" inflammatory genes such as TNF, IL6, and IL1B and utilize overlapping epigenetic mechanisms. We review how interferons (IFNs), best known for activating JAK-STAT signaling and interferon-stimulated genes, also play a key role in regulating training, priming, and tolerance via chromatin-mediated mechanisms. We present new data on how monocyte-to-macrophage differentiation modulates IFN-γ-mediated priming, affects regulation of AP-1 and CEBP activity, and attenuates superinduction of inflammatory genes. We present a "training-priming continuum" model that integrates IFN-mediated priming into current concepts about training and tolerance and proposes a central role for STAT1 and IRF1.
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Affiliation(s)
- Bikash Mishra
- HSS Research Institute and David Z. Rosensweig Genomics Research Center, Hospital for Special Surgery, New York, New York, USA
- Immunology and Microbial Pathogenesis Program, Weill Cornell Medicine, New York, New York, USA
| | - Lionel B Ivashkiv
- HSS Research Institute and David Z. Rosensweig Genomics Research Center, Hospital for Special Surgery, New York, New York, USA
- Immunology and Microbial Pathogenesis Program, Weill Cornell Medicine, New York, New York, USA
- Department of Medicine, Weill Cornell Medicine, New York, New York, USA
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4
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Sakaue S, Weinand K, Isaac S, Dey KK, Jagadeesh K, Kanai M, Watts GFM, Zhu Z, Brenner MB, McDavid A, Donlin LT, Wei K, Price AL, Raychaudhuri S. Tissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles. Nat Genet 2024; 56:615-626. [PMID: 38594305 DOI: 10.1038/s41588-024-01682-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 02/07/2024] [Indexed: 04/11/2024]
Abstract
Translating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer-gene maps from disease-relevant tissues. Building enhancer-gene maps is essential but challenging with current experimental methods in primary human tissues. Here we developed a nonparametric statistical method, SCENT (single-cell enhancer target gene mapping), that models association between enhancer chromatin accessibility and gene expression in single-cell or nucleus multimodal RNA sequencing and ATAC sequencing data. We applied SCENT to 9 multimodal datasets including >120,000 single cells or nuclei and created 23 cell-type-specific enhancer-gene maps. These maps were highly enriched for causal variants in expression quantitative loci and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases and linked somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer-gene maps, essential for defining noncoding variant function.
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Affiliation(s)
- Saori Sakaue
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kathryn Weinand
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Shakson Isaac
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kushal K Dey
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Karthik Jagadeesh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Masahiro Kanai
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Gerald F M Watts
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Zhu Zhu
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael B Brenner
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrew McDavid
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
| | - Laura T Donlin
- Hospital for Special Surgery, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Kevin Wei
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alkes L Price
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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5
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Lin CMA, Isaacs JD, Cooles FAH. Role of IFN-α in Rheumatoid Arthritis. Curr Rheumatol Rep 2024; 26:37-52. [PMID: 38051494 PMCID: PMC10787895 DOI: 10.1007/s11926-023-01125-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/07/2023] [Indexed: 12/07/2023]
Abstract
PURPOSE OF REVIEW Type 1 interferons (IFN-I) are of increasing interest across a wide range of autoimmune rheumatic diseases. Historically, research into their role in rheumatoid arthritis (RA) has been relatively neglected, but recent work continues to highlight a potential contribution to RA pathophysiology. RECENT FINDINGS We emphasise the importance of disease stage when examining IFN-I in RA and provide an overview on how IFN-I may have a direct role on a variety of relevant cellular functions. We explore how clinical trajectory may be influenced by increased IFN-I signalling, and also, the limitations of scores composed of interferon response genes. Relevant environmental triggers and inheritable RA genetic risk relating to IFN-I signalling are explored with emphasis on intriguing data potentially linking IFN-I exposure, epigenetic changes, and disease relevant processes. Whilst these data cumulatively illustrate a likely role for IFN-I in RA, they also highlight the knowledge gaps, particularly in populations at risk for RA, and suggest directions for future research to both better understand IFN-I biology and inform targeted therapeutic strategies.
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Affiliation(s)
- Chung M A Lin
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
- Musculoskeletal Unit, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - John D Isaacs
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
- Musculoskeletal Unit, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Faye A H Cooles
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.
- Musculoskeletal Unit, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK.
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6
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M. S. Barron A, Fabre T, De S. Distinct fibroblast functions associated with fibrotic and immune-mediated inflammatory diseases and their implications for therapeutic development. F1000Res 2024; 13:54. [PMID: 38681509 PMCID: PMC11053351 DOI: 10.12688/f1000research.143472.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/28/2023] [Indexed: 05/01/2024] Open
Abstract
Fibroblasts are ubiquitous cells that can adopt many functional states. As tissue-resident sentinels, they respond to acute damage signals and shape the earliest events in fibrotic and immune-mediated inflammatory diseases. Upon sensing an insult, fibroblasts produce chemokines and growth factors to organize and support the response. Depending on the size and composition of the resulting infiltrate, these activated fibroblasts may also begin to contract or relax thus changing local stiffness within the tissue. These early events likely contribute to the divergent clinical manifestations of fibrotic and immune-mediated inflammatory diseases. Further, distinct changes to the cellular composition and signaling dialogue in these diseases drive progressive fibroblasts specialization. In fibrotic diseases, fibroblasts support the survival, activation and differentiation of myeloid cells, granulocytes and innate lymphocytes, and produce most of the pathogenic extracellular matrix proteins. Whereas, in immune-mediated inflammatory diseases, sequential accumulation of dendritic cells, T cells and B cells programs fibroblasts to support local, destructive adaptive immune responses. Fibroblast specialization has clear implications for the development of effective induction and maintenance therapies for patients with these clinically distinct diseases.
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Affiliation(s)
- Alexander M. S. Barron
- Inflammation & Immunology Research Unit, Pfizer, Inc., Cambridge, Massachusetts, 02139, USA
| | - Thomas Fabre
- Inflammation & Immunology Research Unit, Pfizer, Inc., Cambridge, Massachusetts, 02139, USA
| | - Saurav De
- Inflammation & Immunology Research Unit, Pfizer, Inc., Cambridge, Massachusetts, 02139, USA
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7
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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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