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Sharma SD, Leung SH, Viatte S. Genetics of rheumatoid arthritis. Best Pract Res Clin Rheumatol 2024; 38:101968. [PMID: 38955657 DOI: 10.1016/j.berh.2024.101968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 06/17/2024] [Accepted: 06/24/2024] [Indexed: 07/04/2024]
Abstract
In the past four decades, a plethora of genetic association studies have been carried out in cohorts of patients with rheumatoid arthritis. These studies have highlighted key aspects of disease pathogenesis and suggested causal mechanisms. In this review, we discuss major advances in our understanding of the genetic architecture of rheumatoid arthritis susceptibility, severity and treatment response and explain how genetics supports current models of disease pathogenesis and outcome. We outline future research directions, like Mendelian randomisation, and present a number of potential avenues for clinical translation, including risk and outcome prediction, patient stratification into treatment response groups and pharmacological applications.
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Affiliation(s)
- Seema D Sharma
- Versus Arthritis Centre for Genetics and Genomics, Centre for Musculoskeletal Research, The University of Manchester, Oxford Road, Manchester, M13 9PT, UK; NIHR Manchester Musculoskeletal Biomedical Research Centre, Central Manchester NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK.
| | - Shek H Leung
- Versus Arthritis Centre for Genetics and Genomics, Centre for Musculoskeletal Research, The University of Manchester, Oxford Road, Manchester, M13 9PT, UK.
| | - Sebastien Viatte
- Versus Arthritis Centre for Genetics and Genomics, Centre for Musculoskeletal Research, The University of Manchester, Oxford Road, Manchester, M13 9PT, UK; NIHR Manchester Musculoskeletal Biomedical Research Centre, Central Manchester NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK; Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
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2
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Qiu Y, Feng D, Jiang W, Zhang T, Lu Q, Zhao M. 3D genome organization and epigenetic regulation in autoimmune diseases. Front Immunol 2023; 14:1196123. [PMID: 37346038 PMCID: PMC10279977 DOI: 10.3389/fimmu.2023.1196123] [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: 03/29/2023] [Accepted: 05/17/2023] [Indexed: 06/23/2023] Open
Abstract
Three-dimensional (3D) genomics is an emerging field of research that investigates the relationship between gene regulatory function and the spatial structure of chromatin. Chromatin folding can be studied using chromosome conformation capture (3C) technology and 3C-based derivative sequencing technologies, including chromosome conformation capture-on-chip (4C), chromosome conformation capture carbon copy (5C), and high-throughput chromosome conformation capture (Hi-C), which allow scientists to capture 3D conformations from a single site to the entire genome. A comprehensive analysis of the relationships between various regulatory components and gene function also requires the integration of multi-omics data such as genomics, transcriptomics, and epigenomics. 3D genome folding is involved in immune cell differentiation, activation, and dysfunction and participates in a wide range of diseases, including autoimmune diseases. We describe hierarchical 3D chromatin organization in this review and conclude with characteristics of C-techniques and multi-omics applications of the 3D genome. In addition, we describe the relationship between 3D genome structure and the differentiation and maturation of immune cells and address how changes in chromosome folding contribute to autoimmune diseases.
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Affiliation(s)
- Yueqi Qiu
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China
- Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences, Nanjing, China
| | - Delong Feng
- Department of Dermatology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Wenjuan Jiang
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China
- Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences, Nanjing, China
| | - Tingting Zhang
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China
- Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences, Nanjing, China
- State Key Laboratory of Natural Medicines, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Qianjin Lu
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China
- Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences, Nanjing, China
- Department of Dermatology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Ming Zhao
- Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China
- Key Laboratory of Basic and Translational Research on Immune-Mediated Skin Diseases, Institute of Dermatology, Chinese Academy of Medical Sciences, Nanjing, China
- Department of Dermatology, The Second Xiangya Hospital of Central South University, Changsha, China
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González‐Serna D, Shi C, Kerick M, Hankinson J, Ding J, McGovern A, Tutino M, Villanueva‐Martin G, Ortego‐Centeno N, Callejas JL, Martin J, Orozco G. Identification of Mechanisms by Which Genetic Susceptibility Loci Influence Systemic Sclerosis Risk Using Functional Genomics in Primary T Cells and Monocytes. Arthritis Rheumatol 2023; 75:1007-1020. [PMID: 36281738 PMCID: PMC10953390 DOI: 10.1002/art.42396] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 09/08/2022] [Accepted: 10/18/2022] [Indexed: 11/08/2022]
Abstract
OBJECTIVE Systemic sclerosis (SSc) is a complex autoimmune disease with a strong genetic component. However, most of the genes associated with the disease are still unknown because associated variants affect mostly noncoding intergenic elements of the genome. We used functional genomics to translate the genetic findings into a better understanding of the disease. METHODS Promoter capture Hi-C and RNA-sequencing experiments were performed in CD4+ T cells and CD14+ monocytes from 10 SSc patients and 5 healthy controls to link SSc-associated variants with their target genes, followed by differential expression and differential interaction analyses between cell types. RESULTS We linked SSc-associated loci to 39 new potential target genes and confirmed 7 previously known SSc-associated genes. We highlight novel causal genes, such as CXCR5, as the most probable candidate gene for the DDX6 locus. Some previously known SSc-associated genes, such as IRF8, STAT4, and CD247, showed cell type-specific interactions. We also identified 15 potential drug targets already in use in other similar immune-mediated diseases that could be repurposed for SSc treatment. Furthermore, we observed that interactions were directly correlated with the expression of important genes implicated in cell type-specific pathways and found evidence that chromatin conformation is associated with genotype. CONCLUSION Our study revealed potential causal genes for SSc-associated loci, some of them acting in a cell type-specific manner, suggesting novel biologic mechanisms that might mediate SSc pathogenesis.
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Affiliation(s)
- David González‐Serna
- Institute of Parasitology and Biomedicine López‐Neyra, Consejo Superior de Investigaciones Científicas (IPBLN‐CSIC)GranadaSpain
| | - Chenfu Shi
- Division of Musculoskeletal and Dermatological Sciences, Centre for Genetics and Genomics Versus Arthritis, School of Biological Sciences, Faculty of Biology, Medicine and HealthThe University of ManchesterManchesterUK
| | - Martin Kerick
- Institute of Parasitology and Biomedicine López‐Neyra, Consejo Superior de Investigaciones Científicas (IPBLN‐CSIC)GranadaSpain
| | - Jenny Hankinson
- Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine and HealthThe University of ManchesterManchesterUK
| | - James Ding
- Division of Musculoskeletal and Dermatological Sciences, Centre for Genetics and Genomics Versus Arthritis, School of Biological Sciences, Faculty of Biology, Medicine and HealthThe University of ManchesterManchesterUK
| | - Amanda McGovern
- Division of Musculoskeletal and Dermatological Sciences, Centre for Genetics and Genomics Versus Arthritis, School of Biological Sciences, Faculty of Biology, Medicine and HealthThe University of ManchesterManchesterUK
| | - Mauro Tutino
- Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, Faculty of Biology, Medicine and HealthThe University of ManchesterManchesterUK
| | - Gonzalo Villanueva‐Martin
- Institute of Parasitology and Biomedicine López‐Neyra, Consejo Superior de Investigaciones Científicas (IPBLN‐CSIC)GranadaSpain
| | - Norberto Ortego‐Centeno
- Department of Internal Medicine, Hospital Universitario San CecilioInstitute for Biosanitary Research of Granada (ibs.GRANADA)GranadaSpain
| | - José Luis Callejas
- Department of Internal Medicine, Hospital Universitario San CecilioInstitute for Biosanitary Research of Granada (ibs.GRANADA)GranadaSpain
| | - Javier Martin
- Institute of Parasitology and Biomedicine López‐Neyra, Consejo Superior de Investigaciones Científicas (IPBLN‐CSIC)GranadaSpain
| | - Gisela Orozco
- Division of Musculoskeletal and Dermatological Sciences, Centre for Genetics and Genomics Versus Arthritis, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, and NIHR Manchester Biomedical Research CentreManchester University NHS Foundation Trust, Manchester Academic Health Science CentreManchesterUK
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4
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Orozco G. Fine mapping with epigenetic information and 3D structure. Semin Immunopathol 2022; 44:115-125. [PMID: 35022890 PMCID: PMC8837508 DOI: 10.1007/s00281-021-00906-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 12/13/2021] [Indexed: 12/12/2022]
Abstract
Since 2005, thousands of genome-wide association studies (GWAS) have been published, identifying hundreds of thousands of genetic variants that increase risk of complex traits such as autoimmune diseases. This wealth of data has the potential to improve patient care, through personalized medicine and the identification of novel drug targets. However, the potential of GWAS for clinical translation has not been fully achieved yet, due to the fact that the functional interpretation of risk variants and the identification of causal variants and genes are challenging. The past decade has seen the development of great advances that are facilitating the overcoming of these limitations, by utilizing a plethora of genomics and epigenomics tools to map and characterize regulatory elements and chromatin interactions, which can be used to fine map GWAS loci, and advance our understanding of the biological mechanisms that cause disease.
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Affiliation(s)
- Gisela Orozco
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, AV Hill Building, Oxford Road, Manchester, M13 9LJ, UK. .,NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK.
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Advanced genomics and clinical phenotypes in psoriatic arthritis. Semin Immunol 2021; 58:101665. [PMID: 36307312 DOI: 10.1016/j.smim.2022.101665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Psoriatic Arthritis (PsA) is a complex polygenic inflammatory disease showing a variable musculoskeletal involvement in patients with skin psoriasis. PsA coexist in 25-40 % of patients with the dermatological manifestations, but PsA may also predate the appearance of psoriasis. Nonetheless, the immunopathogenesis of psoriasis and PsA manifest significant similarities, with a major role of the individual susceptibility in both cases. Genome wide association studies (GWAS) identified several genes/loci associated with the risk to develop PsA, both dependent and independent of psoriasis. The major challenge is thus represented by the need to translate the identification of functional polymorphisms and other genetics findings into biological mechanisms along with the identification of novel putative drug targets. A functional genomics approach aims to increase GWAS power and recent evidence supports the use of a multilayer process, including eQTL, methylome, chromatin conformation analysis and genome editing to discover novel genes that can be affected by disease-associated variants, such as PsA. The available data have considered PsA as a unique homogeneous clinical entity while the clinical experience supports a wide variability of skin and joint manifestations coexisting in diverse patients with different mechanisms underlying the musculoskeletal and dermatological domains. A better discrimination of the patient features is encouraged by the limited data on functional genomics. We provide herein a review of the latest findings on PsA functional genomics highlighting the exciting developments in the field and how these might lead to a better understanding of gene regulation underpinning disease mechanisms and ultimately refine clinical phenotyping.
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Nakagawa C, Yokoyama S, Hosomi K, Takada M. Repurposing haloperidol for the treatment of rheumatoid arthritis: an integrative approach using data mining techniques. Ther Adv Musculoskelet Dis 2021; 13:1759720X211047057. [PMID: 34589142 PMCID: PMC8474350 DOI: 10.1177/1759720x211047057] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 08/31/2021] [Indexed: 01/16/2023] Open
Abstract
Introduction Treatment of rheumatoid arthritis (RA) has advanced with the introduction of biological disease-modifying antirheumatic drugs. However, more than 20% of patients with RA still have moderate or severe disease activity. Hence, novel antirheumatic drugs are required. Recently, drug repurposing, a process of identifying new indications for existing drugs, has received great attention. Furthermore, a few reports have shown that antipsychotics are capable of affecting several cytokines that are also modulated by existing antirheumatic drugs. Therefore, we investigated the association between antipsychotics and RA by data mining using real-world data and bioinformatics databases. Methods Disproportionality and sequence symmetry analyses were employed to identify the associations between the investigational drugs and RA using the US Food and Drug Administration Adverse Event Reporting System (2004-2016) and JMDC administrative claims database (January 2005-April 2017; JMDC Inc., Tokyo, Japan), respectively. The reporting odds ratio (ROR) and information component (IC) were used in the disproportionality analysis to indicate a signal. The adjusted sequence ratio (SR) was used in the sequence symmetry analysis to indicate a signal. The bioinformatics analysis suite, BaseSpace Correlation Engine (Illumina, CA, USA) was employed to explore the molecular mechanisms associated with the potential candidates identified by the drug-repurposing approach. Results A potential inverse association between the antipsychotic haloperidol and RA, which exhibited significant inverse signals with ROR, IC, and adjusted SR, was found. Furthermore, the results suggested that haloperidol may exert antirheumatic effects by modulating various signaling pathways, including cytokine and chemokine signaling, major histocompatibility complex class-II antigen presentation, and Toll-like receptor cascade pathways. Conclusion Our drug-repurposing approach using data mining techniques identified haloperidol as a potential antirheumatic drug candidate.
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Affiliation(s)
- Chihiro Nakagawa
- Division of Drug Informatics, School of Pharmacy, Kindai University, Higashiosaka City, Japan
| | - Satoshi Yokoyama
- Division of Drug Informatics, School of Pharmacy, Kindai University, 3-4-1 Kowakae, Higashiosaka City 577-8502, Osaka, Japan
| | - Kouichi Hosomi
- Division of Drug Informatics, School of Pharmacy, Kindai University, Higashiosaka City, Japan
| | - Mitsutaka Takada
- Division of Drug Informatics, School of Pharmacy, Kindai University, Higashiosaka City, Japan
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Freire-Pritchett P, Ray-Jones H, Della Rosa M, Eijsbouts CQ, Orchard WR, Wingett SW, Wallace C, Cairns J, Spivakov M, Malysheva V. Detecting chromosomal interactions in Capture Hi-C data with CHiCAGO and companion tools. Nat Protoc 2021; 16:4144-4176. [PMID: 34373652 PMCID: PMC7612634 DOI: 10.1038/s41596-021-00567-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 04/28/2021] [Indexed: 11/10/2022]
Abstract
Capture Hi-C is widely used to obtain high-resolution profiles of chromosomal interactions involving, at least on one end, regions of interest such as gene promoters. Signal detection in Capture Hi-C data is challenging and cannot be adequately accomplished with tools developed for other chromosome conformation capture methods, including standard Hi-C. Capture Hi-C Analysis of Genomic Organization (CHiCAGO) is a computational pipeline developed specifically for Capture Hi-C analysis. It implements a statistical model accounting for biological and technical background components, as well as bespoke normalization and multiple testing procedures for this data type. Here we provide a step-by-step guide to the CHiCAGO workflow that is aimed at users with basic experience of the command line and R. We also describe more advanced strategies for tuning the key parameters for custom experiments and provide guidance on data preprocessing and downstream analysis using companion tools. In a typical experiment, CHiCAGO takes ~2-3 h to run, although pre- and postprocessing steps may take much longer.
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Affiliation(s)
| | - Helen Ray-Jones
- Functional Gene Control Group, Epigenetics Section, MRC London Institute of Medical Sciences, London, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK
| | - Monica Della Rosa
- Functional Gene Control Group, Epigenetics Section, MRC London Institute of Medical Sciences, London, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK
| | - Chris Q Eijsbouts
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | | | - Steven W Wingett
- Bioinformatics, The Babraham Institute, Cambridge, UK
- Cell Biology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
| | - Chris Wallace
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
- MRC Biostatistics Unit, Cambridge Biomedical Campus, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge, UK
| | | | - Mikhail Spivakov
- Functional Gene Control Group, Epigenetics Section, MRC London Institute of Medical Sciences, London, UK.
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK.
| | - Valeriya Malysheva
- Functional Gene Control Group, Epigenetics Section, MRC London Institute of Medical Sciences, London, UK.
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK.
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8
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Ge X, Frank-Bertoncelj M, Klein K, McGovern A, Kuret T, Houtman M, Burja B, Micheroli R, Shi C, Marks M, Filer A, Buckley CD, Orozco G, Distler O, Morris AP, Martin P, Eyre S, Ospelt C. Functional genomics atlas of synovial fibroblasts defining rheumatoid arthritis heritability. Genome Biol 2021; 22:247. [PMID: 34433485 PMCID: PMC8385949 DOI: 10.1186/s13059-021-02460-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 08/10/2021] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Genome-wide association studies have reported more than 100 risk loci for rheumatoid arthritis (RA). These loci are shown to be enriched in immune cell-specific enhancers, but the analysis so far has excluded stromal cells, such as synovial fibroblasts (FLS), despite their crucial involvement in the pathogenesis of RA. Here we integrate DNA architecture, 3D chromatin interactions, DNA accessibility, and gene expression in FLS, B cells, and T cells with genetic fine mapping of RA loci. RESULTS We identify putative causal variants, enhancers, genes, and cell types for 30-60% of RA loci and demonstrate that FLS account for up to 24% of RA heritability. TNF stimulation of FLS alters the organization of topologically associating domains, chromatin state, and the expression of putative causal genes such as TNFAIP3 and IFNAR1. Several putative causal genes constitute RA-relevant functional networks in FLS with roles in cellular proliferation and activation. Finally, we demonstrate that risk variants can have joint-specific effects on target gene expression in RA FLS, which may contribute to the development of the characteristic pattern of joint involvement in RA. CONCLUSION Overall, our research provides the first direct evidence for a causal role of FLS in the genetic susceptibility for RA accounting for up to a quarter of RA heritability.
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Affiliation(s)
- Xiangyu Ge
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Mojca Frank-Bertoncelj
- Department of Rheumatology, Center of Experimental Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Kerstin Klein
- Department of Rheumatology, Center of Experimental Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Amanda McGovern
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Tadeja Kuret
- Department of Rheumatology, Center of Experimental Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Rheumatology, University Medical Centre, Ljubljana, Slovenia
| | - Miranda Houtman
- Department of Rheumatology, Center of Experimental Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Blaž Burja
- Department of Rheumatology, Center of Experimental Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Rheumatology, University Medical Centre, Ljubljana, Slovenia
| | - Raphael Micheroli
- Department of Rheumatology, Center of Experimental Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Chenfu Shi
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | | | - Andrew Filer
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust, University of Birmingham, Birmingham, UK
| | - Christopher D Buckley
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust, University of Birmingham, Birmingham, UK
- Kennedy Institute of Rheumatology, University of Oxford, Roosevelt Drive, Headington, Oxford, UK
| | - Gisela Orozco
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Manchester University Foundation Trust, Manchester, UK
| | - Oliver Distler
- Department of Rheumatology, Center of Experimental Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Paul Martin
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Manchester University Foundation Trust, Manchester, UK
- The Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Stephen Eyre
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Manchester University Foundation Trust, Manchester, UK
| | - Caroline Ospelt
- Department of Rheumatology, Center of Experimental Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
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Claringbould A, Zaugg JB. Enhancers in disease: molecular basis and emerging treatment strategies. Trends Mol Med 2021; 27:1060-1073. [PMID: 34420874 DOI: 10.1016/j.molmed.2021.07.012] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 07/22/2021] [Accepted: 07/26/2021] [Indexed: 02/07/2023]
Abstract
Enhancers are genomic sequences that play a key role in regulating tissue-specific gene expression levels. An increasing number of diseases are linked to impaired enhancer function through chromosomal rearrangement, genetic variation within enhancers, or epigenetic modulation. Here, we review how these enhancer disruptions have recently been implicated in congenital disorders, cancers, and common complex diseases and address the implications for diagnosis and treatment. Although further fundamental research into enhancer function, target genes, and context is required, enhancer-targeting drugs and gene editing approaches show great therapeutic promise for a range of diseases.
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Affiliation(s)
- Annique Claringbould
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Judith B Zaugg
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstraße 1, 69117 Heidelberg, Germany.
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10
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Shi C, Ray-Jones H, Ding J, Duffus K, Fu Y, Gaddi VP, Gough O, Hankinson J, Martin P, McGovern A, Yarwood A, Gaffney P, Eyre S, Rattray M, Warren RB, Orozco G. Chromatin Looping Links Target Genes with Genetic Risk Loci for Dermatological Traits. J Invest Dermatol 2021; 141:1975-1984. [PMID: 33607115 PMCID: PMC8315765 DOI: 10.1016/j.jid.2021.01.015] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 01/12/2021] [Accepted: 01/21/2021] [Indexed: 02/08/2023]
Abstract
Chromatin looping between regulatory elements and gene promoters presents a potential mechanism whereby disease risk variants affect their target genes. In this study, we use H3K27ac HiChIP, a method for assaying the active chromatin interactome in two cell lines: keratinocytes and skin lymphoma-derived CD8+ T cells. We integrate public datasets for a lymphoblastoid cell line and primary CD4+ T cells and identify gene targets at risk loci for skin-related disorders. Interacting genes enrich for pathways of known importance in each trait, such as cytokine response (psoriatic arthritis and psoriasis) and replicative senescence (melanoma). We show examples of how our analysis can inform changes in the current understanding of multiple psoriasis-associated risk loci. For example, the variant rs10794648, which is generally assigned to IFNLR1, was linked to GRHL3, a gene essential in skin repair and development, in our dataset. Our findings, therefore, indicate a renewed importance of skin-related factors in the risk of disease.
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Affiliation(s)
- Chenfu Shi
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.
| | - Helen Ray-Jones
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom; Dermatology Centre, Salford Royal NHS Foundation Trust, NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - James Ding
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom
| | - Kate Duffus
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom
| | - Yao Fu
- Genes & Human Disease Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA
| | - Vasanthi Priyadarshini Gaddi
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom
| | - Oliver Gough
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom
| | - Jenny Hankinson
- Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, University of Manchester, Manchester, United Kingdom
| | - Paul Martin
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom; Lydia Becker Institute of Immunology and Inflammation, The University of Manchester, Manchester, United Kingdom
| | - Amanda McGovern
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom
| | - Annie Yarwood
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom; Dermatology Centre, Salford Royal NHS Foundation Trust, NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Patrick Gaffney
- Genes & Human Disease Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA
| | - Steve Eyre
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom; NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Magnus Rattray
- Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Richard B Warren
- Dermatology Centre, Salford Royal NHS Foundation Trust, NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Gisela Orozco
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom; NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom
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11
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Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 2021; 25:1315-1360. [PMID: 33844136 PMCID: PMC8040371 DOI: 10.1007/s11030-021-10217-3] [Citation(s) in RCA: 322] [Impact Index Per Article: 107.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/22/2021] [Indexed: 02/06/2023]
Abstract
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.
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Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Devesh Srivastava
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Swati Tiwari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India.
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12
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Diaz-Gallo LM, Brynedal B, Westerlind H, Sandberg R, Ramsköld D. Understanding interactions between risk factors, and assessing the utility of the additive and multiplicative models through simulations. PLoS One 2021; 16:e0250282. [PMID: 33901204 PMCID: PMC8075235 DOI: 10.1371/journal.pone.0250282] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 04/02/2021] [Indexed: 01/04/2023] Open
Abstract
Understanding the genetic background of complex diseases requires the expansion of studies beyond univariate associations. Therefore, it is important to use interaction assessments of risk factors in order to discover whether, and how genetic risk variants act together on disease development. The principle of interaction analysis is to explore the magnitude of the combined effect of risk factors on disease causation. In this study, we use simulations to investigate different scenarios of causation to show how the magnitude of the effect of two risk factors interact. We mainly focus on the two most commonly used interaction models, the additive and multiplicative risk scales, since there is often confusion regarding their use and interpretation. Our results show that the combined effect is multiplicative when two risk factors are involved in the same chain of events, an interaction called synergism. Synergism is often described as a deviation from additivity, which is a broader term. Our results also confirm that it is often relevant to estimate additive effect relationships, because they correspond to independent risk factors at low disease prevalence. Importantly, we evaluate the threshold of more than two required risk factors for disease causation, called the multifactorial threshold model. We found a simple mathematical relationship (square root) between the threshold and an additive-to-multiplicative linear effect scale (AMLES), where 0 corresponds to an additive effect and 1 to a multiplicative. We propose AMLES as a metric that could be used to test different effects relationships at the same time, given that it can simultaneously reveal additive, multiplicative and intermediate risk effects relationships. Finally, the utility of our simulation study was demonstrated using real data by analyzing and interpreting gene-gene interaction odds ratios from a rheumatoid arthritis case-control cohort.
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Affiliation(s)
- Lina-Marcela Diaz-Gallo
- Division of Rheumatology, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Boel Brynedal
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Helga Westerlind
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| | - Rickard Sandberg
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Daniel Ramsköld
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
- * E-mail:
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13
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Shi C, Rattray M, Barton A, Bowes J, Orozco G. Using functional genomics to advance the understanding of psoriatic arthritis. Rheumatology (Oxford) 2021; 59:3137-3146. [PMID: 32778885 PMCID: PMC7590405 DOI: 10.1093/rheumatology/keaa283] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 04/17/2020] [Accepted: 04/28/2020] [Indexed: 01/03/2023] Open
Abstract
Psoriatic arthritis (PsA) is a complex disease where susceptibility is determined by genetic and environmental risk factors. Clinically, PsA involves inflammation of the joints and the skin, and, if left untreated, results in irreversible joint damage. There is currently no cure and the few treatments available to alleviate symptoms do not work in all patients. Over the past decade, genome-wide association studies (GWAS) have uncovered a large number of disease-associated loci but translating these findings into functional mechanisms and novel targets for therapeutic use is not straightforward. Most variants have been predicted to affect primarily long-range regulatory regions such as enhancers. There is now compelling evidence to support the use of chromatin conformation analysis methods to discover novel genes that can be affected by disease-associated variants. Here, we will review the studies published in the field that have given us a novel understanding of gene regulation in the context of functional genomics and how this relates to the study of PsA and its underlying disease mechanism.
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Affiliation(s)
- Chenfu Shi
- Division of Musculoskeletal and Dermatological Sciences, Faculty of Biology, Medicine and Health, Centre for Genetics and Genomics Versus Arthritis
| | - Magnus Rattray
- Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester.,NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre
| | - Anne Barton
- Division of Musculoskeletal and Dermatological Sciences, Faculty of Biology, Medicine and Health, Centre for Genetics and Genomics Versus Arthritis.,NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre.,Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - John Bowes
- Division of Musculoskeletal and Dermatological Sciences, Faculty of Biology, Medicine and Health, Centre for Genetics and Genomics Versus Arthritis.,NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre
| | - Gisela Orozco
- Division of Musculoskeletal and Dermatological Sciences, Faculty of Biology, Medicine and Health, Centre for Genetics and Genomics Versus Arthritis.,NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre.,Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
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14
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González-Serna D, Villanueva-Martin G, Acosta-Herrera M, Márquez A, Martín J. Approaching Shared Pathophysiology in Immune-Mediated Diseases through Functional Genomics. Genes (Basel) 2020; 11:E1482. [PMID: 33317201 PMCID: PMC7762979 DOI: 10.3390/genes11121482] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 12/01/2020] [Accepted: 12/04/2020] [Indexed: 12/14/2022] Open
Abstract
Immune-mediated diseases (IMDs) are complex pathologies that are strongly influenced by environmental and genetic factors. Associations between genetic loci and susceptibility to these diseases have been widely studied, and hundreds of risk variants have emerged during the last two decades, with researchers observing a shared genetic pattern among them. Nevertheless, the pathological mechanism behind these associations remains a challenge that has just started to be understood thanks to functional genomic approaches. Transcriptomics, regulatory elements, chromatin interactome, as well as the experimental characterization of genomic findings, constitute key elements in the emerging understandings of how genetics affects the etiopathogenesis of IMDs. In this review, we will focus on the latest advances in the field of functional genomics, centering our attention on systemic rheumatic IMDs.
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Affiliation(s)
- David González-Serna
- Institute of Parasitology and Biomedicine López-Neyra, Consejo Superior de Investigaciones Científicas (IPBLN-CSIC), 18016 Granada, Spain; (D.G.-S.); (G.V.-M.); (M.A.-H.); (A.M.)
| | - Gonzalo Villanueva-Martin
- Institute of Parasitology and Biomedicine López-Neyra, Consejo Superior de Investigaciones Científicas (IPBLN-CSIC), 18016 Granada, Spain; (D.G.-S.); (G.V.-M.); (M.A.-H.); (A.M.)
| | - Marialbert Acosta-Herrera
- Institute of Parasitology and Biomedicine López-Neyra, Consejo Superior de Investigaciones Científicas (IPBLN-CSIC), 18016 Granada, Spain; (D.G.-S.); (G.V.-M.); (M.A.-H.); (A.M.)
| | - Ana Márquez
- Institute of Parasitology and Biomedicine López-Neyra, Consejo Superior de Investigaciones Científicas (IPBLN-CSIC), 18016 Granada, Spain; (D.G.-S.); (G.V.-M.); (M.A.-H.); (A.M.)
- Systemic Autoimmune Disease Unit, Hospital Clínico San Cecilio, Instituto de Investigación Biosanitaria ibs.GRANADA, 18016 Granada, Spain
| | - Javier Martín
- Institute of Parasitology and Biomedicine López-Neyra, Consejo Superior de Investigaciones Científicas (IPBLN-CSIC), 18016 Granada, Spain; (D.G.-S.); (G.V.-M.); (M.A.-H.); (A.M.)
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15
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Ding J, Frantzeskos A, Orozco G. Functional genomics in autoimmune diseases. Hum Mol Genet 2020; 29:R59-R65. [PMID: 32420598 PMCID: PMC7530520 DOI: 10.1093/hmg/ddaa097] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 05/13/2020] [Accepted: 05/14/2020] [Indexed: 12/11/2022] Open
Abstract
Associations between genetic loci and increased susceptibility to autoimmune disease have been well characterized, however, translating this knowledge into mechanistic insight and patient benefit remains a challenge. While improvements in the precision, completeness and accuracy of our genetic understanding of autoimmune diseases will undoubtedly be helpful, meeting this challenge will require two interlinked problems to be addressed: first which of the highly correlated variants at an individual locus is responsible for increased disease risk, and second what are the downstream effects of this variant. Given that the majority of loci are thought to affect non-coding regulatory elements, the second question is often reframed as what are the target gene(s) and pathways affected by causal variants. Currently, these questions are being addressed using a wide variety of novel techniques and datasets. In many cases, these approaches are complementary and it is likely that the most accurate picture will be generated by consolidating information relating to transcription, regulatory activity, chromatin accessibility, chromatin conformation and readouts from functional experiments, such as genome editing and reporter assays. It is clear that it will be necessary to gather this information from disease relevant cell types and conditions and that by doing so our understanding of disease etiology will be improved. This review is focused on the field of autoimmune disease functional genomics with a particular focus on the most exciting and significant research to be published within the last couple of years.
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Affiliation(s)
- James Ding
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9LJ, UK
| | - Antonios Frantzeskos
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9LJ, UK
| | - Gisela Orozco
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9LJ, UK
- NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
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16
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Yang J, McGovern A, Martin P, Duffus K, Ge X, Zarrineh P, Morris AP, Adamson A, Fraser P, Rattray M, Eyre S. Analysis of chromatin organization and gene expression in T cells identifies functional genes for rheumatoid arthritis. Nat Commun 2020; 11:4402. [PMID: 32879318 PMCID: PMC7468106 DOI: 10.1038/s41467-020-18180-7] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 08/06/2020] [Indexed: 12/16/2022] Open
Abstract
Genome-wide association studies have identified genetic variation contributing to complex disease risk. However, assigning causal genes and mechanisms has been more challenging because disease-associated variants are often found in distal regulatory regions with cell-type specific behaviours. Here, we collect ATAC-seq, Hi-C, Capture Hi-C and nuclear RNA-seq data in stimulated CD4+ T cells over 24 h, to identify functional enhancers regulating gene expression. We characterise changes in DNA interaction and activity dynamics that correlate with changes in gene expression, and find that the strongest correlations are observed within 200 kb of promoters. Using rheumatoid arthritis as an example of T cell mediated disease, we demonstrate interactions of expression quantitative trait loci with target genes, and confirm assigned genes or show complex interactions for 20% of disease associated loci, including FOXO1, which we confirm using CRISPR/Cas9.
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Affiliation(s)
- Jing Yang
- Division of Informatics, Imaging & Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PT, UK
| | - Amanda McGovern
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, University of Manchester, Manchester, M13 9PT, UK
| | - Paul Martin
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, University of Manchester, Manchester, M13 9PT, UK
- Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PT, UK
| | - Kate Duffus
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, University of Manchester, Manchester, M13 9PT, UK
| | - Xiangyu Ge
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, University of Manchester, Manchester, M13 9PT, UK
| | - Peyman Zarrineh
- Division of Informatics, Imaging & Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PT, UK
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, University of Manchester, Manchester, M13 9PT, UK
| | - Antony Adamson
- The Genome Editing Unit, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PT, UK
| | - Peter Fraser
- Department of Biological Science, Florida State University, Tallahassee, FL, 32306, USA
| | - Magnus Rattray
- Division of Informatics, Imaging & Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PT, UK.
| | - Stephen Eyre
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, University of Manchester, Manchester, M13 9PT, UK.
- NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester, UK.
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17
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Casares-Marfil D, Martín J, Acosta-Herrera M. Genomic opportunities for drug repositioning in systemic seropositive rheumatic diseases. Expert Rev Clin Immunol 2020; 16:343-346. [DOI: 10.1080/1744666x.2020.1738926] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
| | - Javier Martín
- Institute of Parasitology and Biomedicine López-Neyra, IPBLN-CSIC, Granada, Spain
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18
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Fang H, Chen L, Knight JC. From genome-wide association studies to rational drug target prioritisation in inflammatory arthritis. THE LANCET. RHEUMATOLOGY 2020; 2:e50-e62. [PMID: 38258277 DOI: 10.1016/s2665-9913(19)30134-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 11/04/2019] [Accepted: 11/08/2019] [Indexed: 12/24/2022]
Abstract
Early identification of genetically validated drug targets can increase the chances of successful late-stage drug development. 81 high-quality genome-wide association studies (GWAS) in diseases related to inflammatory arthritis have been curated into the GWAS catalogue; however, translation of genetic findings from GWAS into rational drug target discovery has been poor. No human genetic findings have completely driven drug development for inflammatory arthritis; however, genetic associations have partly driven the development of abatacept (CTLA-4-Ig) in rheumatoid arthritis and secukinumab (anti-IL-23R) in ankylosing spondylitis. Roadblocks to progress exist, including little knowledge of the genetic architecture and regulatory mechanisms underlying associations, and the need to identify gene regulatory networks and assess target tractability. New opportunities are arising that could maximise the informativeness of GWAS for drug target validation. Genetic variants can be linked to core genes by using functional genomics and then to peripheral genes interconnected to core genes using network information. Moreover, identification of crosstalk between biological pathways might highlight key points for therapeutic intervention.
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Affiliation(s)
- Hai Fang
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Liye Chen
- Botnar Research Centre, University of Oxford, Oxford, UK
| | - Julian C Knight
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
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19
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Drug repurposing to improve treatment of rheumatic autoimmune inflammatory diseases. Nat Rev Rheumatol 2019; 16:32-52. [PMID: 31831878 DOI: 10.1038/s41584-019-0337-0] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/01/2019] [Indexed: 02/08/2023]
Abstract
The past century has been characterized by intensive efforts, within both academia and the pharmaceutical industry, to introduce new treatments to individuals with rheumatic autoimmune inflammatory diseases (RAIDs), often by 'borrowing' treatments already employed in one RAID or previously used in an entirely different disease, a concept known as drug repurposing. However, despite sharing some clinical manifestations and immune dysregulation, disease pathogenesis and phenotype vary greatly among RAIDs, and limited understanding of their aetiology has made repurposing drugs for RAIDs challenging. Nevertheless, the past century has been characterized by different 'waves' of repurposing. Early drug repurposing occurred in academia and was based on serendipitous observations or perceived disease similarity, often driven by the availability and popularity of drug classes. Since the 1990s, most biologic therapies have been developed for one or several RAIDs and then tested among the others, with varying levels of success. The past two decades have seen data-driven repurposing characterized by signature-based approaches that rely on molecular biology and genomics. Additionally, many data-driven strategies employ computational modelling and machine learning to integrate multiple sources of data. Together, these repurposing periods have led to advances in the treatment for many RAIDs.
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20
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Towards a Better Classification and Novel Therapies Based on the Genetics of Systemic Sclerosis. Curr Rheumatol Rep 2019; 21:44. [PMID: 31304568 DOI: 10.1007/s11926-019-0845-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
PURPOSE OF THE REVIEW Nowadays, important advances have occurred in our understanding of the pathogenesis of systemic sclerosis (SSc), which is a rare immune-mediated inflammatory disease (IMID) characterized by vascular damage, immune imbalance, and fibrosis. Its etiology remains unknown; nevertheless, both environmental and genetic factors play a major role in the disease. This review will focus on the main advances made in the field of genetics of SSc. RECENT FINDINGS The assessment of how interindividual genetic variability affects disease onset and progression has enhanced our knowledge of disease biology, and this will eventually translate in the development of new diagnostic and therapeutic tools, which is the final goal of personalized medicine. We will provide an overview of the most relevant achievements in the genetics of SSc, its shared genetics among IMIDs with special attention on drug repurposing, current challenges for the functional characterization of risk variants, and future directions.
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